Alibaba AI Docs

Appearance

Image and video understanding ​

Visual understanding models analyze the images or videos you provide and return natural-language answers. They support single and multiple image inputs and cover tasks such as image captioning, visual question answering, OCR, object detection, document parsing, and video content analysis. This document covers image and video understanding only. For video generation, see the video generation documentation. Try online: Go to the Model Studio console, select a region in the upper-right corner, and navigate to the Vision page.

Getting started ​

Prerequisites

  • Create an API key and set the API key as an environment variable.

  • If you make calls using an SDK, install the SDK. The DashScope Python SDK must be version 1.24.6 or later, and the DashScope Java SDK must be version 2.21.10 or later.

The following example calls a model to describe an image. For other input methods, see Pass local files and Image limits.

OpenAI compatible ​

Python ​

HELPCODEESCAPE-python
from openai import OpenAI
import os

client = OpenAI(
    # API keys vary by region. To get an API key, see https://www.alibabacloud.com/help/en/model-studio/get-api-key
    api_key=os.getenv("DASHSCOPE_API_KEY"),
    # Configurations vary by region. Modify the base URL based on your actual region.
    base_url="https://dashscope-intl.aliyuncs.com/compatible-mode/v1"
)

completion = client.chat.completions.create(
    model="qwen3.6-plus",  # This example uses qwen3.6-plus. You can replace it with another model name as needed. For a list of models, see https://www.alibabacloud.com/help/model-studio/getting-started/models
    messages=[
        {
            "role": "user",
            "content": [
                {
                    "type": "image_url",
                    "image_url": {
                        "url": "https://help-static-aliyun-doc.aliyuncs.com/file-manage-files/zh-CN/20241022/emyrja/dog_and_girl.jpeg"
                    },
                },
                {"type": "text", "text": "What is depicted in the image?"},
            ],
        },
    ],
)
print(completion.choices[0].message.content)

Response ​

HELPCODEESCAPE-plaintext
This is a photo taken on a beach. In the photo, a person and a dog are sitting on the sand, with the sea and sky in the background. The person and the dog seem to be interacting, with the dog's front paw resting on the person's hand. The sunlight is coming from the right side of the frame, adding a warm atmosphere to the whole scene.

Node.js ​

HELPCODEESCAPE-nodejs
import OpenAI from "openai";

const openai = new OpenAI({
  // API keys vary by region. To get an API key, visit: https://www.alibabacloud.com/help/en/model-studio/get-api-key
  // If an environment variable is not set, replace the following line with your Model Studio API key: apiKey: "sk-xxx"
  apiKey: process.env.DASHSCOPE_API_KEY,
  // Configurations vary by region. Modify this based on your region.
  baseURL: "https://dashscope-intl.aliyuncs.com/compatible-mode/v1"
});

async function main() {
  const response = await openai.chat.completions.create({
    model: "qwen3.6-plus",   // This example uses qwen3.6-plus. Change the model name as needed. Model List: https://www.alibabacloud.com/help/model-studio/getting-started/models
    messages: [
      {
        role: "user",
        content: [{
            type: "image_url",
            image_url: {
              "url": "https://help-static-aliyun-doc.aliyuncs.com/file-manage-files/zh-CN/20241022/emyrja/dog_and_girl.jpeg"
            }
          },
          {
            type: "text",
            text: "What is shown in the picture?"
          }
        ]
      }
    ]
  });
  console.log(response.choices[0].message.content);
}
main()

Response ​

HELPCODEESCAPE-plaintext
This is a photo taken on a beach. In the photo, a person and a dog are sitting on the sand, with the sea and sky in the background. The person and the dog seem to be interacting, and the dog's front paw is on the person's hand. Sunlight is coming from the right side of the frame, which adds a warm atmosphere to the scene.

curl ​

HELPCODEESCAPE-curl

# Configurations vary by region. Modify the base URL based on your actual region.
# API keys vary by region. To get an API key, see https://www.alibabacloud.com/help/en/model-studio/get-api-key
# === Delete this comment before execution ===

curl --location 'https://dashscope-intl.aliyuncs.com/compatible-mode/v1/chat/completions' \
--header "Authorization: Bearer $DASHSCOPE_API_KEY" \
--header 'Content-Type: application/json' \
--data '{
  "model": "qwen3.6-plus",
  "messages": [
    {"role": "user",
     "content": [
        {"type": "image_url", "image_url": {"url": "https://help-static-aliyun-doc.aliyuncs.com/file-manage-files/zh-CN/20241022/emyrja/dog_and_girl.jpeg"}​},
        {"type": "text", "text": "What is depicted in the image?"}
    ]
  }]
}'

Response ​

HELPCODEESCAPE-json
{
  "choices": [
    {
      "message": {
        "content": "This is a photo taken on a beach. In the photo, a person and a dog are sitting on the sand, with the sea and sky in the background. The person and the dog seem to be interacting, with the dog's front paw resting on the person's hand. The sunlight is coming from the right side of the frame, adding a warm atmosphere to the whole scene.",
        "role": "assistant"
      },
      "finish_reason": "stop",
      "index": 0,
      "logprobs": null
    }
  ],
  "object": "chat.completion",
  "usage": {
    "prompt_tokens": 1270,
    "completion_tokens": 54,
    "total_tokens": 1324
  },
  "created": 1725948561,
  "system_fingerprint": null,
  "model": "qwen3.6-plus",
  "id": "chatcmpl-0fd66f46-b09e-9164-a84f-3ebbbedbac15"
}

DashScope ​

Python ​

HELPCODEESCAPE-python
import os
import dashscope

# Configurations vary by region. Modify the base URL based on your actual region.
dashscope.base_http_api_url = 'https://dashscope-intl.aliyuncs.com/api/v1'

messages = [
{
    "role": "user",
    "content": [
    {"image": "https://help-static-aliyun-doc.aliyuncs.com/file-manage-files/zh-CN/20241022/emyrja/dog_and_girl.jpeg"},
    {"text": "What is depicted in the image?"}]
}]

response = dashscope.MultiModalConversation.call(
    # API keys vary by region. To get an API key, see https://www.alibabacloud.com/help/en/model-studio/get-api-key
    # If you have not configured environment variables, replace the following line with your Model Studio API key: api_key="sk-xxx"
    api_key=os.getenv('DASHSCOPE_API_KEY'),
    model='qwen3.6-plus',   # This example uses qwen3.6-plus. You can replace it with another model name as needed. For a list of models, see https://www.alibabacloud.com/help/model-studio/getting-started/models
    messages=messages
)

print(response.output.choices[0].message.content[0]["text"])

Response ​

HELPCODEESCAPE-plaintext
This is a photo taken on a beach. In the photo, there is a woman and a dog. The woman is sitting on the sand, smiling and interacting with the dog. The dog is wearing a collar and seems to be shaking hands with the woman. The background is the sea and the sky, and the sunlight shining on them creates a warm atmosphere.

Java ​

HELPCODEESCAPE-java
import java.util.Arrays;
import java.util.Collections;

import com.alibaba.dashscope.aigc.multimodalconversation.MultiModalConversation;
import com.alibaba.dashscope.aigc.multimodalconversation.MultiModalConversationParam;
import com.alibaba.dashscope.aigc.multimodalconversation.MultiModalConversationResult;
import com.alibaba.dashscope.common.MultiModalMessage;
import com.alibaba.dashscope.common.Role;
import com.alibaba.dashscope.exception.ApiException;
import com.alibaba.dashscope.exception.NoApiKeyException;
import com.alibaba.dashscope.exception.UploadFileException;
import com.alibaba.dashscope.utils.Constants;

public class Main {

    // Configurations vary by region. Modify the base URL based on your actual region.
    static {
        Constants.baseHttpApiUrl="https://dashscope-intl.aliyuncs.com/api/v1";
    }

    public static void simpleMultiModalConversationCall()
            throws ApiException, NoApiKeyException, UploadFileException {
        MultiModalConversation conv = new MultiModalConversation();
        MultiModalMessage userMessage = MultiModalMessage.builder().role(Role.USER.getValue())
                .content(Arrays.asList(
                        Collections.singletonMap("image", "https://help-static-aliyun-doc.aliyuncs.com/file-manage-files/zh-CN/20241022/emyrja/dog_and_girl.jpeg"),
                        Collections.singletonMap("text", "What is depicted in the image?"))).build();
        MultiModalConversationParam param = MultiModalConversationParam.builder()
                // API keys vary by region. To get an API key, see https://www.alibabacloud.com/help/en/model-studio/get-api-key
                // If you have not configured environment variables, replace the following line with your Model Studio API key: .apiKey("sk-xxx")
                .apiKey(System.getenv("DASHSCOPE_API_KEY"))
                .model("qwen3.6-plus")  //  This example uses qwen3.6-plus. You can replace it with another model name as needed. For a list of models, see https://www.alibabacloud.com/help/model-studio/getting-started/models
                .messages(Arrays.asList(userMessage))
                .build();
        MultiModalConversationResult result = conv.call(param);
        System.out.println(result.getOutput().getChoices().get(0).getMessage().getContent().get(0).get("text"));
    }
    public static void main(String[] args) {
        try {
            simpleMultiModalConversationCall();
        } catch (ApiException | NoApiKeyException | UploadFileException e) {
            System.out.println(e.getMessage());
        }
        System.exit(0);
    }
}

Response ​

HELPCODEESCAPE-plaintext
This is a photo taken on a beach. In the photo, there is a person wearing a plaid shirt and a dog wearing a collar. The person and the dog are sitting face to face, seemingly interacting. The background is the sea and the sky, and the sunlight shining on them creates a warm atmosphere.

curl ​

HELPCODEESCAPE-curl
# ======= Important =======
# Configurations vary by region. Modify the base URL based on your actual region.
# API keys vary by region. To get an API key, see https://www.alibabacloud.com/help/en/model-studio/get-api-key
# === Delete this comment before execution ===

curl -X POST https://dashscope-intl.aliyuncs.com/api/v1/services/aigc/multimodal-generation/generation \
-H "Authorization: Bearer $DASHSCOPE_API_KEY" \
-H 'Content-Type: application/json' \
-d '{
    "model": "qwen3.6-plus",
    "input":{
        "messages":[
            {
                "role": "user",
                "content": [
                    {"image": "https://help-static-aliyun-doc.aliyuncs.com/file-manage-files/zh-CN/20241022/emyrja/dog_and_girl.jpeg"},
                    {"text": "What is depicted in the image?"}
                ]
            }
        ]
    }
}'

Response ​

HELPCODEESCAPE-json
{
  "output": {
    "choices": [
      {
        "finish_reason": "stop",
        "message": {
          "role": "assistant",
          "content": [
            {
              "text": "This is a photo taken on a beach. In the photo, there is a person wearing a plaid shirt and a dog wearing a collar. They are sitting on the sand, with the sea and sky in the background. The sunlight is coming from the right side of the frame, adding a warm atmosphere to the whole scene."
            }
          ]
        }
      }
    ]
  },
  "usage": {
    "output_tokens": 55,
    "input_tokens": 1271,
    "image_tokens": 1247
  },
  "request_id": "ccf845a3-dc33-9cda-b581-20fe7dc23f70"
}

Select a model ​

  • Qwen3.6: The latest generation of visual understanding models. Offers significant improvements in code development, generalization, and multimodal capabilities such as object recognition, OCR, and object detection compared to Qwen3.5.

    • qwen3.6-plus: The most capable model in the series. Recommended.

    • qwen3.6-flash: Faster and more cost-effective.

    • qwen3.6-35b-a3b: The open source model from the Qwen3.6 series.

  • Qwen3.5: Excels at multimodal reasoning, 2D/3D image understanding, complex document parsing, visual programming, video understanding, and multimodal agents.

    • qwen3.5-plus: A powerful visual understanding model that excels at multimodal reasoning and video understanding.

    • qwen3.5-flash: Faster and more cost-effective. Suitable for latency-sensitive scenarios.

    • qwen3.5-397b-a17b, qwen3.5-122b-a10b, qwen3.5-27b, and qwen3.5-35b-a3b: The open source models from the Qwen3.5 series.

  • Qwen3-VL: Suited for high-precision object recognition and detection (including 3D detection), agent tool calling, document and webpage parsing, complex problem-solving, and long video understanding. The models in this series:

    • qwen3-vl-plus: The most capable model in the Qwen3-VL series.

    • qwen3-vl-flash: Faster and more cost-effective. Suitable for latency-sensitive scenarios.

  • Qwen2.5-VL: Suited for general tasks such as simple image captioning and short video synopsis extraction. The models in this series:

    • qwen-vl-max (belongs to Qwen2.5-VL): The best-performing model in the Qwen2.5-VL series.

    • qwen-vl-plus (belongs to Qwen2.5-VL): Faster and provides a good balance between performance and cost.

For model names, context length, prices, and snapshot versions, see the Model Studio console. For rate limiting, see Rate limiting. Model feature comparison

ModelDeep thinkingTool callingContext cacheStructured outputSupported languages
Qwen3.6 series, Qwen3.5 seriesSupportedSupportedqwen3.6-plus, qwen3.6-flash, qwen3.5-plus, qwen3.5-flash ** qwen3.6-flash and qwen3.5-flash support only explicit caching.Supported in non-thinking mode33 languages: Chinese, Japanese, Korean, Indonesian, Vietnamese, Thai, English, French, German, Russian, Portuguese, Spanish, Italian, Swedish, Danish, Czech, Norwegian, Dutch, Finnish, Turkish, Polish, Swahili, Romanian, Serbian, Greek, Kazakh, Uzbek, Cebuano, Arabic, Urdu, Persian, Hindi/Devanagari, and Hebrew.
Qwen3-VL seriesSupportedSupportedSupport for stable versions of qwen3-vl-plus and qwen3-vl-flashSupported in non-thinking mode33 languages: Chinese, Japanese, Korean, Indonesian, Vietnamese, Thai, English, French, German, Russian, Portuguese, Spanish, Italian, Swedish, Danish, Czech, Norwegian, Dutch, Finnish, Turkish, Polish, Swahili, Romanian, Serbian, Greek, Kazakh, Uzbek, Cebuano, Arabic, Urdu, Persian, Hindi/Devanagari, and Hebrew.
Qwen2.5-VL seriesNot supportedNot supportedSupport for stable versions of qwen-vl-max and qwen-vl-plusThe stable and latest versions of qwen-vl-max and qwen-vl-plus are supported.11 languages: Chinese, English, Japanese, Korean, Arabic, Vietnamese, French, German, Italian, Spanish, and Russian.

Model capabilities ​

Visual understanding models excel in enterprise use cases such as:

  • Image Q&A: identify objects, answer questions about image content, and classify or label images

  • Creative writing: generate captions, social media copy, and short video scripts from visual content

  • Text recognition and information extraction: extract structured data from receipts, certificates, and forms using OCR

  • Multidisciplinary problem-solving: solve math, physics, and chemistry problems presented as images

  • Visual programming: generate HTML, CSS, and JavaScript code from design mockups or screenshots

  • Object detection: locate objects in 2D and 3D space, with bounding box and centroid coordinate output

  • Document parsing: convert image-based documents and PDFs into structured HTML or Markdown

  • Video understanding: analyze video content, locate specific events, and generate time-stamped summaries

    Image Q&A ​

    Describe image content, or classify and label it, such as identifying people, places, animals, and plants.

* If the sun is glaring, what item from this picture should I use?When the sun is glaring, you should use the pink sunglasses in the picture. Sunglasses can effectively block strong light, reduce UV damage to the eyes, and help protect your vision and improve visual comfort in bright sunlight.

Creative writing ​

Generate text descriptions from image or video content, suitable for storytelling, copywriting, and short video scripts.

Please help me write an interesting social media post based on the content of the picture.Of course. This picture is filled with a rich Mid-Autumn Festival atmosphere, cleverly combining classical and modern elements. Based on the main elements in the picture, I have prepared several social media posts in different styles for you to choose from. Poetic and aesthetic style** Tonight, the moon is bright for all to see, but where do autumn thoughts fall? Chang'e flies to the moon, the Jade Rabbit pounds medicine---the romance of the ancients is lit up tonight. May this bright moon light your way home and carry my deepest thoughts to you. Happy Mid-Autumn Festival! Warm and blessing style A full moon brings people together, making the Mid-Autumn night the gentlest. Watch the fireworks bloom, admire the full moon in the sky, take a bite of a mooncake, and want for good health. May all that you and I want for come true. Happy Mid-Autumn Festival and a happy family reunion to everyone!

Text recognition and information extraction ​

Recognize text and formulas in images, or extract information from receipts, certificates, and forms. Supports formatted text output. For supported languages, see Model feature comparison.

Extract the following from the image: ['Invoice Code', 'Invoice Number', 'Destination', 'Fuel Surcharge', 'Fare', 'Date of Travel', 'Departure Time', 'Train Number', 'Seat Number']. Please output in JSON format.

Multidisciplinary problem-solving ​

Solve problems in images related to mathematics, physics, chemistry, and other subjects, at primary, secondary, university, and adult education levels.

Please solve the math problem in the image step by step.

Visual programming ​

Generate code from images or videos, such as creating HTML, CSS, and JavaScript from design drafts or website screenshots.

Create a webpage using HTML and CSS based on my sketch, with black as the main color.Webpage preview

Object detection ​

Supports 2D and 3D detection for determining object orientation, perspective, and occlusion relationships. 3D detection requires the Qwen3-VL model series.

The object detection performance of the Qwen2.5-VL model is robust within a resolution range of 480 × 480 to 2560 × 2560. Outside this range, the detection accuracy may decrease, and detection frame drift may occasionally occur. To draw detection results on the original image, see the FAQ section.

2D positioning - Return Box (bounding box) coordinates: Detect all food items in the image and output their bbox coordinates in JSON format. - Return Point (centroid) coordinates: Locate all food items in the image as points and output their point coordinates in XML format.Visualization of 2D positioning results
3D positioning Detect the car in the image and predict its 3D position. Output JSON: \[{"bbox_3d": \[x_center, y_center, z_center, x_size, y_size, z_size, roll, pitch, yaw\], "label": "category"}\].Visualization of 3D positioning results

Document parsing ​

Parse image-based documents, such as scans or image PDFs, into QwenVL HTML or QwenVL Markdown format. This format accurately recognizes text and retrieves position information for elements such as images and tables. The Qwen3-VL model also supports parsing into standard Markdown format.

The recommended prompts are: qwenvl html (to parse into HTML format) or qwenvl markdown (to parse into Markdown format).

qwenvl markdown.Visualization of results

Video understanding ​

Analyze video content, locate specific events with timestamps, or generate summaries of key time periods. This section covers video understanding only. For video generation, see the video generation documentation.

视频地址 Please describe the series of actions of the person in the video. Output the start time (start_time), end time (end_time), and event (event) in JSON format. Please use HH:mm:ss to represent the timestamp.{ "events": [ { "start_time": "00:00:00", "end_time": "00:00:05", "event": "A person walks towards a table holding a cardboard box and places it on the table." }, { "start_time": "00:00:05", "end_time": "00:00:15", "event": "The person picks up a scanner and scans the label on the cardboard box." }, { "start_time": "00:00:15", "end_time": "00:00:21", "event": "The person puts the scanner back in its place and then picks up a pen to record information in a notebook."}] }

Key features ​

Enable or disable thinking mode ​

  • The qwen3.6, qwen3.5, qwen3-vl-plus, and qwen3-vl-flash series are hybrid thinking models that can respond with or without a reasoning step. Use the enable_thinking parameter to control thinking mode:

    • true: Enables thinking mode. The default value for qwen3.6 and qwen3.5 series models is true.

    • false: Disables thinking mode. The default value for qwen3-vl-plus and qwen3-vl-flash series models is false.

  • Models with the thinking suffix, such as qwen3-vl-235b-a22b-thinking, are thinking-only models. They always reason before responding; this cannot be disabled.

Important

  • Model configuration: In general conversation scenarios without agent tool calling, omit the System Message for optimal performance. Pass role settings and output format requirements through the User Message instead.

  • Use streaming output: When thinking mode is enabled, both streaming and non-streaming output are supported. To avoid timeouts from long responses, use streaming output.

  • Limit thinking length: Deep thinking models can produce lengthy reasoning. Use the thinking_budget parameter to cap the thinking length. If tokens generated during thinking exceed thinking_budget, the thinking is truncated and the model generates the final response immediately. The default value of thinking_budget is the model's maximum chain-of-thought length. For details, see the model list.

OpenAI compatible ​

The enable_thinking parameter is not a standard OpenAI parameter. If you use the OpenAI Python SDK, you can pass it through extra_body. Python

HELPCODEESCAPE-python
import os
from openai import OpenAI

client = OpenAI(
    # API keys vary by region. To get an API key, see https://www.alibabacloud.com/help/en/model-studio/get-api-key
    api_key=os.getenv("DASHSCOPE_API_KEY"),
    # Configurations vary by region. Modify the base URL based on your actual region.
    base_url="https://dashscope-intl.aliyuncs.com/compatible-mode/v1"
)

reasoning_content = ""  # Define the complete thinking process
answer_content = ""     # Define the complete response
is_answering = False   # Determine whether to end the thinking process and start responding
enable_thinking = True
# Create a chat completion request
completion = client.chat.completions.create(
    model="qwen3.6-plus",
    messages=[
        {
            "role": "user",
            "content": [
                {
                    "type": "image_url",
                    "image_url": {
                        "url": "https://img.alicdn.com/imgextra/i1/O1CN01gDEY8M1W114Hi3XcN_!!6000000002727-0-tps-1024-406.jpg"
                    },
                },
                {"type": "text", "text": "How do I solve this problem?"},
            ],
        },
    ],
    stream=True,
    # The enable_thinking parameter enables the thinking process, and the thinking_budget parameter sets the maximum number of tokens for the inference process.
    # Switch thinking mode using the enable_thinking parameter.
    extra_body={
        'enable_thinking': enable_thinking,
        "thinking_budget": 81920},

    # Uncomment the following to return token usage in the last chunk.
    # stream_options={
    #     "include_usage": True
    # }
)

if enable_thinking:
    print("\n" + "=" * 20 + "Thinking Process" + "=" * 20 + "\n")

for chunk in completion:
    # If chunk.choices is empty, print usage.
    if not chunk.choices:
        print("\nUsage:")
        print(chunk.usage)
    else:
        delta = chunk.choices[0].delta
        # Print the thinking process.
        if hasattr(delta, 'reasoning_content') and delta.reasoning_content is not None:
            print(delta.reasoning_content, end='', flush=True)
            reasoning_content += delta.reasoning_content
        else:
            # Start responding.
            if delta.content != "" and is_answering is False:
                print("\n" + "=" * 20 + "Complete Response" + "=" * 20 + "\n")
                is_answering = True
            # Print the response process.
            print(delta.content, end='', flush=True)
            answer_content += delta.content

# print("=" * 20 + "Complete Thinking Process" + "=" * 20 + "\n")
# print(reasoning_content)
# print("=" * 20 + "Complete Response" + "=" * 20 + "\n")
# print(answer_content)

Node.js

HELPCODEESCAPE-nodejs
import OpenAI from "openai";

// Initialize the OpenAI client.
const openai = new OpenAI({
  // API keys vary by region. To get an API key, see https://www.alibabacloud.com/help/en/model-studio/get-api-key
  // If you have not configured environment variables, replace the following line with your Model Studio API key: apiKey: "sk-xxx"
  apiKey: process.env.DASHSCOPE_API_KEY,
  // Configurations vary by region. Modify the base URL based on your actual region.
  baseURL: "https://dashscope-intl.aliyuncs.com/compatible-mode/v1"
});

let reasoningContent = '';
let answerContent = '';
let isAnswering = false;
let enableThinking = true;

let messages = [
    {
        role: "user",
        content: [
        { type: "image_url", image_url: { "url": "https://img.alicdn.com/imgextra/i1/O1CN01gDEY8M1W114Hi3XcN_!!6000000002727-0-tps-1024-406.jpg" } },
        { type: "text", text: "Solve this problem" },
    ]
}]

async function main() {
    try {
        const stream = await openai.chat.completions.create({
            model: 'qwen3.6-plus',
            messages: messages,
            stream: true,
          // Note: In the Node.js SDK, non-standard parameters like enableThinking are passed as top-level properties and do not need to be placed in extra_body.
          enable_thinking: enableThinking,
          thinking_budget: 81920

        });

        if (enableThinking){console.log('\n' + '='.repeat(20) + 'Thinking Process' + '='.repeat(20) + '\n');}

        for await (const chunk of stream) {
            if (!chunk.choices?.length) {
                console.log('\nUsage:');
                console.log(chunk.usage);
                continue;
            }

            const delta = chunk.choices[0].delta;

            // Process the thinking process.
            if (delta.reasoning_content) {
                process.stdout.write(delta.reasoning_content);
                reasoningContent += delta.reasoning_content;
            }
            // Process the formal response.
            else if (delta.content) {
                if (!isAnswering) {
                    console.log('\n' + '='.repeat(20) + 'Complete Response' + '='.repeat(20) + '\n');
                    isAnswering = true;
                }
                process.stdout.write(delta.content);
                answerContent += delta.content;
            }
        }
    } catch (error) {
        console.error('Error:', error);
    }
}

main();

curl

HELPCODEESCAPE-curl
# ======= Important =======
# Configurations vary by region. Modify the base URL based on your actual region.
# API keys vary by region. To get an API key, see https://www.alibabacloud.com/help/en/model-studio/get-api-key
# === Delete this comment before execution ===

curl --location 'https://dashscope-intl.aliyuncs.com/compatible-mode/v1/chat/completions' \
--header "Authorization: Bearer $DASHSCOPE_API_KEY" \
--header 'Content-Type: application/json' \
--data '{
    "model": "qwen3.6-plus",
    "messages": [
    {
      "role": "user",
      "content": [
        {
          "type": "image_url",
          "image_url": {
            "url": "https://img.alicdn.com/imgextra/i1/O1CN01gDEY8M1W114Hi3XcN_!!6000000002727-0-tps-1024-406.jpg"
          }
        },
        {
          "type": "text",
          "text": "Please solve this problem"
        }
      ]
    }
  ],
    "stream":true,
    "stream_options":{"include_usage":true},
    "enable_thinking": true,
    "thinking_budget": 81920
}'

DashScope ​

Python

HELPCODEESCAPE-python
import os
import dashscope
from dashscope import MultiModalConversation

# Configurations vary by region. Modify the base URL based on your actual region.
dashscope.base_http_api_url = "https://dashscope-intl.aliyuncs.com/api/v1"

enable_thinking=True

messages = [
    {
        "role": "user",
        "content": [
            {"image": "https://img.alicdn.com/imgextra/i1/O1CN01gDEY8M1W114Hi3XcN_!!6000000002727-0-tps-1024-406.jpg"},
            {"text": "Solve this problem?"}
        ]
    }
]

response = MultiModalConversation.call(
    # If you have not configured environment variables, replace the following line with your Model Studio API key: api_key="sk-xxx",
    # API keys vary by region. To get an API key, see https://www.alibabacloud.com/help/en/model-studio/get-api-key
    api_key=os.getenv('DASHSCOPE_API_KEY'),
    model="qwen3.6-plus",
    messages=messages,
    stream=True,
    # The enable_thinking parameter enables the thinking process.
    # Switch thinking mode using the enable_thinking parameter.
    enable_thinking=enable_thinking,
    # The thinking_budget parameter sets the maximum number of tokens for the inference process.
    thinking_budget=81920,

)

# Define the complete thinking process.
reasoning_content = ""
# Define the complete response.
answer_content = ""
# Determine whether to end the thinking process and start responding.
is_answering = False

if enable_thinking:
    print("=" * 20 + "Thinking Process" + "=" * 20)

for chunk in response:
    # If both the thinking process and the response are empty, ignore.
    message = chunk.output.choices[0].message
    reasoning_content_chunk = message.get("reasoning_content", None)
    if (chunk.output.choices[0].message.content == [] and
        reasoning_content_chunk == ""):
        pass
    else:
        # If it is currently in the thinking process.
        if reasoning_content_chunk is not None and chunk.output.choices[0].message.content == []:
            print(chunk.output.choices[0].message.reasoning_content, end="")
            reasoning_content += chunk.output.choices[0].message.reasoning_content
        # If it is currently responding.
        elif chunk.output.choices[0].message.content != []:
            if not is_answering:
                print("\n" + "=" * 20 + "Complete Response" + "=" * 20)
                is_answering = True
            print(chunk.output.choices[0].message.content[0]["text"], end="")
            answer_content += chunk.output.choices[0].message.content[0]["text"]

# To print the complete thinking process and complete response, uncomment and run the following code.
# print("=" * 20 + "Complete Thinking Process" + "=" * 20 + "\n")
# print(f"{reasoning_content}")
# print("=" * 20 + "Complete Response" + "=" * 20 + "\n")
# print(f"{answer_content}")

Java

HELPCODEESCAPE-java
// The DashScope SDK version must be 2.21.10 or later.
import java.util.*;

import org.slf4j.Logger;
import org.slf4j.LoggerFactory;

import com.alibaba.dashscope.common.Role;
import com.alibaba.dashscope.exception.ApiException;
import com.alibaba.dashscope.exception.NoApiKeyException;
import io.reactivex.Flowable;

import com.alibaba.dashscope.aigc.multimodalconversation.MultiModalConversation;
import com.alibaba.dashscope.aigc.multimodalconversation.MultiModalConversationParam;
import com.alibaba.dashscope.aigc.multimodalconversation.MultiModalConversationResult;
import com.alibaba.dashscope.common.MultiModalMessage;
import com.alibaba.dashscope.exception.UploadFileException;
import com.alibaba.dashscope.exception.InputRequiredException;
import java.lang.System;
import com.alibaba.dashscope.utils.Constants;

public class Main {
    // Configurations vary by region. Modify the base URL based on your actual region.
    static {Constants.baseHttpApiUrl="https://dashscope-intl.aliyuncs.com/api/v1";}

    private static final Logger logger = LoggerFactory.getLogger(Main.class);
    private static StringBuilder reasoningContent = new StringBuilder();
    private static StringBuilder finalContent = new StringBuilder();
    private static boolean isFirstPrint = true;

    private static void handleGenerationResult(MultiModalConversationResult message) {
        String re = message.getOutput().getChoices().get(0).getMessage().getReasoningContent();
        String reasoning = Objects.isNull(re)?"":re; // Default value

        List<Map<String, Object>> content = message.getOutput().getChoices().get(0).getMessage().getContent();
        if (!reasoning.isEmpty()) {
            reasoningContent.append(reasoning);
            if (isFirstPrint) {
                System.out.println("====================Thinking Process====================");
                isFirstPrint = false;
            }
            System.out.print(reasoning);
        }

        if (Objects.nonNull(content) && !content.isEmpty()) {
            Object text = content.get(0).get("text");
            finalContent.append(content.get(0).get("text"));
            if (!isFirstPrint) {
                System.out.println("\n====================Complete Response====================");
                isFirstPrint = true;
            }
            System.out.print(text);
        }
    }
    public static MultiModalConversationParam buildMultiModalConversationParam(MultiModalMessage Msg)  {
        return MultiModalConversationParam.builder()
                // If you have not configured environment variables, replace the following line with your Model Studio API key: .apiKey("sk-xxx")
                // API keys vary by region. To get an API key, see https://www.alibabacloud.com/help/en/model-studio/get-api-key
                .apiKey(System.getenv("DASHSCOPE_API_KEY"))
                .model("qwen3.6-plus")
                .messages(Arrays.asList(Msg))
                .enableThinking(true)
                .thinkingBudget(81920)
                .incrementalOutput(true)
                .build();
    }

    public static void streamCallWithMessage(MultiModalConversation conv, MultiModalMessage Msg)
            throws NoApiKeyException, ApiException, InputRequiredException, UploadFileException {
        MultiModalConversationParam param = buildMultiModalConversationParam(Msg);
        Flowable<MultiModalConversationResult> result = conv.streamCall(param);
        result.blockingForEach(message -> {
            handleGenerationResult(message);
        });
    }
    public static void main(String[] args) {
        try {
            MultiModalConversation conv = new MultiModalConversation();
            MultiModalMessage userMsg = MultiModalMessage.builder()
                    .role(Role.USER.getValue())
                    .content(Arrays.asList(Collections.singletonMap("image", "https://img.alicdn.com/imgextra/i1/O1CN01gDEY8M1W114Hi3XcN_!!6000000002727-0-tps-1024-406.jpg"),
                            Collections.singletonMap("text", "Please solve this problem")))
                    .build();
            streamCallWithMessage(conv, userMsg);
//             Print the final result.
//            if (reasoningContent.length() > 0) {
//                System.out.println("\n====================Complete Response====================");
//                System.out.println(finalContent.toString());
//            }
        } catch (ApiException | NoApiKeyException | UploadFileException | InputRequiredException e) {
            logger.error("An exception occurred: {}", e.getMessage());
        }
        System.exit(0);
    }
}

curl

HELPCODEESCAPE-curl
# ======= Important =======
# API keys vary by region. To get an API key, see https://www.alibabacloud.com/help/en/model-studio/get-api-key
# Configurations vary by region. Modify the base URL based on your actual region.
# === Delete this comment before execution ===

curl -X POST https://dashscope-intl.aliyuncs.com/api/v1/services/aigc/multimodal-generation/generation \
-H "Authorization: Bearer $DASHSCOPE_API_KEY" \
-H 'Content-Type: application/json' \
-H 'X-DashScope-SSE: enable' \
-d '{
    "model": "qwen3.6-plus",
    "input":{
        "messages":[
            {
                "role": "user",
                "content": [
                    {"image": "https://img.alicdn.com/imgextra/i1/O1CN01gDEY8M1W114Hi3XcN_!!6000000002727-0-tps-1024-406.jpg"},
                    {"text": "Please solve this problem"}
                ]
            }
        ]
    },
    "parameters":{
        "enable_thinking": true,
        "incremental_output": true,
        "thinking_budget": 81920
    }
}'

Multiple image input ​

Visual understanding models support multiple images in a single request, useful for tasks such as product comparison and multi-page document processing. Include multiple image objects in the user message content array. Important

The number of images is limited by the model's total token limit for images and text. The total token count for all images and text must not exceed the model's maximum input limit.

OpenAI compatible ​

Python ​

HELPCODEESCAPE-python
import os
from openai import OpenAI

client = OpenAI(
    # API keys vary by region. To get an API key, see https://www.alibabacloud.com/help/en/model-studio/get-api-key
    api_key=os.getenv("DASHSCOPE_API_KEY"),
    # Configurations vary by region. Modify the configuration based on your region.
    base_url="https://dashscope-intl.aliyuncs.com/compatible-mode/v1",
)

completion = client.chat.completions.create(
    model="qwen3.6-plus",  #  This example uses qwen3.6-plus. You can replace it with another model name as needed. For a list of models, see https://www.alibabacloud.com/help/model-studio/getting-started/models
    messages=[
        {"role": "user","content": [
            {"type": "image_url","image_url": {"url": "https://help-static-aliyun-doc.aliyuncs.com/file-manage-files/zh-CN/20241022/emyrja/dog_and_girl.jpeg"},},
            {"type": "image_url","image_url": {"url": "https://dashscope.oss-cn-beijing.aliyuncs.com/images/tiger.png"},},
            {"type": "text", "text": "What do these images depict?"},
            ],
        }
    ],
)

print(completion.choices[0].message.content)

Response ​

HELPCODEESCAPE-plaintext
Image 1 shows a woman and a Labrador Retriever interacting on a beach. The woman is wearing a plaid shirt and sitting on the sand, shaking hands with the dog. The background shows ocean waves and the sky. The whole scene has a warm and pleasant atmosphere.

Image 2 shows a tiger walking in a forest. The tiger has orange and black stripes and is walking forward. It is surrounded by dense trees and vegetation, and the ground is covered with fallen leaves. The scene gives a feeling of wild nature.

Node.js ​

HELPCODEESCAPE-nodejs
import OpenAI from "openai";

const openai = new OpenAI(
    {
        // API keys vary by region. To get an API key, see https://www.alibabacloud.com/help/en/model-studio/get-api-key
        // If you have not configured the environment variable, replace the following line with your Model Studio API key: apiKey: "sk-xxx"
        apiKey: process.env.DASHSCOPE_API_KEY,
        // Configurations vary by region. Modify the configuration based on your region.
        baseURL: "https://dashscope-intl.aliyuncs.com/compatible-mode/v1"
    }
);

async function main() {
    const response = await openai.chat.completions.create({
        model: "qwen3.6-plus",  // This example uses qwen3.6-plus. You can replace it with another model name as needed. For a list of models, see https://www.alibabacloud.com/help/model-studio/getting-started/models
        messages: [
          {role: "user",content: [
            {type: "image_url",image_url: {"url": "https://help-static-aliyun-doc.aliyuncs.com/file-manage-files/zh-CN/20241022/emyrja/dog_and_girl.jpeg"}​},
            {type: "image_url",image_url: {"url": "https://dashscope.oss-cn-beijing.aliyuncs.com/images/tiger.png"}​},
            {type: "text", text: "What do these images depict?" },
        ]}]
    });
    console.log(response.choices[0].message.content);
}

main()

Response ​

HELPCODEESCAPE-plaintext
In the first image, a person and a dog are interacting on a beach. The person is wearing a plaid shirt, and the dog is wearing a collar. They appear to be shaking hands or giving a high-five.

In the second image, a tiger is walking in a forest. The tiger has orange and black stripes, and the background consists of green trees and vegetation.

curl ​

HELPCODEESCAPE-curl
# ======= Important =======
# API keys vary by region. To get an API key, see https://www.alibabacloud.com/help/en/model-studio/get-api-key
# Configurations vary by region. Modify the configuration based on your region.
# === Delete this comment before execution ===

curl -X POST https://dashscope-intl.aliyuncs.com/compatible-mode/v1/chat/completions \
-H "Authorization: Bearer $DASHSCOPE_API_KEY" \
-H 'Content-Type: application/json' \
-d '{
  "model": "qwen3.6-plus",
  "messages": [
    {
      "role": "user",
      "content": [
        {
          "type": "image_url",
          "image_url": {
            "url": "https://help-static-aliyun-doc.aliyuncs.com/file-manage-files/zh-CN/20241022/emyrja/dog_and_girl.jpeg"
          }
        },
        {
          "type": "image_url",
          "image_url": {
            "url": "https://dashscope.oss-cn-beijing.aliyuncs.com/images/tiger.png"
          }
        },
        {
          "type": "text",
          "text": "What do these images depict?"
        }
      ]
    }
  ]
}'

Response ​

HELPCODEESCAPE-json
{
  "choices": [
    {
      "message": {
        "content": "Image 1 shows a woman and a Labrador Retriever interacting on a beach. The woman is wearing a plaid shirt and sitting on the sand, shaking hands with the dog. The background shows a sea view and a sunset sky, making the whole scene look very warm and harmonious.\n\nImage 2 shows a tiger walking in a forest. The tiger has orange and black stripes and is walking forward. It is surrounded by dense trees and vegetation, and the ground is covered with fallen leaves. The whole scene is full of natural wildness and vitality.",
        "role": "assistant"
      },
      "finish_reason": "stop",
      "index": 0,
      "logprobs": null
    }
  ],
  "object": "chat.completion",
  "usage": {
    "prompt_tokens": 2497,
    "completion_tokens": 109,
    "total_tokens": 2606
  },
  "created": 1725948561,
  "system_fingerprint": null,
  "model": "qwen3.6-plus",
  "id": "chatcmpl-0fd66f46-b09e-9164-a84f-3ebbbedbac15"
}

DashScope ​

Python ​

HELPCODEESCAPE-python
import os
import dashscope

# Configurations vary by region. Modify the configuration based on your region.
dashscope.base_http_api_url = 'https://dashscope-intl.aliyuncs.com/api/v1'

messages = [
    {
        "role": "user",
        "content": [
            {"image": "https://help-static-aliyun-doc.aliyuncs.com/file-manage-files/zh-CN/20241022/emyrja/dog_and_girl.jpeg"},
            {"image": "https://dashscope.oss-cn-beijing.aliyuncs.com/images/tiger.png"},
            {"text": "What do these images depict?"}
        ]
    }
]

response = dashscope.MultiModalConversation.call(
    # API keys vary by region. To get an API key, see https://www.alibabacloud.com/help/en/model-studio/get-api-key
    # If you have not configured the environment variable, replace the following line with your Model Studio API key: api_key="sk-xxx"
    api_key=os.getenv('DASHSCOPE_API_KEY'),
    model='qwen3.6-plus', #  This example uses qwen3.6-plus. You can replace it with another model name as needed. For a list of models, see https://www.alibabacloud.com/help/en/model-studio/getting-started/models
    messages=messages
)

print(response.output.choices[0].message.content[0]["text"])

Response ​

HELPCODEESCAPE-plaintext
These images show some animals and natural scenes. The first image shows a person and a dog interacting on a beach. The second image shows a tiger walking in a forest.

Java ​

HELPCODEESCAPE-java
import java.util.Arrays;
import java.util.Collections;
import com.alibaba.dashscope.aigc.multimodalconversation.MultiModalConversation;
import com.alibaba.dashscope.aigc.multimodalconversation.MultiModalConversationParam;
import com.alibaba.dashscope.aigc.multimodalconversation.MultiModalConversationResult;
import com.alibaba.dashscope.common.MultiModalMessage;
import com.alibaba.dashscope.common.Role;
import com.alibaba.dashscope.exception.ApiException;
import com.alibaba.dashscope.exception.NoApiKeyException;
import com.alibaba.dashscope.exception.UploadFileException;
import com.alibaba.dashscope.utils.Constants;

public class Main {
    static {
    // Configurations vary by region. Modify the configuration based on your region.
        Constants.baseHttpApiUrl="https://dashscope-intl.aliyuncs.com/api/v1";
    }
    public static void simpleMultiModalConversationCall()
            throws ApiException, NoApiKeyException, UploadFileException {
        MultiModalConversation conv = new MultiModalConversation();
        MultiModalMessage userMessage = MultiModalMessage.builder().role(Role.USER.getValue())
                .content(Arrays.asList(
                        Collections.singletonMap("image", "https://help-static-aliyun-doc.aliyuncs.com/file-manage-files/zh-CN/20241022/emyrja/dog_and_girl.jpeg"),
                        Collections.singletonMap("image", "https://dashscope.oss-cn-beijing.aliyuncs.com/images/tiger.png"),
                        Collections.singletonMap("text", "What do these images depict?"))).build();
        MultiModalConversationParam param = MultiModalConversationParam.builder()
                // API keys vary by region. To get an API key, see https://www.alibabacloud.com/help/en/model-studio/get-api-key
                .apiKey(System.getenv("DASHSCOPE_API_KEY"))
                .model("qwen3.6-plus")  //  This example uses qwen3.6-plus. You can replace it with another model name as needed. For a list of models, see https://www.alibabacloud.com/help/model-studio/getting-started/models
                .messages(Arrays.asList(userMessage))
                .build();
        MultiModalConversationResult result = conv.call(param);
        System.out.println(result.getOutput().getChoices().get(0).getMessage().getContent().get(0).get("text"));    }
    public static void main(String[] args) {
        try {
            simpleMultiModalConversationCall();
        } catch (ApiException | NoApiKeyException | UploadFileException e) {
            System.out.println(e.getMessage());
        }
        System.exit(0);
    }
}

Response ​

HELPCODEESCAPE-plaintext
These images show some animals and natural scenes.

1. First image: A woman and a dog are interacting on a beach. The woman is wearing a plaid shirt and sitting on the sand. The dog is wearing a collar and extending its paw to shake hands with the woman.
2. Second image: A tiger is walking in a forest. The tiger has orange and black stripes, and the background consists of trees and leaves.

curl ​

HELPCODEESCAPE-curl
# ======= Important =======
# Configurations vary by region. Modify the configuration based on your region.
# API keys vary by region. To get an API key, see https://www.alibabacloud.com/help/en/model-studio/get-api-key
# === Delete this comment before execution ===

curl --location 'https://dashscope-intl.aliyuncs.com/api/v1/services/aigc/multimodal-generation/generation' \
--header "Authorization: Bearer $DASHSCOPE_API_KEY" \
--header 'Content-Type: application/json' \
--data '{
    "model": "qwen3.6-plus",
    "input":{
        "messages":[
            {
                "role": "user",
                "content": [
                    {"image": "https://help-static-aliyun-doc.aliyuncs.com/file-manage-files/zh-CN/20241022/emyrja/dog_and_girl.jpeg"},
                    {"image": "https://dashscope.oss-cn-beijing.aliyuncs.com/images/tiger.png"},
                    {"text": "What do these images show?"}
                ]
            }
        ]
    }
}'

Response ​

HELPCODEESCAPE-json
{
  "output": {
    "choices": [
      {
        "finish_reason": "stop",
        "message": {
          "role": "assistant",
          "content": [
            {
              "text": "These images show some animals and natural scenes. The first image shows a person and a dog interacting on a beach. The second image shows a tiger walking in a forest."
            }
          ]
        }
      }
    ]
  },
  "usage": {
    "output_tokens": 81,
    "input_tokens": 1277,
    "image_tokens": 2497
  },
  "request_id": "ccf845a3-dc33-9cda-b581-20fe7dc23f70"
}

Video understanding ​

Visual understanding models process video content from image lists (pre-extracted frames) or video files. For video limits and the maximum number of images per image list, see the Video limits section.

Use the latest or a recent snapshot version of a high-performance model to understand video files.

Video files ​

Visual understanding models analyze video by extracting frames. Control frame extraction with the following parameters:

  • fps : Controls the frame extraction frequency. One frame is extracted every fps1 seconds. The value ranges from 0.1 to 10. The default value is 2.0.

    • High-motion scenarios: Set a higher fps value to capture more details.

    • Static or long videos: Set a lower fps value to improve processing efficiency.

  • max_frames : Caps the maximum number of frames extracted from a video. If the frame count calculated from fps exceeds this limit, the system samples frames evenly within the cap. This parameter is only available when using the DashScope SDK.

OpenAI compatible ​

When you input a video file to a visual understanding model using the OpenAI SDK or HTTP, you must set the "type" parameter in the user message to "video_url" .

Python ​

HELPCODEESCAPE-python
import os
from openai import OpenAI

client = OpenAI(
    # API keys vary by region. To get an API key, see https://www.alibabacloud.com/help/model-studio/get-api-key
    # If you have not configured the environment variable, replace the following line with your Model Studio API key: api_key="sk-xxx",
    api_key=os.getenv("DASHSCOPE_API_KEY"),
    # Configurations vary by region. Modify the configuration as needed.
    base_url="https://dashscope-intl.aliyuncs.com/compatible-mode/v1",
)
completion = client.chat.completions.create(
    model="qwen3.6-plus",
    messages=[
        {
            "role": "user",
            "content": [
                # When you input a video file directly, set the value of type to video_url.
                {
                    "type": "video_url",
                    "video_url": {
                        "url": "https://help-static-aliyun-doc.aliyuncs.com/file-manage-files/zh-CN/20241115/cqqkru/1.mp4"
                    },
                    "fps": 2
                },
                {
                    "type": "text",
                    "text": "What is the content of this video?"
                }
            ]
        }
    ]
)

print(completion.choices[0].message.content)

Node.js ​

HELPCODEESCAPE-nodejs
import OpenAI from "openai";

const openai = new OpenAI(
    {
        // API keys vary by region. To get an API key, see https://www.alibabacloud.com/help/model-studio/get-api-key
        // If you have not configured the environment variable, replace the following line with your Model Studio API key: apiKey: "sk-xxx"
        apiKey: process.env.DASHSCOPE_API_KEY,
        // Configurations vary by region. Modify the configuration as needed.
        baseURL: "https://dashscope-intl.aliyuncs.com/compatible-mode/v1"
    }
);

async function main() {
    const response = await openai.chat.completions.create({
        model: "qwen3.6-plus",
        messages: [
            {
                role: "user",
                content: [
                    // When you input a video file directly, set the value of type to video_url.
                    {
                        type: "video_url",
                        video_url: {
                            "url": "https://help-static-aliyun-doc.aliyuncs.com/file-manage-files/zh-CN/20241115/cqqkru/1.mp4"
                        },
                        "fps": 2
                    },
                    {
                        type: "text",
                        text: "What is the content of this video?"
                    }
                ]
            }
        ]
    });

    console.log(response.choices[0].message.content);
}

main();

curl ​

HELPCODEESCAPE-curl
# ======= Important =======
# API keys vary by region. To get an API key, see https://www.alibabacloud.com/help/model-studio/get-api-key
# Configurations vary by region. Modify the configuration as needed.
# === Delete this comment before execution ===

curl -X POST https://dashscope-intl.aliyuncs.com/compatible-mode/v1/chat/completions \
  -H "Authorization: Bearer $DASHSCOPE_API_KEY" \
  -H 'Content-Type: application/json' \
  -d '{
    "model": "qwen3.6-plus",
    "messages": [
      {
        "role": "user",
        "content": [
          {
            "type": "video_url",
            "video_url": {
              "url": "https://help-static-aliyun-doc.aliyuncs.com/file-manage-files/zh-CN/20241115/cqqkru/1.mp4"
            },
            "fps":2
          },
          {
            "type": "text",
            "text": "What is the content of this video?"
          }
        ]
      }
    ]
  }'

DashScope ​

Python ​

HELPCODEESCAPE-python
import dashscope
import os

# Configurations vary by region. Modify the configuration as needed.
dashscope.base_http_api_url = 'https://dashscope-intl.aliyuncs.com/api/v1'
messages = [
    {"role": "user",
        "content": [
            # The fps parameter controls the video frame extraction frequency. It indicates that one frame is extracted every 1/fps seconds. For complete usage, see https://www.alibabacloud.com/help/model-studio/use-qwen-by-calling-api?#2ed5ee7377fum
            {"video": "https://help-static-aliyun-doc.aliyuncs.com/file-manage-files/zh-CN/20241115/cqqkru/1.mp4","fps":2},
            {"text": "What is the content of this video?"}
        ]
    }
]

response = dashscope.MultiModalConversation.call(
    # API keys vary by region. To get an API key, see https://www.alibabacloud.com/help/model-studio/get-api-key
    # If you have not configured the environment variable, replace the following line with your Model Studio API key: api_key ="sk-xxx"
    api_key=os.getenv('DASHSCOPE_API_KEY'),
    model='qwen3.6-plus',
    messages=messages
)

print(response.output.choices[0].message.content[0]["text"])

Java ​

HELPCODEESCAPE-java
import java.util.Arrays;
import java.util.Collections;
import java.util.HashMap;
import java.util.Map;

import com.alibaba.dashscope.aigc.multimodalconversation.MultiModalConversation;
import com.alibaba.dashscope.aigc.multimodalconversation.MultiModalConversationParam;
import com.alibaba.dashscope.aigc.multimodalconversation.MultiModalConversationResult;
import com.alibaba.dashscope.common.MultiModalMessage;
import com.alibaba.dashscope.common.Role;
import com.alibaba.dashscope.exception.ApiException;
import com.alibaba.dashscope.exception.NoApiKeyException;
import com.alibaba.dashscope.exception.UploadFileException;
import com.alibaba.dashscope.utils.Constants;

public class Main {
   static {
            // Configurations vary by region. Modify the configuration as needed.
            Constants.baseHttpApiUrl="https://dashscope-intl.aliyuncs.com/api/v1";
        }
    public static void simpleMultiModalConversationCall()
            throws ApiException, NoApiKeyException, UploadFileException {
        MultiModalConversation conv = new MultiModalConversation();
        // The fps parameter controls the video frame extraction frequency. It indicates that one frame is extracted every 1/fps seconds. For complete usage, see https://www.alibabacloud.com/help/model-studio/use-qwen-by-calling-api?#2ed5ee7377fum
        Map&lt;String, Object&gt; params = new HashMap<>();
        params.put("video", "https://help-static-aliyun-doc.aliyuncs.com/file-manage-files/zh-CN/20241115/cqqkru/1.mp4");
        params.put("fps", 2);
        MultiModalMessage userMessage = MultiModalMessage.builder().role(Role.USER.getValue())
                .content(Arrays.asList(
                        params,
                        Collections.singletonMap("text", "What is the content of this video?"))).build();
        MultiModalConversationParam param = MultiModalConversationParam.builder()
                // API keys vary by region. To get an API key, see https://www.alibabacloud.com/help/model-studio/get-api-key
                // If you have not configured the environment variable, replace the following line with your Model Studio API key: .apiKey("sk-xxx")
                .apiKey(System.getenv("DASHSCOPE_API_KEY"))
                .model("qwen3.6-plus")
                .messages(Arrays.asList(userMessage))
                .build();
        MultiModalConversationResult result = conv.call(param);
        System.out.println(result.getOutput().getChoices().get(0).getMessage().getContent().get(0).get("text"));
    }
    public static void main(String[] args) {
        try {
            simpleMultiModalConversationCall();
        } catch (ApiException | NoApiKeyException | UploadFileException e) {
            System.out.println(e.getMessage());
        }
        System.exit(0);
    }
}

curl ​

HELPCODEESCAPE-curl
# ======= Important =======
# Configurations vary by region. Modify the configuration as needed.
# API keys vary by region. To get an API key, see https://www.alibabacloud.com/help/model-studio/get-api-key
# === Delete this comment before execution ===

curl -X POST https://dashscope-intl.aliyuncs.com/api/v1/services/aigc/multimodal-generation/generation \
-H "Authorization: Bearer $DASHSCOPE_API_KEY" \
-H 'Content-Type: application/json' \
-d '{
    "model": "qwen3.6-plus",
    "input":{
        "messages":[
            {"role": "user","content": [{"video": "https://help-static-aliyun-doc.aliyuncs.com/file-manage-files/zh-CN/20241115/cqqkru/1.mp4","fps":2},
            {"text": "What is the content of this video?"}]}]}
}'

Image list ​

When you provide a video as an image list (pre-extracted video frames), you can use the fps parameter to specify the time interval between frames. This helps the model better understand the sequence, duration, and dynamic changes of events. The model uses the fps parameter to determine the frame rate of the original video, indicating that frames were extracted every fps1 seconds. This parameter is supported by the Qwen3.6 , Qwen3-VL , and Qwen2.5-VL models.

OpenAI compatible ​

When you input a video as an image list to a visual understanding model using the OpenAI SDK or HTTP, you must set the "type" parameter in the user message to "video" .

Python ​

HELPCODEESCAPE-python
import os
from openai import OpenAI

client = OpenAI(
    # API keys vary by region. To get an API key, see https://www.alibabacloud.com/help/model-studio/get-api-key
    # If you have not configured the environment variable, replace the following line with your Model Studio API key: api_key="sk-xxx",
    api_key=os.getenv("DASHSCOPE_API_KEY"),
    # Configurations vary by region. Modify the configuration as needed.
    base_url="https://dashscope-intl.aliyuncs.com/compatible-mode/v1",
)

completion = client.chat.completions.create(
    model="qwen3.6-plus", # This example uses qwen3.6-plus. You can replace it with another model name as needed. For a list of models, see https://www.alibabacloud.com/help/model-studio/getting-started/models
    messages=[{"role": "user","content": [
        # When you provide an image list, the "type" parameter in the user message is "video".
         {"type": "video","video": [
         "https://help-static-aliyun-doc.aliyuncs.com/file-manage-files/zh-CN/20241108/xzsgiz/football1.jpg",
         "https://help-static-aliyun-doc.aliyuncs.com/file-manage-files/zh-CN/20241108/tdescd/football2.jpg",
         "https://help-static-aliyun-doc.aliyuncs.com/file-manage-files/zh-CN/20241108/zefdja/football3.jpg",
         "https://help-static-aliyun-doc.aliyuncs.com/file-manage-files/zh-CN/20241108/aedbqh/football4.jpg"],
         "fps":2},
         {"type": "text","text": "Describe the specific process in this video"},
    ]}]
)

print(completion.choices[0].message.content)

Node.js ​

HELPCODEESCAPE-nodejs
// Make sure you have specified "type": "module" in your package.json file.
import OpenAI from "openai";

const openai = new OpenAI({
    // API keys vary by region. To get an API key, see https://www.alibabacloud.com/help/model-studio/get-api-key
    // If you have not configured the environment variable, replace the following line with your Model Studio API key: apiKey: "sk-xxx",
    apiKey: process.env.DASHSCOPE_API_KEY,
    // Configurations vary by region. Modify the configuration as needed.
    baseURL: "https://dashscope-intl.aliyuncs.com/compatible-mode/v1"
});

async function main() {
    const response = await openai.chat.completions.create({
        model: "qwen3.6-plus",  // This example uses qwen3.6-plus. You can replace it with another model name as needed. For a list of models, see https://www.alibabacloud.com/help/model-studio/getting-started/models
        messages: [{
            role: "user",
            content: [
                {
                    // When you provide an image list, the "type" parameter in the user message is "video".
                    type: "video",
                    video: [
                        "https://help-static-aliyun-doc.aliyuncs.com/file-manage-files/zh-CN/20241108/xzsgiz/football1.jpg",
                        "https://help-static-aliyun-doc.aliyuncs.com/file-manage-files/zh-CN/20241108/tdescd/football2.jpg",
                        "https://help-static-aliyun-doc.aliyuncs.com/file-manage-files/zh-CN/20241108/zefdja/football3.jpg",
                        "https://help-static-aliyun-doc.aliyuncs.com/file-manage-files/zh-CN/20241108/aedbqh/football4.jpg"],
                        "fps": 2
                },
                {
                    type: "text",
                    text: "Describe the specific process in this video"
                }
            ]
        }]
    });
    console.log(response.choices[0].message.content);
}

main();

curl ​

HELPCODEESCAPE-curl
# ======= Important =======
# API keys vary by region. To get an API key, see https://www.alibabacloud.com/help/model-studio/get-api-key
# Configurations vary by region. Modify the configuration as needed.
# === Delete this comment before execution ===

curl -X POST https://dashscope-intl.aliyuncs.com/compatible-mode/v1/chat/completions \
-H "Authorization: Bearer $DASHSCOPE_API_KEY" \
-H 'Content-Type: application/json' \
-d '{
    "model": "qwen3.6-plus",
    "messages": [{"role": "user","content": [{"type": "video","video": [
                  "https://help-static-aliyun-doc.aliyuncs.com/file-manage-files/zh-CN/20241108/xzsgiz/football1.jpg",
                  "https://help-static-aliyun-doc.aliyuncs.com/file-manage-files/zh-CN/20241108/tdescd/football2.jpg",
                  "https://help-static-aliyun-doc.aliyuncs.com/file-manage-files/zh-CN/20241108/zefdja/football3.jpg",
                  "https://help-static-aliyun-doc.aliyuncs.com/file-manage-files/zh-CN/20241108/aedbqh/football4.jpg"],
                  "fps":2},
                {"type": "text","text": "Describe the specific process in this video"}]}]
}'

DashScope ​

Python ​

HELPCODEESCAPE-python
import os
import dashscope

# Configurations vary by region. Modify the configuration as needed.
dashscope.base_http_api_url = 'https://dashscope-intl.aliyuncs.com/api/v1'
messages = [{"role": "user",
             "content": [
                  # When you provide an image list, the fps parameter applies to the Qwen3.6, Qwen3-VL, and Qwen2.5-VL series models.
                 {"video":["https://help-static-aliyun-doc.aliyuncs.com/file-manage-files/zh-CN/20241108/xzsgiz/football1.jpg",
                           "https://help-static-aliyun-doc.aliyuncs.com/file-manage-files/zh-CN/20241108/tdescd/football2.jpg",
                           "https://help-static-aliyun-doc.aliyuncs.com/file-manage-files/zh-CN/20241108/zefdja/football3.jpg",
                           "https://help-static-aliyun-doc.aliyuncs.com/file-manage-files/zh-CN/20241108/aedbqh/football4.jpg"],
                   "fps":2},
                 {"text": "Describe the specific process in this video"}]}]
response = dashscope.MultiModalConversation.call(
    # API keys vary by region. To get an API key, see https://www.alibabacloud.com/help/model-studio/get-api-key
    # If you have not configured the environment variable, replace the following line with your Model Studio API key: api_key="sk-xxx",
    api_key=os.getenv("DASHSCOPE_API_KEY"),
    model='qwen3.6-plus',  # This example uses qwen3.6-plus. You can replace it with another model name as needed. For a list of models, see https://www.alibabacloud.com/help/model-studio/getting-started/models
    messages=messages
)
print(response.output.choices[0].message.content[0]["text"])

Java ​

HELPCODEESCAPE-java
// The DashScope SDK version must be 2.21.10 or later.
import java.util.Arrays;
import java.util.Collections;
import java.util.Map;

import com.alibaba.dashscope.aigc.multimodalconversation.MultiModalConversation;
import com.alibaba.dashscope.aigc.multimodalconversation.MultiModalConversationParam;
import com.alibaba.dashscope.aigc.multimodalconversation.MultiModalConversationResult;
import com.alibaba.dashscope.common.MultiModalMessage;
import com.alibaba.dashscope.common.Role;
import com.alibaba.dashscope.exception.ApiException;
import com.alibaba.dashscope.exception.NoApiKeyException;
import com.alibaba.dashscope.exception.UploadFileException;
import com.alibaba.dashscope.utils.Constants;

public class Main {
    static {
        // Configurations vary by region. Modify the configuration as needed.
        Constants.baseHttpApiUrl="https://dashscope-intl.aliyuncs.com/api/v1";
    }
    private static final String MODEL_NAME = "qwen3.6-plus";  // This example uses qwen3.6-plus. You can replace it with another model name as needed. For a list of models, see https://www.alibabacloud.com/help/model-studio/getting-started/models
    public static void videoImageListSample() throws ApiException, NoApiKeyException, UploadFileException {
        MultiModalConversation conv = new MultiModalConversation();
        // When you provide an image list, the fps parameter applies to the Qwen3.6, Qwen3-VL, and Qwen2.5-VL series models.
        Map&lt;String, Object&gt; params = new HashMap<>();
        params.put("video", Arrays.asList("https://help-static-aliyun-doc.aliyuncs.com/file-manage-files/zh-CN/20241108/xzsgiz/football1.jpg",
                        "https://help-static-aliyun-doc.aliyuncs.com/file-manage-files/zh-CN/20241108/tdescd/football2.jpg",
                        "https://help-static-aliyun-doc.aliyuncs.com/file-manage-files/zh-CN/20241108/zefdja/football3.jpg",
                        "https://help-static-aliyun-doc.aliyuncs.com/file-manage-files/zh-CN/20241108/aedbqh/football4.jpg"));
        params.put("fps", 2);
        MultiModalMessage userMessage = MultiModalMessage.builder()
                .role(Role.USER.getValue())
                .content(Arrays.asList(
                        params,
                        Collections.singletonMap("text", "Describe the specific process in this video")))
                .build();
        MultiModalConversationParam param = MultiModalConversationParam.builder()
                // API keys vary by region. To get an API key, see https://www.alibabacloud.com/help/model-studio/get-api-key
                // If you have not configured the environment variable, replace the following line with your Model Studio API key: .apiKey("sk-xxx")
                .apiKey(System.getenv("DASHSCOPE_API_KEY"))
                .model(MODEL_NAME)
                .messages(Arrays.asList(userMessage)).build();
        MultiModalConversationResult result = conv.call(param);
        System.out.print(result.getOutput().getChoices().get(0).getMessage().getContent().get(0).get("text"));
    }
    public static void main(String[] args) {
        try {
            videoImageListSample();
        } catch (ApiException | NoApiKeyException | UploadFileException e) {
            System.out.println(e.getMessage());
        }
        System.exit(0);
    }
}

curl ​

HELPCODEESCAPE-curl
# ======= Important =======
# Configurations vary by region. Modify the configuration as needed.
# API keys vary by region. To get an API key, see https://www.alibabacloud.com/help/model-studio/get-api-key
# === Delete this comment before execution ===

curl -X POST https://dashscope-intl.aliyuncs.com/api/v1/services/aigc/multimodal-generation/generation \
-H "Authorization: Bearer $DASHSCOPE_API_KEY" \
-H 'Content-Type: application/json' \
-d '{
  "model": "qwen3.6-plus",
  "input": {
    "messages": [
      {
        "role": "user",
        "content": [
          {
            "video": [
              "https://help-static-aliyun-doc.aliyuncs.com/file-manage-files/zh-CN/20241108/xzsgiz/football1.jpg",
              "https://help-static-aliyun-doc.aliyuncs.com/file-manage-files/zh-CN/20241108/tdescd/football2.jpg",
              "https://help-static-aliyun-doc.aliyuncs.com/file-manage-files/zh-CN/20241108/zefdja/football3.jpg",
              "https://help-static-aliyun-doc.aliyuncs.com/file-manage-files/zh-CN/20241108/aedbqh/football4.jpg"
            ],
            "fps":2

          },
          {
            "text": "Describe the specific process in this video"
          }
        ]
      }
    ]
  }
}'

Pass a local file (Base64 encoding or file path) ​

Visual understanding models support two methods for uploading local files: Base64 encoding and direct file path upload. Choose based on your file size and SDK:

  • Conversational or chat history scenarios: Use a public URL. The request size stays small and server-side caching reduces latency.

  • Offline or private deployment scenarios: Use Base64 encoding or a local file path to avoid server-side download timeouts.

  • DashScope SDK only: Direct local file path upload is supported.

For a full decision table, see How to choose a file upload method. Both methods must meet the file requirements in Image limitations.

Upload using Base64 encoding ​

Convert the file to a Base64-encoded string and pass it to the model. Suitable for OpenAI and DashScope SDKs, and HTTP requests. Steps to pass a Base64-encoded string (image example)

  1. Encode the file: Convert the local image to a Base64-encoded string.

    Example code for converting an image to Base64 encoding

    HELPCODEESCAPE-python
# Encoding function: Converts a local file to a Base64-encoded string
import base64
def encode_image(image_path):
    with open(image_path, "rb") as image_file:
        return base64.b64encode(image_file.read()).decode("utf-8")

# Replace xxx/eagle.png with the absolute path of your local image
base64_image = encode_image("xxx/eagle.png")

  2. Build a Data URL in the following format: data:[MIME_type];base64,{base64_image}.

    1. Replace MIME_type with the actual media type. The value must match a MIME Type listed in the Supported image formats table, such as image/jpeg or image/png.

    2. base64_image is the Base64-encoded string generated in the previous step.

  3. Invoke the model: Pass the Data URL in the image or image_url parameter and invoke the model.

Upload using a file path ​

Directly pass the local file path to the model. This method is supported only by the DashScope Python and Java SDKs. It is not supported for DashScope HTTP requests or the OpenAI-compatible mode.

Refer to the following table to specify the file path based on your programming language and operating system. Specify a file path (image example)

SystemSDKFile path to passExample
Linux or macOS systemPython SDKfile://file:///home/images/test.png
Java SDK
Windows systemPython SDKfile://file://D:/images/test.png
Java SDKfile:///file:///D:/images/test.png

Images ​

Pass using a file path ​

Python ​

HELPCODEESCAPE-python
import os
import dashscope

# Configurations vary by region. Modify this based on your actual region.
dashscope.base_http_api_url = 'https://dashscope-intl.aliyuncs.com/api/v1'

# Replace xxx/eagle.png with the absolute path of your local image.
local_path = "xxx/eagle.png"
image_path = f"file://{local_path}"
messages = [
                {'role':'user',
                'content': [{'image': image_path},
                            {'text': 'What scene is depicted in the image?'}]}]
response = dashscope.MultiModalConversation.call(
    # API keys vary by region. To obtain an API key, see https://www.alibabacloud.com/help/en/model-studio/get-api-key
    # If the environment variable is not configured, replace the following line with your Model Studio API key: api_key="sk-xxx"
    api_key=os.getenv('DASHSCOPE_API_KEY'),
    model='qwen3.6-plus',  # This example uses qwen3.6-plus. You can replace it with another model name as needed. For a list of models, see https://www.alibabacloud.com/help/model-studio/getting-started/models
    messages=messages)
print(response.output.choices[0].message.content[0]["text"])

Java ​

HELPCODEESCAPE-java
import java.util.Arrays;
import java.util.Collections;
import java.util.HashMap;
import com.alibaba.dashscope.aigc.multimodalconversation.MultiModalConversation;
import com.alibaba.dashscope.aigc.multimodalconversation.MultiModalConversationParam;
import com.alibaba.dashscope.aigc.multimodalconversation.MultiModalConversationResult;
import com.alibaba.dashscope.common.MultiModalMessage;
import com.alibaba.dashscope.common.Role;
import com.alibaba.dashscope.exception.ApiException;
import com.alibaba.dashscope.exception.NoApiKeyException;
import com.alibaba.dashscope.exception.UploadFileException;
import com.alibaba.dashscope.utils.Constants;

public class Main {

    static {
        // Configurations vary by region. Modify this based on your actual region.
        Constants.baseHttpApiUrl="https://dashscope-intl.aliyuncs.com/api/v1";
    }

    public static void callWithLocalFile(String localPath)
            throws ApiException, NoApiKeyException, UploadFileException {
        String filePath = "file://"+localPath;
        MultiModalConversation conv = new MultiModalConversation();
        MultiModalMessage userMessage = MultiModalMessage.builder().role(Role.USER.getValue())
                .content(Arrays.asList(new HashMap&lt;String, Object&gt;(){​{put("image", filePath);}​},
                        new HashMap&lt;String, Object&gt;(){​{put("text", "What scene is depicted in the image?");}​})).build();
        MultiModalConversationParam param = MultiModalConversationParam.builder()
                // API keys vary by region. To obtain an API key, see https://www.alibabacloud.com/help/en/model-studio/get-api-key
                // If the environment variable is not configured, replace the following line with your Model Studio API key: .apiKey("sk-xxx")
                .apiKey(System.getenv("DASHSCOPE_API_KEY"))
                .model("qwen3.6-plus")  // This example uses qwen3.6-plus. You can replace it with another model name as needed. For a list of models, see https://www.alibabacloud.com/help/model-studio/getting-started/models
                .messages(Arrays.asList(userMessage))
                .build();
        MultiModalConversationResult result = conv.call(param);
        System.out.println(result.getOutput().getChoices().get(0).getMessage().getContent().get(0).get("text"));}

    public static void main(String[] args) {
        try {
            // Replace xxx/eagle.png with the absolute path of your local image.
            callWithLocalFile("xxx/eagle.png");
        } catch (ApiException | NoApiKeyException | UploadFileException e) {
            System.out.println(e.getMessage());
        }
        System.exit(0);
    }
}

Passing in Base64-encoded data ​

OpenAI compatible ​

Python ​

HELPCODEESCAPE-python
from openai import OpenAI
import os
import base64

# Encoding function: Converts a local file to a Base64-encoded string.
def encode_image(image_path):
    with open(image_path, "rb") as image_file:
        return base64.b64encode(image_file.read()).decode("utf-8")

# Replace xxx/eagle.png with the absolute path of your local image.
base64_image = encode_image("xxx/eagle.png")
client = OpenAI(
    # API keys vary by region. To obtain an API key, see https://www.alibabacloud.com/help/en/model-studio/get-api-key
    # If the environment variable is not configured, replace the following line with your Model Studio API key: api_key="sk-xxx"
    api_key=os.getenv('DASHSCOPE_API_KEY'),
    # Configurations vary by region. Modify this based on your actual region.
    base_url="https://dashscope-intl.aliyuncs.com/compatible-mode/v1",
)
completion = client.chat.completions.create(
    model="qwen3.6-plus", # This example uses qwen3.6-plus. You can replace it with another model name as needed. For a list of models, see https://www.alibabacloud.com/help/model-studio/getting-started/models
    messages=[
        {
            "role": "user",
            "content": [
                {
                    "type": "image_url",
                    # When passing Base64 image data, note that the image format (image/{format}) must match the Content-Type in the list of supported images. "f" is a string formatting method.
                    # PNG image:  f"data:image/png;base64,{base64_image}"
                    # JPEG image: f"data:image/jpeg;base64,{base64_image}"
                    # WEBP image: f"data:image/webp;base64,{base64_image}"
                    "image_url": {"url": f"data:image/png;base64,{base64_image}"},
                },
                {"type": "text", "text": "What scene is depicted in the image?"},
            ],
        }
    ],
)
print(completion.choices[0].message.content)

Node.js ​

HELPCODEESCAPE-nodejs
import OpenAI from "openai";
import { readFileSync } from 'fs';

const openai = new OpenAI(
    {
        // API keys vary by region. To obtain an API key, see https://www.alibabacloud.com/help/en/model-studio/get-api-key
        // If the environment variable is not configured, replace the following line with your Model Studio API key: apiKey: "sk-xxx"
        apiKey: process.env.DASHSCOPE_API_KEY,
        // Configurations vary by region. Modify this based on your actual region.
        baseURL: "https://dashscope-intl.aliyuncs.com/compatible-mode/v1"
    }
);

const encodeImage = (imagePath) => {
    const imageFile = readFileSync(imagePath);
    return imageFile.toString('base64');
  };
// Replace xxx/eagle.png with the absolute path of your local image.
const base64Image = encodeImage("xxx/eagle.png")
async function main() {
    const completion = await openai.chat.completions.create({
        model: "qwen3.6-plus",  // This example uses qwen3.6-plus. You can replace it with another model name as needed. For a list of models, see https://www.alibabacloud.com/help/model-studio/getting-started/models
        messages: [
            {"role": "user",
            "content": [{"type": "image_url",
                            // Note: When passing Base64 data, the image format (image/{format}) must match the Content-Type in the list of supported images.
                           // PNG image:  data:image/png;base64,${base64Image}
                          // JPEG image: data:image/jpeg;base64,${base64Image}
                         // WEBP image: data:image/webp;base64,${base64Image}
                        "image_url": {"url": `data:image/png;base64,${base64Image}`},},
                        {"type": "text", "text": "What scene is depicted in the image?"}]}]
    });
    console.log(completion.choices[0].message.content);
}

main();

curl ​

  • To convert a file to a Base64-encoded string, see the example code.

  • For demonstration purposes, the Base64-encoded string in the code, "data:image/jpg;base64,/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAA...", is truncated. In practice, you must pass the complete encoded string.

HELPCODEESCAPE-curl
# ======= Important =======
# API keys vary by region. To obtain an API key, see https://www.alibabacloud.com/help/en/model-studio/get-api-key
# Configurations vary by region. Modify this based on your actual region.
# === Delete this comment before execution ===

curl --location 'https://dashscope-intl.aliyuncs.com/compatible-mode/v1/chat/completions' \
--header "Authorization: Bearer $DASHSCOPE_API_KEY" \
--header 'Content-Type: application/json' \
--data '{
  "model": "qwen3.6-plus",
  "messages": [
  {
    "role": "user",
    "content": [
      {"type": "image_url", "image_url": {"url": "data:image/jpg;base64,/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAA"}​},
      {"type": "text", "text": "What scene is depicted in the image?"}
    ]
  }]
}'

DashScope ​

Python ​

HELPCODEESCAPE-python
import base64
import os
import dashscope

# Configurations vary by region. Modify this based on your actual region.
dashscope.base_http_api_url = 'https://dashscope-intl.aliyuncs.com/api/v1'

# Encoding function: Converts a local file to a Base64-encoded string.
def encode_image(image_path):
    with open(image_path, "rb") as image_file:
        return base64.b64encode(image_file.read()).decode("utf-8")

# Replace xxxx/eagle.png with the absolute path of your local image.
base64_image = encode_image("xxxx/eagle.png")

messages = [
    {
        "role": "user",
        "content": [
            # Note: When passing Base64 data, the image format (image/{format}) must match the Content-Type in the list of supported images. "f" is a string formatting method.
            # PNG image:  f"data:image/png;base64,{base64_image}"
            # JPEG image: f"data:image/jpeg;base64,{base64_image}"
            # WEBP image: f"data:image/webp;base64,{base64_image}"
            {"image": f"data:image/png;base64,{base64_image}"},
            {"text": "What scene is depicted in the image?"},
        ],
    },
]

response = dashscope.MultiModalConversation.call(
    # API keys vary by region. To obtain an API key, see https://www.alibabacloud.com/help/en/model-studio/get-api-key
    # If the environment variable is not configured, replace the following line with your Model Studio API key: api_key="sk-xxx"
    api_key=os.getenv("DASHSCOPE_API_KEY"),
    model="qwen3.6-plus",  # This example uses qwen3.6-plus. You can replace it with another model name as needed. For a list of models, see https://www.alibabacloud.com/help/model-studio/getting-started/models
    messages=messages,
)
print(response.output.choices[0].message.content[0]["text"])

Java ​

HELPCODEESCAPE-java
import java.io.IOException;
import java.util.Arrays;
import java.util.Collections;
import java.util.HashMap;
import java.util.Base64;
import java.nio.file.Files;
import java.nio.file.Path;
import java.nio.file.Paths;

import com.alibaba.dashscope.aigc.multimodalconversation.*;
import com.alibaba.dashscope.common.MultiModalMessage;
import com.alibaba.dashscope.common.Role;
import com.alibaba.dashscope.exception.ApiException;
import com.alibaba.dashscope.exception.NoApiKeyException;
import com.alibaba.dashscope.exception.UploadFileException;
import com.alibaba.dashscope.utils.Constants;

public class Main {

    static {
        // Configurations vary by region. Modify this based on your actual region.
        Constants.baseHttpApiUrl="https://dashscope-intl.aliyuncs.com/api/v1";
    }

    private static String encodeImageToBase64(String imagePath) throws IOException {
        Path path = Paths.get(imagePath);
        byte[] imageBytes = Files.readAllBytes(path);
        return Base64.getEncoder().encodeToString(imageBytes);
    }

    public static void callWithLocalFile(String localPath) throws ApiException, NoApiKeyException, UploadFileException, IOException {

        String base64Image = encodeImageToBase64(localPath); // Base64 encoding

        MultiModalConversation conv = new MultiModalConversation();
        MultiModalMessage userMessage = MultiModalMessage.builder().role(Role.USER.getValue())
                .content(Arrays.asList(
                        new HashMap&lt;String, Object&gt;() {​{ put("image", "data:image/png;base64," + base64Image); }​},
                        new HashMap&lt;String, Object&gt;() {​{ put("text", "What scene is depicted in the image?"); }​}
                )).build();

        MultiModalConversationParam param = MultiModalConversationParam.builder()
                // API keys vary by region. To obtain an API key, see https://www.alibabacloud.com/help/en/model-studio/get-api-key
                .apiKey(System.getenv("DASHSCOPE_API_KEY"))
                .model("qwen3.6-plus")
                .messages(Arrays.asList(userMessage))
                .build();

        MultiModalConversationResult result = conv.call(param);
        System.out.println(result.getOutput().getChoices().get(0).getMessage().getContent().get(0).get("text"));
    }

    public static void main(String[] args) {
        try {
            // Replace xxx/eagle.png with the absolute path of your local image.
            callWithLocalFile("xxx/eagle.png");
        } catch (ApiException | NoApiKeyException | UploadFileException | IOException e) {
            System.out.println(e.getMessage());
        }
        System.exit(0);
    }
}

curl ​

  • To convert a file to a Base64-encoded string, see the example code.

  • For demonstration purposes, the Base64-encoded string in the code, "data:image/jpg;base64,/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAA...", is truncated. In practice, you must pass the complete encoded string.

HELPCODEESCAPE-curl
# ======= Important =======
# Configurations vary by region. Modify this based on your actual region.
# API keys vary by region. To obtain an API key, see https://www.alibabacloud.com/help/en/model-studio/get-api-key
# === Delete this comment before execution ===

curl -X POST https://dashscope.aliyuncs.com/api/v1/services/aigc/multimodal-generation/generation \
-H "Authorization: Bearer $DASHSCOPE_API_KEY" \
-H 'Content-Type: application/json' \
-d '{
    "model": "qwen3.6-plus",
    "input":{
        "messages":[
            {
             "role": "user",
             "content": [
               {"image": "data:image/jpeg;base64,/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAA..."},
               {"text": "What scene is depicted in the image?"}
                ]
            }
        ]
    }
}'

Video files ​

This topic uses a local file named test.mp4 as an example.

Pass the file path ​

Python ​

HELPCODEESCAPE-python
import os
import dashscope

# Configurations vary by region. Modify the settings based on your region.
dashscope.base_http_api_url = 'https://dashscope-intl.aliyuncs.com/api/v1'

# Replace xxx/test.mp4 with the absolute path of your local video file.
local_path = "xxx/test.mp4"
video_path = f"file://{local_path}"
messages = [
                {'role':'user',
                # The fps parameter controls the number of frames extracted from the video. It extracts one frame every 1/fps seconds.
                'content': [{'video': video_path,"fps":2},
                            {'text': 'What scene does this video depict?'}]}]
response = MultiModalConversation.call(
    # API keys vary by region. To obtain an API key, see https://www.alibabacloud.com/help/en/model-studio/get-api-key
    # If the environment variable is not set, replace the following line with your Model Studio API key: api_key="sk-xxx"
    api_key=os.getenv('DASHSCOPE_API_KEY'),
    model='qwen3.6-plus',
    messages=messages)
print(response.output.choices[0].message.content[0]["text"])

Java ​

HELPCODEESCAPE-java
import java.util.Arrays;
import java.util.Collections;
import java.util.HashMap;
import com.alibaba.dashscope.aigc.multimodalconversation.MultiModalConversation;
import com.alibaba.dashscope.aigc.multimodalconversation.MultiModalConversationParam;
import com.alibaba.dashscope.aigc.multimodalconversation.MultiModalConversationResult;
import com.alibaba.dashscope.common.MultiModalMessage;
import com.alibaba.dashscope.common.Role;
import com.alibaba.dashscope.exception.ApiException;
import com.alibaba.dashscope.exception.NoApiKeyException;
import com.alibaba.dashscope.exception.UploadFileException;
import com.alibaba.dashscope.utils.Constants;

public class Main {

    static {
        // Configurations vary by region. Modify the settings based on your region.
        Constants.baseHttpApiUrl="https://dashscope-intl.aliyuncs.com/api/v1";
    }

    public static void callWithLocalFile(String localPath)
            throws ApiException, NoApiKeyException, UploadFileException {
        String filePath = "file://"+localPath;
        MultiModalConversation conv = new MultiModalConversation();
        MultiModalMessage userMessage = MultiModalMessage.builder().role(Role.USER.getValue())
                .content(Arrays.asList(new HashMap&lt;String, Object&gt;()
                                       {​{
                                           put("video", filePath);// The fps parameter controls the number of frames extracted from the video. It extracts one frame every 1/fps seconds.
                                           put("fps", 2);
                                       }​},
                        new HashMap&lt;String, Object&gt;(){​{put("text", "What scene does this video depict?");}​})).build();
        MultiModalConversationParam param = MultiModalConversationParam.builder()
                // API keys vary by region. To obtain an API key, see https://www.alibabacloud.com/help/en/model-studio/get-api-key
                // If the environment variable is not set, replace the following line with your Model Studio API key: .apiKey("sk-xxx")
                .apiKey(System.getenv("DASHSCOPE_API_KEY"))
                .model("qwen3.6-plus")
                .messages(Arrays.asList(userMessage))
                .build();
        MultiModalConversationResult result = conv.call(param);
        System.out.println(result.getOutput().getChoices().get(0).getMessage().getContent().get(0).get("text"));}

    public static void main(String[] args) {
        try {
            // Replace xxx/test.mp4 with the absolute path of your local video file.
            callWithLocalFile("xxx/test.mp4");
        } catch (ApiException | NoApiKeyException | UploadFileException e) {
            System.out.println(e.getMessage());
        }
        System.exit(0);
    }
}

Pass the Base64 encoded string ​

OpenAI compatible ​

Python ​

HELPCODEESCAPE-python
from openai import OpenAI
import os
import base64

# Encoding function: Converts a local file to a Base64 encoded string.
def encode_video(video_path):
    with open(video_path, "rb") as video_file:
        return base64.b64encode(video_file.read()).decode("utf-8")

# Replace xxx/test.mp4 with the absolute path of your local video file.
base64_video = encode_video("xxx/test.mp4")
client = OpenAI(
    # API keys vary by region. To obtain an API key, see https://www.alibabacloud.com/help/en/model-studio/get-api-key
    # If the environment variable is not set, replace the following line with your Model Studio API key: api_key="sk-xxx"
    api_key=os.getenv('DASHSCOPE_API_KEY'),
    # Configurations vary by region. Modify the settings based on your region.
    base_url="https://dashscope-intl.aliyuncs.com/compatible-mode/v1",
)
completion = client.chat.completions.create(
    model="qwen3.6-plus",
    messages=[
        {
            "role": "user",
            "content": [
                {
                    # When passing a video file directly, set the value of type to video_url.
                    "type": "video_url",
                    "video_url": {"url": f"data:video/mp4;base64,{base64_video}"},
                    "fps":2
                },
                {"type": "text", "text": "What scene does this video depict?"},
            ],
        }
    ],
)
print(completion.choices[0].message.content)

Node.js ​

HELPCODEESCAPE-nodejs
import OpenAI from "openai";
import { readFileSync } from 'fs';

const openai = new OpenAI(
    {
        // API keys vary by region. To obtain an API key, see https://www.alibabacloud.com/help/en/model-studio/get-api-key
        // If the environment variable is not set, replace the following line with your Model Studio API key: apiKey: "sk-xxx"
        apiKey: process.env.DASHSCOPE_API_KEY,
        // Configurations vary by region. Modify the settings based on your region.
        baseURL: "https://dashscope-intl.aliyuncs.com/compatible-mode/v1"
    }
);

const encodeVideo = (videoPath) => {
    const videoFile = readFileSync(videoPath);
    return videoFile.toString('base64');
  };
// Replace xxx/test.mp4 with the absolute path of your local video file.
const base64Video = encodeVideo("xxx/test.mp4")
async function main() {
    const completion = await openai.chat.completions.create({
        model: "qwen3.6-plus",
        messages: [
            {"role": "user",
             "content": [{
                 // When passing a video file directly, set the value of type to video_url.
                "type": "video_url",
                "video_url": {"url": `data:video/mp4;base64,${base64Video}`},
                "fps":2},
                 {"type": "text", "text": "What scene does this video depict?"}]}]
    });
    console.log(completion.choices[0].message.content);
}

main();

curl ​

  • To convert a file to a Base64-encoded string, see the sample code.

  • For demonstration purposes, the Base64-encoded string in the code, "data:video/mp4;base64,/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAA...", is truncated. In practice, you must pass the complete encoded string.

HELPCODEESCAPE-curl
# ======= Important =======
# API keys vary by region. To obtain an API key, see https://www.alibabacloud.com/help/en/model-studio/get-api-key
# Configurations vary by region. Modify the settings based on your region.
# === Delete this comment before execution ===

curl --location 'https://dashscope.aliyuncs.com/compatible-mode/v1/chat/completions' \
--header "Authorization: Bearer $DASHSCOPE_API_KEY" \
--header 'Content-Type: application/json' \
--data '{
  "model": "qwen3.6-plus",
  "messages": [
  {
    "role": "user",
    "content": [
      {"type": "video_url", "video_url": {"url": "data:video/mp4;base64,/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAA..."},"fps":2},
      {"type": "text", "text": "What scene does this video depict?"}
    ]
  }]
}'

DashScope ​

Python ​

HELPCODEESCAPE-python
import base64
import os
import dashscope

# Configurations vary by region. Modify the settings based on your region.
dashscope.base_http_api_url = 'https://dashscope-intl.aliyuncs.com/api/v1'

# Encoding function: Converts a local file to a Base64 encoded string.
def encode_video(video_path):
    with open(video_path, "rb") as video_file:
        return base64.b64encode(video_file.read()).decode("utf-8")

# Replace xxxx/test.mp4 with the absolute path of your local video file.
base64_video = encode_video("xxxx/test.mp4")

messages = [{'role':'user',
                # The fps parameter controls the number of frames extracted from the video. It extracts one frame every 1/fps seconds.
             'content': [{'video': f"data:video/mp4;base64,{base64_video}","fps":2},
                            {'text': 'What scene does this video depict?'}]}]
response = dashscope.MultiModalConversation.call(
    # API keys vary by region. To obtain an API key, see https://www.alibabacloud.com/help/en/model-studio/get-api-key
    # If the environment variable is not set, replace the following line with your Model Studio API key: api_key="sk-xxx"
    api_key=os.getenv('DASHSCOPE_API_KEY'),
    model='qwen3.6-plus',
    messages=messages)

print(response.output.choices[0].message.content[0]["text"])

Java ​

HELPCODEESCAPE-java
import java.io.IOException;
import java.util.*;
import java.nio.file.Files;
import java.nio.file.Path;
import java.nio.file.Paths;

import com.alibaba.dashscope.aigc.multimodalconversation.*;
import com.alibaba.dashscope.common.MultiModalMessage;
import com.alibaba.dashscope.common.Role;
import com.alibaba.dashscope.exception.ApiException;
import com.alibaba.dashscope.exception.NoApiKeyException;
import com.alibaba.dashscope.exception.UploadFileException;
import com.alibaba.dashscope.utils.Constants;

public class Main {

    static {
        // Configurations vary by region. Modify the settings based on your region.
        Constants.baseHttpApiUrl="https://dashscope-intl.aliyuncs.com/api/v1";
    }

    private static String encodeVideoToBase64(String videoPath) throws IOException {
        Path path = Paths.get(videoPath);
        byte[] videoBytes = Files.readAllBytes(path);
        return Base64.getEncoder().encodeToString(videoBytes);
    }

    public static void callWithLocalFile(String localPath)
            throws ApiException, NoApiKeyException, UploadFileException, IOException {

        String base64Video = encodeVideoToBase64(localPath); // Base64 encoding

        MultiModalConversation conv = new MultiModalConversation();
        MultiModalMessage userMessage = MultiModalMessage.builder().role(Role.USER.getValue())
                .content(Arrays.asList(new HashMap&lt;String, Object&gt;()
                                       {​{
                                           put("video", "data:video/mp4;base64," + base64Video);// The fps parameter controls the number of frames extracted from the video. It extracts one frame every 1/fps seconds.
                                           put("fps", 2);
                                       }​},
                        new HashMap&lt;String, Object&gt;(){​{put("text", "What scene does this video depict?");}​})).build();

        MultiModalConversationParam param = MultiModalConversationParam.builder()
                // API keys vary by region. To obtain an API key, see https://www.alibabacloud.com/help/en/model-studio/get-api-key
                // If the environment variable is not set, replace the following line with your Model Studio API key: .apiKey("sk-xxx")
                .apiKey(System.getenv("DASHSCOPE_API_KEY"))
                .model("qwen3.6-plus")
                .messages(Arrays.asList(userMessage))
                .build();

        MultiModalConversationResult result = conv.call(param);
        System.out.println(result.getOutput().getChoices().get(0).getMessage().getContent().get(0).get("text"));
    }

    public static void main(String[] args) {
        try {
            // Replace xxx/test.mp4 with the absolute path of your local video file.
            callWithLocalFile("xxx/test.mp4");
        } catch (ApiException | NoApiKeyException | UploadFileException | IOException e) {
            System.out.println(e.getMessage());
        }
        System.exit(0);
    }
}

curl ​

  • To convert a file to a Base64-encoded string, see the sample code.

  • For demonstration purposes, the Base64-encoded string in the code, "data:video/mp4;base64,/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAA...", is truncated. In practice, you must pass the complete encoded string.

HELPCODEESCAPE-curl
# ======= Important =======
# Configurations vary by region. Modify the settings based on your region.
# API keys vary by region. To obtain an API key, see https://www.alibabacloud.com/help/en/model-studio/get-api-key
# === Delete this comment before execution ===

curl -X POST https://dashscope-intl.aliyuncs.com/api/v1/services/aigc/multimodal-generation/generation \
-H "Authorization: Bearer $DASHSCOPE_API_KEY" \
-H 'Content-Type: application/json' \
-d '{
    "model": "qwen3.6-plus",
    "input":{
        "messages":[
            {
             "role": "user",
             "content": [
               {"video": "data:video/mp4;base64,/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAA..."},
               {"text": "What scene does this video depict? "}
                ]
            }
        ]
    }
}'

Image list ​

This example uses the local files football1.jpg, football2.jpg, football3.jpg, and football4.jpg.

Pass by file path ​

Python ​

HELPCODEESCAPE-python
import os
import dashscope

# Configurations vary by region. Modify the settings based on your region.
dashscope.base_http_api_url = 'https://dashscope-intl.aliyuncs.com/api/v1'

local_path1 = "football1.jpg"
local_path2 = "football2.jpg"
local_path3 = "football3.jpg"
local_path4 = "football4.jpg"

image_path1 = f"file://{local_path1}"
image_path2 = f"file://{local_path2}"
image_path3 = f"file://{local_path3}"
image_path4 = f"file://{local_path4}"

messages = [{'role':'user',
              #  When you pass an image list, the fps parameter applies to the Qwen3.6, Qwen3-VL, and Qwen2.5-VL series models.
             'content': [{'video': [image_path1,image_path2,image_path3,image_path4],"fps":2},
                         {'text': 'What scene does this video depict?'}]}]
response = MultiModalConversation.call(
    # API keys vary by region. To obtain an API key, see https://www.alibabacloud.com/help/en/model-studio/get-api-key
    # If you have not configured the environment variable, replace the following line with your Model Studio API key: api_key="sk-xxx"
    api_key=os.getenv('DASHSCOPE_API_KEY'),
    model='qwen3.6-plus',  # This example uses qwen3.6-plus. You can replace it with another model name as needed. For a list of models, see https://www.alibabacloud.com/help/model-studio/getting-started/models
    messages=messages)

print(response.output.choices[0].message.content[0]["text"])

Java ​

HELPCODEESCAPE-java
// The DashScope SDK version must be 2.21.10 or later.
import java.util.Arrays;
import java.util.Map;
import java.util.Collections;
import com.alibaba.dashscope.aigc.multimodalconversation.MultiModalConversation;
import com.alibaba.dashscope.aigc.multimodalconversation.MultiModalConversationParam;
import com.alibaba.dashscope.aigc.multimodalconversation.MultiModalConversationResult;
import com.alibaba.dashscope.common.MultiModalMessage;
import com.alibaba.dashscope.common.Role;
import com.alibaba.dashscope.exception.ApiException;
import com.alibaba.dashscope.exception.NoApiKeyException;
import com.alibaba.dashscope.exception.UploadFileException;
import com.alibaba.dashscope.utils.Constants;

public class Main {

    static {
        // Configurations vary by region. Modify the settings based on your region.
        Constants.baseHttpApiUrl="https://dashscope-intl.aliyuncs.com/api/v1";
    }

    private static final String MODEL_NAME = "qwen3.6-plus";  // This example uses qwen3.6-plus. You can replace it with another model name as needed. For a list of models, see https://www.alibabacloud.com/help/model-studio/getting-started/models
    public static void videoImageListSample(String localPath1, String localPath2, String localPath3, String localPath4)
            throws ApiException, NoApiKeyException, UploadFileException {
        MultiModalConversation conv = new MultiModalConversation();
        String filePath1 = "file://" + localPath1;
        String filePath2 = "file://" + localPath2;
        String filePath3 = "file://" + localPath3;
        String filePath4 = "file://" + localPath4;
        Map&lt;String, Object&gt; params = new HashMap<>();
        params.put("video", Arrays.asList(filePath1,filePath2,filePath3,filePath4));
        //  When you pass an image list, the fps parameter applies to the Qwen3.6, Qwen3-VL, and Qwen2.5-VL series models.
        params.put("fps", 2);
        MultiModalMessage userMessage = MultiModalMessage.builder()
                .role(Role.USER.getValue())
                .content(Arrays.asList(params,
                        Collections.singletonMap("text", "Describe the specific process in this video")))
                .build();
        MultiModalConversationParam param = MultiModalConversationParam.builder()
                // API keys vary by region. To obtain an API key, see https://www.alibabacloud.com/help/en/model-studio/get-api-key
                // If you have not configured the environment variable, replace the following line with your Model Studio API key: .apiKey("sk-xxx")
                .apiKey(System.getenv("DASHSCOPE_API_KEY"))
                .model(MODEL_NAME)
                .messages(Arrays.asList(userMessage)).build();
        MultiModalConversationResult result = conv.call(param);
        System.out.print(result.getOutput().getChoices().get(0).getMessage().getContent().get(0).get("text"));
    }
    public static void main(String[] args) {
        try {
            videoImageListSample(
                    "xxx/football1.jpg",
                    "xxx/football2.jpg",
                    "xxx/football3.jpg",
                    "xxx/football4.jpg");
        } catch (ApiException | NoApiKeyException | UploadFileException e) {
            System.out.println(e.getMessage());
        }
        System.exit(0);
    }
}

Base64-encoded input ​

OpenAI compatible ​

Python ​

HELPCODEESCAPE-python
import os
from openai import OpenAI
import base64

# Encoding function: Converts a local file to a Base64-encoded string.
def encode_image(image_path):
    with open(image_path, "rb") as image_file:
        return base64.b64encode(image_file.read()).decode("utf-8")

base64_image1 = encode_image("football1.jpg")
base64_image2 = encode_image("football2.jpg")
base64_image3 = encode_image("football3.jpg")
base64_image4 = encode_image("football4.jpg")
client = OpenAI(
    # API keys vary by region. To obtain an API key, see https://www.alibabacloud.com/help/en/model-studio/get-api-key
    # If you have not configured the environment variable, replace the following line with your Model Studio API key: api_key="sk-xxx",
    api_key=os.getenv("DASHSCOPE_API_KEY"),
    # Configurations vary by region. Modify the settings based on your region.
    base_url="https://dashscope-intl.aliyuncs.com/compatible-mode/v1",
)
completion = client.chat.completions.create(
    model="qwen3.6-plus",  # This example uses qwen3.6-plus. You can replace it with another model name as needed. For a list of models, see https://www.alibabacloud.com/help/model-studio/getting-started/models
    messages=[
    {"role": "user","content": [
        {"type": "video","video": [
            f"data:image/jpeg;base64,{base64_image1}",
            f"data:image/jpeg;base64,{base64_image2}",
            f"data:image/jpeg;base64,{base64_image3}",
            f"data:image/jpeg;base64,{base64_image4}",]},
        {"type": "text","text": "Describe the specific process in this video"},
    ]}]
)
print(completion.choices[0].message.content)

Node.js ​

HELPCODEESCAPE-nodejs
import OpenAI from "openai";
import { readFileSync } from 'fs';

const openai = new OpenAI(
    {
        // API keys vary by region. To obtain an API key, see https://www.alibabacloud.com/help/en/model-studio/get-api-key
        // If you have not configured the environment variable, replace the following line with your Model Studio API key: apiKey: "sk-xxx"
        apiKey: process.env.DASHSCOPE_API_KEY,
        // Configurations vary by region. Modify the settings based on your region.
        baseURL: "https://dashscope-intl.aliyuncs.com/compatible-mode/v1"
    }
);

const encodeImage = (imagePath) => {
    const imageFile = readFileSync(imagePath);
    return imageFile.toString('base64');
  };

const base64Image1 = encodeImage("football1.jpg")
const base64Image2 = encodeImage("football2.jpg")
const base64Image3 = encodeImage("football3.jpg")
const base64Image4 = encodeImage("football4.jpg")
async function main() {
    const completion = await openai.chat.completions.create({
        model: "qwen3.6-plus",  // This example uses qwen3.6-plus. You can replace it with another model name as needed. For a list of models, see https://www.alibabacloud.com/help/model-studio/getting-started/models
        messages: [
            {"role": "user",
             "content": [{"type": "video",
                        "video": [
                            `data:image/jpeg;base64,${base64Image1}`,
                            `data:image/jpeg;base64,${base64Image2}`,
                            `data:image/jpeg;base64,${base64Image3}`,
                            `data:image/jpeg;base64,${base64Image4}`]},
                        {"type": "text", "text": "What scene does this video depict?"}]}]
    });
    console.log(completion.choices[0].message.content);
}

main();

curl ​

  • To convert a file to a Base64-encoded string, see the sample code.

  • For demonstration purposes, the Base64-encoded string "data:image/jpg;base64,/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAA..." in the code is truncated. In practice, you must pass the complete encoded string.

HELPCODEESCAPE-curl
# ======= Important =======
# Configurations vary by region. Modify the settings based on your region.
# API keys vary by region. To obtain an API key, see https://www.alibabacloud.com/help/en/model-studio/get-api-key
# === Delete this comment before execution ===

curl -X POST https://dashscope-intl.aliyuncs.com/compatible-mode/v1/chat/completions \
-H "Authorization: Bearer $DASHSCOPE_API_KEY" \
-H 'Content-Type: application/json' \
-d '{
    "model": "qwen3.6-plus",
    "messages": [{"role": "user",
                "content": [{"type": "video",
                "video": [
                          "data:image/jpeg;base64,/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAA...",
                          "data:image/jpeg;base64,nEpp6jpnP57MoWSyOWwrkXMJhHRCWYeFYb...",
                          "data:image/jpeg;base64,JHWQnJPc40GwQ7zERAtRMK6iIhnWw4080s...",
                          "data:image/jpeg;base64,adB6QOU5HP7dAYBBOg/Fb7KIptlbyEOu58..."
                          ]},
                {"type": "text",
                "text": "Describe the specific process in this video"}]}]
}'

DashScope ​

Python ​

HELPCODEESCAPE-python
import base64
import os
import dashscope

# Configurations vary by region. Modify the settings based on your region.
dashscope.base_http_api_url = 'https://dashscope-intl.aliyuncs.com/api/v1'

#  Encoding function: Converts a local file to a Base64-encoded string.
def encode_image(image_path):
    with open(image_path, "rb") as image_file:
        return base64.b64encode(image_file.read()).decode("utf-8")

base64_image1 = encode_image("football1.jpg")
base64_image2 = encode_image("football2.jpg")
base64_image3 = encode_image("football3.jpg")
base64_image4 = encode_image("football4.jpg")

messages = [{'role':'user',
            'content': [
                    {'video':
                         [f"data:image/jpeg;base64,{base64_image1}",
                          f"data:image/jpeg;base64,{base64_image2}",
                          f"data:image/jpeg;base64,{base64_image3}",
                          f"data:image/jpeg;base64,{base64_image4}"
                         ]
                    },
                    {'text': 'Describe the specific process in this video.'}]}]
response = dashscope.MultiModalConversation.call(
    # API keys vary by region. To obtain an API key, see https://www.alibabacloud.com/help/en/model-studio/get-api-key
    api_key=os.getenv("DASHSCOPE_API_KEY"),
    model='qwen3.6-plus',  # This example uses qwen3.6-plus. You can replace it with another model name as needed. For a list of models, see https://www.alibabacloud.com/help/model-studio/getting-started/models
    messages=messages)

print(response.output.choices[0].message.content[0]["text"])

Java ​

HELPCODEESCAPE-java
import java.io.IOException;
import java.util.*;
import java.nio.file.Files;
import java.nio.file.Path;
import java.nio.file.Paths;

import com.alibaba.dashscope.aigc.multimodalconversation.*;
import com.alibaba.dashscope.common.MultiModalMessage;
import com.alibaba.dashscope.common.Role;
import com.alibaba.dashscope.exception.ApiException;
import com.alibaba.dashscope.exception.NoApiKeyException;
import com.alibaba.dashscope.exception.UploadFileException;
import com.alibaba.dashscope.utils.Constants;

public class Main {

    static {
        // Configurations vary by region. Modify the settings based on your region.
        Constants.baseHttpApiUrl="https://dashscope-intl.aliyuncs.com/api/v1";
    }

    private static String encodeImageToBase64(String imagePath) throws IOException {
        Path path = Paths.get(imagePath);
        byte[] imageBytes = Files.readAllBytes(path);
        return Base64.getEncoder().encodeToString(imageBytes);
    }

    public static void videoImageListSample(String localPath1,String localPath2,String localPath3,String localPath4)
            throws ApiException, NoApiKeyException, UploadFileException, IOException {

        String base64Image1 = encodeImageToBase64(localPath1); // Base64 encoding
        String base64Image2 = encodeImageToBase64(localPath2);
        String base64Image3 = encodeImageToBase64(localPath3);
        String base64Image4 = encodeImageToBase64(localPath4);

        MultiModalConversation conv = new MultiModalConversation();
        Map&lt;String, Object&gt; params = new HashMap<>();
        params.put("video", Arrays.asList(
                        "data:image/jpeg;base64," + base64Image1,
                        "data:image/jpeg;base64," + base64Image2,
                        "data:image/jpeg;base64," + base64Image3,
                        "data:image/jpeg;base64," + base64Image4));
        //  When you pass an image list, the fps parameter applies to the Qwen3.6, Qwen3-VL, and Qwen2.5-VL series models.
        params.put("fps", 2);
        MultiModalMessage userMessage = MultiModalMessage.builder()
                .role(Role.USER.getValue())
                .content(Arrays.asList(params,
                        Collections.singletonMap("text", "Describe the specific process in this video")))
                .build();

        MultiModalConversationParam param = MultiModalConversationParam.builder()
                // API keys vary by region. To obtain an API key, see https://www.alibabacloud.com/help/en/model-studio/get-api-key
                .apiKey(System.getenv("DASHSCOPE_API_KEY"))
                .model("qwen3.6-plus")
                .messages(Arrays.asList(userMessage))
                .build();

        MultiModalConversationResult result = conv.call(param);
        System.out.println(result.getOutput().getChoices().get(0).getMessage().getContent().get(0).get("text"));
    }

    public static void main(String[] args) {
        try {
            // Replace xxx/football1.png and other placeholders with the absolute paths of your local images.
            videoImageListSample(
                    "xxx/football1.jpg",
                    "xxx/football2.jpg",
                    "xxx/football3.jpg",
                    "xxx/football4.jpg"
            );
        } catch (ApiException | NoApiKeyException | UploadFileException | IOException e) {
            System.out.println(e.getMessage());
        }
        System.exit(0);
    }
}

curl ​

  • To convert a file to a Base64-encoded string, see the sample code.

  • For demonstration purposes, the Base64-encoded string "data:image/jpg;base64,/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAA..." in the code is truncated. In practice, you must pass the complete encoded string.

HELPCODEESCAPE-curl
# ======= Important =======
# Configurations vary by region. Modify the settings based on your region.
# API keys vary by region. To obtain an API key, see https://www.alibabacloud.com/help/en/model-studio/get-api-key
# === Delete this comment before execution ===

curl -X POST https://dashscope-intl.aliyuncs.com/api/v1/services/aigc/multimodal-generation/generation \
-H "Authorization: Bearer $DASHSCOPE_API_KEY" \
-H 'Content-Type: application/json' \
-d '{
  "model": "qwen3.6-plus",
  "input": {
    "messages": [
      {
        "role": "user",
        "content": [
          {
            "video": [
                      "data:image/jpeg;base64,/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAA...",
                      "data:image/jpeg;base64,nEpp6jpnP57MoWSyOWwrkXMJhHRCWYeFYb...",
                      "data:image/jpeg;base64,JHWQnJPc40GwQ7zERAtRMK6iIhnWw4080s...",
                      "data:image/jpeg;base64,adB6QOU5HP7dAYBBOg/Fb7KIptlbyEOu58..."
            ],
            "fps":2
          },
          {
            "text": "Describe the specific process in this video"
          }
        ]
      }
    ]
  }
}'

Processing high-resolution images ​

The API limits visual tokens per image. With default settings, high-resolution images are compressed and fine details can be lost. Use vl_high_resolution_images or adjust max_pixels to retain more detail. View the pixels per visual token, token limit, and pixel limit for each model

If the input image resolution exceeds the model's pixel limit, the image is scaled down to fit within the limit.

ModelPixels per tokenvl_high_resolution_imagesmax_pixelsToken limitPixel limit
Qwen3.6 and Qwen3-VL series models32*32truemax_pixels is invalid16384 tokens16777216 (which is 16384&lt;i&gt;32*32)
false (default)Customizable. The default is 2621440, and the maximum is 16777216.Determined by max_pixels, which is max_pixels/32/32max_pixels
qwen-vl-max, qwen-vl-max-latest, qwen-vl-max-2025-08-13, qwen-vl-plus, qwen-vl-plus-latest, qwen-vl-plus-2025-08-15, and models32*32truemax_pixels is invalid16384 tokens16777216 (which is 16384*32*32)
false (default)Customizable. The default is 1310720, and the maximum is 16777216.Determined by max_pixels, which is max_pixels/32/32max_pixels
Other qwen-vl-max, other qwen-vl-plus, Qwen2.5-VL open source series, and QVQ series models28*28truemax_pixels is invalid16384 tokens12845056 (which is 16384*28*28)
false (default)Customizable. The default is 1003520, and the maximum is 12845056.Determined by max_pixels, which is max_pixels/28/28max_pixels

  • When vl_high_resolution_images=true, the API applies a fixed high-resolution policy and ignores max_pixels. Use this when you need to detect fine text, small objects, or rich visual detail.

  • When vl_high_resolution_images=false, the pixel limit is controlled by max_pixels.

    • For high processing speed or cost-sensitive scenarios: use the default max_pixels value or set it smaller.

    • To prioritize detail over processing speed: increase max_pixels.

OpenAI compatibility ​

vl_high_resolution_images is not a standard OpenAI parameter. Pass it as follows:

  • Python SDK: pass through the extra_body dictionary.

  • Node.js SDK: pass as a top-level parameter.

Python ​

HELPCODEESCAPE-python
import os
import time
from openai import OpenAI

client = OpenAI(
    # API keys vary by region. To get an API key, see https://www.alibabacloud.com/help/en/model-studio/get-api-key
    api_key=os.getenv("DASHSCOPE_API_KEY"),
    # Configurations vary by region. Modify the configuration based on your region.
    base_url="https://dashscope-intl.aliyuncs.com/compatible-mode/v1",
)

completion = client.chat.completions.create(
    model="qwen3.6-plus",
    messages=[
        {"role": "user","content": [
            {"type": "image_url","image_url": {"url": "https://help-static-aliyun-doc.aliyuncs.com/file-manage-files/zh-CN/20250212/earbrt/vcg_VCG211286867973_RF.jpg"},
            # max_pixels specifies the maximum pixel threshold for the input image. It is invalid when vl_high_resolution_images=True. When vl_high_resolution_images=False, it is customizable, and the maximum value varies by model.
            # "max_pixels": 16384 * 32 * 32
            },
           {"type": "text", "text": "What festival atmosphere does this picture show"},
            ],
        }
    ],
    extra_body={"vl_high_resolution_images":True}

)
print(f"Model output: {completion.choices[0].message.content}")
print(f"Total input tokens: {completion.usage.prompt_tokens}")

Node.js ​

HELPCODEESCAPE-nodejs
import OpenAI from "openai";

const openai = new OpenAI(
    {
        // API keys vary by region. To get an API key, see https://www.alibabacloud.com/help/en/model-studio/get-api-key
        // If you have not configured the environment variable, replace the following line with your Model Studio API key: apiKey: "sk-xxx"
        apiKey: process.env.DASHSCOPE_API_KEY,
        // Configurations vary by region. Modify the configuration based on your region.
        baseURL: "https://dashscope-intl.aliyuncs.com/compatible-mode/v1"
    }
);

const response = await openai.chat.completions.create({
        model: "qwen3.6-plus",
        messages: [
        {role: "user",content: [
            {type: "image_url",
            image_url: {"url": "https://help-static-aliyun-doc.aliyuncs.com/file-manage-files/zh-CN/20250212/earbrt/vcg_VCG211286867973_RF.jpg"},
            // max_pixels specifies the maximum pixel threshold for the input image. It has no effect when vl_high_resolution_images=True. When vl_high_resolution_images=False, it is customizable, and the maximum value varies by model.
            // "max_pixels": 2560 * 32 * 32
            },
            {type: "text", text: "What festival atmosphere does this picture show?" },
        ]}],
        vl_high_resolution_images:true
    })

console.log("Model output: ",response.choices[0].message.content);
console.log("Total input tokens",response.usage.prompt_tokens);

curl ​

HELPCODEESCAPE-curl
# ======= Important notes =======
# API keys vary by region. To get an API key, see https://www.alibabacloud.com/help/en/model-studio/get-api-key
# Configurations vary by region. Modify the configuration based on your region.
# === Delete this comment before execution ===

curl -X POST https://dashscope.aliyuncs.com/compatible-mode/v1/chat/completions \
-H "Authorization: Bearer $DASHSCOPE_API_KEY" \
-H 'Content-Type: application/json' \
-d '{
  "model": "qwen3.6-plus",
  "messages": [
    {
      "role": "user",
      "content": [
        {
          "type": "image_url",
          "image_url": {
            "url": "https://help-static-aliyun-doc.aliyuncs.com/file-manage-files/zh-CN/20250212/earbrt/vcg_VCG211286867973_RF.jpg"
          }
        },
        {
          "type": "text",
          "text": "What festival atmosphere does this picture show?"
        }
      ]
    }
  ],
  "vl_high_resolution_images":true
}'

DashScope ​

Python ​

HELPCODEESCAPE-python
import os
import time

import dashscope

# Configurations vary by region. Modify the configuration based on your region.
dashscope.base_http_api_url = 'https://dashscope-intl.aliyuncs.com/api/v1'

messages = [
    {
        "role": "user",
        "content": [
            {"image": "https://help-static-aliyun-doc.aliyuncs.com/file-manage-files/zh-CN/20250212/earbrt/vcg_VCG211286867973_RF.jpg",
            # max_pixels specifies the maximum pixel threshold for the input image. It is invalid when vl_high_resolution_images=True. When vl_high_resolution_images=False, it is customizable, and the maximum value varies by model.
            # "max_pixels": 16384 * 32 * 32
            },
            {"text": "What festival atmosphere does this picture show?"}
        ]
    }
]

response = dashscope.MultiModalConversation.call(
        # If you have not configured the environment variable, replace the following line with your Model Studio API key: api_key="sk-xxx"
        # API keys vary by region. To get an API key, see https://www.alibabacloud.com/help/en/model-studio/get-api-key
        api_key=os.getenv('DASHSCOPE_API_KEY'),
        model='qwen3.6-plus',
        messages=messages,
        vl_high_resolution_images=True
    )

print("Model output",response.output.choices[0].message.content[0]["text"])
print("Total input tokens:",response.usage.input_tokens)

Java ​

HELPCODEESCAPE-java
import java.util.Arrays;
import java.util.Collections;
import java.util.Map;
import java.util.HashMap;
import com.alibaba.dashscope.aigc.multimodalconversation.MultiModalConversation;
import com.alibaba.dashscope.aigc.multimodalconversation.MultiModalConversationParam;
import com.alibaba.dashscope.aigc.multimodalconversation.MultiModalConversationResult;
import com.alibaba.dashscope.common.MultiModalMessage;
import com.alibaba.dashscope.common.Role;
import com.alibaba.dashscope.exception.ApiException;
import com.alibaba.dashscope.exception.NoApiKeyException;
import com.alibaba.dashscope.exception.UploadFileException;
import com.alibaba.dashscope.utils.Constants;

public class Main {

    static {
        // Configurations vary by region. Modify the configuration based on your region.
        Constants.baseHttpApiUrl="https://dashscope-intl.aliyuncs.com/api/v1";
    }

    public static void simpleMultiModalConversationCall()
            throws ApiException, NoApiKeyException, UploadFileException {
        MultiModalConversation conv = new MultiModalConversation();
        Map&lt;String, Object&gt; map = new HashMap<>();
        map.put("image", "https://help-static-aliyun-doc.aliyuncs.com/file-manage-files/zh-CN/20250212/earbrt/vcg_VCG211286867973_RF.jpg");
        // max_pixels specifies the maximum pixel threshold for the input image. It is invalid when vl_high_resolution_images=True. When vl_high_resolution_images=False, it is customizable, and the maximum value varies by model.
        // map.put("max_pixels", 2621440);
        MultiModalMessage userMessage = MultiModalMessage.builder().role(Role.USER.getValue())
                .content(Arrays.asList(
                        map,
                        Collections.singletonMap("text", "What festival atmosphere does this picture show?"))).build();
        MultiModalConversationParam param = MultiModalConversationParam.builder()
                // If you have not configured the environment variable, replace the following line with your Model Studio API key: .apiKey("sk-xxx")
                .apiKey(System.getenv("DASHSCOPE_API_KEY"))
                .model("qwen3.6-plus")
                .message(userMessage)
                .vlHighResolutionImages(true)
                .build();
        MultiModalConversationResult result = conv.call(param);
        System.out.println(result.getOutput().getChoices().get(0).getMessage().getContent().get(0).get("text"));
        System.out.println(result.getUsage().getInputTokens());
    }

    public static void main(String[] args) {
        try {
            simpleMultiModalConversationCall();
        } catch (ApiException | NoApiKeyException | UploadFileException e) {
            System.out.println(e.getMessage());
        }
        System.exit(0);
    }
}

curl ​

HELPCODEESCAPE-curl
# ======= Important notes =======
# API keys vary by region. To get an API key, see https://www.alibabacloud.com/help/en/model-studio/get-api-key
# Configurations vary by region. Modify the configuration based on your region.
# === Delete this comment before execution ===

curl -X POST https://dashscope-intl.aliyuncs.com/api/v1/services/aigc/multimodal-generation/generation \
-H "Authorization: Bearer $DASHSCOPE_API_KEY" \
-H 'Content-Type: application/json' \
-d '{
    "model": "qwen3.6-plus",
    "input":{
        "messages":[
            {
             "role": "user",
             "content": [
               {"image": "https://help-static-aliyun-doc.aliyuncs.com/file-manage-files/zh-CN/20250212/earbrt/vcg_VCG211286867973_RF.jpg"},
               {"text": "What festival atmosphere does this picture show?"}
                ]
            }
        ]
    },
    "parameters": {
        "vl_high_resolution_images": true
    }
}'

More examples ​

  • Multi-turn conversation

  • Streaming output

Limitations ​

Input file limits ​

Image limits ​

  • Image resolution

    • Minimum size: Both width and height must exceed 10 pixels.

    • Aspect ratio: The ratio of the long side to the short side must not exceed 200:1 for both the original and scaled images.

      For the image scaling logic, see the smart_resize function in Calculate image tokens

    • Pixel limit:

      • Keep the image resolution within 8K (7680x4320). Higher resolutions can cause API timeouts due to large file sizes and long transfer times.

      • The model scales images automatically using max_pixels and min_pixels, so ultra-high resolution does not improve accuracy and increases the risk of failed calls. Scale images to a reasonable size on the client side before calling the API.

  • Supported image formats

    • For resolutions below 4K (3840x2160), the following image formats are supported:
Image formatCommon extensionsMIME type
BMP.bmpimage/bmp
JPEG.jpe, .jpeg, .jpgimage/jpeg
PNG.pngimage/png
TIFF.tif, .tiffimage/tiff
WEBP.webpimage/webp
HEIC.heicimage/heic

  • For resolutions between 4K (3840x2160) and 8K (7680x4320), only the JPEG, JPG, and PNG formats are supported.

  • Image size:

    • When passed as a public URL: A single image cannot exceed 20 MB for Qwen3.6 series and Qwen3.5 series models. For other models, a single image cannot exceed 10 MB.

    • When passed as a local path: A single image cannot exceed 10 MB.

    • When passed as a Base64-encoded string: The encoded string cannot exceed 10 MB.

    To compress a file, see How to compress an image or video to meet the size limit .

  • Image count limit: For multi-image inputs, the maximum number of supported images varies based on the input method:

    • When passed as public URLs or local paths: Up to 256 images.

    • When passed as Base64-encoded strings: Up to 250 images.

      The total number of tokens for all images is also limited by the model's maximum input token limit. The total token count must be less than the model's maximum input limit.

Video limits ​

  • When passed as an image list, the number of images in the list is limited as follows:

    • qwen3.6 series and qwen3.5 series: A minimum of 4 images and a maximum of 8,000 images.

    • qwen3-vl-plus series, qwen3-vl-flash series, qwen3-vl-235b-a22b-thinking, and qwen3-vl-235b-a22b-instruct: A minimum of 4 images and a maximum of 2,000 images.

    • Other open source Qwen3-VL, Qwen2.5-VL (including commercial and open source versions), and QVQ series models: A minimum of 4 images and a maximum of 512 images.

    • Other models: A minimum of 4 images and a maximum of 80 images.

  • When passed as a video file:

    • Video size:

      • When passed as a public URL:

        • qwen3.6 series, qwen3.5 series, Qwen3-VL series, and qwen-vl-max (including qwen-vl-max-latest, qwen-vl-max-2025-04-08, and all later versions): Up to 2 GB.

        • qwen-vl-plus series, other qwen-vl-max models, open source Qwen2.5-VL series, and QVQ series models: Up to 1 GB.

        • Other models: Up to 150 MB.

      • When passed as a Base64-encoded string: The encoded string must be less than 10 MB.

      • When passed as a local file path: The video file cannot exceed 100 MB.

      To compress a file, see How to compress an image or video to meet the size limit .

    • Video duration:

      • qwen3.6 series and qwen3.5 series: 2 seconds to 2 hours.

      • qwen3-vl-plus series, qwen3-vl-flash series, qwen3-vl-235b-a22b-thinking, and qwen3-vl-235b-a22b-instruct: 2 seconds to 1 hour.

      • Other open source Qwen3-VL series and qwen-vl-max (including qwen-vl-max-latest, qwen-vl-max-2025-04-08, and later updated versions): 2 seconds to 20 minutes.

      • qwen-vl-plus series, other qwen-vl-max models, open source Qwen2.5-VL series, and QVQ series models: 2 seconds to 10 minutes.

      • Other models: 2 seconds to 40 seconds.

    • Video format: MP4, AVI, MKV, MOV, FLV, WMV, and more.

    • Video dimensions: No specific limits. The model can automatically adjust video dimensions using max_pixels and min_pixels. Larger video files do not result in better understanding.

    • Video count limit: Up to 64 videos can be passed.

    • Audio understanding: Audio understanding for video files is not supported.

File input methods ​

  • Public URL : Provide an HTTP or HTTPS URL that the API can download. For the best stability and performance, upload the file to OSS to get a public URL.

    Important

    The response header of the public URL must include Content-Length (file size) and Content-Type (media type, such as image/jpeg). If either field is missing or incorrect, the download fails.

  • Base64 encoding: Convert the file to a Base64-encoded string before passing it. Suitable for all SDK types and HTTP. Note that Base64 encoding increases data size by ~33%, so the original file must be under 7 MB to stay within the 10 MB encoded limit.

  • Local file path (DashScope SDK only): Pass the path to a local file directly. Files must not exceed 10 MB.

For recommendations on how to choose a file upload method, see How to choose a file upload method?

Going live ​

  • Image and video pre-processing: Visual understanding models have input file size limits. To compress files, see image or video compression methods.

  • Processing text files: Visual understanding models only support image files and cannot process text files directly. Workarounds:

    • Convert the text file to an image format. Use an image editing library, such as Python's pdf2image, to convert the file into multiple high-quality images, one per page. Then pass the images to the model using the multi-image input method.

    • Qwen-Long supports processing text files and can be used to parse file content.

  • Fault tolerance and stability

    • Timeout handling: In non-streaming calls, a timeout error occurs if the model does not complete output within 180 seconds. After a timeout, the generated content is returned in the response body. Check for the x-dashscope-partialresponse: true response header to detect timeouts. To continue generation, use the partial mode feature (supported by select models): add the generated content to the messages array and resend the request. See Continue from incomplete output.

    • Retry mechanism: Implement retry logic, such as exponential backoff, for API calls to handle network fluctuations or temporary service unavailability.

Billing and rate limiting ​

  • Billing: The total cost is calculated from the total number of input and output tokens. For prices, see the Model Studio console.

    • Token composition: Input tokens include text tokens and tokens converted from images or videos. Output tokens are the text generated by the model. In thinking mode, the reasoning chain also counts as output tokens. If the reasoning chain is suppressed, billing uses the non-thinking mode price.

    • Calculate tokens for images and videos: Use the following code to calculate token consumption for an image or video. The estimate is for reference only. Actual usage is subject to the API response.

      Calculate tokens for images and videos

      Images ​

      Formula: Image Tokens = h_bar * w_bar / token_pixels + 2

      • h_bar, w_bar: The height and width of the scaled image. The model scales images to a pixel limit based on max_pixels and vl_high_resolution_images. See Process high-resolution images.

      • token_pixels: The pixel value corresponding to each visual Token. This varies by model:

        • qwen3.6 series, qwen3.5 series, Qwen3-VL, qwen-vl-max, qwen-vl-max-latest, qwen-vl-max-2025-08-13, qwen-vl-plus, qwen-vl-plus-latest, qwen-vl-plus-2025-08-15: Each Token corresponds to 32x32 pixels.

        • QVQ and other Qwen2.5-VL models: Each Token corresponds to 28x28 pixels.

      The following code demonstrates the approximate image scaling logic used by the model. Use it to estimate token consumption. Actual billing is subject to the API response.

      Example: A 4000×3000 image passed to a qwen3.6-plus model (token_pixels = 32×32 = 1024). With default max_pixels (2621440), the model scales the image down. After scaling, the adjusted dimensions are 1376×1856. Token count = (1376 × 1856) / 1024 + 2 = 2494 + 2 = 2496 tokens. Use the code below to calculate the exact scaled dimensions for your image.

      HELPCODEESCAPE-python
import math
from PIL import Image  # pip install Pillow

def smart_size(image_path, max_pixels, vl_high_resolution_images):
    """Calculates the scaled dimensions of an image based on model parameters to estimate image tokens."""
    image = Image.open(image_path)
    height, width = image.height, image.width

    # The zoom factor is 32 for models such as Qwen3.6, Qwen3.5, and Qwen3-VL. For other models, it is 28.
    factor = 32
    h_bar = round(height / factor) * factor
    w_bar = round(width / factor) * factor

    # Token lower limit: 4 tokens
    min_pixels = 4 * factor * factor

    # When vl_high_resolution_images=True, the token upper limit is fixed at 16384, and max_pixels is ignored.
    if vl_high_resolution_images:
        max_pixels = 16384 * factor * factor

    # Constrain the total number of pixels to the range of [min_pixels, max_pixels].
    if h_bar * w_bar > max_pixels:
        beta = math.sqrt((height * width) / max_pixels)
        h_bar = math.floor(height / beta / factor) * factor
        w_bar = math.floor(width / beta / factor) * factor
    elif h_bar * w_bar < min_pixels:
        beta = math.sqrt(min_pixels / (height * width))
        h_bar = math.ceil(height * beta / factor) * factor
        w_bar = math.ceil(width * beta / factor) * factor

    return h_bar, w_bar

if __name__ == "__main__":
    # Note: The values of max_pixels and vl_high_resolution_images must be consistent with the parameters passed when calling the model.
    h_bar, w_bar = smart_size("xxx/test.jpg", max_pixels=2560 * 32 * 32, vl_high_resolution_images=False)
    print(f"Scaled image dimensions: height {h_bar}, width {w_bar}")

    # Each image includes one &lt;vision_bos&gt; token and one &lt;vision_eos&gt; token.
    token = int(h_bar * w_bar / (32 * 32)) + 2
    print(f"Number of image tokens: {token}")

      Videos ​

      • Video files:

        When processing a video file, the model first extracts frames and then calculates the total number of tokens for all video frames. Because this calculation is complex, you can use the following code to estimate the total token consumption for a video by passing its path:

        HELPCODEESCAPE-python
# Before use, install with: pip install opencv-python
import math
import os
import logging
import cv2

logger = logging.getLogger(__name__)

FRAME_FACTOR = 2

# For models such as Qwen3.6, Qwen3.5, Qwen3-VL, qwen-vl-max-0813, qwen-vl-plus-0815, and qwen-vl-plus-0710, the image zoom factor is 32.
IMAGE_FACTOR = 32

#  For other models, the image zoom factor is 28.
# IMAGE_FACTOR = 28

# Maximum aspect ratio for video frames
MAX_RATIO = 200
# Minimum pixel count for video frames
VIDEO_MIN_PIXELS = 4 * 32 * 32
# Maximum pixel count for video frames. For the Qwen3-VL-Plus model, VIDEO_MAX_PIXELS is 640 * 32 * 32. For other models, it is 768 * 32 * 32.
VIDEO_MAX_PIXELS = 640 * 32 * 32

# If the user does not pass the FPS parameter, the default value is used.
FPS = 2.0
# Minimum number of extracted frames
FPS_MIN_FRAMES = 4
# Maximum number of extracted frames (default: 2000 for Qwen3-VL-Plus, 512 for Qwen3-VL-Flash/Qwen2.5-VL, 80 for others)
FPS_MAX_FRAMES = 2000

# Maximum pixel value for video input (default: 131072*32*32 for Qwen3-VL-Plus, 65536*32*32 for others)
VIDEO_TOTAL_PIXELS = int(float(os.environ.get('VIDEO_MAX_PIXELS', 131072 * 32 * 32)))

def round_by_factor(number: int, factor: int) -> int:
    """Returns the integer closest to 'number' that is divisible by 'factor'."""
    return round(number / factor) * factor

def ceil_by_factor(number: int, factor: int) -> int:
    """Returns the smallest integer greater than or equal to 'number' that is divisible by 'factor'."""
    return math.ceil(number / factor) * factor

def floor_by_factor(number: int, factor: int) -> int:
    """Returns the largest integer less than or equal to 'number' that is divisible by 'factor'."""
    return math.floor(number / factor) * factor

def extract_vision_info(conversations):
    vision_infos = []
    if isinstance(conversations[0], dict):
        conversations = [conversations]
    for conversation in conversations:
        for message in conversation:
            if isinstance(message["content"], list):
                for ele in message["content"]:
                    if (
                        "image" in ele
                        or "image_url" in ele
                        or "video" in ele
                        or ele.get("type","") in ("image", "image_url", "video")
                    ):
                        vision_infos.append(ele)
    return vision_infos

def smart_nframes(ele,total_frames,video_fps):
    """Calculates the number of extracted video frames.

    Args:
        ele (dict): A dictionary containing video configuration.
            - fps: Controls the number of frames extracted for model input.
        total_frames (int): The original total number of frames in the video.
        video_fps (int | float): The original frame rate of the video.

    Raises:
        An error is raised if nframes is not within the interval [FRAME_FACTOR, total_frames].

    Returns:
        The number of video frames for model input.
    """
    assert not ("fps" in ele and "nframes" in ele), "Only accept either `fps` or `nframes`"
    fps = ele.get("fps", FPS)
    min_frames = ceil_by_factor(ele.get("min_frames", FPS_MIN_FRAMES), FRAME_FACTOR)
    max_frames = floor_by_factor(ele.get("max_frames", min(FPS_MAX_FRAMES, total_frames)), FRAME_FACTOR)
    duration = total_frames / video_fps if video_fps != 0 else 0
    if duration-int(duration)>(1/fps):
        total_frames = math.ceil(duration * video_fps)
    else:
        total_frames = math.ceil(int(duration)*video_fps)
    nframes = total_frames / video_fps * fps
    if nframes > total_frames:
        logger.warning(f"smart_nframes: nframes[{nframes}] > total_frames[{total_frames}]")
    nframes = int(min(min(max(nframes, min_frames), max_frames), total_frames))
    if not (FRAME_FACTOR <= nframes and nframes <= total_frames):
        raise ValueError(f"nframes should in interval [{FRAME_FACTOR}, {total_frames}], but got {nframes}.")

    return nframes

def get_video(video_path):
    # Get video information
    cap = cv2.VideoCapture(video_path)

    frame_width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
    # Get video height
    frame_height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
    total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))

    video_fps = cap.get(cv2.CAP_PROP_FPS)
    return frame_height, frame_width, total_frames, video_fps

def smart_resize(ele, path, factor=IMAGE_FACTOR):
    # Get the original video's width and height
    height, width, total_frames, video_fps = get_video(path)
    # Token lower limit for video frames
    min_pixels = VIDEO_MIN_PIXELS
    total_pixels = VIDEO_TOTAL_PIXELS
    # Number of extracted video frames
    nframes = smart_nframes(ele, total_frames, video_fps)
    max_pixels = max(min(VIDEO_MAX_PIXELS, total_pixels / nframes * FRAME_FACTOR),int(min_pixels * 1.05))

    # The aspect ratio of the video should not exceed 200:1 or 1:200.
    if max(height, width) / min(height, width) > MAX_RATIO:
        raise ValueError(
            f"absolute aspect ratio must be smaller than {MAX_RATIO}, got {max(height, width) / min(height, width)}"
        )

    h_bar = max(factor, round_by_factor(height, factor))
    w_bar = max(factor, round_by_factor(width, factor))
    if h_bar * w_bar > max_pixels:
        beta = math.sqrt((height * width) / max_pixels)
        h_bar = floor_by_factor(height / beta, factor)
        w_bar = floor_by_factor(width / beta, factor)
    elif h_bar * w_bar < min_pixels:
        beta = math.sqrt(min_pixels / (height * width))
        h_bar = ceil_by_factor(height * beta, factor)
        w_bar = ceil_by_factor(width * beta, factor)
    return h_bar, w_bar

def token_calculate(video_path, fps):
    # Pass the video path and fps frame extraction parameter
    messages = [{"content": [{"video": video_path, "fps": fps}]}]
    vision_infos = extract_vision_info(messages)[0]

    resized_height, resized_width = smart_resize(vision_infos, video_path)

    height, width, total_frames, video_fps = get_video(video_path)
    num_frames = smart_nframes(vision_infos, total_frames, video_fps)
    print(f"Original video dimensions: {height}*{width}, model input dimensions: {resized_height}*{resized_width}, total video frames: {total_frames}, total frames extracted when fps is {fps}: {num_frames}", end=", ")
    video_token = int(math.ceil(num_frames / 2) * resized_height / 32 * resized_width / 32)
    video_token += 2   # The system automatically adds <|vision_bos|> and <|vision_eos|> visual markers (1 token each).
    return video_token

video_token = token_calculate("xxx/test.mp4", 1)
print("Video tokens:", video_token)

      • Image list:

        When a video is passed as a list of images, it means that frame extraction has already been completed. You can use the following code to calculate the token consumption by passing the path and number of images:

        HELPCODEESCAPE-python
# Before use, install with: pip install Pillow
import math
import os
import logging
from typing import Tuple
from PIL import Image

logger = logging.getLogger(__name__)

# ==================== Constant Definitions ====================
FRAME_FACTOR = 2
# For models like Qwen3-VL, qwen-vl-max-0813, qwen-vl-plus-0815, and qwen-vl-plus-0710, the zoom factor is 32.
IMAGE_FACTOR = 32

#  For other models, the zoom factor is 28.
# IMAGE_FACTOR = 28

# Constants for token calculation
TOKEN_DIVISOR = 32  # Divisor for token calculation
VISION_SPECIAL_TOKENS = 2  # <|vision_bos|> and <|vision_eos|> markers

# Maximum aspect ratio for video frames
MAX_RATIO = 200
# Minimum pixel count for video frames
VIDEO_MIN_PIXELS = 4 * 32 * 32
# Maximum pixel count for video frames. For the Qwen3-VL-Plus model, VIDEO_MAX_PIXELS is 640 * 32 * 32. For other models, it is 768 * 32 * 32.
VIDEO_MAX_PIXELS = 640 * 32 * 32

# Maximum pixel value for video input (default: 131072*32*32 for Qwen3-VL-Plus, 65536*32*32 for others)
VIDEO_TOTAL_PIXELS = int(float(os.environ.get('VIDEO_MAX_PIXELS', 131072 * 32 * 32)))

def round_by_factor(number: int, factor: int) -> int:
    """Returns the integer closest to 'number' that is divisible by 'factor'."""
    return round(number / factor) * factor

def ceil_by_factor(number: int, factor: int) -> int:
    """Returns the smallest integer greater than or equal to 'number' that is divisible by 'factor'."""
    return math.ceil(number / factor) * factor

def floor_by_factor(number: int, factor: int) -> int:
    """Returns the largest integer less than or equal to 'number' that is divisible by 'factor'."""
    return math.floor(number / factor) * factor

def get_image_size(image_path: str) -> Tuple[int, int]:
    if not os.path.exists(image_path):
        raise FileNotFoundError(f"Image file not found: {image_path}")

    try:
        image = Image.open(image_path)
        height = image.height
        width = image.width
        image.close()  # Close the file promptly
        return height, width
    except Exception as e:
        raise ValueError(f"Cannot read image file {image_path}: {str(e)}")

def smart_resize(height: int, width: int, nframes: int, factor: int = IMAGE_FACTOR) -> Tuple[int, int]:
    """
    Calculates the scaled dimensions of an image.

    Args:
        height: Original image height
        width: Original image width
        nframes: Number of video frames
        factor: Zoom factor, defaults to IMAGE_FACTOR

    Returns:
        (resized_height, resized_width) Scaled height and width

    Raises:
        ValueError: Aspect ratio exceeds the limit
    """
    # Token lower limit for video frames
    min_pixels = VIDEO_MIN_PIXELS
    total_pixels = VIDEO_TOTAL_PIXELS
    # Number of extracted video frames
    max_pixels = max(min(VIDEO_MAX_PIXELS, total_pixels / nframes * FRAME_FACTOR), int(min_pixels * 1.05))

    # The aspect ratio of the video should not exceed 200:1 or 1:200.
    aspect_ratio = max(height, width) / min(height, width)
    if aspect_ratio > MAX_RATIO:
        raise ValueError(
            f"Image aspect ratio must be less than {MAX_RATIO}:1, current is {aspect_ratio:.2f}:1"
        )

    h_bar = max(factor, round_by_factor(height, factor))
    w_bar = max(factor, round_by_factor(width, factor))
    if h_bar * w_bar > max_pixels:
        beta = math.sqrt((height * width) / max_pixels)
        h_bar = floor_by_factor(height / beta, factor)
        w_bar = floor_by_factor(width / beta, factor)
    elif h_bar * w_bar < min_pixels:
        beta = math.sqrt(min_pixels / (height * width))
        h_bar = ceil_by_factor(height * beta, factor)
        w_bar = ceil_by_factor(width * beta, factor)
    return h_bar, w_bar

def calculate_video_tokens(image_path: str, nframes: int = 1, factor: int = IMAGE_FACTOR, verbose: bool = True) -> int:
    """

    Args:
        image_path: Path to the video frame file
        nframes: Number of video frames,
        factor: Zoom factor, defaults to IMAGE_FACTOR
        verbose: Whether to print detailed information

    Returns:
        The number of tokens consumed

    Raises:
        FileNotFoundError: File does not exist
        ValueError: Invalid file format or aspect ratio out of bounds
    """
    # Get original image dimensions (read only once)
    height, width = get_image_size(image_path)

    # Calculate scaled dimensions
    resized_height, resized_width = smart_resize(height, width, nframes, factor)

    # Calculate the number of tokens
    # Formula: ceil(frames/2) * (height/TOKEN_DIVISOR) * (width/TOKEN_DIVISOR) + VISION_SPECIAL_TOKENS
    video_token = int(
        math.ceil(nframes / 2) *
        (resized_height / TOKEN_DIVISOR) *
        (resized_width / TOKEN_DIVISOR)
    )
    # Add visual marker tokens (<|vision_bos|> and <|vision_eos|>)
    video_token += VISION_SPECIAL_TOKENS

    if verbose:
        print(f"Original video frame dimensions: {height}×{width}, model input dimensions: {resized_height}×{resized_width}, ", end="")

    return video_token

if __name__ == "__main__":
    try:
        video_token = calculate_video_tokens("xxx/test.jpg", nframes=30)
        print(f"Video tokens: {video_token}\n")
    except Exception as e:
        print(f"Error: {str(e)}\n")

  • View bills: View bills or top up your account on the Expenses and Costs page of the Alibaba Cloud Management Console.

  • Rate limiting: For rate limits for visual understanding models, see Rate limiting.

  • Free quota (Singapore region only): A free quota of 1 million tokens is provided for visual understanding models, valid for 90 days from the date you enable Model Studio or your model request is approved.

API reference ​

For input and output parameters of the visual understanding model, see Text generation.

FAQ ​

How to choose a file upload method ​

Choose an upload method based on SDK type, file size, and network stability.

File typeFile specificationsDashScope SDK (Python, Java)OpenAI-compatible / DashScope HTTP
ImageGreater than 7 MB and less than 10 MBPass the local pathOnly public URLs are supported. Use Alibaba Cloud Object Storage Service.
Less than 7 MBPass the local pathBase64 encoding
VideoGreater than 100 MBOnly public URLs are supported. Use Alibaba Cloud Object Storage Service.Only public URLs are supported. Use Alibaba Cloud Object Storage Service.
Greater than 7 MB and less than 100 MBPass the local pathOnly public URLs are supported. Use Alibaba Cloud Object Storage Service.
Less than 7 MBPass the local pathBase64 encoding

Base64 encoding increases the data size. The original file must be smaller than 7 MB. Use Base64 or a local path to prevent server-side download timeouts and improve stability.

How to compress an image or video to meet the size limit ​

Visual understanding models have input file size limits. Use the following methods to compress your files. Image compression methods

  • Online tools: Use an online tool such as CompressJPEG to compress the image.

  • Local software: Use software such as Photoshop to adjust the quality when exporting the image.

  • Code implementation:

    HELPCODEESCAPE-python
# pip install pillow

from PIL import Image
def compress_image(input_path, output_path, quality=85):
    with Image.open(input_path) as img:
        img.save(output_path, "JPEG", optimize=True, quality=quality)

# Pass a local image
compress_image("/xxx/before-large.jpeg","/xxx/after-min.jpeg")

Video compression methods

  • Online tools: Use an online tool such as FreeConvert to compress the video.

  • Local software: Use software such as HandBrake.

  • Code implementation: Use the FFmpeg tool. See the official FFmpeg website.

    HELPCODEESCAPE-bash
# Basic transform command
# -i: Specifies the path of the input file. Example: input.mp4
# -vcodec: Specifies the video encoder. Common values include libx264 (recommended for general use) and libx265 (higher compression ratio).
# -crf: Controls the video quality. The value ranges from 18 to 28. A smaller value indicates higher quality and a larger file size.
# --preset: Controls the balance between encoding speed and compression efficiency. Common values include slow, fast, and faster.
# -y: Overwrites the output file if it already exists. No value is required.
# output.mp4: Specifies the path of the output file.

ffmpeg -i input.mp4 -vcodec libx264 -crf 28 -preset slow output.mp4

How to draw detection frames on the original image after object detection ​

After the model outputs object locations, use the following code to draw detection frames and labels on the original image.

  • Qwen2.5-VL: Returns coordinates as absolute pixel values relative to the top-left corner of the scaled image. To draw detection frames, see the code in qwen2_5_vl_2d.py.

  • Qwen3-VL: Returns relative coordinates with values normalized to the range of [0, 999]. To draw detection frames, see the code in qwen3_vl_2d.py (2D localization) or qwen3_vl_3d.zip (3D localization).

Error codes ​

If the model call fails and returns an error message, see Error messages for resolution.

Mirror of Alibaba Cloud Model Studio docs for reference and RAG. Not affiliated with Alibaba Cloud.

Source: alibabacloud.com/help/en/model-studio