party model integration tutorial-Kimi

Call Kimi models deployed on Alibaba Cloud Model Studio.

Supported regions: US (Virginia), China (Beijing), and Germany (Frankfurt).

Model experience: Try Kimi models in the Model Experience Center. The endpoint varies by region. Select the base URL for the region where you activated the service.

OpenAI compatible

US (Virginia)

SDK base_url: https://dashscope-us.aliyuncs.com/compatible-mode/v1

HTTP request URL: POST https://dashscope-us.aliyuncs.com/compatible-mode/v1/chat/completions

Germany (Frankfurt)

SDK base_url: https://{WorkspaceId}.eu-central-1.maas.aliyuncs.com/compatible-mode/v1

HTTP request URL: POST https://{WorkspaceId}.eu-central-1.maas.aliyuncs.com/compatible-mode/v1/chat/completions

Replace WorkspaceId with your actual Workspace ID.

China (Beijing)

SDK base_url: https://dashscope.aliyuncs.com/compatible-mode/v1

HTTP request URL: POST https://dashscope.aliyuncs.com/compatible-mode/v1/chat/completions

DashScope

US (Virginia)

For text-only models such as kimi-k2-thinking, the HTTP request URL is POST https://dashscope-us.aliyuncs.com/api/v1/services/aigc/text-generation/generation

For multimodal models such as kimi-k2.5 and kimi-k2.6, the HTTP request URL is POST https://dashscope-us.aliyuncs.com/api/v1/services/aigc/multimodal-generation/generation SDK base_url configuration:

Python

dashscope.base_http_api_url = 'https://dashscope-us.aliyuncs.com/api/v1'

Java

Method 1:
import com.alibaba.dashscope.protocol.Protocol; Generation gen = new Generation(Protocol.HTTP.getValue(), "https://dashscope-us.aliyuncs.com/api/v1");
Method 2:
import com.alibaba.dashscope.utils.Constants; Constants.baseHttpApiUrl="https://dashscope-us.aliyuncs.com/api/v1";

Germany (Frankfurt)

For text-only models such as kimi-k2-thinking, the HTTP request URL is POST https://{WorkspaceId}.eu-central-1.maas.aliyuncs.com/api/v1/services/aigc/text-generation/generation

For multimodal models such as kimi-k2.5 and kimi-k2.6, the HTTP request URL is POST https://{WorkspaceId}.eu-central-1.maas.aliyuncs.com/api/v1/services/aigc/multimodal-generation/generation

Replace WorkspaceId with your actual Workspace ID. SDK base_url configuration:

Python

Replace WorkspaceId with your actual Workspace ID.

dashscope.base_http_api_url = 'https://{WorkspaceId}.eu-central-1.maas.aliyuncs.com/api/v1'

Java

Replace WorkspaceId with your actual Workspace ID.

Method 1:
import com.alibaba.dashscope.protocol.Protocol; Generation gen = new Generation(Protocol.HTTP.getValue(), "https://{WorkspaceId}.eu-central-1.maas.aliyuncs.com/api/v1");
Method 2:
import com.alibaba.dashscope.utils.Constants; Constants.baseHttpApiUrl="https://{WorkspaceId}.eu-central-1.maas.aliyuncs.com/api/v1";

China (Beijing)

For text-only models such as kimi-k2-thinking, the HTTP request URL is POST https://dashscope.aliyuncs.com/api/v1/services/aigc/text-generation/generation

For multimodal models such as kimi-k2.5 and kimi-k2.6, the HTTP request URL is POST https://dashscope.aliyuncs.com/api/v1/services/aigc/multimodal-generation/generation

No base_url configuration is required for SDK calls.

Prerequisites: Get an API key and set it as an environment variable. If you use the OpenAI SDK, install the SDK.

Getting started

The following examples use text-only input. For multimodal examples, see Multimodal call.

OpenAI compatible

Python

HELPCODEESCAPE-python
import os
from openai import OpenAI

client = OpenAI(
    api_key=os.getenv("DASHSCOPE_API_KEY"),
    base_url="https://dashscope.aliyuncs.com/compatible-mode/v1",
)

completion = client.chat.completions.create(
    model="kimi-k2-thinking",
    messages=[{"role": "user", "content": "Who are you?"}],
    stream=True,
)

reasoning_content = ""  # Complete thinking process
answer_content = ""     # Complete response
is_answering = False    # Flag to track if the response has started

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

for chunk in completion:
    if chunk.choices:
        delta = chunk.choices[0].delta
        # Collect the thinking content
        if hasattr(delta, "reasoning_content") and delta.reasoning_content is not None:
            if not is_answering:
                print(delta.reasoning_content, end="", flush=True)
            reasoning_content += delta.reasoning_content
        # Start generating the response when content is received
        if hasattr(delta, "content") and delta.content:
            if not is_answering:
                print("\n" + "=" * 20 + "Complete Response" + "=" * 20 + "\n")
                is_answering = True
            print(delta.content, end="", flush=True)
            answer_content += delta.content

Response

HELPCODEESCAPE-plaintext
====================Thinking Process====================

The user asks "Who are you?", which is a direct question about my identity. I need to answer truthfully based on my actual identity.

I am Kimi, an AI assistant developed by Moonshot AI. I should introduce myself clearly and concisely, including:
1. My identity: AI assistant
2. My developer: Moonshot AI
3. My name: Kimi
4. My core capabilities: long-text processing, intelligent conversation, file processing, search, etc.

I should maintain a friendly and professional tone, avoiding overly technical terms so that general users can understand. I should also emphasize that I am an AI without personal consciousness, emotions, or personal experiences.

Response structure:
- Directly state my identity
- Mention my developer
- Briefly introduce core capabilities
- Keep it clear and concise
====================Complete Response====================

I am Kimi, an AI assistant developed by Moonshot AI. I am based on a Mixture-of-Experts (MoE) architecture and have capabilities such as ultra-long context understanding, intelligent conversation, file processing, code generation, and complex task reasoning. How can I help you?

Node.js

HELPCODEESCAPE-nodejs
import OpenAI from "openai";
import process from 'process';

// Initialize the OpenAI client
const openai = new OpenAI({
    // If the environment variable is not set, replace this with your Model Studio API key: apiKey: "sk-xxx"
    apiKey: process.env.DASHSCOPE_API_KEY,
    baseURL: 'https://dashscope.aliyuncs.com/compatible-mode/v1'
});

let reasoningContent = ''; // Complete thinking process
let answerContent = ''; // Complete response
let isAnswering = false; // Flag to track if the response has started

async function main() {
    const messages = [{ role: 'user', content: 'Who are you?' }];

    const stream = await openai.chat.completions.create({
        model: 'kimi-k2-thinking',
        messages,
        stream: true,
    });

    console.log('\n' + '='.repeat(20) + 'Thinking Process' + '='.repeat(20) + '\n');

    for await (const chunk of stream) {
        if (chunk.choices?.length) {
            const delta = chunk.choices[0].delta;
            // Collect the thinking content
            if (delta.reasoning_content !== undefined && delta.reasoning_content !== null) {
                if (!isAnswering) {
                    process.stdout.write(delta.reasoning_content);
                }
                reasoningContent += delta.reasoning_content;
            }

            // Start generating the response when content is received
            if (delta.content !== undefined && delta.content) {
                if (!isAnswering) {
                    console.log('\n' + '='.repeat(20) + 'Complete Response' + '='.repeat(20) + '\n');
                    isAnswering = true;
                }
                process.stdout.write(delta.content);
                answerContent += delta.content;
            }
        }
    }
}

main();

Response

HELPCODEESCAPE-plaintext
====================Thinking Process====================

The user asks "Who are you?", which is a direct question about my identity. I need to answer truthfully based on my actual identity.

I am Kimi, an AI assistant developed by Moonshot AI. I should introduce myself clearly and concisely, including:
1. My identity: AI assistant
2. My developer: Moonshot AI
3. My name: Kimi
4. My core capabilities: long-text processing, intelligent conversation, file processing, search, etc.

I should maintain a friendly and professional tone and avoid overly technical terms for clarity. I should also emphasize that I am an AI without personal consciousness, emotions, or experiences to prevent misunderstandings.

Response structure:
- Directly state my identity
- Mention my developer
- Briefly introduce core capabilities
- Keep it clear and concise
====================Complete Response====================

I am Kimi, an AI assistant developed by Moonshot AI.

I am skilled in:
- Long-text understanding and generation
- Intelligent conversation and question answering
- File processing and analysis
- Information retrieval and integration

As an AI assistant, I do not have personal consciousness, emotions, or experiences, but I am designed to provide accurate and helpful assistance. How can I help you?

HTTP

Curl

HELPCODEESCAPE-curl
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": "kimi-k2-thinking",
    "messages": [
        {
            "role": "user",
            "content": "Who are you?"
        }
    ]
}'

Response

HELPCODEESCAPE-json
{
    "choices": [
        {
            "message": {
                "content": "I am Kimi, an AI assistant developed by Moonshot AI. I am skilled in long-text processing, intelligent conversation, file analysis, programming assistance, and complex task reasoning. I can help you answer questions, create content, and analyze documents. How can I assist you?",
                "reasoning_content": "The user asks 'Who are you?', which is a direct question about my identity. I must answer truthfully based on my actual identity.\n\nI am Kimi, an AI assistant developed by Moonshot AI. I should introduce myself clearly and concisely, including:\n1. My identity: AI assistant\n2. My developer: Moonshot AI\n3. My name: Kimi\n4. My core capabilities: long-text processing, intelligent conversation, file processing, search, etc.\n\nI should maintain a friendly and professional tone while providing useful information. No need to overcomplicate; a direct answer is sufficient.",
                "role": "assistant"
            },
            "finish_reason": "stop",
            "index": 0,
            "logprobs": null
        }
    ],
    "object": "chat.completion",
    "usage": {
        "prompt_tokens": 8,
        "completion_tokens": 183,
        "total_tokens": 191
    },
    "created": 1762753998,
    "system_fingerprint": null,
    "model": "kimi-k2-thinking",
    "id": "chatcmpl-485ab490-90ec-48c3-85fa-1c732b683db2"
}

DashScope

The following DashScope examples use the text-generation endpoint, which only applies to text-only models (kimi-k2-thinking and Moonshot-Kimi-K2-Instruct). For multimodal models (kimi-k2.5 and kimi-k2.6), use the multimodal-generation endpoint. See Multimodal call.

Python

HELPCODEESCAPE-python
import os
from dashscope import Generation


messages = [{"role": "user", "content": "Who are you?"}]

completion = Generation.call(
    # If the environment variable is not set, replace this with your Model Studio API key: api_key="sk-xxx"
    api_key=os.getenv("DASHSCOPE_API_KEY"),
    model="kimi-k2-thinking",
    messages=messages,
    result_format="message",  # Set the result format to message
    stream=True,              # Enable streaming output
    incremental_output=True,  # Enable incremental output
)

reasoning_content = ""  # Complete thinking process
answer_content = ""     # Complete response
is_answering = False    # Indicates whether response generation has started

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

for chunk in completion:
    message = chunk.output.choices[0].message

    # Collect only the thinking content
    if message.reasoning_content:
        if not is_answering:
            print(message.reasoning_content, end="", flush=True)
        reasoning_content += message.reasoning_content

    # When content is received, start generating the response
    if message.content:
        if not is_answering:
            print("\n" + "=" * 20 + "Complete Response" + "=" * 20 + "\n")
            is_answering = True
        print(message.content, end="", flush=True)
        answer_content += message.content

# After the loop, `reasoning_content` and `answer_content` contain the full thinking process and response.
# You can add any subsequent processing logic here.
# print(f"\n\nComplete thinking process:\n{reasoning_content}")
# print(f"\nComplete response:\n{answer_content}")

Response

HELPCODEESCAPE-plaintext
====================Thinking Process====================

The user asks "Who are you?", which is a direct question about my identity. I need to answer truthfully based on my actual identity.

I am Kimi, an AI assistant developed by Moonshot AI. I should state this clearly and concisely.

Key information to include:
1. My name: Kimi
2. My developer: Moonshot AI
3. My nature: AI assistant
4. What I can do: answer questions, assist with content creation, etc.

I should maintain a friendly and helpful tone while accurately stating my identity. I should not pretend to be human or have a personal identity.

A suitable response would be:
"I am Kimi, an AI assistant developed by Moonshot AI. I can help you with a variety of tasks such as answering questions, creating content, and analyzing documents. How can I help you?"

This response is direct, accurate, and encourages further interaction.
====================Complete Response====================

I am Kimi, an AI assistant developed by Moonshot AI. I can help you with a variety of tasks such as answering questions, creating content, and analyzing documents. How can I help you?

Java

HELPCODEESCAPE-java
// DashScope SDK version >= 2.19.4
import com.alibaba.dashscope.aigc.generation.Generation;
import com.alibaba.dashscope.aigc.generation.GenerationParam;
import com.alibaba.dashscope.aigc.generation.GenerationResult;
import com.alibaba.dashscope.common.Message;
import com.alibaba.dashscope.common.Role;
import com.alibaba.dashscope.exception.ApiException;
import com.alibaba.dashscope.exception.InputRequiredException;
import com.alibaba.dashscope.exception.NoApiKeyException;
import io.reactivex.Flowable;
import java.lang.System;
import java.util.Arrays;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;

public class Main {
    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(GenerationResult message) {
        String reasoning = message.getOutput().getChoices().get(0).getMessage().getReasoningContent();
        String content = message.getOutput().getChoices().get(0).getMessage().getContent();

        if (reasoning!= null&&!reasoning.isEmpty()) {
            reasoningContent.append(reasoning);
            if (isFirstPrint) {
                System.out.println("====================Thinking Process====================");
                isFirstPrint = false;
            }
            System.out.print(reasoning);
        }

        if (content!= null&&!content.isEmpty()) {
            finalContent.append(content);
            if (!isFirstPrint) {
                System.out.println("\n====================Complete Response====================");
                isFirstPrint = true;
            }
            System.out.print(content);
        }
    }
    private static GenerationParam buildGenerationParam(Message userMsg) {
        return GenerationParam.builder()
                // 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("kimi-k2-thinking")
                .incrementalOutput(true)
                .resultFormat("message")
                .messages(Arrays.asList(userMsg))
                .build();
    }
    public static void streamCallWithMessage(Generation gen, Message userMsg)
            throws NoApiKeyException, ApiException, InputRequiredException {
        GenerationParam param = buildGenerationParam(userMsg);
        Flowable&lt;GenerationResult&gt; result = gen.streamCall(param);
        result.blockingForEach(message -> handleGenerationResult(message));
    }

    public static void main(String[] args) {
        try {
            Generation gen = new Generation();
            Message userMsg = Message.builder().role(Role.USER.getValue()).content("Who are you?").build();
            streamCallWithMessage(gen, userMsg);
            // Print the final result
            // if (reasoningContent.length() > 0) {
            //     System.out.println("\n====================Complete Response====================");
            //     System.out.println(finalContent.toString());
            // }
        } catch (ApiException | NoApiKeyException | InputRequiredException e) {
            logger.error("An exception occurred: {}", e.getMessage());
        }
        System.exit(0);
    }
}

Response

HELPCODEESCAPE-plaintext
====================Thinking Process====================
The user asks "Who are you?", which is a direct question about my identity. I need to answer truthfully based on my actual identity.

I am Kimi, an AI assistant developed by Moonshot AI. I should state this clearly and concisely.

The response should include:
1. My identity: AI assistant
2. My developer: Moonshot AI
3. My name: Kimi
4. My core capabilities: long-text processing, intelligent conversation, file processing, etc.

I should not pretend to be human or provide excessive technical details. A clear and friendly answer is sufficient.
====================Complete Response====================
I am Kimi, an AI assistant developed by Moonshot AI. My skills include long-text processing, intelligent conversation, question answering, content creation, and file analysis and processing. How can I assist you?

HTTP

Curl

HELPCODEESCAPE-curl
curl -X POST "https://dashscope.aliyuncs.com/api/v1/services/aigc/text-generation/generation" \
-H "Authorization: Bearer $DASHSCOPE_API_KEY" \
-H "Content-Type: application/json" \
-d '{
    "model": "kimi-k2-thinking",
    "input":{
        "messages":[
            {
                "role": "user",
                "content": "Who are you?"
            }
        ]
    },
    "parameters": {
        "result_format": "message"
    }
}'

Response

HELPCODEESCAPE-json
{
    "output": {
        "choices": [
            {
                "finish_reason": "stop",
                "message": {
                    "content": "I am Kimi, an AI assistant developed by Moonshot AI. I can help you answer questions, create content, analyze documents, and write code. How can I help you?",
                    "reasoning_content": "The user asks \"Who are you?\", which is a direct question about my identity. I need to answer truthfully based on my actual identity.\n\nI am Kimi, an AI assistant developed by Moonshot AI. I should state this clearly and concisely.\n\nKey information to include:\n1. My name: Kimi\n2. My developer: Moonshot AI\n3. My nature: AI assistant\n4. What I can do: answer questions, assist with content creation, etc.\n\nThe response should be friendly, direct, and easy to understand.",
                    "role": "assistant"
                }
            }
        ]
    },
    "usage": {
        "input_tokens": 9,
        "output_tokens": 156,
        "total_tokens": 165
    },
    "request_id": "709a0697-ed1f-4298-82c9-a4b878da1849"
}

Multimodal call

kimi-k2.5 and kimi-k2.6 process text, image, and video input. The enable_thinking parameter controls whether thinking mode is active. The following examples demonstrate multimodal capabilities.

Enable or disable thinking mode

kimi-k2.5 and kimi-k2.6 are hybrid thinking models that respond either directly or after reasoning. The enable_thinking parameter controls this behavior:

true: Enables thinking mode.
false (default): Disables thinking mode.

kimi-k2.6 supports passing the thinking process in multi-turn conversations through the preserve_thinking parameter. For more information, see Pass thinking process.

The examples below show single-image input with thinking mode enabled. Commented-out code demonstrates multi-image input.

OpenAI compatible

Python

HELPCODEESCAPE-python
import os
from openai import OpenAI

client = OpenAI(
    api_key=os.getenv("DASHSCOPE_API_KEY"),
    base_url="https://dashscope.aliyuncs.com/compatible-mode/v1",
)

# Single-image input example (thinking mode enabled)
completion = client.chat.completions.create(
    model="kimi-k2.6",
    messages=[
        {
            "role": "user",
            "content": [
                {"type": "text", "text": "What scene is depicted in the image?"},
                {
                    "type": "image_url",
                    "image_url": {
                        "url": "https://help-static-aliyun-doc.aliyuncs.com/file-manage-files/zh-CN/20241022/emyrja/dog_and_girl.jpeg"
                    }
                }
            ]
        }
    ],
    extra_body={"enable_thinking":True}  # Enable thinking mode
)

# Print the thinking process
if hasattr(completion.choices[0].message, 'reasoning_content') and completion.choices[0].message.reasoning_content:
    print("\n" + "=" * 20 + "Thinking Process" + "=" * 20 + "\n")
    print(completion.choices[0].message.reasoning_content)

# Print the response content
print("\n" + "=" * 20 + "Complete Response" + "=" * 20 + "\n")
print(completion.choices[0].message.content)

# Multi-image input example (thinking mode enabled, uncomment to use)
# completion = client.chat.completions.create(
#     model="kimi-k2.6",
#     messages=[
#         {
#             "role": "user",
#             "content": [
#                 {"type": "text", "text": "What do these images depict?"},
#                 {
#                     "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"}
#                 }
#             ]
#         }
#     ],
#     extra_body={"enable_thinking":True}
# )
#
# # Print the thinking process and response
# if hasattr(completion.choices[0].message, 'reasoning_content') and completion.choices[0].message.reasoning_content:
#     print("\nThinking process:\n" + completion.choices[0].message.reasoning_content)
# print("\nComplete response:\n" + completion.choices[0].message.content)

Node.js

HELPCODEESCAPE-nodejs
import OpenAI from "openai";
import process from 'process';

const openai = new OpenAI({
    apiKey: process.env.DASHSCOPE_API_KEY,
    baseURL: 'https://dashscope.aliyuncs.com/compatible-mode/v1'
});

// Single-image input example (thinking mode enabled)
const completion = await openai.chat.completions.create({
    model: 'kimi-k2.6',
    messages: [
        {
            role: 'user',
            content: [
                { type: 'text', text: 'What scene is depicted in the image?' },
                {
                    type: 'image_url',
                    image_url: {
                        url: 'https://help-static-aliyun-doc.aliyuncs.com/file-manage-files/zh-CN/20241022/emyrja/dog_and_girl.jpeg'
                    }
                }
            ]
        }
    ],
    enable_thinking: true  // Enable thinking mode
});

// Print the thinking process
if (completion.choices[0].message.reasoning_content) {
    console.log('\n' + '='.repeat(20) + 'Thinking Process' + '='.repeat(20) + '\n');
    console.log(completion.choices[0].message.reasoning_content);
}

// Print the response content
console.log('\n' + '='.repeat(20) + 'Complete Response' + '='.repeat(20) + '\n');
console.log(completion.choices[0].message.content);

// Multi-image input example (thinking mode enabled, uncomment to use)
// const multiCompletion = await openai.chat.completions.create({
//     model: 'kimi-k2.6',
//     messages: [
//         {
//             role: 'user',
//             content: [
//                 { type: 'text', text: 'What do these images depict?' },
//                 {
//                     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' }
//                 }
#             ]
#         }
#     ],
#     enable_thinking: true
# });
#
# // Print the thinking process and response
# if (multiCompletion.choices[0].message.reasoning_content) {
#     console.log('\nThinking process:\n' + multiCompletion.choices[0].message.reasoning_content);
# }
# console.log('\nComplete response:\n' + multiCompletion.choices[0].message.content);

Curl

HELPCODEESCAPE-bash
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": "kimi-k2.6",
    "messages": [
        {
            "role": "user",
            "content": [
                {
                    "type": "text",
                    "text": "What scene is depicted in the image?"
                },
                {
                    "type": "image_url",
                    "image_url": {
                        "url": "https://help-static-aliyun-doc.aliyuncs.com/file-manage-files/zh-CN/20241022/emyrja/dog_and_girl.jpeg"
                    }
                }
            ]
        }
    ],
    "enable_thinking": true
}'

# Multi-image input example (uncomment to use)
# 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": "kimi-k2.6",
#     messages": [
#         {
#             "role": "user",
#             "content": [
#                 {
#                     "type": "text",
#                     "text": "What do these images depict?"
#                 },
#                 {
#                     "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"
#                     }
#                 }
#             ]
#         }
#     ],
#     "enable_thinking": true,
#     "stream": false
# }'

DashScope

Python

HELPCODEESCAPE-python
import os
from dashscope import MultiModalConversation

# Single-image input example (thinking mode enabled)
response = MultiModalConversation.call(
    api_key=os.getenv("DASHSCOPE_API_KEY"),
    model="kimi-k2.6",
    messages=[
        {
            "role": "user",
            "content": [
                {"text": "What scene is depicted in the image?"},
                {"image": "https://help-static-aliyun-doc.aliyuncs.com/file-manage-files/zh-CN/20241022/emyrja/dog_and_girl.jpeg"}
            ]
        }
    ],
    enable_thinking=True  # Enable thinking mode
)

# Print the thinking process
if hasattr(response.output.choices[0].message, 'reasoning_content') and response.output.choices[0].message.reasoning_content:
    print("\n" + "=" * 20 + "Thinking Process" + "=" * 20 + "\n")
    print(response.output.choices[0].message.reasoning_content)

# Print the response content
print("\n" + "=" * 20 + "Complete Response" + "=" * 20 + "\n")
print(response.output.choices[0].message.content[0]["text"])

# Multi-image input example (thinking mode enabled, uncomment to use)
# response = MultiModalConversation.call(
#     api_key=os.getenv("DASHSCOPE_API_KEY"),
#     model="kimi-k2.6",
#     messages=[
#         {
#             "role": "user",
#             "content": [
#                 {"text": "What do these images depict?"},
#                 {"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"}
#             ]
#         }
#     ],
#     enable_thinking=True
# )
#
# # Print the thinking process and response
# if hasattr(response.output.choices[0].message, 'reasoning_content') and response.output.choices[0].message.reasoning_content:
#     print("\nThinking process:\n" + response.output.choices[0].message.reasoning_content)
# print("\nComplete response:\n" + response.output.choices[0].message.content[0]["text"])

Java

HELPCODEESCAPE-java
// Requires DashScope SDK v2.19.4 or later.
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.InputRequiredException;
import com.alibaba.dashscope.exception.NoApiKeyException;
import com.alibaba.dashscope.utils.JsonUtils;
import java.util.Arrays;
import java.util.HashMap;
import java.util.Map;

public class KimiK26MultiModalExample {
    public static void main(String[] args) {
        try {
            // Single-image input example (thinking mode enabled)
            MultiModalConversation conv = new MultiModalConversation();

            // Build the message content
            Map&lt;String, Object&gt; textContent = new HashMap<>();
            textContent.put("text", "What scene is depicted in the image?");

            Map&lt;String, Object&gt; imageContent = new HashMap<>();
            imageContent.put("image", "https://help-static-aliyun-doc.aliyuncs.com/file-manage-files/zh-CN/20241022/emyrja/dog_and_girl.jpeg");

            MultiModalMessage userMessage = MultiModalMessage.builder()
                    .role(Role.USER.getValue())
                    .content(Arrays.asList(textContent, imageContent))
                    .build();

            // Build the request parameters
            MultiModalConversationParam param = MultiModalConversationParam.builder()
                    // If the environment variable is not set, replace this with your API key from Model Studio.
                    .apiKey(System.getenv("DASHSCOPE_API_KEY"))
                    .model("kimi-k2.6")
                    .messages(Arrays.asList(userMessage))
                    .enableThinking(true)  // Enable thinking mode
                    .build();

            // Call the model
            MultiModalConversationResult result = conv.call(param);

            // Print the result
            String content = result.getOutput().getChoices().get(0).getMessage().getContent().get(0).get("text");
            System.out.println("Response: " + content);

            // If thinking mode is enabled, print the thinking process
            if (result.getOutput().getChoices().get(0).getMessage().getReasoningContent() != null) {
                System.out.println("\nThinking process: " +
                    result.getOutput().getChoices().get(0).getMessage().getReasoningContent());
            }

            // Multi-image input example (uncomment to use)
            // Map&lt;String, Object&gt; imageContent1 = new HashMap<>();
            // imageContent1.put("image", "https://help-static-aliyun-doc.aliyuncs.com/file-manage-files/zh-CN/20241022/emyrja/dog_and_girl.jpeg");
            // Map&lt;String, Object&gt; imageContent2 = new HashMap<>();
            // imageContent2.put("image", "https://dashscope.oss-cn-beijing.aliyuncs.com/images/tiger.png");
            //
            // Map&lt;String, Object&gt; textContent2 = new HashMap<>();
            // textContent2.put("text", "What do these images depict?");
            //
            // MultiModalMessage multiImageMessage = MultiModalMessage.builder()
            //         .role(Role.USER.getValue())
            //         .content(Arrays.asList(textContent2, imageContent1, imageContent2))
            //         .build();
            //
            // MultiModalConversationParam multiParam = MultiModalConversationParam.builder()
            //         .apiKey(System.getenv("DASHSCOPE_API_KEY"))
            //         .model("kimi-k2.6")
            //         .messages(Arrays.asList(multiImageMessage))
            //         .enableThinking(true)
            //         .build();
            //
            // MultiModalConversationResult multiResult = conv.call(multiParam);
            // System.out.println(multiResult.getOutput().getChoices().get(0).getMessage().getContent().get(0).get("text"));

        } catch (ApiException | NoApiKeyException | InputRequiredException e) {
            System.err.println("Call failed: " + e.getMessage());
        }
    }
}

Curl

HELPCODEESCAPE-bash
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": "kimi-k2.6",
    "input": {
        "messages": [
            {
                "role": "user",
                "content": [
                    {
                        "text": "What scene is depicted in the image?"
                    },
                    {
                        "image": "https://help-static-aliyun-doc.aliyuncs.com/file-manage-files/zh-CN/20241022/emyrja/dog_and_girl.jpeg"
                    }
                ]
            }
        ]
    },
    "parameters": {
        "enable_thinking": true
    }
}'

# Multi-image input example (uncomment to use)
# 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": "kimi-k2.6",
#     "input": {
#         "messages": [
#             {
#                 "role": "user",
#                 "content": [
#                     {
#                         "text": "What do these images depict?"
#                     },
#                     {
#                         "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"
#                     }
#                 ]
#             }
#         ]
#     },
#     "parameters": {
#         "enable_thinking": true
#     }
# }'

Video understanding

Video file

kimi-k2.5 and kimi-k2.6 analyze video by extracting frames. Two parameters control frame extraction:

fps : Sets the frame extraction frequency. The model extracts one frame every \frac 1 {fps} seconds. The value must be in the range of [0.1, 10], with a default of 2.0.
- For high-motion scenes: Set a higher fps value to capture more detail.
- For static or long videos: Set a lower fps value to improve processing efficiency.
max_frames: Sets the maximum number of frames to extract from a video. The default and maximum value is 2000.
If the total number of frames calculated from the fps value exceeds this limit, the system automatically applies uniform frame extraction to stay within the max_frames limit. This parameter is available only when using the DashScope SDK.

OpenAI compatible

When passing a video file to the model using the OpenAI SDK or an HTTP request, set the "type" parameter in the user message to "video_url" .

Python

HELPCODEESCAPE-python
import os
from openai import OpenAI

client = OpenAI(
    api_key=os.getenv("DASHSCOPE_API_KEY"),
    base_url="https://dashscope.aliyuncs.com/compatible-mode/v1",
)

completion = client.chat.completions.create(
    model="kimi-k2.6",
    messages=[
        {
            "role": "user",
            "content": [
                # When passing a video file directly, set the "type" parameter 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({
    apiKey: process.env.DASHSCOPE_API_KEY,
    baseURL: "https://dashscope.aliyuncs.com/compatible-mode/v1"
});

async function main() {
    const response = await openai.chat.completions.create({
        model: "kimi-k2.6",
        messages: [
            {
                role: "user",
                content: [
                    // When passing a video file directly, set the "type" parameter 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
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": "kimi-k2.6",
    "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

dashscope.base_http_api_url = "https://dashscope.aliyuncs.com/api/v1"

messages = [
    {"role": "user",
        "content": [
            # The fps parameter sets the frame extraction frequency; one frame is extracted every 1/fps seconds.
            {"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(
    # If the environment variable is not set, use your Model Studio API key directly: api_key="sk-xxx"
    api_key=os.getenv('DASHSCOPE_API_KEY'),
    model='kimi-k2.6',
    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.JsonUtils;
import com.alibaba.dashscope.utils.Constants;

public class Main {
    static {Constants.baseHttpApiUrl="https://dashscope.aliyuncs.com/api/v1";}

    public static void simpleMultiModalConversationCall()
            throws ApiException, NoApiKeyException, UploadFileException {
        MultiModalConversation conv = new MultiModalConversation();
        // The fps parameter sets the frame extraction frequency; one frame is extracted every 1/fps seconds.
        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()
                .apiKey(System.getenv("DASHSCOPE_API_KEY"))
                .model("kimi-k2.6")
                .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
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": "kimi-k2.6",
    "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 frames), set the fps parameter to the original video's frame extraction rate. This value, representing one frame extracted every \frac 1 {fps} seconds, helps the model understand the sequence, duration, and dynamic changes in the video.

OpenAI compatible

When passing a video as an image list using the OpenAI SDK or an HTTP request, set the "type" parameter in the user message to "video" .

Python

HELPCODEESCAPE-python
import os
from openai import OpenAI

client = OpenAI(
    api_key=os.getenv("DASHSCOPE_API_KEY"),
    base_url="https://dashscope.aliyuncs.com/compatible-mode/v1",
)

completion = client.chat.completions.create(
    model="kimi-k2.6",
    messages=[{"role": "user","content": [
        # When passing an image list, the "type" parameter in the user message must be set to "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 action in this video."},
    ]}]
)

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

Node.js

HELPCODEESCAPE-nodejs
import OpenAI from "openai";

const openai = new OpenAI({
    apiKey: process.env.DASHSCOPE_API_KEY,
    baseURL: "https://dashscope.aliyuncs.com/compatible-mode/v1"
});

async function main() {
    const response = await openai.chat.completions.create({
        model: "kimi-k2.6",
        messages: [{
            role: "user",
            content: [
                {
                    // When passing an image list, the "type" parameter in the user message must be set to "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 action in this video."
                }
            ]
        }]
    });
    console.log(response.choices[0].message.content);
}

main();

Curl

HELPCODEESCAPE-curl
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": "kimi-k2.6",
    "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 action in this video."}]}]
}'

DashScope

Python

HELPCODEESCAPE-python
import os
import dashscope

dashscope.base_http_api_url = "https://dashscope.aliyuncs.com/api/v1"

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 action in this video."}]}]
response = dashscope.MultiModalConversation.call(
    # If the environment variable is not set, use your Model Studio API key directly: api_key="sk-xxx",
    api_key=os.getenv("DASHSCOPE_API_KEY"),
    model='kimi-k2.6',
    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.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 {Constants.baseHttpApiUrl="https://dashscope.aliyuncs.com/api/v1";}

    private static final String MODEL_NAME = "kimi-k2.6";
    public static void videoImageListSample() throws ApiException, NoApiKeyException, UploadFileException {
        MultiModalConversation conv = new MultiModalConversation();
        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 action in this video.")))
                .build();
        MultiModalConversationParam param = MultiModalConversationParam.builder()
                .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
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": "kimi-k2.6",
  "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 action in this video."
          }
        ]
      }
    ]
  }
}'

Pass a local file

The OpenAI-compatible API supports only Base64 encoding. DashScope supports both Base64 encoding and file paths.

OpenAI-compatible

To use Base64 encoding, construct a Data URL. See Construct a Data URL for instructions.

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 to your local image.
base64_image = encode_image("xxx/eagle.png")

client = OpenAI(
    api_key=os.getenv('DASHSCOPE_API_KEY'),
    base_url="https://dashscope.aliyuncs.com/compatible-mode/v1",
)
completion = client.chat.completions.create(
    model="kimi-k2.6",
    messages=[
        {
            "role": "user",
            "content": [
                {
                    "type": "image_url",
                    "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)

# The following are examples of passing a local video file and a local image list.

# [Local video file] Encode the local video as a Data URL and pass it to the video_url parameter:
#   def encode_video_to_data_url(video_path):
#       with open(video_path, "rb") as f:
#           return "data:video/mp4;base64," + base64.b64encode(f.read()).decode("utf-8")

#   video_data_url = encode_video_to_data_url("xxx/local.mp4")
#   content = [{"type": "video_url", "video_url": {"url": video_data_url}, "fps": 2}, {"type": "text", "text": "What is the content of this video?"}]

# [Local image list] Encode multiple local images with Base64 and pass them as a list to the video parameter:
#   image_data_urls = [f"data:image/jpeg;base64,{encode_image(p)}" for p in ["xxx/f1.jpg", "xxx/f2.jpg", "xxx/f3.jpg", "xxx/f4.jpg"]]
#   content = [{"type": "video", "video": image_data_urls, "fps": 2}, {"type": "text", "text": "Describe the sequence of events in this video."}]

Node.js

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

const openai = new OpenAI(
    {
        apiKey: process.env.DASHSCOPE_API_KEY,
        baseURL: "https://dashscope.aliyuncs.com/compatible-mode/v1"
    }
);

const encodeImage = (imagePath) => {
    const imageFile = readFileSync(imagePath);
    return imageFile.toString('base64');
  };
// Replace xxx/eagle.png with the absolute path to your local image.
const base64Image = encodeImage("xxx/eagle.png")
async function main() {
    const completion = await openai.chat.completions.create({
        model: "kimi-k2.6",
        messages: [
            {"role": "user",
             "content": [{"type": "image_url",
                        "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();

// The following are examples of passing a local video file and a local image list.

// [Local video file] Encode the local video as a Data URL and pass it to the video_url parameter:
//   const encodeVideoToDataUrl = (videoPath) => "data:video/mp4;base64," + readFileSync(videoPath).toString("base64");
//   const videoDataUrl = encodeVideoToDataUrl("xxx/local.mp4");
//   content: [{ type: "video_url", video_url: { url: videoDataUrl }, fps: 2 }, { type: "text", text: "What is the content of this video?" }]

// [Local image list] Encode multiple local images with Base64 and pass them as a list to the video parameter:
//   const imageDataUrls = ["xxx/f1.jpg","xxx/f2.jpg","xxx/f3.jpg","xxx/f4.jpg"].map(p => `data:image/jpeg;base64,${encodeImage(p)}`);
//   content: [{ type: "video", video: imageDataUrls, fps: 2 }, { type: "text", text: "Describe the sequence of events in this video." }]

//   messages: [{"role": "user", "content": content}]
//   Then call openai.chat.completions.create({model: "kimi-k2.6", messages: messages})

DashScope

Base64 encoding

To use Base64 encoding, construct a Data URL. See Construct a Data URL for instructions.

Python

HELPCODEESCAPE-python
import base64
import os
import dashscope
from dashscope import MultiModalConversation

dashscope.base_http_api_url = "https://dashscope.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 xxx/eagle.png with the absolute path to your local image.
base64_image = encode_image("xxx/eagle.png")

messages = [
    {
        "role": "user",
        "content": [
            {"image": f"data:image/png;base64,{base64_image}"},
            {"text": "What scene is depicted in the image?"},
        ],
    },
]
response = MultiModalConversation.call(
    # 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="kimi-k2.6",
    messages=messages,
)
print(response.output.choices[0].message.content[0]["text"])

# The following are examples of passing a local video file and a local image list.

# [Local video file]
#   video_data_url = "data:video/mp4;base64," + base64.b64encode(open("xxx/local.mp4","rb").read()).decode("utf-8")
#   content: [{"video": video_data_url, "fps": 2}, {"text": "What is the content of this video?"}]

# [Local image list]
#   Base64: image_data_urls = [f"data:image/jpeg;base64,{encode_image(p)}" for p in ["xxx/f1.jpg","xxx/f2.jpg","xxx/f3.jpg","xxx/f4.jpg"]]
#   content: [{"video": image_data_urls, "fps": 2}, {"text": "Describe the sequence of events in this video."}]

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 {Constants.baseHttpApiUrl="https://dashscope.aliyuncs.com/api/v1";}

    private static String encodeToBase64(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 = encodeToBase64(localPath); // Encode the file to a Base64 string.

        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()
                .apiKey(System.getenv("DASHSCOPE_API_KEY"))
                .model("kimi-k2.6")
                .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 to your local image.
            callWithLocalFile("xxx/eagle.png");
        } catch (ApiException | NoApiKeyException | UploadFileException | IOException e) {
            System.out.println(e.getMessage());
        }
        System.exit(0);
    }

    // The following are examples of passing a local video file and a local image list.
    // [Local video file]
    // String base64Image = encodeToBase64(localPath);
    // 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;}},
   //                     new HashMap&lt;String, Object&gt;() {{ put("text", "What scene is depicted in this video?"); }}
   //             )).build();

    // [Local image list]
    // List&lt;String&gt; urls = Arrays.asList(
    //                                   "data:image/jpeg;base64,"+encodeToBase64(path/f1.jpg),
    //                                   "data:image/jpeg;base64,"+encodeToBase64(path/f2.jpg),
    //                                   "data:image/jpeg;base64,"+encodeToBase64(path/f3.jpg),
    //                                   "data:image/jpeg;base64,"+encodeToBase64(path/f4.jpg));
   //  MultiModalConversation conv = new MultiModalConversation();
   //  MultiModalMessage userMessage = MultiModalMessage.builder().role(Role.USER.getValue())
   //             .content(Arrays.asList(
   //                     new HashMap&lt;String, Object&gt;() {{ put("video", urls;}},
   //                     new HashMap&lt;String, Object&gt;() {{ put("text", "What scene is depicted in this video?"); }}
   //             )).build();

}

File path

Pass a local file path directly to the model. Only the DashScope Python and Java SDKs support this method. Use the table below to format the path for your SDK and operating system. Specify a file path (image example)

System	SDK	File path format	Example
Linux or macOS	Python SDK	file://	file:///home/images/test.png
Java SDK
Windows	Python SDK	file://	file://D:/images/test.png
Java SDK	file:///	file:///D:/images/test.png

Python

HELPCODEESCAPE-python
import os
from dashscope import MultiModalConversation
import dashscope

dashscope.base_http_api_url = "https://dashscope.aliyuncs.com/api/v1"

# Replace xxx/eagle.png with the absolute path to 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 = MultiModalConversation.call(
    api_key=os.getenv('DASHSCOPE_API_KEY'),
    model='kimi-k2.6',
    messages=messages)
print(response.output.choices[0].message.content[0]["text"])

# The following examples show how to pass a local video file or a local image list by using file paths.
# [Local video file]
#  video_path = "file:///path/to/local.mp4"
#  content: [{"video": video_path, "fps": 2}, {"text": "What is the content of this video?"}]

# [Local image list]
# image_paths = ["file:///path/f1.jpg", "file:///path/f2.jpg", "file:///path/f3.jpg", "file:///path/f4.jpg"]
# content: [{"video": image_paths, "fps": 2}, {"text": "Describe the sequence of events in this video."}]

Java

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

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 {Constants.baseHttpApiUrl="https://dashscope.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()
                .apiKey(System.getenv("DASHSCOPE_API_KEY"))
                .model("kimi-k2.6")
                .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 to your local image.
            callWithLocalFile("xxx/eagle.png");
        } catch (ApiException | NoApiKeyException | UploadFileException e) {
            System.out.println(e.getMessage());
        }
        System.exit(0);
    }

    // The following examples show how to pass a local video file or a local image list by using file paths.

    // [Local video file]
    //  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);}},
    //                    new HashMap&lt;String, Object&gt;(){{put("text", "What scene is depicted in the video?");}})).build();

    // [Local image list]

    //    MultiModalConversation conv = new MultiModalConversation();
    //    List&lt;String&gt; filePath = Arrays.asList("file:///path/f1.jpg", "file:///path/f2.jpg", "file:///path/f3.jpg", "file:///path/f4.jpg")
    //    MultiModalMessage userMessage = MultiModalMessage.builder().role(Role.USER.getValue())
    //            .content(Arrays.asList(new HashMap&lt;String, Object&gt;(){{put("video", filePath);}},
    //                    new HashMap&lt;String, Object&gt;(){{put("text", "What scene is depicted in the video?");}})).build();
}

File limitations

Image limitations

Image resolution:
- Minimum size: Width and height must both be greater than 10 pixels.
- Aspect ratio: The ratio of the longest side to the shortest side must not exceed 200:1.
- Maximum resolution: Keep image resolution within 8K (7680x4320). Higher resolutions may cause API timeouts due to large file sizes.
Supported image formats
- For resolutions below 4K (3840x2160), the following image formats are supported:

Format	File extension	MIME type
BMP	.bmp	image/bmp
JPEG	.jpe, .jpeg, .jpg	image/jpeg
PNG	.png	image/png
TIFF	.tif, .tiff	image/tiff
WEBP	.webp	image/webp
HEIC	.heic	image/heic

For resolutions between 4K (3840x2160) and 8K (7680x4320), only the JPEG, JPG, and PNG formats are supported.
Image size:
- When an image is provided via a public URL or local path, its size must not exceed 10 MB.
- When using Base64 encoding, the encoded string must not exceed 10 MB.
To reduce the file size, see How to compress an image or video to the required size .
Number of supported images: The total token count across all images and text must stay within the model's maximum input limit.

Video limitations

As an image list: Minimum of 4 images, maximum of 2,000 images.
As a video file:
- Video size:
  - Via a public URL: Up to 2 GB.
  - Via Base64 encoding, the encoded string must be less than 10 MB.
  - Via a local file path, the video file cannot exceed 100 MB.
- Video duration: 2 seconds to 1 hour.
Video format: MP4, AVI, MKV, MOV, FLV, WMV, and more.
Video resolution: No specific limit. For best results, use 2K or lower. Higher resolutions increase processing time without improving understanding.
Audio understanding: The model does not process audio tracks in video files.

Other features

Model	Multi-turn conversation	Deep thinking	Function calling	Structured output	Web search	Prefix completion	Context cache
kimi-k2.6	Supported	Supported	Supported	Not supported	Not supported	Not supported	Supported
kimi-k2.5	Supported	Supported	Supported	Not supported	Not supported	Not supported	Supported
kimi-k2-thinking	Supported	Supported	Supported	Supported	Not supported	Not supported	Supported
Moonshot-Kimi-K2-Instruct	Supported	Not supported	Supported	Not supported	Supported	Not supported	Supported

Default values

Model	enable_thinking	temperature	top_p	presence_penalty	fps	max_frames
kimi-k2.6	false	thinking mode: 1.0 non-thinking mode: 0.6	thinking/non-thinking mode: 0.95	thinking/non-thinking mode: 0.0	2	2000
kimi-k2.5	false	thinking mode: 1.0 non-thinking mode: 0.6	thinking/non-thinking mode: 0.95	thinking/non-thinking mode: 0.0	2	2000
kimi-k2-thinking	-	1.0	-	-	-	-
Moonshot-Kimi-K2-Instruct	-	0.6	1.0	0.0	-	-

A hyphen (-) means the parameter is not supported.

Models and billing

The Kimi series are large language models from Moonshot AI.

kimi-k2.6: The latest model in the Kimi series with stronger long-horizon coding, improved instruction following, and self-correction. Supports text, image, and video input with thinking and non-thinking modes for conversation and agent tasks.
kimi-k2.5: Achieves open-source SOTA on agent tasks, code generation, visual understanding, and general intelligence. Supports image, video, and text input with thinking and non-thinking modes for conversation and agent tasks.
kimi-k2-thinking: Deep thinking mode only. Displays the reasoning process through the reasoning_content field. Excels at coding and tool calling --- ideal for logical analysis, planning, or in-depth understanding.
Moonshot-Kimi-K2-Instruct: No deep thinking mode. Generates responses directly with lower latency --- ideal for quick, direct answers.

For pricing and context window details, see the Model Studio console.

Billing is based on input and output token counts.

In thinking mode, the chain of thought counts as output tokens.

Error codes

If a model call fails, see Error messages.

party model integration tutorial-Kimi ​

OpenAI compatible ​

US (Virginia) ​

Germany (Frankfurt) ​

China (Beijing) ​

DashScope ​

US (Virginia) ​

Python ​

Java ​

Germany (Frankfurt) ​

Python ​

Java ​

China (Beijing) ​

Getting started ​

OpenAI compatible ​

Python ​

Response ​

Node.js ​

Response ​

HTTP ​

Curl ​

Response ​

DashScope ​

Python ​

Response ​

Java ​

Response ​

HTTP ​

Curl ​

Response ​

Multimodal call ​

Enable or disable thinking mode ​

OpenAI compatible ​

Python ​

Node.js ​

Curl ​

DashScope ​

Python ​

Java ​

Curl ​

Video understanding ​

Video file ​

OpenAI compatible ​

Python ​

Node.js ​

Curl ​

DashScope ​

Python ​

Java ​

Curl ​

Image list ​

OpenAI compatible ​

Python ​

Node.js ​

Curl ​

DashScope ​

Python ​

Java ​

Curl ​

Pass a local file ​

OpenAI-compatible ​

Python ​

Node.js ​

DashScope ​

Base64 encoding ​

Python ​

Java ​

File path ​

Python ​

Java ​

File limitations ​

Image limitations ​

Video limitations ​

Other features ​

Default values ​

Models and billing ​

Error codes ​

party model integration tutorial-Kimi

OpenAI compatible

US (Virginia)

Germany (Frankfurt)

China (Beijing)

DashScope

US (Virginia)

Python

Java

Germany (Frankfurt)

Python

Java

China (Beijing)

Getting started

OpenAI compatible

Python

Response

Node.js

Response

HTTP

Curl

Response

DashScope

Python

Response

Java

Response

HTTP

Curl

Response

Multimodal call

Enable or disable thinking mode

OpenAI compatible

Python

Node.js

Curl

DashScope

Python

Java

Curl

Video understanding

Video file

OpenAI compatible

Python

Node.js

Curl

DashScope

Python

Java

Curl

Image list

OpenAI compatible

Python

Node.js

Curl

DashScope

Python

Java

Curl

Pass a local file

OpenAI-compatible

Python

Node.js

DashScope

Base64 encoding

Python

Java

File path

Python

Java

File limitations

Image limitations

Video limitations

Other features

Default values

Models and billing

Error codes