Kimi - Alibaba Cloud Model Studio: Central de Documentação do Alibaba Cloud

Este documento descreve como chamar o service de inferência do modelo Kimi implantado no Alibaba Cloud Model Studio.

ImportanteOs modelos Moonshot-Kimi-K2-Instruct e kimi-k2-thinking foram descontinuados em 9 de julho de 2026. Recomendamos a migração para qwen3.7-plus, qwen3.8-max ou qwen3.8-flash.

Regiões suportadas: China (Beijing), Singapore, Japan (Tokyo), China (Hong Kong), Germany (Frankfurt) e US (Virginia).

Experiência com o modelo: Experimente o modelo Kimi no centro de testes de modelos.

Os endpoints de service são específicos por região. Configure a URL base correta para sua região.

OpenAI compatible

US (Virginia)

O parâmetro base_url para chamadas SDK é: https://{WorkspaceId}.us-east-1.maas.aliyuncs.com/compatible-mode/v1

URL da requisição HTTP: POST https://{WorkspaceId}.us-east-1.maas.aliyuncs.com/compatible-mode/v1/chat/completions

Germany (Frankfurt)

O parâmetro base_url para chamadas SDK é: https://{WorkspaceId}.eu-central-1.maas.aliyuncs.com/compatible-mode/v1

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

Singapore

O parâmetro base_url para chamadas SDK é: https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/compatible-mode/v1

URL da requisição HTTP: POST https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/compatible-mode/v1/chat/completions

Japan (Tokyo)

O parâmetro base_url para chamadas SDK é: https://{WorkspaceId}.ap-northeast-1.maas.aliyuncs.com/compatible-mode/v1

URL da requisição HTTP: POST https://{WorkspaceId}.ap-northeast-1.maas.aliyuncs.com/compatible-mode/v1/chat/completions

China (Beijing)

O parâmetro base_url para chamadas SDK é: https://{WorkspaceId}.cn-beijing.maas.aliyuncs.com/compatible-mode/v1

URL da requisição HTTP: POST https://{WorkspaceId}.cn-beijing.maas.aliyuncs.com/compatible-mode/v1/chat/completions

China (Hong Kong)

O parâmetro base_url para chamadas SDK é: https://{WorkspaceId}.cn-hongkong.maas.aliyuncs.com/compatible-mode/v1

URL da requisição HTTP: POST https://{WorkspaceId}.cn-hongkong.maas.aliyuncs.com/compatible-mode/v1/chat/completions

DashScope

US (Virginia)

A URL da requisição HTTP para modelos de texto, como kimi-k2-thinking, é POST https://{WorkspaceId}.us-east-1.maas.aliyuncs.com/api/v1/services/aigc/text-generation/generation

A URL da requisição HTTP para modelos multimodais, como kimi-k2.6 e kimi-k2.5, é POST https://{WorkspaceId}.us-east-1.maas.aliyuncs.com/api/v1/services/aigc/multimodal-generation/generation

O parâmetro base_url para chamadas SDK é:

Python code

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

Java code

Método 1:

import com.alibaba.dashscope.protocol.Protocol;
Generation gen = new Generation(Protocol.HTTP.getValue(), "https://{WorkspaceId}.us-east-1.maas.aliyuncs.com/api/v1");

Método 2:

import com.alibaba.dashscope.utils.Constants;
Constants.baseHttpApiUrl="https://{WorkspaceId}.us-east-1.maas.aliyuncs.com/api/v1";

Germany (Frankfurt)

A URL da requisição HTTP para modelos de texto, como kimi-k2-thinking, é POST https://{WorkspaceId}.eu-central-1.maas.aliyuncs.com/api/v1/services/aigc/text-generation/generation

A URL da requisição HTTP para modelos multimodais, como kimi-k2.7-code, kimi-k2.6 e kimi-k2.5, é POST https://{WorkspaceId}.eu-central-1.maas.aliyuncs.com/api/v1/services/aigc/multimodal-generation/generation

O parâmetro base_url para chamadas SDK é:

Python code

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

Java code

Método 1:

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

Método 2:

import com.alibaba.dashscope.utils.Constants;
Constants.baseHttpApiUrl="https://{WorkspaceId}.eu-central-1.maas.aliyuncs.com/api/v1";

Singapore

A URL da requisição HTTP para modelos de texto, como kimi-k2-thinking, é POST https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/api/v1/services/aigc/text-generation/generation

A URL da requisição HTTP para modelos multimodais, como kimi-k2.7-code, kimi-k2.6 e kimi-k2.5, é POST https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/api/v1/services/aigc/multimodal-generation/generation

O parâmetro base_url para chamadas SDK é:

Python code

# Singapore region. Replace {WorkspaceId} with your Bailian workspace ID. URLs vary by region.
dashscope.base_http_api_url = 'https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/api/v1'

Java code

Opção 1:

import com.alibaba.dashscope.protocol.Protocol;
// Singapore region. Replace {WorkspaceId} with your Bailian workspace ID. URLs vary by region.
Generation gen = new Generation(Protocol.HTTP.getValue(), "https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/api/v1");

Opção 2:

import com.alibaba.dashscope.utils.Constants;
// Singapore region. Replace {WorkspaceId} with your Bailian workspace ID. URLs vary by region.
Constants.baseHttpApiUrl="https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/api/v1";

Japan (Tokyo)

A URL da requisição HTTP para modelos de texto, como kimi-k2-thinking, é POST https://{WorkspaceId}.ap-northeast-1.maas.aliyuncs.com/api/v1/services/aigc/text-generation/generation

A URL da requisição HTTP para modelos multimodais, como kimi-k2.7-code, kimi-k2.6 e kimi-k2.5, é POST https://{WorkspaceId}.ap-northeast-1.maas.aliyuncs.com/api/v1/services/aigc/multimodal-generation/generation

O parâmetro base_url para chamadas SDK é:

Python code

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

Java code

Opção 1:

import com.alibaba.dashscope.protocol.Protocol;
Generation gen = new Generation(Protocol.HTTP.getValue(), "https://{WorkspaceId}.ap-northeast-1.maas.aliyuncs.com/api/v1");

Opção 2:

import com.alibaba.dashscope.utils.Constants;
Constants.baseHttpApiUrl="https://{WorkspaceId}.ap-northeast-1.maas.aliyuncs.com/api/v1";

China (Hong Kong)

A URL da requisição HTTP para modelos de texto, como kimi-k2-thinking, é POST https://{WorkspaceId}.cn-hongkong.maas.aliyuncs.com/api/v1/services/aigc/text-generation/generation

A URL da requisição HTTP para modelos multimodais, como kimi-k2.7-code, kimi-k2.6 e kimi-k2.5, é POST https://{WorkspaceId}.cn-hongkong.maas.aliyuncs.com/api/v1/services/aigc/multimodal-generation/generation

O parâmetro base_url para chamadas SDK é:

Python code

dashscope.base_http_api_url = 'https://{WorkspaceId}.cn-hongkong.maas.aliyuncs.com/api/v1'

Java code

Método 1:

import com.alibaba.dashscope.protocol.Protocol;
Generation gen = new Generation(Protocol.HTTP.getValue(), "https://{WorkspaceId}.cn-hongkong.maas.aliyuncs.com/api/v1");

Método 2:

import com.alibaba.dashscope.utils.Constants;
Constants.baseHttpApiUrl="https://{WorkspaceId}.cn-hongkong.maas.aliyuncs.com/api/v1";

China (Beijing)

A URL da requisição HTTP para modelos de texto, como kimi-k2-thinking, é POST https://{WorkspaceId}.cn-beijing.maas.aliyuncs.com/api/v1/services/aigc/text-generation/generation

A URL da requisição HTTP para modelos multimodais, como kimi-k2.7-code, kimi-k2.6 e kimi-k2.5, é POST https://{WorkspaceId}.cn-beijing.maas.aliyuncs.com/api/v1/services/aigc/multimodal-generation/generation

O parâmetro base_url para chamadas SDK é:

Python code

# China (Beijing) region. Replace {WorkspaceId} with your Bailian workspace ID. URLs vary by region.
dashscope.base_http_api_url = 'https://{WorkspaceId}.cn-beijing.maas.aliyuncs.com/api/v1'

Java code

Opção 1:

import com.alibaba.dashscope.protocol.Protocol;
// China (Beijing) region. Replace {WorkspaceId} with your Bailian workspace ID. URLs vary by region.
Generation gen = new Generation(Protocol.HTTP.getValue(), "https://{WorkspaceId}.cn-beijing.maas.aliyuncs.com/api/v1");

Opção 2:

import com.alibaba.dashscope.utils.Constants;
// China (Beijing) region. Replace {WorkspaceId} with your Bailian workspace ID. URLs vary by region.
Constants.baseHttpApiUrl="https://{WorkspaceId}.cn-beijing.maas.aliyuncs.com/api/v1";

Substitua {WorkspaceId} pelo seu workspace ID real.

Pré-requisitos: Você deve get an API key e set it as an environment variable. Se usar o SDK, você deve install the SDK.

Primeiros passos

Os exemplos a seguir usam apenas entrada de texto. Para exemplos multimodais, consulte chamada multimodal.

OpenAI compatible

Python

import os
from openai import OpenAI

client = OpenAI(
    api_key=os.getenv("DASHSCOPE_API_KEY"),
    # China (Beijing) region. Replace {WorkspaceId} with your Bailian workspace ID. URLs vary by region.
    base_url="https://{WorkspaceId}.cn-beijing.maas.aliyuncs.com/compatible-mode/v1",
)

completion = client.chat.completions.create(
    model="kimi-k2.6",
    messages=[{"role": "user", "content": "Who are you?"}],
    stream=True,
    extra_body={"enable_thinking": True},  # Enable thinking mode to get reasoning_content
)

reasoning_content = ""  # Complete thinking process
answer_content = ""     # Complete response
is_answering = False    # Tracks if the main response has started.

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

for chunk in completion:
    if chunk.choices:
        delta = chunk.choices[0].delta
        # Store content from the thinking process.
        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 printing the main response once its content arrives.
        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

Resposta

====================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 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 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

import OpenAI from "openai";
import process from 'process';

// Initialize the OpenAI client
const openai = new OpenAI({
    // If not using an environment variable, replace `process.env.DASHSCOPE_API_KEY` with your API key string (e.g., "sk-xxx").
    apiKey: process.env.DASHSCOPE_API_KEY,
    // China (Beijing) region. Replace {WorkspaceId} with your Bailian workspace ID. URLs vary by region.
    baseURL: 'https://{WorkspaceId}.cn-beijing.maas.aliyuncs.com/compatible-mode/v1'
});

let reasoningContent = ''; // Complete thinking process
let answerContent = ''; // Complete response
let isAnswering = false; // Tracks if the main response has started.

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

    const stream = await openai.chat.completions.create({
        model: 'kimi-k2.6',
        messages,
        stream: true,
        enable_thinking: true,  // Enable thinking mode to get reasoning_content
    });

    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;
            // Store content from the thinking process.
            if (delta.reasoning_content !== undefined && delta.reasoning_content !== null) {
                if (!isAnswering) {
                    process.stdout.write(delta.reasoning_content);
                }
                reasoningContent += delta.reasoning_content;
            }

            // Start printing the main response once its content arrives.
            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();

Resposta

====================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

# China (Beijing) region. Replace {WorkspaceId} with your Bailian workspace ID. URLs vary by region.
curl -X POST https://{WorkspaceId}.cn-beijing.maas.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": "Who are you?"
        }
    ],
    "enable_thinking": true
}'

Resposta

{
    "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-k2.6",
    "id": "chatcmpl-485ab490-90ec-48c3-85fa-1c732b683db2"
}

DashScope

Os exemplos do DashScope a seguir usam o endpoint multimodal-generation para chamar o kimi-k2.6, que suporta entrada de texto e multimodal. Para mais exemplos multimodais, consulte chamada multimodal .

Python

import os
import dashscope
from dashscope import MultiModalConversation
# China (Beijing) region configuration. Replace {WorkspaceId} with your actual workspace ID. For other regions, use the corresponding base URL.
dashscope.base_http_api_url = "https://{WorkspaceId}.cn-beijing.maas.aliyuncs.com/api/v1"

# Define the request messages.
messages = [{"role": "user", "content": "Who are you?"}]

completion = MultiModalConversation.call(
    api_key=os.getenv("DASHSCOPE_API_KEY"),  # If not using an environment variable, provide your key directly, e.g., api_key="sk-xxx"
    model="kimi-k2.6",
    messages=messages,
    result_format="message",  # Set the result format to message
    stream=True,              # Enable streaming.
    incremental_output=True,  # Enable incremental output
    enable_thinking=True,     # Enable thinking mode to get reasoning_content
)

reasoning_content = ""  # Complete thinking process
answer_content = ""     # Complete response
is_answering = False    # Tracks if the main response has started.

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

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

    # Store content from the thinking process.
    reasoning_chunk = message.get("reasoning_content")
    if reasoning_chunk:
        if not is_answering:
            print(reasoning_chunk, end="", flush=True)
        reasoning_content += reasoning_chunk

    # Start printing the main response once its content arrives. content is a list, so extract the text from it.
    if message.get("content"):
        text = message.content[0].get("text", "")
        if not is_answering:
            print("\n" + "=" * 20 + "Complete Response" + "=" * 20 + "\n")
            is_answering = True
        print(text, end="", flush=True)
        answer_content += text

Resposta

====================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

// DashScope SDK version >= 2.19.4
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;
import java.util.Arrays;
import java.util.Collections;

public class Main {
    public static void main(String[] args) {
        // China (Beijing) region configuration. Replace {WorkspaceId} with your actual workspace ID. For other regions, use the corresponding base URL.
        Constants.baseHttpApiUrl = "https://{WorkspaceId}.cn-beijing.maas.aliyuncs.com/api/v1";
        try {
            MultiModalConversation conv = new MultiModalConversation();

            MultiModalMessage userMsg = MultiModalMessage.builder()
                    .role(Role.USER.getValue())
                    .content(Arrays.asList(Collections.singletonMap("text", "Who are you?")))
                    .build();

            MultiModalConversationParam param = MultiModalConversationParam.builder()
                    // If not using an environment variable, replace the following line with your API key, e.g., .apiKey("sk-xxx")
                    .apiKey(System.getenv("DASHSCOPE_API_KEY"))
                    .model("kimi-k2.6")
                    .messages(Arrays.asList(userMsg))
                    .build();

            MultiModalConversationResult result = conv.call(param);

            String content = (String) result.getOutput().getChoices().get(0).getMessage().getContent().get(0).get("text");
            System.out.println("Response: " + content);
        } catch (ApiException | NoApiKeyException | UploadFileException e) {
            System.err.println("An exception occurred: " + e.getMessage());
        }
        System.exit(0);
    }
}

Resposta

====================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

# China (Beijing) region. Replace {WorkspaceId} with your Bailian workspace ID. URLs vary by region.
curl -X POST "https://{WorkspaceId}.cn-beijing.maas.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": "Who are you?"
            }
        ]
    },
    "parameters": {
        "result_format": "message",
        "enable_thinking": true
    }
}'

Resposta

{
    "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"
}

Anthropic compatible

Python

Código de exemplo

import anthropic
import os

client = anthropic.Anthropic(
    # If the environment variable is not configured, replace the value with your Model Studio API key: api_key="sk-xxx"
    api_key=os.getenv("DASHSCOPE_API_KEY"),
    # When you make a call, replace {WorkspaceId} with your actual Workspace ID.
    base_url="https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/apps/anthropic",
)

message = client.messages.create(
    model="kimi-k2.6",
    max_tokens=1024,
    messages=[
        {"role": "user", "content": "Who are you?"}
    ],
    stream=True,
)

for event in message:
    if event.type == "content_block_delta":
        if hasattr(event.delta, "thinking"):
            print(event.delta.thinking, end="", flush=True)
        if hasattr(event.delta, "text"):
            print(event.delta.text, end="", flush=True)

HTTP

Código de exemplo

curl

# Singapore region. Replace {WorkspaceId} with your Bailian workspace ID. URLs vary by region.
curl -X POST https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/apps/anthropic/v1/messages \
-H "Authorization: Bearer $DASHSCOPE_API_KEY" \
-H "Content-Type: application/json" \
-H "anthropic-version: 2023-06-01" \
-d '{
    "model": "kimi-k2.6",
    "max_tokens": 1024,
    "messages": [
        {
            "role": "user",
            "content": "Who are you?"
        }
    ]
}'

Chamadas multimodais

Os modelos kimi-k2.7-code, kimi-k2.6 e kimi-k2.5 processam simultaneamente texto, imagens ou vídeo. Use o parâmetro enable_thinking para ativar o modo de raciocínio. Os exemplos a seguir mostram como utilizar esse recurso.

Ativar ou desativar o modo de raciocínio

Os modelos kimi-k2.6 e kimi-k2.5 são híbridos de raciocínio. Eles podem responder após raciocinar ou responder diretamente. Utilize o parâmetro enable_thinking para controlar a ativação do modo de raciocínio:

true: Ativa o modo de raciocínio
false (padrão): Desativa o modo de raciocínio

O modelo kimi-k2.7-code opera exclusivamente com raciocínio: o modo de raciocínio está sempre ativado (enable_thinking tem como padrão true e não pode ser desativado), e preserve_thinking tem como padrão true.

O kimi-k2.6 permite transmitir o processo de raciocínio em conversas de múltiplas turnos por meio do parâmetro preserve_thinking. Para mais informações, consulte Pass the thinking process.

Os exemplos abaixo demonstram como usar uma URL de imagem e ativar o modo de raciocínio. O exemplo principal ilustra a entrada de uma única imagem, enquanto o código comentado serve como exemplo para entrada de múltiplas imagens.

OpenAI compatible

Python

import os
from openai import OpenAI

client = OpenAI(
    api_key=os.getenv("DASHSCOPE_API_KEY"),
    # China (Beijing) region. Replace {WorkspaceId} with your Bailian workspace ID. URLs vary by region.
    base_url="https://{WorkspaceId}.cn-beijing.maas.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 complete response
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 complete 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

import OpenAI from "openai";
import process from 'process';

const openai = new OpenAI({
    apiKey: process.env.DASHSCOPE_API_KEY,
    // China (Beijing) region. Replace {WorkspaceId} with your Bailian workspace ID. URLs vary by region.
    baseURL: 'https://{WorkspaceId}.cn-beijing.maas.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 complete response
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 complete 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

# China (Beijing) region. Replace {WorkspaceId} with your Bailian workspace ID. URLs vary by region.
curl -X POST https://{WorkspaceId}.cn-beijing.maas.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)
# China (Beijing) region. Replace {WorkspaceId} with your Bailian workspace ID. URLs vary by region.
# curl -X POST https://{WorkspaceId}.cn-beijing.maas.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

import os
import dashscope
from dashscope import MultiModalConversation
# China (Beijing) region configuration. Replace {WorkspaceId} with your actual workspace ID. For other regions, use the corresponding base URL.
dashscope.base_http_api_url = "https://{WorkspaceId}.cn-beijing.maas.aliyuncs.com/api/v1"

# 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 complete response
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 complete 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

// Requires DashScope SDK v2.22.24 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.NoApiKeyException;
import com.alibaba.dashscope.exception.UploadFileException;
import com.alibaba.dashscope.utils.JsonUtils;
import com.alibaba.dashscope.utils.Constants;
import java.util.Arrays;
import java.util.HashMap;
import java.util.Map;

public class KimiK26MultiModalExample {
    public static void main(String[] args) {
        // China (Beijing) region configuration. Replace {WorkspaceId} with your actual workspace ID. For other regions, use the corresponding base URL.
        Constants.baseHttpApiUrl = "https://{WorkspaceId}.cn-beijing.maas.aliyuncs.com/api/v1";
        try {
            // Single-image input example (thinking mode enabled)
            MultiModalConversation conv = new MultiModalConversation();

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

            Map<String, Object> 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 response
            String content = (String) 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<String, Object> 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<String, Object> imageContent2 = new HashMap<>();
            // imageContent2.put("image", "https://dashscope.oss-cn-beijing.aliyuncs.com/images/tiger.png");
            //
            // Map<String, Object> 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 | UploadFileException e) {
            System.err.println("Call failed: " + e.getMessage());
        }
    }
}

Curl

# China (Beijing) region. Replace {WorkspaceId} with your Bailian workspace ID. URLs vary by region.
curl -X POST "https://{WorkspaceId}.cn-beijing.maas.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)
# China (Beijing) region. Replace {WorkspaceId} with your Bailian workspace ID. URLs vary by region.
# curl -X POST "https://{WorkspaceId}.cn-beijing.maas.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
#     }
# }'

Compreensão de vídeo

Video file

Os modelos kimi-k2.7-code, kimi-k2.6 e kimi-k2.5 analisam vídeos extraindo uma sequência de quadros. Controle a estratégia de extração de quadros com os seguintes parâmetros:

fps: Controla a frequência de extração de quadros. O intervalo entre os quadros extraídos é de \frac 1 {fps} segundos. O valor deve estar no intervalo de [0,1, 10]. O valor padrão é 2,0.
- Para cenas com muito movimento: Defina um valor de fps mais alto para capturar mais detalhes.
- Para vídeos estáticos ou longos: Defina um valor de fps mais baixo para melhorar a eficiência do processamento.
max_frames: Especifica o número máximo de quadros a serem extraídos de um vídeo. O valor padrão e máximo é 2000.

Se o número de quadros calculado a partir do valor de fps exceder esse limite, o sistema extrairá quadros uniformemente para permanecer dentro do limite de max_frames. Este parâmetro está disponível apenas quando você usa o DashScope SDK.

OpenAI compatible

Ao passar um arquivo de vídeo para o modelo usando o OpenAI SDK ou uma requisição HTTP, defina o parâmetro "type" na mensagem do usuário como "video_url" .

Python

import os
from openai import OpenAI

client = OpenAI(
    api_key=os.getenv("DASHSCOPE_API_KEY"),
    # China (Beijing) region. Replace {WorkspaceId} with your Bailian workspace ID. URLs vary by region.
    base_url="https://{WorkspaceId}.cn-beijing.maas.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

import OpenAI from "openai";

const openai = new OpenAI({
    apiKey: process.env.DASHSCOPE_API_KEY,
    // China (Beijing) region. Replace {WorkspaceId} with your Bailian workspace ID. URLs vary by region.
    baseURL: "https://{WorkspaceId}.cn-beijing.maas.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

# China (Beijing) region. Replace {WorkspaceId} with your Bailian workspace ID. URLs vary by region.
curl -X POST https://{WorkspaceId}.cn-beijing.maas.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

import dashscope
import os

# China (Beijing) region. Replace {WorkspaceId} with your Bailian workspace ID. URLs vary by region.
dashscope.base_http_api_url = "https://{WorkspaceId}.cn-beijing.maas.aliyuncs.com/api/v1"

messages = [
    {"role": "user",
        "content": [
            # The fps parameter sets the frame extraction frequency; the interval between frames is 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 DASHSCOPE_API_KEY environment variable is not set, replace this 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"])

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 {
    public static void simpleMultiModalConversationCall()
            throws ApiException, NoApiKeyException, UploadFileException {
        MultiModalConversation conv = new MultiModalConversation();
        // The fps parameter sets the frame extraction frequency; the interval between frames is 1/fps seconds.
        Map<String, Object> 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) {
        // China (Beijing) region configuration. Replace {WorkspaceId} with your actual workspace ID. For other regions, use the corresponding base URL.
        Constants.baseHttpApiUrl = "https://{WorkspaceId}.cn-beijing.maas.aliyuncs.com/api/v1";
        try {
            simpleMultiModalConversationCall();
        } catch (ApiException | NoApiKeyException | UploadFileException e) {
            System.out.println(e.getMessage());
        }
        System.exit(0);
    }
}

curl

# China (Beijing) region. Replace {WorkspaceId} with your Bailian workspace ID. URLs vary by region.
curl -X POST https://{WorkspaceId}.cn-beijing.maas.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

Ao fornecer um vídeo como uma lista de imagens (quadros pré-extraídos), use o parâmetro fps para especificar a taxa de extração de quadros do vídeo original. Esse valor indica que os quadros foram extraídos a cada \frac 1 {fps} segundos, permitindo que o modelo compreenda melhor a sequência de eventos, a duração e as mudanças dinâmicas.

OpenAI compatible

Ao passar um vídeo como uma lista de imagens usando o OpenAI SDK ou uma requisição HTTP, defina o parâmetro "type" na mensagem do usuário como "video" .

Python

import os
from openai import OpenAI

client = OpenAI(
    api_key=os.getenv("DASHSCOPE_API_KEY"),
    # China (Beijing) region. Replace {WorkspaceId} with your Bailian workspace ID. URLs vary by region.
    base_url="https://{WorkspaceId}.cn-beijing.maas.aliyuncs.com/compatible-mode/v1",
)

completion = client.chat.completions.create(
    model="kimi-k2.6",
    messages=[{"role": "user","content": [
        # When passing an image list, set the "type" parameter in the user message 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

import OpenAI from "openai";

const openai = new OpenAI({
    apiKey: process.env.DASHSCOPE_API_KEY,
    // China (Beijing) region. Replace {WorkspaceId} with your Bailian workspace ID. URLs vary by region.
    baseURL: "https://{WorkspaceId}.cn-beijing.maas.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, set the "type" parameter in the user message 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

# China (Beijing) region. Replace {WorkspaceId} with your Bailian workspace ID. URLs vary by region.
curl -X POST https://{WorkspaceId}.cn-beijing.maas.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

import os
import dashscope

# China (Beijing) region. Replace {WorkspaceId} with your Bailian workspace ID. URLs vary by region.
dashscope.base_http_api_url = "https://{WorkspaceId}.cn-beijing.maas.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 DASHSCOPE_API_KEY environment variable is not set, replace this 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"])

Java

// Requires DashScope SDK v2.21.10 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 {
    // China (Beijing) region. Replace {WorkspaceId} with your Bailian workspace ID. URLs vary by region.
    static {Constants.baseHttpApiUrl="https://{WorkspaceId}.cn-beijing.maas.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<String, Object> 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

# China (Beijing) region. Replace {WorkspaceId} with your Bailian workspace ID. URLs vary by region.
curl -X POST https://{WorkspaceId}.cn-beijing.maas.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."
          }
        ]
      }
    ]
  }
}'

Passar um arquivo local

Os exemplos a seguir mostram como passar um arquivo local. A API compatível com OpenAI suporta apenas codificação Base64, enquanto o DashScope suporta tanto codificação Base64 quanto caminhos de arquivo.

OpenAI compatible

Para passar um arquivo local usando codificação Base64, construa uma Data URL. Para instruções, consulte Construct a Data URL.

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'),
    # China (Beijing) region. Replace {WorkspaceId} with your Bailian workspace ID. URLs vary by region.
    base_url="https://{WorkspaceId}.cn-beijing.maas.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 examples show how to pass 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

import OpenAI from "openai";
import { readFileSync } from 'fs';

const openai = new OpenAI(
    {
        apiKey: process.env.DASHSCOPE_API_KEY,
        // China (Beijing) region. Replace {WorkspaceId} with your Bailian workspace ID. URLs vary by region.
        baseURL: "https://{WorkspaceId}.cn-beijing.maas.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 examples show how to pass 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

Para passar um arquivo local usando codificação Base64, construa uma Data URL. Para instruções, consulte Construct a Data URL.

Python

import base64
import os
import dashscope
from dashscope import MultiModalConversation

# China (Beijing) region. Replace {WorkspaceId} with your Bailian workspace ID. URLs vary by region.
dashscope.base_http_api_url = "https://{WorkspaceId}.cn-beijing.maas.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 DASHSCOPE_API_KEY environment variable is not set, pass your Model Studio API key directly, for example: 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 examples show how to pass 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]
#   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

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 {

   // China (Beijing) region. Replace {WorkspaceId} with your Bailian workspace ID. URLs vary by region.
   static {Constants.baseHttpApiUrl="https://{WorkspaceId}.cn-beijing.maas.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);

        MultiModalConversation conv = new MultiModalConversation();
        MultiModalMessage userMessage = MultiModalMessage.builder().role(Role.USER.getValue())
                .content(Arrays.asList(
                        new HashMap<String, Object>() {{ put("image", "data:image/png;base64," + base64Image); }},
                        new HashMap<String, Object>() {{ 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 examples show how to pass a local video file and a local image list.
    // [Local video file]
    // String base64Video = encodeToBase64(localPath);
    // MultiModalConversation conv = new MultiModalConversation();
   //  MultiModalMessage userMessage = MultiModalMessage.builder().role(Role.USER.getValue())
   //             .content(Arrays.asList(
   //                     new HashMap<String, Object>() {{ put("video", "data:video/mp4;base64," + base64Video); }},
   //                     new HashMap<String, Object>() {{ put("text", "What scene is depicted in this video?"); }}
   //             )).build();

    // [Local image list]
    // List<String> 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<String, Object>() {{ put("video", urls); }},
   //                     new HashMap<String, Object>() {{ put("text", "What scene is depicted in this video?"); }}
   //             )).build();

}

File path

É possível passar um caminho de arquivo local diretamente para o modelo. Este método é suportado apenas pelos SDKs Python e Java do DashScope; não está disponível para DashScope HTTP ou para a API compatível com OpenAI. A tabela abaixo mostra o formato de caminho de arquivo necessário para cada linguagem de programação e sistema operacional.

Especificar um caminho de arquivo (exemplo de imagem)

Sistema	SDK	Formato do caminho	Exemplo
Linux ou macOS	Python SDK	file://{caminho absoluto do arquivo}	file:///home/images/test.png
Linux ou macOS	Java SDK	file://{caminho absoluto do arquivo}	file:///home/images/test.png
Windows	Python SDK	file://{caminho absoluto do arquivo}	file://D:/images/test.png
Windows	Java SDK	file:///{caminho absoluto do arquivo}	file:///D:/images/test.png

Python

import os
from dashscope import MultiModalConversation
import dashscope

# China (Beijing) region. Replace {WorkspaceId} with your Bailian workspace ID. URLs vary by region.
dashscope.base_http_api_url = "https://{WorkspaceId}.cn-beijing.maas.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 and a list of local images 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

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 {
    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<String, Object>(){{put("image", filePath);}},
                        new HashMap<String, Object>(){{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) {
        // China (Beijing) region configuration. Replace {WorkspaceId} with your actual workspace ID. For other regions, use the corresponding base URL.
        Constants.baseHttpApiUrl = "https://{WorkspaceId}.cn-beijing.maas.aliyuncs.com/api/v1";
        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 and a list of local images 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<String, Object>(){{put("video", filePath);}},
    //                    new HashMap<String, Object>(){{put("text", "What scene is depicted in the video?");}})).build();

    // [Local image list]

    //    MultiModalConversation conv = new MultiModalConversation();
    //    List<String> 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<String, Object>(){{put("video", filePath);}},
    //                    new HashMap<String, Object>(){{put("text", "What scene is depicted in the video?");}})).build();
}

Limitações de arquivos

Image limitations

Resolução da imagem:
- Tamanho mínimo: Largura e altura devem exceder 10 pixels cada.
- Proporção: A razão entre o lado mais longo e o mais curto não deve exceder 200:1.
- Resolução máxima: O máximo recomendado é 8K(7680x4320). Resoluções maiores podem causar timeouts na chamada da API devido ao tamanho grande dos arquivos ou transferências de rede lentas.
Formatos de imagem suportados
- Os seguintes formatos são suportados para resoluções abaixo de 4K (3840x2160):
  
  Formato de imagem
  Extensão de arquivo
  Tipo MIME
  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
- Para resoluções entre 4K(3840x2160) e 8K(7680x4320), apenas JPEG, JPG e PNG são suportados.
Tamanho da imagem:
- Ao fornecer uma imagem via URL pública ou caminho local, seu tamanho não deve exceder 10 MB.
- Ao usar codificação Base64, a string codificada não deve exceder 10 MB.
Para compactar um arquivo, consulte How to compress an image or video to meet the size limit .
Número de imagens suportadas: Ao fornecer várias imagens, o número total de tokens para todas as imagens e texto não deve exceder o limite máximo de entrada do modelo.

Formato de imagem	Extensão de arquivo	Tipo MIME
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

Video limitations

Como lista de imagens: De 4 a 2.000 imagens.
Como arquivo de vídeo:
- Tamanho do vídeo:
  - Via URL pública: Até 2 GB.
  - Via codificação Base64: a string codificada deve ter menos de 10 MB.
  - Via caminho de arquivo local: Até 100 MB.
- Duração do vídeo: De 2 segundos a 1 hora.
Formato de vídeo: Os formatos suportados incluem MP4, AVI, MKV, MOV, FLV e WMV.
Resolução do vídeo: Embora não haja um limite estrito de resolução, use 2K ou inferior para obter melhores resultados. Resoluções mais altas aumentam o tempo de processamento sem melhorar a compreensão do modelo.
Compreensão de áudio: O modelo não processa a faixa de áudio em arquivos de vídeo.

Outros recursos

Modelo	Multi-turn conversation	Deep thinking	Function calling	Structured output	Web search	Prefix completion	Context cache
kimi-k2.7-code	Suportado	Suportado	Suportado	Não suportado	Não suportado	Não suportado	Suportado
kimi-k2.6	Suportado	Suportado	Suportado	Não suportado	Não suportado	Não suportado	Suportado
kimi-k2.5	Suportado	Suportado	Suportado	Não suportado	Não suportado	Não suportado	Suportado
kimi-k2-thinking	Suportado	Suportado	Suportado	Suportado	Não suportado	Não suportado	Suportado
Moonshot-Kimi-K2-Instruct	Suportado	Não suportado	Suportado	Não suportado	Suportado	Não suportado	Suportado

Parâmetros padrão

Modelo	enable_thinking	temperature	top_p	presence_penalty	fps	max_frames
kimi-k2.7-code	true (apenas modo de raciocínio)	1.0	0.95	0.0	2	2000
kimi-k2.6	false	modo de raciocínio: 1.0 modo sem raciocínio: 0.6	Ambos os modos: 0.95	Ambos os modos: 0.0	2	2000
kimi-k2.5	false	modo de raciocínio: 1.0 modo sem raciocínio: 0.6	Ambos os modos: 0.95	Ambos os modos: 0.0	2	2000
kimi-k2-thinking	-	1.0	-	-	-	-
Moonshot-Kimi-K2-Instruct	-	0.6	1.0	0	-	-

Um hífen (-) indica que o parâmetro não se aplica.

Modelos e faturamento

A série Kimi consiste em grandes modelos de linguagem da Moonshot AI.

kimi-k2.7-code: O modelo Kimi mais capaz para codificação. Segue instruções de contexto longo com mais confiabilidade e alcança taxas de sucesso mais altas em tarefas de programação. Suporta entrada de texto, imagem e vídeo, modo de raciocínio, conversação e tarefas de agente.
kimi-k2.6: O modelo mais novo e capaz da série Kimi. Oferece desempenho aprimorado em codificação de longo horizonte, seguimento de instruções e autocorreção. Suporta entrada de texto, imagem e vídeo, modos de raciocínio e sem raciocínio, conversação e tarefas de agente.
kimi-k2.5: Alcança desempenho de última geração (SOTA) em benchmarks de código aberto para tarefas de agente, geração de código, compreensão visual e outras tarefas de inteligência geral. Suporta entrada de imagem, vídeo e texto, modos de raciocínio e sem raciocínio, conversação e tarefas de agente.
kimi-k2-thinking: Suporta apenas o modo de raciocínio profundo. Expõe o processo de raciocínio através do campo reasoning_content. Destaca-se em codificação e chamada de ferramentas, sendo adequado para casos de uso que exigem análise lógica, planejamento ou compreensão profunda.
Moonshot-Kimi-K2-Instruct: Não suporta raciocínio profundo. Gera respostas com menor latência, sendo adequado para casos de uso que precisam de respostas rápidas e diretas.

Para preços do kimi-k2.7-code, consulte model invocation billing .

Para detalhes sobre preços e janela de contexto, consulte o console do Model Studio.

O faturamento é baseado nas contagens de tokens de entrada e saída.

No modo de raciocínio, a cadeia de pensamento conta como tokens de saída.

Códigos de erro

Se uma chamada de modelo falhar e retornar uma mensagem de erro, consulte Error codes.