moonshotai/kimi-k2.6

Kimi K2.6 is an advanced large language model with strong reasoning and upgraded native multimodality. It natively understands and processes text and images, delivering more accurate analysis, better instruction following, and stable performance across complex tasks. Designed for production use, Kimi K2.6 is ideal for AI assistants, enterprise applications, and multimodal workflows that require reliable and high-quality outputs.

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moonshotai/kimi-k2.6
Kimi K2.6
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Kimi K2.6 is an advanced large language model with strong reasoning and upgraded native multimodality. It natively understands and processes text and images, delivering more accurate analysis, better instruction following, and stable performance across complex tasks. Designed for production use, Kimi K2.6 is ideal for AI assistants, enterprise applications, and multimodal workflows that require reliable and high-quality outputs.

參數

程式碼範例

import os
from openai import OpenAI

# Vision Understanding Example
# Image: Use base64 encoding (data:image/png;base64,...)
# Video: Use URL (recommended for large files)

client = OpenAI(
    api_key=os.getenv("ATLASCLOUD_API_KEY"),
    base_url="https://api.atlascloud.ai/v1"
)

response = client.chat.completions.create(
    model="moonshotai/kimi-k2.6",
    messages=[
    {
        "role": "user",
        "content": [
            {
                "type": "image_url",
                "image_url": {
                    "url": "data:image/png;base64,<BASE64_IMAGE_DATA>"
                }
            },
            {
                "type": "video_url",
                "video_url": {
                    "url": "https://example.com/your-video.mp4"
                }
            },
            {
                "type": "text",
                "text": "Please describe the content of this image/video"
            }
        ]
    }
],
    max_tokens=1024,
    temperature=0.7
)

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

安裝

為您的程式語言安裝所需的套件。

bash
pip install requests

驗證

所有 API 請求都需要透過 API 金鑰進行驗證。您可以從 Atlas Cloud 儀表板取得 API 金鑰。

bash
export ATLASCLOUD_API_KEY="your-api-key-here"

HTTP 標頭

python
import os

API_KEY = os.environ.get("ATLASCLOUD_API_KEY")
headers = {
    "Content-Type": "application/json",
    "Authorization": f"Bearer {API_KEY}"
}
請妥善保管您的 API 金鑰

切勿在客戶端程式碼或公開儲存庫中暴露您的 API 金鑰。請改用環境變數或後端代理。

提交請求

import requests

url = "https://api.atlascloud.ai/v1/chat/completions"
headers = {
    "Content-Type": "application/json",
    "Authorization": "Bearer $ATLASCLOUD_API_KEY"
}
data = {
    "model": "your-model",
    "messages": [{"role": "user", "content": "Hello"}],
    "max_tokens": 1024
}

response = requests.post(url, headers=headers, json=data)
print(response.json())

輸入 Schema

以下參數可在請求主體中使用。

總計: 9必填: 2選填: 7
modelstringrequired
The model ID to use for the completion.
Example: "moonshotai/kimi-k2.6"
messagesarray[object]required
A list of messages comprising the conversation so far.
rolestringrequired
The role of the message author. One of "system", "user", or "assistant".
systemuserassistant
contentstringrequired
The content of the message.
max_tokensinteger
The maximum number of tokens to generate in the completion.
Default: 1024Min: 1
temperaturenumber
Sampling temperature between 0 and 2. Higher values make output more random, lower values more focused and deterministic.
Default: 0.7Min: 0Max: 2
top_pnumber
Nucleus sampling parameter. The model considers the tokens with top_p probability mass.
Default: 1Min: 0Max: 1
streamboolean
If set to true, partial message deltas will be sent as server-sent events.
Default: false
stoparray[string]
Up to 4 sequences where the API will stop generating further tokens.
frequency_penaltynumber
Penalizes new tokens based on their existing frequency in the text so far. Between -2.0 and 2.0.
Default: 0Min: -2Max: 2
presence_penaltynumber
Penalizes new tokens based on whether they appear in the text so far. Between -2.0 and 2.0.
Default: 0Min: -2Max: 2

範例請求主體

json
{
  "model": "moonshotai/kimi-k2.6",
  "messages": [
    {
      "role": "user",
      "content": "Hello"
    }
  ],
  "max_tokens": 1024,
  "temperature": 0.7,
  "stream": false
}

輸出 Schema

API 傳回與 ChatCompletion 相容的回應。

idstringrequired
Unique identifier for the completion.
objectstringrequired
Object type, always "chat.completion".
Default: "chat.completion"
createdintegerrequired
Unix timestamp of when the completion was created.
modelstringrequired
The model used for the completion.
choicesarray[object]required
List of completion choices.
indexintegerrequired
Index of the choice.
messageobjectrequired
The generated message.
finish_reasonstringrequired
The reason generation stopped.
stoplengthcontent_filter
usageobjectrequired
Token usage statistics.
prompt_tokensintegerrequired
Number of tokens in the prompt.
completion_tokensintegerrequired
Number of tokens in the completion.
total_tokensintegerrequired
Total tokens used.

範例回應

json
{
  "id": "chatcmpl-abc123",
  "object": "chat.completion",
  "created": 1700000000,
  "model": "model-name",
  "choices": [
    {
      "index": 0,
      "message": {
        "role": "assistant",
        "content": "Hello! How can I assist you today?"
      },
      "finish_reason": "stop"
    }
  ],
  "usage": {
    "prompt_tokens": 10,
    "completion_tokens": 20,
    "total_tokens": 30
  }
}

Atlas Cloud Skills

Atlas Cloud Skills 將 300 多個 AI 模型直接整合至您的 AI 程式碼助手。一鍵安裝,即可使用自然語言生成圖片、影片,以及與 LLM 對話。

支援的客戶端

Claude Code
OpenAI Codex
Gemini CLI
Cursor
Windsurf
VS Code
Trae
GitHub Copilot
Cline
Roo Code
Amp
Goose
Replit
40+ 支援的客戶端

安裝

bash
npx skills add AtlasCloudAI/atlas-cloud-skills

設定 API 金鑰

從 Atlas Cloud 儀表板取得 API 金鑰,並設為環境變數。

bash
export ATLASCLOUD_API_KEY="your-api-key-here"

功能

安裝完成後,您可以在 AI 助手中使用自然語言存取所有 Atlas Cloud 模型。

圖片生成使用 Nano Banana 2、Z-Image 等模型生成圖片。
影片創作使用 Kling、Vidu、Veo 等從文字或圖片創建影片。
LLM 對話與 Qwen、DeepSeek 及其他大型語言模型對話。
媒體上傳上傳本機檔案,用於圖片編輯和圖片轉影片工作流程。

MCP Server

Atlas Cloud MCP Server 透過 Model Context Protocol 將您的 IDE 與 300 多個 AI 模型連接。支援任何 MCP 相容的客戶端。

支援的客戶端

Cursor
VS Code
Windsurf
Claude Code
OpenAI Codex
Gemini CLI
Cline
Roo Code
100+ 支援的客戶端

安裝

bash
npx -y atlascloud-mcp

設定

將以下設定新增至您 IDE 的 MCP 設定檔中。

json
{
  "mcpServers": {
    "atlascloud": {
      "command": "npx",
      "args": [
        "-y",
        "atlascloud-mcp"
      ],
      "env": {
        "ATLASCLOUD_API_KEY": "your-api-key-here"
      }
    }
  }
}

可用工具

atlas_generate_image根據文字提示生成圖片。
atlas_generate_video從文字或圖片創建影片。
atlas_chat與大型語言模型對話。
atlas_list_models瀏覽 300 多個可用的 AI 模型。
atlas_quick_generate一步完成內容創建,自動選擇模型。
atlas_upload_media上傳本機檔案用於 API 工作流程。

Kimi K2.5 Large Language Model

Overview

Kimi K2.5 is an advanced large language model developed by Moonshot AI, designed to deliver high-quality reasoning, ultra-long context comprehension, and professional-grade language generation. It is an enhanced iteration within the Kimi model family, focusing on improved reliability, stronger analytical performance, and better alignment with real-world, high-complexity use cases.

Kimi K2.5 is particularly optimized for document-centric intelligence, making it suitable for enterprise knowledge systems, research assistants, and applications where long-context understanding and accuracy are critical.


Model Positioning

Kimi K2.5 is positioned as a reasoning- and context-oriented foundation model, rather than a purely conversational model. Its primary goal is to support tasks that require:

  • Sustained attention across long inputs
  • Precise interpretation of complex instructions
  • Structured reasoning over large bodies of text
  • Stable and predictable output behavior

This positioning makes Kimi K2.5 especially well suited for professional, enterprise, and research-oriented AI products.


Design Philosophy

The design of Kimi K2.5 emphasizes depth over superficial fluency. Instead of optimizing solely for short responses or casual chat, the model focuses on:

  • Preserving semantic coherence across long documents
  • Maintaining logical consistency throughout multi-step reasoning
  • Reducing hallucinations in factual and analytical outputs
  • Respecting instruction hierarchy and task constraints

This approach allows Kimi K2.5 to perform reliably in scenarios where correctness, traceability, and clarity are more important than creativity or stylistic variation.


Key Capabilities

Ultra-Long Context Processing

Kimi K2.5 is designed to process very large context inputs, enabling it to:

  • Read and analyze long reports, contracts, or manuals
  • Understand relationships across distant sections of text
  • Perform holistic summarization and synthesis
  • Answer questions that depend on information scattered throughout a document

This capability is essential for applications involving legal documents, research papers, financial disclosures, and technical documentation.


Structured Reasoning & Analysis

The model demonstrates strong performance in:

  • Logical reasoning and step-by-step analysis
  • Comparing multiple viewpoints or data sources
  • Drawing conclusions from large, unstructured inputs
  • Handling abstract or ambiguous problem statements

Kimi K2.5 is particularly effective when tasks require explicit reasoning chains, such as evaluations, reviews, or decision-support systems.


Instruction Following & Task Control

Kimi K2.5 is optimized to follow complex instructions with high fidelity:

  • Supports multi-part and nested instructions
  • Maintains task objectives over long interactions
  • Reduces instruction drift during extended sessions
  • Handles professional constraints such as tone, format, and structure

This makes it well suited for workflow-based AI systems and agent-style applications.


High-Precision Language Generation

Rather than focusing on stylistic creativity, Kimi K2.5 emphasizes:

  • Clear and unambiguous language
  • Structured outputs suitable for professional use
  • Consistent terminology across long responses
  • Reduced verbosity unless explicitly requested

As a result, the model performs well in technical writing, analytical reports, summaries, and professional correspondence.


Multilingual Understanding

Kimi K2.5 supports multilingual natural language processing and can:

  • Understand and generate content in multiple languages
  • Maintain reasoning quality across language boundaries
  • Support cross-lingual document analysis

This enables its use in global enterprise environments and multilingual knowledge systems.


Application Scenarios

Kimi K2.5 can be applied across a wide range of real-world scenarios, including:

Enterprise Knowledge Systems

  • Internal document search and Q&A
  • Policy and compliance analysis
  • Knowledge base construction and maintenance
  • Decision-support assistants

Research & Analysis

  • Literature review and research synthesis
  • Long-form academic summarization
  • Comparative analysis across multiple documents
  • Hypothesis exploration and reasoning support

Professional Content Processing

  • Technical documentation analysis
  • Legal and regulatory document review
  • Financial and business report summarization
  • Structured information extraction

AI Product Development

  • Long-context conversational assistants
  • Agent-based reasoning systems
  • Retrieval-augmented generation (RAG) pipelines
  • Document-centric AI applications

API & System Integration

Kimi K2.5 is provided through cloud-based APIs and is designed for:

  • Scalable backend deployment
  • Integration with existing AI pipelines
  • Use in multi-component AI systems and agents

It works particularly well when combined with:

  • Document chunking and indexing systems
  • Vector databases and retrieval systems
  • Workflow orchestration and agent frameworks

Technical Characteristics

CategoryDescription
Model NameKimi K2.5
Model TypeLarge Language Model (LLM)
Model FamilyKimi
Core StrengthLong-context reasoning
Context HandlingUltra-long context support
Reasoning StyleStructured, analytical
Output StyleProfessional, precise
DeploymentCloud-based API
Target AudienceEnterprise, research, professional users

Reliability & Production Readiness

Kimi K2.5 is designed with production environments in mind:

  • Stable behavior across repeated queries
  • Consistent output quality
  • Predictable response structure
  • Suitable for high-stakes applications requiring reliability

These characteristics make it appropriate for enterprise-grade AI deployments.


Why Choose Kimi K2.5?

  • Strong focus on long-context comprehension
  • Reliable reasoning across complex inputs
  • Professional-grade language output
  • Well suited for document-heavy and analytical tasks
  • Designed for real-world, high-complexity AI workloads

Summary

Kimi K2.5 is a professional-oriented large language model built to handle long documents, complex reasoning, and structured analysis with high reliability. It provides a solid foundation for enterprise AI systems, research assistants, and document-centric applications where depth, accuracy, and consistency are essential.

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