moonshotai/Kimi-K2-Instruct

LLM

Kimi-K2 Instruct API by Moonshot

moonshotai/Kimi-K2-Instruct

Kimi-K2-Instruct

Kimi's latest and most powerful open-source model.

参数

代码示例
import os
from openai import OpenAI

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-Instruct",
    messages=[
    {
        "role": "user",
        "content": "hello"
    }
],
    max_tokens=1024,
    temperature=0.7
)

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

安装

安装所需的依赖包。

bash

pip install requests

认证

所有 API 请求需要通过 API Key 进行认证。您可以在 Atlas Cloud 控制台获取 API Key。

bash

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

HTTP 请求头

python

import os

API_KEY = os.environ.get("ATLASCLOUD_API_KEY")
headers = {
    "Content-Type": "application/json",
    "Authorization": f"Bearer {API_KEY}"
}

保护好您的 API Key

切勿在客户端代码或公开仓库中暴露您的 API Key。请使用环境变量或后端代理。

提交请求

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())

Input Schema

以下参数在请求体中被接受。

总计: 9必填: 2可选: 7

modelstringrequired

The model ID to use for the completion.

Example: "moonshotai/Kimi-K2-Instruct"

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

请求体示例

json

{
  "model": "moonshotai/Kimi-K2-Instruct",
  "messages": [
    {
      "role": "user",
      "content": "Hello"
    }
  ],
  "max_tokens": 1024,
  "temperature": 0.7,
  "stream": false
}

Output Schema

API 返回兼容 ChatCompletion 的响应格式。

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.

响应示例

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 将 300+ AI 模型直接集成到您的 AI 编程助手中。一条命令安装，即可用自然语言生成图像、视频和与 LLM 对话。

支持的客户端

Claude Code

OpenAI Codex

Gemini CLI

Cursor

Windsurf

VS Code

Trae

GitHub Copilot

Cline

Roo Code

Amp

Goose

Replit

40+ 支持的客户端

安装

bash

npx skills add AtlasCloudAI/atlas-cloud-skills

设置 API Key

从 Atlas Cloud 控制台获取 API Key，并将其设置为环境变量。

bash

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

功能

安装后，您可以在 AI 助手中使用自然语言访问所有 Atlas Cloud 模型。

图像生成使用 Nano Banana 2、Z-Image 等模型生成图像。

视频创作使用 Kling、Vidu、Veo 等模型从文本或图像创建视频。

LLM 对话与 Qwen、DeepSeek 等大语言模型对话。

媒体上传上传本地文件用于图像编辑和图生视频工作流。

MCP Server

Atlas Cloud MCP Server 通过 Model Context Protocol 将您的 IDE 与 300+ AI 模型连接。支持任何兼容 MCP 的客户端。

支持的客户端

Cursor

VS Code

Windsurf

Claude Code

OpenAI Codex

Gemini CLI

Cline

Roo Code

100+ 支持的客户端

安装

bash

npx -y atlascloud-mcp

配置

将以下配置添加到您的 IDE 的 MCP 设置文件中。

json

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

可用工具

atlas_generate_image从文本提示生成图像。

atlas_generate_video从文本或图像创建视频。

atlas_chat与大语言模型对话。

atlas_list_models浏览 300+ 可用 AI 模型。

atlas_quick_generate一步式内容创建，自动选择最佳模型。

atlas_upload_media上传本地文件用于 API 工作流。

了解更多

github.com/AtlasCloudAI/mcp-server

Kimi-K2-Instruct

1. Model Introduction

Kimi K2 is a state-of-the-art mixture-of-experts (MoE) language model with 32 billion activated parameters and 1 trillion total parameters. Trained with the Muon optimizer, Kimi K2 achieves exceptional performance across frontier knowledge, reasoning, and coding tasks while being meticulously optimized for agentic capabilities.

Key Features

Large-Scale Training: Pre-trained a 1T parameter MoE model on 15.5T tokens with zero training instability.
MuonClip Optimizer: We apply the Muon optimizer to an unprecedented scale, and develop novel optimization techniques to resolve instabilities while scaling up.
Agentic Intelligence: Specifically designed for tool use, reasoning, and autonomous problem-solving.

Model Variants

Kimi-K2-Base: The foundation model, a strong start for researchers and builders who want full control for fine-tuning and custom solutions.
Kimi-K2-Instruct: The post-trained model best for drop-in, general-purpose chat and agentic experiences. It is a reflex-grade model without long thinking.

2. Model Summary


Architecture	Mixture-of-Experts (MoE)
Total Parameters	1T
Activated Parameters	32B
Number of Layers (Dense layer included)	61
Number of Dense Layers	1
Attention Hidden Dimension	7168
MoE Hidden Dimension (per Expert)	2048
Number of Attention Heads	64
Number of Experts	384
Selected Experts per Token	8
Number of Shared Experts	1
Vocabulary Size	160K
Context Length	128K
Attention Mechanism	MLA
Activation Function	SwiGLU

3. Evaluation Results

Instruction model evaluation results

Benchmark	Metric	Kimi K2 Instruct	DeepSeek-V3-0324	Qwen3-235B-A22B (non-thinking)	Claude Sonnet 4 (w/o extended thinking)	Claude Opus 4 (w/o extended thinking)	GPT-4.1	Gemini 2.5 Flash Preview (05-20)
Coding Tasks
LiveCodeBench v6 (Aug 24 - May 25)	Pass@1	53.7	46.9	37.0	48.5	47.4	44.7	44.7
OJBench	Pass@1	27.1	24.0	11.3	15.3	19.6	19.5	19.5
MultiPL-E	Pass@1	85.7	83.1	78.2	88.6	89.6	86.7	85.6
SWE-bench Verified (Agentless Coding)	Single Patch w/o Test (Acc)	51.8	36.6	39.4	50.2	53.0	40.8	32.6
SWE-bench Verified (Agentic Coding)	Single Attempt (Acc)	65.8	38.8	34.4	72.7*	72.5*	54.6	—
Multiple Attempts (Acc)	71.6	—	—	80.2	79.4*	—	—
SWE-bench Multilingual (Agentic Coding)	Single Attempt (Acc)	47.3	25.8	20.9	51.0	—	31.5	—
TerminalBench	Inhouse Framework (Acc)	30.0	—	—	35.5	43.2	8.3	—
Terminus (Acc)	25.0	16.3	6.6	—	—	30.3	16.8
Aider-Polyglot	Acc	60.0	55.1	61.8	56.4	70.7	52.4	44.0
Tool Use Tasks
Tau2 retail	Avg@4	70.6	69.1	57.0	75.0	81.8	74.8	64.3
Tau2 airline	Avg@4	56.5	39.0	26.5	55.5	60.0	54.5	42.5
Tau2 telecom	Avg@4	65.8	32.5	22.1	45.2	57.0	38.6	16.9
AceBench	Acc	76.5	72.7	70.5	76.2	75.6	80.1	74.5
Math & STEM Tasks
AIME 2024	Avg@64	69.6	59.4*	40.1*	43.4	48.2	46.5	61.3
AIME 2025	Avg@64	49.5	46.7	24.7*	33.1*	33.9*	37.0	46.6
MATH-500	Acc	97.4	94.0*	91.2*	94.0	94.4	92.4	95.4
HMMT 2025	Avg@32	38.8	27.5	11.9	15.9	15.9	19.4	34.7
CNMO 2024	Avg@16	74.3	74.7	48.6	60.4	57.6	56.6	75.0
PolyMath-en	Avg@4	65.1	59.5	51.9	52.8	49.8	54.0	49.9
ZebraLogic	Acc	89.0	84.0	37.7*	73.7	59.3	58.5	57.9
AutoLogi	Acc	89.5	88.9	83.3	89.8	86.1	88.2	84.1
GPQA-Diamond	Avg@8	75.1	68.4*	62.9*	70.0*	74.9*	66.3	68.2
SuperGPQA	Acc	57.2	53.7	50.2	55.7	56.5	50.8	49.6
Humanity's Last Exam (Text Only)	-	4.7	5.2	5.7	5.8	7.1	3.7	5.6
General Tasks
MMLU	EM	89.5	89.4	87.0	91.5	92.9	90.4	90.1
MMLU-Redux	EM	92.7	90.5	89.2	93.6	94.2	92.4	90.6
MMLU-Pro	EM	81.1	81.2*	77.3	83.7	86.6	81.8	79.4
IFEval	Prompt Strict	89.8	81.1	83.2*	87.6	87.4	88.0	84.3
Multi-Challenge	Acc	54.1	31.4	34.0	46.8	49.0	36.4	39.5
SimpleQA	Correct	31.0	27.7	13.2	15.9	22.8	42.3	23.3
Livebench	Pass@1	76.4	72.4	67.6	74.8	74.6	69.8	67.8

• Bold denotes global SOTA, and underlined denotes open-source SOTA.

• Data points marked with * are taken directly from the model's tech report or blog.

• All metrics, except for SWE-bench Verified (Agentless), are evaluated with an 8k output token length. SWE-bench Verified (Agentless) is limited to a 16k output token length.

• Kimi K2 achieves 65.8% pass@1 on the SWE-bench Verified tests with bash/editor tools (single-attempt patches, no test-time compute). It also achieves a 47.3% pass@1 on the SWE-bench Multilingual tests under the same conditions. Additionally, we report results on SWE-bench Verified tests (71.6%) that leverage parallel test-time compute by sampling multiple sequences and selecting the single best via an internal scoring model.

• To ensure the stability of the evaluation, we employed avg@k on the AIME, HMMT, CNMO, PolyMath-en, GPQA-Diamond, EvalPlus, Tau2.

• Some data points have been omitted due to prohibitively expensive evaluation costs.

Base model evaluation results

Benchmark	Metric	Shot	Kimi K2 Base	Deepseek-V3-Base	Qwen2.5-72B	Llama 4 Maverick
General Tasks
MMLU	EM	5-shot	87.8	87.1	86.1	84.9
MMLU-pro	EM	5-shot	69.2	60.6	62.8	63.5
MMLU-redux-2.0	EM	5-shot	90.2	89.5	87.8	88.2
SimpleQA	Correct	5-shot	35.3	26.5	10.3	23.7
TriviaQA	EM	5-shot	85.1	84.1	76.0	79.3
GPQA-Diamond	Avg@8	5-shot	48.1	50.5	40.8	49.4
SuperGPQA	EM	5-shot	44.7	39.2	34.2	38.8
Coding Tasks
LiveCodeBench v6	Pass@1	1-shot	26.3	22.9	21.1	25.1
EvalPlus	Pass@1	-	80.3	65.6	66.0	65.5
Mathematics Tasks
MATH	EM	4-shot	70.2	60.1	61.0	63.0
GSM8k	EM	8-shot	92.1	91.7	90.4	86.3
Chinese Tasks
C-Eval	EM	5-shot	92.5	90.0	90.9	80.9
CSimpleQA	Correct	5-shot	77.6	72.1	50.5	53.5

探索类似模型

NEW

HOT

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.

LLM

Kimi K2.6

Kimi K2.5 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 K2.5 is ideal for AI assistants, enterprise applications, and multimodal workflows that require reliable and high-quality outputs.

LLM