moonshotai/Kimi-K2-Instruct-0905

LLM

Kimi K2 Instruct 0905 API by Moonshot

moonshotai/Kimi-K2-Instruct-0905

Kimi-K2-Instruct-0905

Improved assistant model with stronger reasoning and reliability.

Parameters

Code Example
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-0905",
    messages=[
    {
        "role": "user",
        "content": "hello"
    }
],
    max_tokens=1024,
    temperature=0.7
)

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

Install

Install the required package for your language.

pip install requests

Authentication

All API requests require authentication via an API key. You can get your API key from the Atlas Cloud dashboard.

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

HTTP Headers

import os

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

Keep your API key secure

Never expose your API key in client-side code or public repositories. Use environment variables or a backend proxy instead.

Submit a request

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

The following parameters are accepted in the request body.

Total: 9Required: 2Optional: 7

modelstringrequired

The model ID to use for the completion.

Example: "moonshotai/Kimi-K2-Instruct-0905"

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

Example Request Body

{
  "model": "moonshotai/Kimi-K2-Instruct-0905",
  "messages": [
    {
      "role": "user",
      "content": "Hello"
    }
  ],
  "max_tokens": 1024,
  "temperature": 0.7,
  "stream": false
}

Output Schema

The API returns a ChatCompletion-compatible response.

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.

Example Response

{
  "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 integrates 300+ AI models directly into your AI coding assistant. One command to install, then use natural language to generate images, videos, and chat with LLMs.

Supported Clients

Claude Code

OpenAI Codex

Gemini CLI

Cursor

Windsurf

VS Code

Trae

GitHub Copilot

Cline

Roo Code

Amp

Goose

Replit

40+ supported clients

Install

npx skills add AtlasCloudAI/atlas-cloud-skills

Setup API Key

Get your API key from the Atlas Cloud dashboard and set it as an environment variable.

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

Capabilities

Once installed, you can use natural language in your AI assistant to access all Atlas Cloud models.

Image GenerationGenerate images with models like Nano Banana 2, Z-Image, and more.

Video CreationCreate videos from text or images with Kling, Vidu, Veo, etc.

LLM ChatChat with Qwen, DeepSeek, and other large language models.

Media UploadUpload local files for image editing and image-to-video workflows.

Learn more

github.com/AtlasCloudAI/atlas-cloud-skills

MCP Server

Atlas Cloud MCP Server connects your IDE with 300+ AI models via the Model Context Protocol. Works with any MCP-compatible client.

Supported Clients

Cursor

VS Code

Windsurf

Claude Code

OpenAI Codex

Gemini CLI

Cline

Roo Code

100+ supported clients

Install

npx -y atlascloud-mcp

Configuration

Add the following configuration to your IDE's MCP settings file.

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

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