Kimi K2.5 API: Complete Developer Guide with Code Examples 2026

The Kimi K2.5 API provides developers with programmatic access to Moonshot AI's flagship model. This comprehensive guide covers everything from authentication to advanced integration patterns, helping you build powerful AI applications with Kimi K2.5.

What is the Kimi K2.5 API?

The Kimi K2.5 API is a RESTful interface that allows developers to integrate Kimi K2.5's capabilities into their applications. Built on the OpenAI-compatible API format, it provides seamless integration with existing tools and frameworks while supporting Kimi K2.5's unique features like 256K context window and multimodal inputs.

Key Features

Feature	Description
256K Context Window	Process documents up to ~200 pages
Multimodal Support	Text, image, and document inputs
Streaming Responses	Real-time token generation
Function Calling	Tool use and agentic workflows
OpenAI Compatible	Drop-in replacement for OpenAI SDK
Context Caching	Reduced costs for repeated context

Getting Started with Kimi K2.5 API

1. Obtain API Credentials

Create an account on the Moonshot AI developer portal
Navigate to API Keys section
Generate a new API key
Store securely (environment variables recommended)

2. API Base URL

https://api.moonshot.cn/v1

3. Authentication

All API requests require authentication via Bearer token:

Authorization: Bearer YOUR_API_KEY

Kimi K2.5 API Code Examples

Python Integration

Basic Chat Completion

import openai

# Configure client
client = openai.OpenAI(
    api_key="your-kimi-api-key",
    base_url="https://api.moonshot.cn/v1"
)

# Simple completion
response = client.chat.completions.create(
    model="kimi-k2.5",
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "Explain machine learning in simple terms."}
    ],
    temperature=0.7,
    max_tokens=1000
)

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

Streaming Responses

import openai

client = openai.OpenAI(
    api_key="your-kimi-api-key",
    base_url="https://api.moonshot.cn/v1"
)

stream = client.chat.completions.create(
    model="kimi-k2.5",
    messages=[
        {"role": "user", "content": "Write a Python function to calculate fibonacci numbers."}
    ],
    stream=True
)

for chunk in stream:
    if chunk.choices[0].delta.content:
        print(chunk.choices[0].delta.content, end="")

Multimodal Request with Image

import openai
import base64

client = openai.OpenAI(
    api_key="your-kimi-api-key",
    base_url="https://api.moonshot.cn/v1"
)

# Read and encode image
with open("chart.png", "rb") as f:
    image_data = base64.b64encode(f.read()).decode()

response = client.chat.completions.create(
    model="kimi-k2.5",
    messages=[
        {
            "role": "user",
            "content": [
                {"type": "text", "text": "Analyze this chart and summarize the key trends."},
                {
                    "type": "image_url",
                    "image_url": {
                        "url": f"data:image/png;base64,{image_data}"
                    }
                }
            ]
        }
    ]
)

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

JavaScript/Node.js Integration

Basic Request with Fetch

const response = await fetch('https://api.moonshot.cn/v1/chat/completions', {
  method: 'POST',
  headers: {
    'Content-Type': 'application/json',
    'Authorization': 'Bearer YOUR_API_KEY'
  },
  body: JSON.stringify({
    model: 'kimi-k2.5',
    messages: [
      { role: 'system', content: 'You are a coding assistant.' },
      { role: 'user', content: 'Generate a React component for a todo list.' }
    ],
    temperature: 0.7,
    max_tokens: 2000
  })
});

const data = await response.json();
console.log(data.choices[0].message.content);

Using OpenAI SDK

import OpenAI from 'openai';

const client = new OpenAI({
  apiKey: 'your-kimi-api-key',
  baseURL: 'https://api.moonshot.cn/v1'
});

async function generateCode() {
  const stream = await client.chat.completions.create({
    model: 'kimi-k2.5',
    messages: [
      { role: 'user', content: 'Create a Python API with FastAPI' }
    ],
    stream: true
  });

  for await (const chunk of stream) {
    process.stdout.write(chunk.choices[0]?.delta?.content || '');
  }
}

generateCode();

cURL Examples

# Basic chat completion
curl https://api.moonshot.cn/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -d '{
    "model": "kimi-k2.5",
    "messages": [
      {"role": "user", "content": "Hello, Kimi!"}
    ]
  }'

# With system prompt and parameters
curl https://api.moonshot.cn/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -d '{
    "model": "kimi-k2.5",
    "messages": [
      {"role": "system", "content": "You are a Python expert."},
      {"role": "user", "content": "Explain decorators."}
    ],
    "temperature": 0.3,
    "max_tokens": 1500
  }'

Advanced Kimi K2.5 API Features

Function Calling / Tool Use

import openai

client = openai.OpenAI(
    api_key="your-kimi-api-key",
    base_url="https://api.moonshot.cn/v1"
)

tools = [
    {
        "type": "function",
        "function": {
            "name": "get_weather",
            "description": "Get weather for a location",
            "parameters": {
                "type": "object",
                "properties": {
                    "location": {
                        "type": "string",
                        "description": "City name"
                    }
                },
                "required": ["location"]
            }
        }
    }
]

response = client.chat.completions.create(
    model="kimi-k2.5",
    messages=[
        {"role": "user", "content": "What's the weather in Beijing?"}
    ],
    tools=tools
)

# Check if model wants to call a function
if response.choices[0].message.tool_calls:
    tool_call = response.choices[0].message.tool_calls[0]
    print(f"Function to call: {tool_call.function.name}")
    print(f"Arguments: {tool_call.function.arguments}")

Long Context Document Processing

import openai

client = openai.OpenAI(
    api_key="your-kimi-api-key",
    base_url="https://api.moonshot.cn/v1"
)

# Read a long document
document = open("annual_report.txt", "r", encoding="utf-8").read()

response = client.chat.completions.create(
    model="kimi-k2.5",
    messages=[
        {
            "role": "user",
            "content": f"Summarize the key financial metrics from this report:\n\n{document}"
        }
    ],
    max_tokens=2000
)

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

Kimi K2.5 API Pricing

Type	Price	Unit
Context Cache Hit	$0.10	/ 1M tokens
Context Cache Miss	$0.60	/ 1M tokens
Output Tokens	$3.00	/ 1M tokens

Cost Optimization Tips

Use Context Caching: For repeated contexts, caching reduces costs significantly
Stream Responses: For real-time applications, streaming improves UX
Optimize Prompts: Clear, concise prompts reduce token usage
Batch Requests: Process multiple items in a single request when possible

Error Handling

Common HTTP Status Codes

Code	Meaning	Resolution
200	Success	Request completed successfully
400	Bad Request	Check request format and parameters
401	Unauthorized	Verify API key
429	Rate Limited	Reduce request frequency
500	Server Error	Retry with exponential backoff

Python Error Handling Example

import openai
from openai import RateLimitError, APIError

client = openai.OpenAI(
    api_key="your-kimi-api-key",
    base_url="https://api.moonshot.cn/v1"
)

try:
    response = client.chat.completions.create(
        model="kimi-k2.5",
        messages=[{"role": "user", "content": "Hello"}]
    )
except RateLimitError:
    print("Rate limit exceeded. Please wait before retrying.")
except APIError as e:
    print(f"API error: {e}")
except Exception as e:
    print(f"Unexpected error: {e}")

Best Practices for Kimi K2.5 API

1. Secure API Key Management

import os
from dotenv import load_dotenv

load_dotenv()

api_key = os.getenv("KIMI_API_KEY")
client = openai.OpenAI(
    api_key=api_key,
    base_url="https://api.moonshot.cn/v1"
)

2. Implement Retry Logic

import time
from functools import wraps

def retry_with_backoff(max_retries=3):
    def decorator(func):
        @wraps(func)
        def wrapper(*args, **kwargs):
            for i in range(max_retries):
                try:
                    return func(*args, **kwargs)
                except Exception as e:
                    if i == max_retries - 1:
                        raise
                    time.sleep(2 ** i)  # Exponential backoff
            return None
        return wrapper
    return decorator

@retry_with_backoff(max_retries=3)
def call_kimi_api(messages):
    return client.chat.completions.create(
        model="kimi-k2.5",
        messages=messages
    )

3. Request Batching

# Instead of multiple individual requests
requests = [
    "Summarize paragraph 1",
    "Summarize paragraph 2",
    "Summarize paragraph 3"
]

# Batch into a single request
batch_prompt = "Summarize each of these paragraphs:\n\n" + "\n\n".join(
    f"{i+1}. {req}" for i, req in enumerate(requests)
)

response = client.chat.completions.create(
    model="kimi-k2.5",
    messages=[{"role": "user", "content": batch_prompt}]
)

Integration Examples

FastAPI Backend

from fastapi import FastAPI
from pydantic import BaseModel
import openai

app = FastAPI()
client = openai.OpenAI(
    api_key="your-kimi-api-key",
    base_url="https://api.moonshot.cn/v1"
)

class ChatRequest(BaseModel):
    message: str
    temperature: float = 0.7

@app.post("/chat")
async def chat(request: ChatRequest):
    response = client.chat.completions.create(
        model="kimi-k2.5",
        messages=[{"role": "user", "content": request.message}],
        temperature=request.temperature
    )
    return {"response": response.choices[0].message.content}