Kimi K2 API Integration: A No-Fluff Getting Started

Moonshot AI's Kimi K2, a Mixture-of-Experts model, supports native image understanding through the standard chat-completions API, enabling multimodal tasks like document QA and screenshot analysis. The integration guide demonstrates setup via cURL and Python, covering streaming, function calling, and token management for production use.

Kimi K2 is Moonshot AI's flagship Mixture-of-Experts model, and the first thing developers notice is what it can see. Unlike text-only models, K2 takes images natively through the same chat-completions interface you already know — an image url array inside the message content is all it takes. If your workload involves long-document QA, screenshot analysis, or an agent swarm that needs to read what's on screen, K2 is worth a serious look. Setup is deliberately boring. You hit the standard v1 endpoint, send the same request shape used everywhere, and turn on multimodal only when you need it. This guide walks the fastest path from zero to a working request: cURL first, then Python, then the parts — function calling, error handling, token math — that tend to trip people up in production. kimi-k2 https://api.moonshot.cn/v1 export MOONSHOT API KEY="sk-..." curl https://api.moonshot.cn/v1/chat/completions \ -H "Authorization: Bearer $MOONSHOT API KEY" \ -H "Content-Type: application/json" \ -d '{ "model": "kimi-k2", "messages": {"role": "user", "content": "Summarize the key points of this contract."} , "max tokens": 1024 }' python import os from openai import OpenAI client = OpenAI api key=os.getenv "MOONSHOT API KEY" , base url="https://api.moonshot.cn/v1", resp = client.chat.completions.create model="kimi-k2", messages= {"role": "user", "content": "Rewrite this error message in plain language: " + err} , max tokens=512, print resp.choices 0 .message.content The biggest difference between K2 and text-only models like DeepSeek V4 is that content can be an array of parts: resp = client.chat.completions.create model="kimi-k2", messages= { "role": "user", "content": {"type": "text", "text": "What is wrong with this dashboard? Be specific."}, {"type": "image url", "image url": {"url": "https://example.com/dashboard.png"}}, , } , Pass remote URLs or base64 data URLs. Each image consumes tokens against the 256K window, so keep images reasonably sized and crop where you can. This capability alone is why K2 often wins for document and screen-understanding tasks — for a text-only comparison, see our DeepSeek V4 guide https://taotok.io/deepseek-v4-api-guide . stream = client.chat.completions.create model="kimi-k2", messages= {"role": "user", "content": "Give me 5 tips for prompt engineering."} , stream=True, for chunk in stream: print chunk.choices 0 .delta.content or "", end="" Define tools the usual way, then let the model emit tool calls : tools = { "type": "function", "function": { "name": "get weather", "description": "Get the current weather for a city", "parameters": { "type": "object", "properties": { "city": {"type": "string"} }, "required": "city" , }, }, } resp = client.chat.completions.create model="kimi-k2", messages= {"role": "user", "content": "What's the weather in Tokyo?"} , tools=tools, print resp.choices 0 .message.tool calls Execute the tool, append the result as a tool role message, and loop until the model finishes. The extended Kimi K2 guide with more examples is on the taotok.io blog at https://taotok.io/kimi-k2-api-integration https://taotok.io/kimi-k2-api-integration , and if you're deciding between K2 and DeepSeek V4, the side-by-side comparison https://taotok.io/deepseek-v4-vs-kimi-k2 will save you an afternoon.