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Kimi API: Complete Guide for Kimi K2.5 Model and Setup

Experience di power of Moonshot AI wit 256K context window and ogbonge vision powers for your projects.

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Kimi API Overview How to start API Pricing Rate Limits Multimodal Vision Other Ways FAQ

Kimi API and wetin you suppose know

Kimi API and wetin you suppose know

Kimi API dey give developers access to Moonshot AI’s Kimi K2.5, one heavy model and e get 1 trillion parameter MoE architecture. Dis model sabi read many tins at once, e get 256K token context window, and e fit work like agent to solve hard tasks. Since e follow REST-based design, pipel fit join am to apps to check vision, read long documents, and manage many AI agents at di same time. People fit get am through Moonshot platform or providers like Together AI, OpenRouter, and NVIDIA NIM.

If you don dey use OpenAI before, to move your work go Moonshot no hard at all. Di API dey work 100 percent like OpenAI SDK structure, so na only di base URL and API key you go change. To enter di house, you need standard Bearer token authorization. You fit use official SDKs for Python and Node.js to manage your requests, and if your team want full control, you fit download di model weights for Hugging Face to host am yourself.

Wetin make dis API special pass others? Na di way e mix 256K context window wit native vision powers, and di price cheap well-well—almost 4x cheap pass Claude Opus 4.5. Complex RAG pipelines dey easy to build now because di model fit read full documentation and even understand images and video content inside one single pass. You fit even get access to Kimi through dis API to use all di four special modes: Instant, Thinking, Agent, and Agent Swarm.

Feature Wetin e be
Current Model Kimi K2.5 (kimi-k2.5)
Context Window 262,144 tokens (256K)
Input Types Text, images, video, documents
Authentication Bearer token for Authorization header
SDKs Python, Node.js (OpenAI-compatible)
Providers Moonshot Official, OpenRouter, Together AI, NVIDIA NIM

Di API endpoints be like mirror for OpenAI chat completions, and e dey support JSON responses and streaming output. Using dis tools dey help developers build beta agents wey fit use functions and tools naturally. Di system also get automatic context caching wey dey make repeated input cost drop by 75 percent.

  • Integrating tools dey easy because di REST endpoints follow OpenAI pattern.
  • Streaming responses dey make UI update sharp-sharp as di AI dey generate words.
  • Function calling support dey help build agents wey sabi use external tools.
  • Multimodal input dey make am possible to use images and video join text.
  • Context caching dey make beta optimization for cost when you reuse old prompts.

How to start to use API

How to start to use API

To register for Moonshot no dey take time at all. Just go platform.moonshot.ai, create account wit your email, and go di API keys section. Di platform get beta documentation for English and Chinese language, and dem get code examples to show how to join am to your work.

  1. Register for platform.moonshot.ai and verify your email address.
  2. Check di developer dashboard and go API Keys section.
  3. Generate your first API key and keep am well because you no go see am again.
  4. Install OpenAI Python SDK or use cURL to start your first request.
from openai import OpenAI

client = OpenAI(
    api_key="your_moonshot_api_key",
    base_url="https://api.moonshot.cn/v1"
)

response = client.chat.completions.create(
    model="kimi-k2.5",
    messages=[
        {"role": "system", "content": "You be beta assistant."},
        {"role": "user", "content": "Explain wetin MoE architecture mean."}
    ],
    temperature=0.7
)

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

Dis code dey work exactly like OpenAI API calls. If you want switch from GPT models, you only need to change di base_url and api_key. Every error handling, retry logic, and streaming setup wey you get before go work without any wahala.

How di pricing dey work

How di pricing dey work

Kimi K2.5 pricing make sense well-well for 2026, especially for people wey dey look for way to save moni. Di official Moonshot API get automatic context caching wey dey cut input cost by 75 percent if you keep using di same system prompt or document.

Provider Input (per 1M tokens) Output (per 1M tokens) Cached Input
Moonshot Official $0.60 $3.00 $0.15 (75% off)
OpenRouter $0.45 $2.20 Varies
Together AI $0.50 $2.80 Varies

Currently, dis prices dey show say Kimi K2.5 cheap pass Claude Opus 4.5 by 4 times for di same kind of work. You no need write any special code to use di context caching—di system go just recognize di same prefix and bill you small moni automatically. Just remember say model versions dey update regularly so price fit change small.

How to use multimodal vision

How to use multimodal vision

Kimi K2.5 get native vision powers, so e fit see images and video directly inside di API request. E dey use MoonViT-3D encoder to check different picture sizes without any need for person to resize or fix am first before sending.

response = client.chat.completions.create(
    model="kimi-k2.5",
    messages=[
        {
            "role": "user",
            "content": [
                {"type": "text", "text": "Tell me wetin dey dis chart and bring out di data."},
                {"type": "image_url", "image_url": {"url": "https://example.com/chart.png"}}
            ]
        }
    ]
)

Di vision part don score high marks for benchmarks like OCRBench (92.3%) and InfoVQA (92.6%). Dis one show say Kimi K2.5 na ogbonge model to use if you want read charts or extract data from complex documents. You fit even send video by using multiple frames or video URLs.

Other ways to use model

Other ways to use model

Apart from di official Moonshot platform, developers fit use other ways to get Kimi K2.5 performance for dia apps.

  • Using OpenRouter to get one single bill for many different AI models.
  • Hosting for Together AI to get low-latency and fast speed for users.
  • Running NVIDIA NIM microservices for big enterprise deployment.
  • Downloading weights from Hugging Face to host di model on your own servers.

If you want host am yourself, you go need many GPU because di 1T model heavy. You fit use tools like vLLM, SGLang, or Docker to set am up, but make you sure say you check di hardware requirement well-well.

Wetin people dey ask frequent

Di API follow OpenAI style?

Yes, e follow am well-well. You only need to change base_url to api.moonshot.cn/v1 and use your key. Everyting like chat completions and function calling go work di same way. You no need change how your code be at all.

How big be di context window?

Kimi K2.5 fit take up to 262,144 tokens inside one single request. Dis one mean say you fit dump full code projects or plenty long documents inside at once without any need to cut dem into pieces.

How context caching dey work?

Context caching dey start to work by itself when you use di same documents for many requests. E dey drop price for tokens from $0.60 to $0.15 for one million. You no need to manage any cache yourself as di system dey check am for you.

I fit host Kimi myself?

Kimi K2.5 dey open-source under Modified MIT License, so you fit download am for Hugging Face. But because di model big reach 1T, you go need strong GPU setup. For people wey no get heavy machine, Kimi Linear na beta alternative because e small pass.

Which language documentation get?

Di platform get beta documentation for English and Chinese language, and dem get code examples to show how to join am to your work.

Which formats vision support?

Kimi K2.5 support text, images, video, and documents inside di same input pass without any need for person to resize or fix am first.