Kimi K2 (Moonshot)
Moonshot AI's 1T-parameter MoE model. Industry-leading agentic coding and tool-use benchmarks.
Kimi K2 (Moonshot) is text & chat AI model from Custom, priced at β¬0.600 per 1M input tokens with a 131.1K tokens context window.
0.7
Pricing
API Integration
Use our OpenAI-compatible API to integrate Kimi K2 (Moonshot) into your application.
npm install railwailimport railwail from "railwail";
const rw = railwail("YOUR_API_KEY");
// Simple β just pass a string
const reply = await rw.run("kimi-k2", "Hello! What can you do?");
console.log(reply);
// With message history
const reply2 = await rw.run("kimi-k2", [
{ role: "system", content: "You are a helpful assistant." },
{ role: "user", content: "Explain quantum computing simply." },
]);
console.log(reply2);
// Full response with usage info
const res = await rw.chat("kimi-k2", [
{ role: "user", content: "Hello!" },
], { temperature: 0.7, max_tokens: 500 });
console.log(res.choices[0].message.content);
console.log(res.usage);Deep dive β Moonshot AI's Kimi K2 (Moonshot)
Moonshot AI (ζδΉζι’, literally 'dark side of the moon') was founded in March 2023 in Beijing by Yang Zhilin (CEO, Tsinghua and CMU alumnus, co-author of XLNet and Transformer-XL), Zhou Xinyu and Wu Yuxin. The company quickly emerged as one of the four 'AI tigers' of China alongside Zhipu AI, MiniMax and 01.AI. Moonshot's flagship product is Kimi (named after the explorer Kimi Raikkonen), a consumer-facing chatbot launched in October 2023 that became famous in China for very long context handling. Kimi initially launched with a 200K Chinese-character context, expanded to 1M and then 2M tokens via long-context architectural research. Moonshot has raised over $1.3B across multiple rounds, including a $1B round in early 2024 led by Alibaba at a $2.5B valuation, and a subsequent round in late 2024 reportedly valuing the company near $3.3B. Investors include Alibaba, Tencent, Sequoia China, Hongshan and HSG. Kimi K2 was released in July 2025 as the lab's open-weight Mixture-of-Experts flagship, marking Moonshot's move to a more open model strategy alongside its consumer app.
Visit Moonshot AI βKimi K2 was released by Moonshot AI in July 2025 as an open-weight Mixture-of-Experts model with 1 trillion total parameters and 32 billion active per token across 384 experts (8 selected per token). The architecture follows DeepSeek-style fine-grained MoE with Multi-head Latent Attention for efficient inference. Kimi K2 was pretrained on 15.5 trillion tokens of multilingual web text, code, books and scientific papers, with a heavy emphasis on Chinese and English. The training run used the Muon optimizer (Momentum Orthogonalized by Newton-Schulz) at unprecedented scale and reportedly improved sample efficiency over AdamW. Moonshot published the MuonClip variant that adds gradient clipping to stabilise Muon at trillion-parameter scale. Post-training emphasised agentic capabilities: Kimi K2 was trained with synthetic and real tool-use trajectories covering multi-step web search, coding, file manipulation and structured tool calling, positioning it as one of the strongest open-weight agentic models. The model supports a 128K context window. Two variants were released: Kimi K2-Base for fine-tuning and Kimi K2-Instruct for general chat and agentic use. Weights ship under a Modified MIT License that requires attribution for very-large-scale commercial deployments.
- Parameters
- 1T total, 32B active per token
- Context
- 128K tokens
- 1T-parameter Mixture-of-Experts with 32B active per token
- Pretrained on 15.5T tokens using the Muon optimizer (MuonClip variant)
- Strong agentic capability: SWE-bench Verified, Terminal-Bench, ToolBench leadership
- 128K context window
- Function calling and parallel tool calls
- Excellent Chinese-English bilingual performance
- Code generation and editing across major languages
- Open weights under Modified MIT License
- Compatible with vLLM, SGLang, llama.cpp, HuggingFace
- Available via Kimi consumer app and Moonshot API
- Best for: agentic workloads, bilingual chat, coding agents, on-prem deployment.
Pretrained on 15.5 trillion tokens of multilingual web text (Chinese and English dominant), code, books and scientific papers using the Muon optimizer with MuonClip stability. Knowledge cutoff is approximately early 2025. Post-training emphasises agentic tool-use trajectories and supervised fine-tuning on curated coding and reasoning data.
License: Modified MIT License: open weights, commercial use permitted; very-large-scale deployments require attribution in product UI.
Known limitations
- Filters Chinese political topics
- Large memory footprint requires multi-GPU inference
- No native vision input in base K2 release
- Limited third-party safety evaluations
- Less integrated tooling ecosystem outside China
Frequently asked questions
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