Kimi K2: Mixture-of-experts language model by Moonshot AI
Trillion-parameter MoE model with Muon-optimized training.
Learn more about Kimi-K2
Kimi K2 is a trillion-parameter mixture-of-experts language model developed by Moonshot AI for natural language processing tasks. The architecture employs sparse activation patterns where only a subset of expert networks processes each input token, enabling massive model scale while maintaining computational efficiency during inference. The model utilizes Muon optimization techniques during training to improve convergence and parameter efficiency across the distributed expert layers. The system is designed for deployment through API endpoints that handle routing between active experts based on input characteristics. This sparse MoE approach provides a favorable trade-off between model capacity and computational cost compared to dense transformer architectures of equivalent capability.
Muon optimizer at scale
Applies the Muon optimizer to a 1 trillion parameter model with novel techniques to resolve training instabilities, achieving stable pre-training on 15.5 trillion tokens without reported convergence issues.
Mixture-of-experts architecture
Uses 384 experts with 8 selected per token and 1 shared expert, enabling efficient scaling with 32 billion activated parameters while maintaining a large total parameter count for knowledge capacity.
Extended context and agentic design
Supports 128K token context length and is specifically optimized for tool use, reasoning, and autonomous problem-solving tasks rather than long-form thinking or extended reasoning modes.
from openai import OpenAI
client = OpenAI(api_key="your_api_key", base_url="https://api.moonshot.cn/v1")
response = client.chat.completions.create(
model="kimi-k2",
messages=[{"role": "user", "content": "Explain quantum computing in simple terms"}],
temperature=0.7
)
print(response.choices[0].message.content)See how people are using Kimi-K2
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