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
Top in AI & ML
Related Repositories
Discover similar tools and frameworks used by developers
Transformers
Unified API for pre-trained transformer models across frameworks.
ControlNet
Dual-branch architecture for conditional diffusion model control.
Open WebUI
Extensible multi-LLM chat platform with RAG pipeline.
CUTLASS
CUDA C++ templates and Python DSLs for high-performance matrix multiplication on GPUs.
ByteTrack
Multi-object tracker associating low-confidence detections across frames.
Related Reading
Guides and comparisons from the Greptile content library
Top AI Code Review Tools for Small Dev Teams 2026
Affordable, lightweight AI code review tools that help small teams catch bugs faster. Practical comparisons of accuracy, ease of use, and value for teams without enterprise budgets.
What is Shift Left Testing? Everything Engineering Leaders Need to Know
The five types of shift-left testing, the benefits and limitations of each, and how the approach changes when agents write and review a growing share of your code.
What is Source Code Management (SCM): Everything Engineering Leaders Need to Know
Source code management is more than branching and merging. Here is how SCM works, the top tools, the best practices, and why good code review is the foundation of source control in an agentic era.
Best AI Code Review Tools for GitHub
Top-rated AI code review tools for GitHub teams in 2025. Compare features, pricing, and accuracy to find the perfect fit for faster PR reviews and bulletproof code quality.