Kimi K2 Model

Kimi K2, developed by Moonshot AI, is a cutting-edge open-source Mixture-of‑Experts (MoE) model that pushes the boundaries of large language models by efficiently scaling to 1 trillion total parameters, while only activating 32 billion parameters during inference.

This architecture enables extremely high capacity without proportional computational cost, making K2 powerful yet resource-aware.

One of Kimi K2’s standout strengths is its ultra-long context window: the model supports up to 128K tokens (and newer versions like K2 0905 boost this to 256K), which means it can handle very large documents, codebases, or multi-turn conversations with deep context retention.

Its pre-training was conducted on 15.5 trillion tokens, with a custom optimizer (MuonClip) that helps stabilize training and maintain efficiency.

Beyond just chat, Kimi K2 is designed for agentic intelligence — it’s capable of planning, tool-using, and executing multi-step tasks autonomously.

This makes it well-suited for applications like autonomous agents, complex problem solving, and intelligent automation.

In terms of performance, K2 demonstrates strong benchmarks in coding, mathematical reasoning, and knowledge tasks, outperforming many open-source rivals.

There are two main variants: Kimi-K2-Base (for research and fine-tuning) and Kimi-K2-Instruct (optimized for drop-in chat and agent workflows).

The model is fully open-source, with weights available for self‑hosting and a permissive license, giving developers deep flexibility.

Meanwhile, Moonshot AI also provides a hosted API, allowing users to integrate Kimi K2 into applications without managing hardware.

Overall, Kimi K2 stands as a powerful and flexible foundation for building intelligent agents, developer tools, and long-context applications — all while remaining open and extensible.