A new large language model — Kimi K2 — has been making waves across the technology industry. Developed by Chinese artificial intelligence company Moonshot AI, which is backed by digital behemoth Alibaba, its release is being hailed as another DeepSeek moment. Like DeepSeek, Kimi K2 is open-weight, which means its trained parameters are freely available to be downloaded and adapted; also like DeepSeek, it’s demonstrated impressive performance over established models.
How does Kimi K2 work?
Kimi uses a mixture-of-experts model architecture. This means it consists of a system of separate subnetworks that each specialize in distinct parts of a given problem. The benefit of such an approach is efficiency in terms of both speed and computation.
Despite having 32 billion active parameters (and a trillion total parameters), it’s relatively inexpensive to use. While Claude Opus 4 costs $15 per million input tokens and $75 per million output tokens, Kimi is a fraction of that at $0.15 per million input tokens and $2.50 per million output tokens.
Kimi is also notably described as an agentic LLM. Moonshot AI says in its product copy that it has been “meticulously optimized for agentic tasks”. This sets it apart from the reasoning approach commonly used in other established models, where the model is constructed in such a way to follow a sophisticated step-by-step approach to problem solving. By contrast, the agentic approach emphasized by Moonshot is intended to allow the model ‘learn’ from external experiences — a point made by researchers David Silver and Richard Sutton in their paper The Era of Experience (which the Moonshot AI team specifically cite).
