Moonshot AI is committed to solving ambitious "moonshot" problems that will lead humanity to AGI. We embrace open source, and contributed the following projects to the community:
- Mooncake pioneered the idea of KV-centric disaggregated LLM serving, winning the Best Paper award at FAST 2025. We released code and data corresponding to our paper.
- Kimi-K1.5: Scaling Reinforcement Learning with LLMs. We released our tech report on building an O1-level multi-modal reasoning model.
- Moonlight for our paper Muon is Scalable for LLM Training. We released (i) checkpoints for SOTA small models; (ii) code for our improved Muon optimizer.
- MoBA: Mixture of Block Attention for Long-Context LLMs. We released code and paper for our looong-context LLM technique.
- Kimi-VL: Mixture-of-Experts Vision-Language Model for Multimodal Reasoning, Long-Context Understanding, and Strong Agent Capabilities
- Kimina-Prover Preview: Towards Large Formal Reasoning Models with Reinforcement Learning. We released technical report, model weights and a rectified version of miniF2F-test benchmark.
- MoonshotAI-Cookbook: examples on how to use our API service.
- moonpalace: debugging tool for our API service.