Moonshot AI

Kimi K2 0905 is the September update of Kimi K2 0711. It is a large-scale Mixture-of-Experts (MoE) language model developed by Moonshot AI, featuring 1 trillion total parameters with 32 billion active per forward pass. It supports long-context inference up to 256k tokens, extended from the previous 128k. This update improves agentic coding with higher accuracy and better generalization across scaffolds, and enhances frontend coding with more aesthetic and functional outputs for web, 3D, and related tasks. The model is trained with a novel stack incorporating the MuonClip optimizer for stable large-scale MoE training.

Kimi K2 base model is a state-of-the-art mixture-of-experts (MoE) language model with 32 billion activated parameters and 1 trillion total parameters. Trained on 15.5 trillion tokens with the MuonClip optimizer, this is the foundation model before instruction tuning. It demonstrates strong performance on knowledge, reasoning, and coding benchmarks while being optimized for agentic capabilities.

Kimi K2 is a state-of-the-art mixture-of-experts (MoE) language model with 32 billion activated parameters and 1 trillion total parameters. Trained with the MuonClip optimizer, it achieves exceptional performance across frontier knowledge, reasoning, and coding tasks while being meticulously optimized for agentic capabilities. The instruct variant is post-trained for drop-in, general-purpose chat and agentic experiences without long thinking.

Thinking Kimi model for slower research passes, planning, and hard technical questions

Kimi reasoning model for long-horizon research, planning, and tool use

Kimi K2-Instruct-0905 is the latest, most capable version of Kimi K2, achieving state-of-the-art performance in frontier knowledge, math, and coding among non-thinking models. This Mixture-of-Experts model features 32 billion activated parameters and 1 trillion total parameters, meticulously optimized for agentic tasks. Key features include enhanced agentic coding intelligence, extended context length to 256K tokens, and a hybrid architecture trained with MuonClip optimizer on 15.5T tokens. The model achieves 65.8% on SWE-bench Verified (single attempt), 47.3% on SWE-bench Multilingual, and excels at tool use with 70.6% on Tau2-retail. It is a reflex-grade model without long thinking, designed to act and execute complex tasks seamlessly.

Earlier Kimi frontier model for long-context agents, coding, and multimodal work

Multimodal Kimi workhorse for agent loops, coding tasks, and visual context

Coding-focused Kimi model, stronger on long-horizon repo work with less overthinking

Lower-latency Kimi Code variant for interactive edits and coding-agent loops

Multimodal Kimi model with 1M context and toggleable max-effort thinking for long-horizon agent work

Kimi 1.5 is a next-generation multimodal large language model developed by Moonshot AI. It incorporates advanced reinforcement learning (RL) and scalable multimodal reasoning, delivering state-of-the-art performance in math, code, vision, and long-context reasoning tasks.