Kimi K3 is a 2.8T-parameter mixture-of-expertsA model built from many specialist sub-networks where only a few activate per token, giving big-model capability at small-model running cost.Full definition → with 104B active, 896 experts of which 16 fire per token, a 1M-token context windowThe maximum amount of text a model can consider at once — its working memory for the current conversation or task.Full definition →, and native vision, with a reported ~2.5x scaling-efficiency gain over K2 from work on numerical stability at scale.
Moonshot shipped the surrounding machinery too: FlashKDA attentionThe mechanism that lets a model weigh which earlier words matter for the word it's currently processing — the core operation of a transformer.Full definition → kernels, the MoonEP library for MoE communication, and AgentENV for distributed AI agentAn AI system that doesn't just answer once but works toward a goal in a loop — taking actions, reading the results, and deciding what to do next.Full definition → environments. That is closer to a full recipe than a weights drop.
"Open weights" is not OSI open source here. Hosting providers above $20M a year need a separate agreement, and products above 100M monthly users or $20M monthly revenue must display "Kimi K3" in the UI.
Agent skills carry a regression tax. Across nearly 6,000 paired runs, adding skills produced real gains but also broke tasks the agent had previously solved without them, so stuffing more procedures into context is not free.
End-to-end RL over a multi-module pipeline causes role drift: overall accuracy rises while modules quietly abandon their assigned jobs, such as a decomposer that embeds the answer instead of structuring the problem.
Terms in this piece · Glossary
mixture-of-experts — A model built from many specialist sub-networks where only a few activate per token, giving big-model capability at small-model running cost.
context window — The maximum amount of text a model can consider at once — its working memory for the current conversation or task.
attention — The mechanism that lets a model weigh which earlier words matter for the word it's currently processing — the core operation of a transformer.
AI agent — An AI system that doesn't just answer once but works toward a goal in a loop — taking actions, reading the results, and deciding what to do next.
Why it matters
Frames a debate whose outcome determines which frontier models remain self-hostable.
Key quotes
“This is more than a model drop; it is a fairly complete recipe for large-scale agentic post-training and serving.”
“That breadth underscores that open-weight frontier launches are now supply-chain events, not just research announcements.”
“As usual, public eval gains and harness-specific production utility are diverging.”
