Thoughts on Kimi K2 Thinking (www.interconnects.ai)

Moonshot AI (Kimi) released Kimi K2 Thinking, a high-profile open reasoning MoE model that pushes open-source capabilities closer to the closed-model frontier. Architecturally it’s a 1T-parameter mixture-of-experts with ~32B active parameters, a 256K token context window, and support for “interleaved thinking” during agentic tool use. Moonshot reports K2 can chain 200–300 sequential tool calls and was post-trained with Quantization-Aware Training (INT4 weight-only for MoE parts), enabling native INT4 inference with ~2x generation speed improvements; all benchmark numbers are reported under INT4. Early public evaluations show K2 surpassing some closed models on selective tasks (e.g., Humanity’s Last Exam, BrowseComp) while still trailing top closed models on others. The release matters because it highlights how fast open and Chinese labs are iterating and shipping competitive systems—shortening the lag to closed leaders and pressuring U.S. closed labs on pricing, narrative, and deployment. Important practical implications: benchmark comparisons are getting trickier (and more favorable to open models when measured at serving precision), hosting and tool-integration needs will spike as models make heavy tool calls, and distribution/serving capacity becomes a key gating factor. The permissive-but-attributive license and an overwhelmed API underline that K2 is already seeing strong demand, setting the stage for intense competition and infrastructure work in 2025–26.