Kimi K2.7-Code cuts thinking tokens 30% — but practitioners say the benchmarks don’t check out

Moonshot AI released Kimi K2.7-Code this week, an open-source update to its K2 coding model family, claiming leaner reasoning and double-digit performance gains.K2.7-Code is built on the same trillion-parameter mixture-of-experts architecture as its predecessor K2.6, and drops in via an… Moonshot AI released Kimi K2.7-Code this week, an open-source update to its K2 coding model family, claiming leaner reasoning and double-digit performance gains.K2.7-Code is built on the same trillion-parameter mixture-of-experts architecture as its predecessor K2.6, and drops in via an OpenAI-compatible API — which matters for teams already running K2.6 in production gateways.When K2.6 launched in April, it topped OpenRouter's weekly LLM leaderboard — a ranking based on actual API routing decisions by developers, not self-reported benchmark scores.Moonshot AI says K2.7-Code addresses what it calls "overthinking," reducing thinking-token usage by 30% compared to K2.6 — a number that would directly affect inference costs for teams running agentic workflows. Whether that efficiency gain holds on independent benchmarks is a question practitioners have already started raising publicly.What Kimi K2.7-Code isK2.7-Code is released under a Modified MIT license, with weights available on HuggingFace. The model is deployable via vLLM or SGLang. It runs exclusively in thinking mode and does not support temperature adjustment…