Moonshot AI has released Kimi K2.7 Code, a new AI model built specifically for programming tasks and agent-based coding workflows. The model builds on its predecessor, Kimi K2.6, and is available as an open-weights version on Hugging Face.
According to Moonshot AI, K2.7 Code is designed to outperform its predecessor on long-running, complex software engineering tasks. For general tasks outside of coding, the company still recommends K2.6. Kimi is also the model that coding tool provider Cursor resells in a modified form.
Gains over K2.6, but still behind the leaders
On Moonshot's in-house Kimi Code Bench v2, performance jumps from 50.9 to 62.0. On Program Bench, it climbs from 48.3 to 53.6, and on MLS Bench Lite, it rises from 26.7 to 35.1. K2.7 Code also improves on agentic benchmarks, hitting 76.0 on MCP Atlas (up from 69.4) and 81.1 on MCPMark Verified (up from 72.8).
In a head-to-head comparison with GPT-5.5 and Claude Opus 4.8, though, K2.7 Code trails on most coding benchmarks. GPT-5.5 scores 69.1 on Program Bench versus 53.6 for K2.7 Code. On Kimi Code Bench v2, it's 69.0 versus 62.0. Program Bench is a particularly tough test. Agents have to reproduce a program's behavior using only a compiled binary and its documentation wihtout source code access, decompilation, or internet.
