Moonshot’s open model Kimi K2.7 Code undercuts GPT-5.5 and Claude by up to 12x on price per token

Moonshot AI has released Kimi K2.7 Code, an open-weights model with one trillion parameters built for programming. It still trails GPT-5.5 and Claude Opus 4.8 in coding benchmarks but costs a fraction of the price.

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. 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).Ad 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.AdDEC_D_Incontent-1 There’s one outlier: MCPMark Verified, a benchmark that tests AI agents across five real-world software environments, including Notion, GitHub, file systems, Postgres databases, and browser automation via Playwright. Here, K2.7 Code beats Claude Opus 4.8 with 81.1 versus 76.4, but falls well short of GPT-5.5 at 92.9. As always, benchmark results and real-world performance can diverge. A trillion parameters, but only 32 billion active at a time K2.7 Code uses a Mixture-of-Experts (MoE) architecture with one trillion total parameters, according to its model card. Only 32 billion of those are active per token. The model has 384 experts, with eight selected per token.