Kimi K2.7-Code Ships Open-Source: 30% Fewer Thinking Tokens, 81% MCP Mark Verified, $4/M Output
Moonshot AI released Kimi K2.7-Code on June 12, 2026 as an open-source successor to K2.6, the model that held open-source SWE-bench Pro SOTA at 58.6% from April. Weights are on Hugging Face under a Modified MIT license. A high-speed variant with 5-6x faster output, capped at approximately 180 tokens per second, launched June 15 at double the standard API price.
Benchmark Numbers
All figures are Moonshot’s own benchmarks. K2.7-Code ran in Kimi Code CLI; comparisons against GPT-5.5 used Codex xhigh and Claude Opus 4.8 used Claude Code xhigh.
| Benchmark | K2.6 | K2.7-Code | GPT-5.5 | Opus 4.8 |
|---|---|---|---|---|
| Kimi Code Bench v2 | 50.9 | 62.0 | 69.0 | 67.4 |
| Program Bench | 48.3 | 53.6 | 69.1 | 63.8 |
| MLS Bench Lite | 26.7 | 35.1 | 35.5 | 42.8 |
| Kimi Claw 24/7 Bench | 42.9 | 46.9 | 52.8 | 50.4 |
| MCP Atlas | 69.4 | 76.0 | 79.4 | 81.3 |
| MCP Mark Verified | 72.8 | 81.1 | 92.9 | 76.4 |
K2.7-Code beats Opus 4.8 on MCP Mark Verified, 81.1 vs 76.4. MCP Mark Verified tests agents across five real-world software environments: Notion, GitHub, file systems, Postgres, and Playwright browser automation. GPT-5.5 leads at 92.9.
On Program Bench — where agents must reproduce a compiled binary’s behavior without source code — K2.7-Code trails GPT-5.5 by 15.5 points. That is the starkest gap in the table.
The Operational Win
The headline that matters most for production workloads is not the benchmark delta. It is the 30% reduction in reasoning token usage versus K2.6. For teams running K2.7-Code in agentic loops with extended thinking enabled, 30% fewer thinking tokens translates directly to lower inference cost and lower latency per turn. Moonshot’s number — unverified independently — is credible enough to test before committing.
Pricing
| Variant | Input | Output | Cache input |
|---|---|---|---|
| Standard | $0.95/M | $4.00/M | $0.19/M |
| High-speed (June 15) | ~$1.90/M | ~$8.00/M | — |
Output at $4/M is 12x cheaper than Claude Fable 5 at $50/M, and 6x cheaper than Opus 4.8 at $25/M. The standard pricing is unchanged from K2.6.
The Caveat
Every benchmark row above is from Moonshot’s own test suites. K2.6 scored 24% on DeepSWE, an independent benchmark with a 70-point spread across models — tied with GPT-5.4-mini. That is a 35-point gap versus Moonshot’s vendor-run Kimi Code Bench v2 score for K2.6. Developer Sugumaran Balasubramaniyan challenged Moonshot publicly on X to submit K2.7-Code to DeepSWE; as of publication, Moonshot has not done so. No third-party SWE-bench Pro run for K2.7-Code exists yet.
That gap does not mean the model is useless — K2.6 still ran production workloads for teams that tested it. It means the benchmark numbers should be treated as directional until independent results land.
The token efficiency improvement and the open-weight, low-cost positioning are the real reasons to evaluate K2.7-Code against your own agentic workloads. Run the high-speed variant if throughput matters; use standard if you want the cheaper thinking budget.