An open-weight model now sits one point behind the best proprietary coding agent on a public benchmark
The gap between open weights and the frontier labs keeps closing, and release notes now read like safety filings.
The same release reports hallucinations cut from 23.4% to 8.3%, with the numbers published.
Moonshot’s Kimi K3.1 Pro is a 3T-class open-weight model with a 1M-token context. On Terminal-Bench 4.0 it scores 65.7%, ahead of GPT-6 Astra at 58.2% and just behind Claude Opus 5.5 at 66.4%, by Moonshot’s own evals. A Highspeed variant runs 2-3x faster. Both are live today on Kimi.ai, Kimi Code, and the API, and the weights are due by November 4. Moonshot also says it deliberately removed cyber-training data and published before-and-after numbers for that too.
My read: the gap between open weights and the frontier labs keeps closing, and release notes now read like safety filings.
Your pick: run K3.1 Pro on one real coding task through the API this week. Keep your evals model-agnostic so you can swap when the weights drop.
12 months: open-weight coding agents are the default for cost-sensitive teams.
24 months: model choice is a routing decision.
36 months: frontier capability concentrates in a few labs and the rest is commoditised.
Why I may be wrong: these are vendor-run benchmarks, and the internal numbers can’t be checked from outside.
What score on your own evals would earn it a slot?