Moonshot AI introduced Kimi K3.1 Pro, the first model in its new K3.1 series and, by the company's description, its most capable 3T-class open-weight model to date, with a 1-million-token context window. A companion model, Kimi K3.1 Pro Highspeed, delivers 2–3 times the speed of K3.1 Pro for more responsive interactions. Both are available on Kimi.com, Kimi Work, Kimi Code and the Kimi API, with full model weights promised by November 4. The announcement page does not carry an explicit publication date. Read the announcement.
How it was built
K3.1 Pro is a post-trained version of Kimi K3 with substantially larger post-training compute. To enable large-scale joint reinforcement learning across hundreds of downstream domains, Moonshot says it optimized every layer of the stack — the RL framework, long-context inference, training efficiency, sandbox infrastructure, rollout scheduling and a shared platform for environment quality checks — so each increment of RL compute delivers stable returns.
Reported results
- On Terminal-Bench 4.0, which evaluates coding agents on realistic, resource-constrained terminal tasks, K3.1 Pro scores 65.7% — ahead of GPT-6 Astra at 58.2% and narrowly behind Claude Opus 5.5 at 66.4%.
- On Kimi Code Bench 3.0 Long-horizon, the company's internal benchmark for multi-step execution, tool use and end-to-end problem solving, it scores 61.2% against K3's 55.95%.
- Across coding, agentic and knowledge benchmarks, Moonshot says K3.1 Pro matches or approaches the strongest proprietary models.
These are vendor-reported figures. The model also works across game engines including Unity, Unreal Engine and Godot, combining them with web search and 3D generation, and Moonshot highlights stronger front-end composition and long-horizon coding from large-scale reinforcement learning.
For how we treat vendor benchmarks, see our verification guide; the Kimi profile lists official entry points.
