Moonshot releases Kimi K3.1 Pro, open weights due November 4

Moonshot AI released Kimi K3.1 Pro on October 23, a model it calls its most capable open-weight release to date. It is available now through the Kimi API, but the weights themselves are not out yet. Moonshot says they will be published by November 4.

Open-weight models usually trail the best closed ones by months. Moonshot's own numbers put K3.1 Pro close to Claude Opus 5.5 and GPT-6 Astra on several tests, and it is priced well below Opus.

What Moonshot released

K3.1 Pro is the first model in the K3.1 series and an upgrade of Kimi K3. Moonshot describes it only as "3T-class", meaning roughly three trillion parameters, and gives no exact total or active count. It has a context window of 1,048,576 tokens, according to Moonshot's pricing documentation.

There is also a Highspeed variant that Moonshot says runs two to three times faster. Both are on the API as kimi-k3.1-pro and kimi-k3.1-pro-highspeed, and in the Kimi apps, Kimi Code and Kimi Work. Enterprise administrators have to switch it on, because access is off by default.

OpenRouter lists the standard model at $2 per million input tokens and $10 per million output tokens. Simon Willison reports that this is half the price of Opus 5.5 and that Highspeed costs twice as much.

What the benchmarks show

All of the following figures are Moonshot's own, run with the highest reasoning setting:

• Terminal-Bench 4.0: 65.7%, against 66.4% for Claude Opus 5.5 and 58.2% for GPT-6 Astra.
• BenchCAD with tools: 92.7%, against 96.2% for Opus 5.5 and 95.9% for GPT-6 Astra.
• OfficeQA Pro v2: 64.4, which Moonshot calls state of the art.
• Claim-level hallucination rate on its internal Hallu Bench Hard: 8.3%, down from 23.4% for K3.

The one independent number comes from Artificial Analysis, cited by Willison: a score of 55, behind only Opus 5.5 at 58. Treat the vendor figures with caution until outside testers have run the weights.

Cyber skills were cut on purpose

Moonshot says it removed cyber-related data from mid-training and from its reinforcement learning environments before the open release. On its internal xdev-bench-hard test, a checkpoint without those changes solved 90.5% of problems. The final model solves 81.0%. K3 solved 33.3%.

Moonshot says the aim is to avoid open weights that push past the best closed models on cyber tasks, and that it will raise the level over time. That is a company's own account of its own tests, and nobody outside can check it until the weights ship.

What to watch

The weights are due by November 4. Moonshot's post does not state a licence, and the API pricing page it links to showed empty tables when fetched, so the OpenRouter rates are the only prices confirmed. Both questions, plus independent benchmark runs, will decide how much of the "open-weight near the frontier" claim holds up.