Moonshot Releases Kimi K3.1 Pro, Says It Cut Cyber Data From Training

Moonshot AI has released Kimi K3.1 Pro, the first model in its K3.1 series, with a 1-million-token context window and a faster Highspeed variant that it says runs two to three times quicker. Both are available now on Kimi.ai, Kimi Work, Kimi Code and the Kimi API, and Moonshot says the full model weights will be released by November 4.

The detail that sets the release apart is what Moonshot says it held back. During development, the company found cyber-related content embedded in its general training data and its reinforcement learning tasks, and for the open-weight release it removed that material from both stages while keeping general coding tasks.

The effect shows up in Moonshot's own testing. On xdev-bench-hard, an internal benchmark of vulnerability discovery and exploitation tasks, a version trained without those removals solved 90.5% of problems at pass@3, while the released model solved 81.0%. Moonshot says the model's cyber capability remains behind frontier proprietary models that have been publicly available for at least three months.

We aim not to release open weights that would extend the absolute frontier of cyber capability,

the company wrote. It also said it intends to raise the cyber capability available in open weights over time, arguing that open models let more teams audit their own code, including legacy systems.

On general performance, Moonshot positions the model close to the top US systems. It reports a 65.7% score on Terminal-Bench 4.0, ahead of GPT-6 Astra at 58.2% and just behind Claude Opus 5.5 at 66.4%. On BenchCAD, a 3D modeling test, it reports 92.7% with tools, against 96.2% for Claude Opus 5.5 and 95.9% for GPT-6 Astra. These results are self-reported, and several come from Moonshot's internal benchmarks. AI Briefing News has not independently verified them.

Moonshot describes K3.1 Pro as a post-trained version of Kimi K3 that used substantially more post-training compute. Enterprise administrators must switch the model on for their organizations, as access is off by default at launch.

The announcement doesn't state the model's exact parameter count, beyond calling it "3T-class," or say which license the weights will carry. It gives no API pricing and no independent review of its cyber evaluation.

Moonshot released Kimi K3 in July. That model has 2.8 trillion total parameters, making it the largest open-weight model released to date, larger than DeepSeek's V4 Pro. Its weights were published on July 27 under a Modified MIT license. Moonshot is backed by Alibaba and Tencent, and raised about $2 billion in May at a valuation above $20 billion. The K3.1 Pro release follows Meta's Muse Spark Open, a US open-weight model released earlier this month, as AI Briefing News reported.