Moonshot AI 在 HuggingFace 发布了 2.8T 参数的 Kimi K3.1 Pro 模型权重，该模型以 4-bit 精度提供以降低部署门槛。模型在 Terminal-Bench 4.0 上得分 65.7%，架构上通过线性注意力机制优化了 1M token 上下文的处理效率，并采用了限制商业转售的修改版 MIT 许可。
Moonshot AI 开源了 2.8T 参数的 Kimi K3.1 Pro 模型权重，其采用 4-bit 量化发布并针对长上下文处理进行了架构优化，为开发者提供了高性能模型的新选择。
Moonshot AI released the massive 2.8T-parameter Kimi K3.1 Pro weights on Huggingface.
On Terminal-Bench, its head-to-head with Claude 5.5
Trained with 4-bit weights, it also ships at that precision, avoiding the 5.6TB a 16-bit copy would need.
Moonshot traced steadily rising exploit skill to cyber content buried in its general mid-training corpus and RL environments, and removed both before opening the weights.
In Moonshot’s own tests the model scores 65.7% on Terminal-Bench 4.0, near Claude Opus 5.5’s 66.4%, but without tools reaches only 39.4% on HLE-Diamond, against GPT-6 Astra’s 65.8%.
Only 104B of its parameters fire per token, and 69 of its 93 layers use linear attention, which makes a 1M-token context cheaper to process.
Its modified MIT license exempts internal use but makes model-hosting firms earning over $20M a year negotiate, reining in resellers that could undercut the company’s own API.
来源：Rohan Paul · x.com