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.