When Moonshot AI launched Kimi K3.1 Pro on October 23, the official blog left a hard promise: "The full model weights will be released by Nov 4." Those weights are now live on the Hugging Face repo moonshotai/Kimi-K3.1-Pro — a model the company calls its most capable 3T-class open-weight release to date. The tracker AI/TLDR logged the upload on November 2, two days inside the deadline. A 2-3x faster Kimi K3.1 Pro Highspeed shipped on the API side at launch; what opened up now is the full standard-Pro weights.
The specs that landed
From the official model card: a 2.8T-parameter MoE with 104B active per token; 93 layers comprising 69 Kimi Delta Attention layers and 24 Gated DSA layers — Moonshot says Gated DSA replaces the full-attention Gated MLA layers of Kimi K3 for more efficient long-context modeling. Each token routes to 16 of 896 experts plus 2 shared experts; attention runs 96 heads over a 160K vocabulary; the context window is 1,048,576 tokens; vision goes through a 401M-parameter MoonViT-V2 encoder, with text-plus-image input. The most practical detail for self-hosters: weights ship directly in MXFP4 with MXFP8 activations, the product of quantization-aware training rather than post-training compression — what you download is a low-bit artifact ready to deploy. The card lists four deployment paths — Transformers, vLLM, SGLang and Docker Model Runner; on the API side, platform.kimi.ai exposes kimi-k3.1-pro with OpenAI/Anthropic-compatible endpoints, and reasoning_effort supports low/high/max, defaulting to max.
The license: an MIT skeleton with two commercial clauses
The grant section of the Kimi K3.1 Pro License reads like standard MIT: use, copy, modify, merge, publish, distribute, sublicense, sell, fine-tune and build derivatives — all permitted. The watershed comes later. First, if your Model-as-a-Service business — one that gives third parties meaningful control over inputs, parameters or training data — plus affiliates exceeds 20 million US dollars in aggregate revenue over any consecutive 12 months, you must sign a separate agreement with Moonshot before commercial use. Second, products with more than 100 million monthly active users or more than 20 million dollars in monthly revenue must prominently display "Kimi K3.1-Pro" in the user interface. Internal use, and use through Moonshot's official products or certified inference partners, is exempt from both.
The official benchmark table shows wins and losses
All numbers below are from the official model card. Terminal-Bench 4.0: 65.7, above GPT-6 Astra's 58.2 and a hair under Claude Opus 5.5's 66.4. HLE-Full with tools: 70.0, and BrowseComp: 92.1 — both the highest scores in the table. On the internal Kimi Hallu Bench Hard, claim-level hallucination fell from 23.4% on K3 to 8.3% (the vendor notes this benchmark deliberately stresses factual accuracy and the rates do not represent everyday error rates). The weak spots are printed too: HLE-Diamond 39.4, far below Astra's 65.8; Program Bench 31.5 against Opus 5.5's 65.0. A vendor table that marks its own losses is worth more than a one-sided press release.
Safety limits delivered, independent test arrives
The safety posture previewed at launch is baked into the open weights: the official blog states Moonshot identified and removed cyber-related data from both the mid-training and RL stages, after observing that vulnerability-research capability rises steadily with post-training scale — the lever is data, while general-purpose coding tasks are retained. There is independent evidence too: on November 3, Simon Willison ran the "read raw weights, name the game" experiment through kimi-k3.1-pro on OpenRouter, and it correctly identified Breakout after 9m 41s and 19,493 reasoning tokens (write-up).
For self-hosting teams the question now reduces to a VRAM budget; for the open-source field, "give a date, ship on it" is becoming the credit metric of flagship open weights — and the 20-million-dollar line is Moonshot pricing "open" and "commercial" separately: free by default, pay at scale.