AI News · 4 min read
Kimi K3.1 Pro open weights: 2.8T MoE hits Hugging Face
Moonshot AI published Kimi K3.1 Pro weights on Nov 4, 2026 — 2.8T total / 104B active, 1M context, custom license. What the card says and who can actually run it.
What dropped on November 4, 2026
Moonshot AI put Kimi K3.1 Pro on Hugging Face at moonshotai/Kimi-K3.1-Pro ↗. The model card describes an open-weight, natively multimodal agentic MoE — Moonshot’s most capable 3T-class model to date — with:
2.8T total parameters, ~104B activated
1,048,576-token context
Text + image (MoonViT-V2 vision encoder, 401M params)
MXFP4 weights / MXFP8 activations (quantization-aware training)
API access via
kimi-k3.1-proon platform.kimi.ai ↗
GIGAZINE ↗ confirmed the free weight release on November 5 local time, noting the earlier October API launch and the promised November 4 weight date. The HF tree is ~1.56 TB across 96 safetensors shards — this is not a laptop download.
License: open with a MaaS tripwire
The Kimi K3.1 Pro License ↗ allows use, modification, distribution, and fine-tuning, with two commercial hooks that matter:
If you run a Model-as-a-Service business and your aggregate revenue exceeds $20M over any consecutive 12 months, you need a separate Moonshot agreement before commercial use of the Software or derivatives.
Products with >100M MAU or >$20M monthly revenue must display “Kimi K3.1-Pro” prominently in the UI.
Internal use and access through Moonshot’s own products / certified partners are carved out. Read the license yourself before you put this behind a paid API.
Community reaction (HN + prior LocalLLaMA)
The Hugging Face drop hit Hacker News as “Kimi K3.1 Pro model weights are out” ↗ (~624 points at fetch time). Hardware reality dominated the thread:
“Too bad you probably need something like an 8x B300 node to run this.”
“Models this size aren’t targeting hobbyists running models in their bedrooms… This model is for researchers who already have racks of equipment.”
On the quality side, daily users pushed back on “benchmaxxed” takes:
“If you use it day-to-day you can definitely feel that K3 (and K3.1) are much stronger than DS4.1-Flash-1017. There’s something the benchmarks don’t capture.”
That matches earlier LocalLLaMA anxiety when the parent K3 weights landed — operators already doing A100 / H200 / B300 memory math and warning that Ampere without native FP4 would be “ugly” (r/LocalLLaMA deployment thread ↗).
Our take: who it’s for
For: labs and companies that want a frontier-class open-weight agentic model with a 1M context, can afford multi-node inference (or a third-party host), and whose MaaS revenue stays under the license tripwire — or who will negotiate with Moonshot.
Not for: solo builders hoping to “run it locally” on a single consumer GPU. Use smaller open models (Qwen / Llama-class) for that; see our open-source LLM comparison and Llama 4 guide.
Compared with closed frontier chat: K3.1 Pro’s card shows competitive coding and agent scores against Claude Fable 5.1 / GPT-6 Astra on Moonshot’s reported tables — but harness, judge, and fallback differences make scoreboard shopping dangerous. Day-to-day coding cost and refusal behavior matter more than a single Terminal-Bench cell.
Practical hosting paths (without inventing benchmarks)
Three realistic paths we see teams take with models this size:
Official / certified API. Use
kimi-k3.1-proon Moonshot’s platform when you want the card’s intended harness and do not want to operate racks.Third-party inference. Hosts appear quickly after open-weight drops (OpenRouter and others were already called out on HN). Compare cache-hit pricing, not just sticker $/M tokens.
Self-host for internal use. Plan for multi-node memory first; then KV-cache for the context length you actually need. Most product teams do not need the full 1M tokens on day one — 128k–256k with a clear compaction policy is enough for many agent loops.
Do not treat a single Terminal-Bench or SWE cell as a buy decision. Harness differences (Claude Code vs Kimi Code vs Codex) move scores. Run your own eval suite on the tasks you pay for.
For local-first builders, stick with smaller open models until your hardware or budget catches up — our open-source LLM comparison is the starting map.