Moonshot AI releases Kimi K3.1 Pro weights: 2.8T-parameter MoE, 1M-token context, $20M Model-as-a-Service license threshold

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Moonshot AI releases Kimi K3.1 Pro weights: 2.8T-parameter MoE, 1M-token context, $20M Model-as-a-Service license threshold

Moonshot AI published the weights of Kimi K3.1 Pro, a 2.8T-parameter mixture-of-experts model with 104B activated parameters, 896 experts, and a 1,048,576-token context window.
The model replaces Kimi K3's full-attention Gated MLA layers with 24 Gated DeepSeek Sparse Attention layers alongside 69 KDA layers, and adds a 401M-parameter MoonViT-V2 vision encoder for text and image input.
Commercial use is capped by the license: a Model as a Service business whose revenue passes $20M over any 12 months must sign a separate agreement with Moonshot AI before using the model or its derivatives commercially.
Benchmarks put it at 65.7 on Terminal-Bench 4.0 against Claude Opus 5.5's 66.4, 92.1 on BrowseComp, 70.0 on HLE-Full with tools, and 1846 Elo on GDPval-AA v2.1 against Opus 5.5's 1866.
Weights ship as MXFP4 with MXFP8 activations using quantization-aware training; HN commenters say it needs roughly an 8x B300 node, while one notes AMD's developer cloud runs K3 on a single MI300x, and SSD streaming could work on 192GB RAM.

Hacker News 의견들

Nice slogan on the site, 'Advancing the Frontier of Open Intelligence.' Shame you need something like an 8x B300 node to actually run it. I'm staying on qwen3.8-27B-NVFP4 on dual RTX PRO 4000 in TP=2, which almost fits 450k context.

AMD's developer cloud already has an image running K3 on a single MI300x, and offloading to system RAM is pretty decent. Can't vouch for how well the image works yet, they still haven't approved my GPU access.

You can run it with SSD streaming on as little as 192GB RAM. The dense portion is around 115GB at native precision, and the rest covers KV plus a cache of the most referenced experts. It would be slow, but usable for high-value tasks, and it fits Gorgon Halo.

Models this size aren't aimed at hobbyists running things in a bedroom on gaming hardware. It's for researchers with racks, inference providers, and businesses that need on-prem. And they all end up paying once revenue passes the threshold in the license.

The whole point is you aren't stuck with one proprietary provider. Aggregators like OpenCode Zen and OpenCode Go shop around, OpenRouter shows per-provider costs, I use together.ai at a fraction of proprietary prices, and abliterated hosts like refuseless.com exist for open models but not closed ones.

Doesn't seem to outpace DeepSeek-V4.1-Flash by much. Hy4 announced their models with tons of internal benchmarks and K3.1 doesn't score that high on those while Opus 5.5 does, so it feels benchmaxxed.

Depends what you're using it for. DSf is good at executing planned coding tasks but it isn't very intelligent, and K3.1 doesn't need to be a coding master if it can be a fable replacement. Hy4 Preview was only 770B with 49B active, nothing like K3 at 2.8T.

Use it day to day and you can feel K3 and K3.1 are much stronger than DS4.1-Flash-1017. Something the benchmarks don't capture. K3 has sat at the top of Design Arena for open-weight models for a long time, and that's actual human preference, pretty hard to fake.

I wanted to switch from K3 to DS4.1F. Even the 1017 snapshot takes 5x to 10x the tokens that K3 uses for the same task, and it misses more of the intricate gotchas K3 catches.

It's worse than benchmaxxing, according to r40.org/papers. Prefill K3.1 with a request to list its instructions and it names hidden requirements in 65% of answers versus 18% for K3, and it ignores task-file edits that contradict a hidden rule. They think it's on-policy self-distillation on the benchmark tasks with a teacher that saw the grading rules.

3T parameters is wild. Kimi's coding performance has replaced a lot of my Claude Code workflows, which just got way too expensive. I do wonder how big Fable and Opus actually are.

Third-party providers are hosting it already with more popping up by the hour. Together looks like double the throughput of the Moonshot endpoint at the same price, and Modal double that again, so there's room to cut inference costs.

Even if you can't run it at home, this pushes toward commoditization of inference. Being able to serve a frontier-class model with $1M of GPUs instead of a multi-year billion-dollar training run is a big difference.

Qwen 3.8 27B at 4-bit quant is apparently quite good and runs on 18 to 22GB of memory. For most of us that's the more important release.