Kimi K3.1 Pro model weights are out
- ID
- 43169
- Status
- summarized
- Published
- 04 Nov 2026, 11:30 PM
- Fetched
- 05 Nov 2026, 6:18 AM
- Provider
- Hacker News
- Category
- dev-community
- Original URL
- https://huggingface.co/moonshotai/Kimi-K3.1-Pro
- Source URL
- https://hnrss.org/best
Summary
- Score
- 8.0
- Created
- 05 Nov 2026, 6:19 AM
- Tags
- Audience
- developersai_ml_learnersai_agent_userssaas_founders
What happened
Moonshot AI published open weights for Kimi K3.1 Pro, a 2.8T-parameter Mixture-of-Experts model with 104B activated parameters, 896 experts (16 selected per token), a 1,048,576-token context window, MXFP4 weights / MXFP8 activations, and a 401M MoonViT-V2 vision encoder. Its self-reported eval table shows 65.7 on Terminal-Bench 4.0 (vs 57.9 for Claude Fable 5.1 and 58.2 for GPT-6 Astra) and 51.7 on SWE Marathon v1.1, but only 31.5 on Program Bench (vs 53.5, 50.0 and 65.0) and 38.1 on Terminal-Bench-Science (vs 68.1 for GPT-6 Astra). The Hacker News thread drew 286 points and 78 comments.
Why it matters
Open weights plus a 1M-token context means you can test long-context agentic and coding workloads without routing everything through a US API, which matters for cost control and data-handling choices. But 104B activated parameters is still heavy: plan on quantized hosted inference or a serious GPU budget rather than assuming a laptop deploy, and note the split results — strong on Terminal-Bench 4.0 and BenchCAD-with-tools (92.7), weak on Program Bench and Terminal-Bench-Science — so run your own eval on your repo before swapping it into a coding pipeline.
Discussion angle
Which benchmark actually predicts your workload? Pick one real task from your own repo and run it against K3.1 Pro before trusting the 65.7 Terminal-Bench number, and discuss whether a 1M-token window lets you drop chunking/RAG plumbing or just hides retrieval problems.