Moonshot releases open-weight Kimi K3.1 Pro multimodal agentic model on Hugging Face
Moonshot describes Kimi K3.1 Pro as an open-weight, natively multimodal agentic model with 2.8T total parameters, 104B activated, and a 1M-token context window.
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Moonshot describes Kimi K3.1 Pro as an open-weight, natively multimodal agentic model with 2.8T total parameters, 104B activated, and a 1M-token context window. It is post-trained from Kimi K3 using large-scale joint reinforcement learning. The model card lists architecture details (MoE with 896 experts, 16 selected per token, Gated DSA attention) and a benchmark table comparing it against several other models across coding, agentic, and multimodal tasks. Reported scores are self-published.
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Kimi K3.1 Pro is described as open-weight, with 2.8T total parameters and 104B activated parameters.
Source: Hugging Face ↗Stored source-based brief · key point 1 · AINewsIQ paraphraseThe model uses a Mixture-of-Experts design with 896 experts, selecting 16 per token plus 2 shared experts.
Source: Hugging Face ↗Stored source-based brief · key point 2 · AINewsIQ paraphraseIt has a 1,048,576-token context length and accepts text and image inputs.
Source: Hugging Face ↗Stored source-based brief · key point 3 · AINewsIQ paraphraseThe model card reports benchmark results against Claude, GPT-6 Astra, DeepSeek-V4.1-Flash-1017, and GLM-5.3 on coding and agentic tasks.
Source: Hugging Face ↗Stored source-based brief · key point 4 · AINewsIQ paraphraseLimits from the stored brief
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Moonshot's official X account posted that Kimi K3.1 Pro weights are on Hugging Face. The model card describes a 2.8T MoE (104B active) with 1M context, image-text inputs, and MXFP4 weights. HN front-page discussion topped 300 points.
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- x post ↗ · 5,685 likes, 595 reposts, 271 replies, 324 quotes · observed 11/4/2026, 11:15:00 PM
- hn post ↗ · 307 points, 84 comments · observed 11/4/2026, 11:15:00 PM
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Why this may matter
Open-weight release of a very large multimodal agentic model gives researchers and developers access to weights and architecture details. Benchmark comparisons are vendor-reported and mixed across tasks.
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