Mistral Large 4 Is Open Weight. Read the License and Do the Memory Math Before You Self-Host

Mistral released the open weights of Mistral Large 4, a roughly 1-trillion-parameter natively multimodal Mixture-of-Experts model with a 1M-token context, on Hugging Face on October 29, 2026. The release ships under the modified-MIT Mistral Ecosystem Open-Weight License, which requires a separate commercial license for anyone operating a Model-as-a-Service business, and self-hosting demands roughly 1 TB of weights at FP8 or about 0.5 TB at NVFP4 because all 128 experts must stay resident even though only 52B parameters are active per token. Mistral reports 41% on Terminal-Bench 4.0 and 48% on the Artificial Analysis Coding Agent Index, though those figures remain vendor claims pending independent evaluation.

On October 29, 2026, Mistral released the weights of Mistral Large 4 nicknamed "Le Chonk" on Hugging Face. It's a roughly 1 trillion parameter, natively multimodal Mixture-of-Experts model with a 1M-token context, and Mistral calls it the best open-weight model for coding. "Open weight" headlines make self-hosting sound like a download away. Two details decide whether it actually is for your team: what the license lets you do , and how much hardware "52B active" really needs . This post walks through both, plus how I'd decide between the API and running it yourself. Benchmark figures below are Mistral's own reported numbers unless noted. Treat them as claims until independent evals land. mistralai/Mistral-Large-4-1T-A52B on Hugging Face, in two official variants: Mistral's reported results include 19.7% on Cognition's private FrontierCode 2.0, 41% on Terminal-Bench 4.0 up 13 points from the preview while using 30% fewer output tokens , 48% on the Artificial Analysis Coding Agent Index roughly tied with GLM-5.3 while using 47% fewer tokens per task , 84.5% on BrowseComp, and 97.1% attack resistance on the B3 Agent Security benchmark. The license is the Mistral Ecosystem Open-Weight License , a modified MIT license. It grants broad rights to use, copy, modify, fine-tune, distribute and sell the model and derivatives. Then section 2 adds the condition that matters: If the Licensee or any of its affiliates operates a Model as a Service business, the Licensee must enter into a separate commercial license with Mistral before using the Model or its derivative works for any commercial purpose. "Model as a Service" means giving a third party access to the model for inference or post-training an API, a hosted endpoint in a way that lets them exercise meaningful control over the inputs, parameters, or training data . The license carves out: In practice, a quick way to read it: | What you're building | Likely bucket | |---|---| | Internal coding assistant or RAG over company docs | Internal use, no extra license | | SaaS feature: "summarize this ticket" button | Embedded in a specific feature | | Public API where customers send their own prompts and params | Model as a Service, needs a commercial license | | Fine-tuning service for customers' data | Model as a Service post-training | Note the condition is about whether you or an affiliate operate a MaaS business, and there's no revenue threshold. If your company runs any hosted-model offering, talk to legal before you ship. I'm not a lawyer; read the full license text linked below. "52B active" sounds like a mid-size model. It isn't, from a memory point of view. In an MoE, the router picks 4 of 128 experts per token . Different tokens pick different experts, so every expert has to be loaded and ready. That means: Rough weight-only arithmetic my estimate, not Mistral's figure : FP8 : ~1 byte per param x ~1T params = ~1 TB of weights NVFP4 : ~0.5 byte per param x ~1T params = ~0.5 TB of weights + scale overhead On top of that you need KV cache, and a 1M-token context makes KV cache a serious line item if you actually use long prompts. This is a multi-GPU, multi-node deployment, which is why the Hugging Face example sets both tensor parallel tp size and expert parallel ep size sizes. GGUF quants make local experiments possible, but heavy quantization can cost quality, so re-run your evals on whatever quant you deploy. Open weights give you options, not a free lunch. Read the license for your exact product shape, size hardware by total parameters, and prove the model wins on your own tasks through the API before you buy a rack of GPUs for it. Would you self-host a 1T MoE model, or is the API good enough for your use case?