# Mistral Large 4 Is Open Weight. Read the License and Do the Memory Math Before You Self-Host > Source: > Published: 2026-11-03 04:07:13+00:00 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?