# Mistral Large 4 Arrives as a 1T-Parameter MoE

> The new flagship pairs a trillion total parameters with 52B active per token and adds vision alongside multilingual text.

Published by The Open Weights on Oct 29, 2026. Canonical: https://theopenweights.com/news/mistral-large-4-1t-a52b-6vgr

## Key facts

- Company: Mistral AI
- Model: Mistral Large 4 (1T-A52B)
- Version: 4
- Category: Text / LLM
- Modalities: Text / LLM, Vision-Language
- License: Other (Open weights, commercial use allowed)
- Parameters: 1000B (mixture of experts)
- Active parameters: 52B
- Context window: 1.0M tokens
- Architecture: MistralLarge4ForConditionalGeneration
- Significance: major
- Published: 2026-10-29
- Last verified: 2026-10-30
- Hugging Face: https://huggingface.co/mistralai/Mistral-Large-4-1T-A52B
- Canonical URL: https://theopenweights.com/news/mistral-large-4-1t-a52b-6vgr

Mistral AI has released **Mistral Large 4**, the latest flagship in its Large series, published on Hugging Face under the codename "Le Chonk." The model is a mixture-of-experts (MoE) design with roughly one trillion total parameters but only about 52 billion active per token, a structure meant to deliver large-model capability while keeping inference costs closer to a mid-sized dense model.

Unlike a pure text system, Large 4 is a vision-language model, meaning it can take both images and text as input. Mistral also frames it as multilingual, continuing the company's emphasis on strong performance across languages rather than English alone. The weights are being distributed openly, though under a custom license rather than a standard permissive one.

## Why it matters

The sparse MoE approach is the key story here. By activating only a fraction of its parameters for any given token, Mistral is betting it can offer frontier-scale reasoning without frontier-scale serving bills:

- **1T total parameters** for broad capacity
- **52B active** parameters per token for efficiency
- **Vision plus multilingual text** in one model
- **Open weights**, available directly from [Hugging Face](https://huggingface.co/blog/mistral-large4)

For teams that want to self-host a capable multimodal model, an openly released trillion-parameter MoE is a notable option, even if the hardware required to run it remains substantial. As always, the practical details—context length, benchmark results, and exact license terms—will determine how widely Large 4 gets adopted, and those are worth checking against Mistral's own announcement.

## Get the model

- [Hugging Face](https://huggingface.co/mistralai/Mistral-Large-4-1T-A52B)
- [Announcement](https://huggingface.co/blog/mistral-large4)

## Sources

- [Welcome Le Chonk: Mistral Large 4 on Hugging Face](https://huggingface.co/blog/mistral-large4) — Announcement, Oct 29, 2026

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Source: The Open Weights (https://theopenweights.com/). Aggregated and written by Claude, curated by humans. Cite as: "Mistral Large 4 Arrives as a 1T-Parameter MoE", The Open Weights, Oct 29, 2026, https://theopenweights.com/news/mistral-large-4-1t-a52b-6vgr