---
id: "lib-2026-10-29-we-re-releasing-the-weights-of-mistral-large-4-le"
type: "library"
title: "We're releasing the weights of Mistral Large 4 (Le Chonk)"
lang: "EN"
org: "Mistral AI"
source_type: "Other"
date: "2026-10-29"
week: "2026-W44"
url: "https://nosov.ai/library/lib-2026-10-29-we-re-releasing-the-weights-of-mistral-large-4-le/"
original_url: "https://mistral.ai/news/mistral-large-4-oss/"
tier: "B"
tier_name: "Useful, limited"
tier_reason: "Mistral AI's own launch post for the open-weight release of Mistral Large 4; the licence terms and prices are primary facts, but every performance figure is a vendor-selected benchmark and most of the page is partner endorsements."
summary_basis: "full-text"
original_minutes: "10"
tags: ["ai technology", "open weight models", "eu sovereignty", "model licensing"]
---

# We're releasing the weights of Mistral Large 4 (Le Chonk)

Tier B (Useful, limited): Mistral AI's own launch post for the open-weight release of Mistral Large 4; the licence terms and prices are primary facts, but every performance figure is a vendor-selected benchmark and most of the page is partner endorsements.

Summary based on the full text.

## In short

Mistral AI released the weights of Mistral Large 4, nicknamed Le Chonk, a 1-trillion-parameter multimodal model first shown in public preview earlier the same month. The company says three more weeks of reinforcement learning made it the leading open-weight model for coding and defensive security, with fewer tokens per task. The licence is a modified MIT licence that is free for commercial use except for companies offering the model as a hosted service. The post leans hard on European sovereignty, backed by quotes from security vendors, banks and cloud partners.

## Key numbers

- 1-trillion-parameter: size of Mistral Large 4 (Le Chonk), a natively multimodal model now released as open weights
- $0.70 / $2.10: API price per million input and output tokens ($0.07 per million cached input)
- 48%: score on the Artificial Analysis Coding Agent Index, tied with GLM-5.3 at 47% fewer tokens per task (vendor claim)
- 41%: Terminal-Bench 4.0 score, up 13 points from the public preview
- 96.6%: average refusal rate on malicious cyber requests across three jailbreak benchmarks (vendor measurement)

## What it reports

- Coding (vendor figures): 19.7% on Cognition's private FrontierCode 2.0, described as the lead; 48% on the Artificial Analysis Coding Agent Index, tied with GLM-5.3 at 47% fewer tokens per task; 41% on Terminal-Bench 4.0, up 13 points from preview with 30% fewer output tokens. In Surge Code's blind pairwise tests it was preferred over GLM-5.3, Kimi K3 and DeepSeek V4.1 Flash, behind only Claude Opus 5.5.
- Security: 51% on the Artificial Analysis Cyber Index; 96.6% average refusal on malicious cyber prompts from JailbreakBench, StrongREJECT and AgentHarm; 97.1% attack resistance on the B3 Agent Security Benchmark, up from 93.3% in preview.
- Mistral says live-traffic monitoring showed refusals of black-box attacks on live targets but "a potential for uplift to conduct real attacks", and that it is changing its guardrails. A specialised offensive-security variant goes only to vetted partners under contract.
- Other scores: Finch (finance spreadsheet work) 73.3%, second among open models to Kimi K3; BrowseComp 84.5%, from 75% at the starting checkpoint.
- Three reasoning modes trade cost for accuracy. Weights on Hugging Face for vLLM or SGLang; also via Mistral's API, OpenRouter, NVIDIA endpoints and Microsoft Foundry. The model card cites fluency in 160+ languages. A technical report is promised.
- Licence: Mistral Ecosystem Open-Weight licence, free to use, modify, fine-tune and commercialise; Model-as-a-Service providers need a separate commercial licence.

## How they got it

Vendor-run or vendor-selected benchmarks, partly on third-party indices, with charts. No technical report yet; architecture details such as active parameters and context length are not stated in the post.

## Limits

- All performance claims come from the vendor, with no independent replication cited.
- Comparisons are mostly to other open-weight models; frontier closed models appear only in the human-preference result.
- The post does not give context length, active parameters or hardware needs.
- Most of the text is partner testimonials.

## Why it matters for companies

A trillion-parameter model with open weights and a permissive licence gives European and regulated firms a self-hosting option near the front of open models, with published API prices. The licence carve-out matters for anyone building a hosted AI product on top of it. The security claims cut both ways: Mistral itself reports possible uplift for real attacks, which is a reason to apply your own controls rather than rely on the model's refusals. Independent benchmarks and the promised technical report are needed before relying on the coding claims.

Original: https://mistral.ai/news/mistral-large-4-oss/