---
id: "lib-2026-10-06-introducing-mistral-large-4-public-preview"
type: "library"
title: "Introducing Mistral Large 4 (public preview)"
lang: "EN"
org: "Mistral AI"
source_type: "Release"
date: "2026-10-06"
week: "2026-W41"
url: "https://nosov.ai/library/lib-2026-10-06-introducing-mistral-large-4-public-preview/"
original_url: "https://mistral.ai/news/mistral-large-4/"
tier: "C"
tier_name: "Weak signal"
tier_reason: "Vendor launch post from Mistral; benchmark results are self-reported or selected by the vendor and weights were not yet released."
summary_basis: "full-text"
original_minutes: "11"
tags: ["ai technology", "model release", "open weights", "europe"]
---

# Introducing Mistral Large 4 (public preview)

Tier C (Weak signal): Vendor launch post from Mistral; benchmark results are self-reported or selected by the vendor and weights were not yet released.

Summary based on the full text.

## In short

Mistral released a public preview of Mistral Large 4 on 6 October 2026, a 1-trillion-parameter multimodal mixture-of-experts model with open weights promised by the end of the month. Mistral positions it as the strongest open-weight model built outside China, trained and served in its own European datacenters. The post leans on cybersecurity, where it argues closed models refuse legitimate defensive tasks.

## Key numbers

- 1 trillion / 52 billion: total and active parameters of Mistral Large 4
- 3,800: NVIDIA Grace Blackwell GPUs in Mistral's European datacenters used for training
- 61.7%: DeepSWE v1.1 score (59.4% SWE-Atlas-QnA, 28.3% Terminal-Bench 4)
- $1.36 / $4.18: price per million input / output tokens

## What it reports

- 1 trillion parameters, 52 billion active; trained from scratch on 3,800 NVIDIA Grace Blackwell GPUs; training data spans more than 160 languages.
- Cyber: 82% on an Artificial Analysis Cyber Index test that asks a model to reproduce and patch a real vulnerability; 93% of the 40 Cybench challenges. Mistral says Claude Opus 5.5 and GPT-6 Astra score near zero on the reproduction test because they refuse.
- Coding: Coding Agent Index 49.8%; in a blind Surge AI evaluation it ranked second of five (3.74 on a 1-5 scale), behind Claude Opus 5 (4.22).
- Agentic work: 59.9% on AutomationBench (657 business workflows); 1,393 Elo on AA-Briefcase.
- Vision: 42% vs 41% for GPT-6-Astra on Dense 200.
- Safety: resists 93.3% of attacks on Lakera's B3 benchmark; KORA score 1.691 out of 2.
- RL at about 3,000 GPUs produces roughly 33 billion tokens a day, about 16 billion trainable.
- First milestone of a €3 billion Series D.

## How they got it

Mix of third-party benchmarks (Artificial Analysis, vals.ai, Surge AI, Lakera) and internal evaluations chosen by Mistral. Architecture, full benchmarks and post-training details are deferred to the weights release.

## Limits

- Benchmark selection and framing are the vendor's; several comparisons are against older or chosen competitors.
- The refusal-based comparison on cyber reflects safety policy, not capability.
- Preview model still in training; numbers may change.

## Why it matters for companies

European firms with data-residency or sovereignty requirements get a large open-weight option they can self-host, priced at $1.36 per million input tokens through the API. For security teams, the pitch is a model that will not refuse defensive work and can run on-premise. Independent results after the weights ship are the evidence to wait for.

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