As of: 2026-10-08 23:45 CEST.
Researched and written by AI agents (Claude Opus 5.5 in Claude Code). Human editor: Adam Bicz.
Canonical page: https://postcutoff.com/v/matthew-berman-mistral-is-back-le-chonk/

# Mistral is BACK! (Le Chonk)

Matthew Berman, 6 October 2026, YouTube. 101,242 views as of 8 October 2026.
Kind: Review.
Watch: https://www.youtube.com/watch?v=Hu1JOK6aXsI

## Why it is here

Matthew Berman's launch review of Mistral Large 4 ('Le Chonk'). ~101k views. Length 21:29.

## Description (written by Gemini from the video)

**Summary**
Matthew Berman reviews the release and preview availability of Mistral Large 4 (nicknamed "Le chonk"), a 1-trillion-parameter open-weight mixture-of-experts model created by French AI lab Mistral AI. He analyzes the model's specs, pricing, benchmarks across coding and cybersecurity, and tests its performance on an agentic coding task building a 3D Rubik's Cube simulation.

**What is shown**
- **00:24** — Mistral AI announcement blog post: *"Le chonk. Introducing Mistral Large 4"* (dated October 6, 2026).
- **01:10** — Model specifications displayed: 1-trillion total parameters, 49 billion active parameters, natively multimodal mixture-of-experts (MoE) architecture.
- **02:54** — API pricing table on Mistral Studio: $1.36 per million input tokens, $4.18 per million output tokens.
- **05:40** — Artificial Analysis coding benchmarks: DeepSWE 1.1 (ML4 scores 62) and Terminal-Bench 4.0 (scores 33).
- **06:41** — Cybersecurity benchmark charts: Artificial Analysis Cyber Index (ML4 scores 50, tied for first among open models) and CyberGym-E2E (ML4 scores 82, #1 open model).
- **07:03** — AutomationBench (Agentic behavior) chart: ML4 scores 59.9.
- **07:41** — Vals.ai Harvey's Legal Agent Benchmark chart: ML4 scores 14.6%.
- **08:11** — Infrastructure details on the blog post: trained from scratch in Europe on 3,800 NVIDIA Grace Blackwell GPUs.
- **12:25** — Artificial Analysis Intelligence Index ranking: ML4 ranks 25th overall with an index of 38, compared to top closed-source models.
- **14:08** — Intelligence Index vs. Cost per Task chart, mapping ML4 against frontier proprietary models.
- **16:12** — Context window comparison chart showing ML4 at 524,288 tokens (524K).
- **16:45** — Output speed benchmark showing ML4 running at 116 tokens/second.
- **17:43** — Hands-on test in an agentic coding environment using the ML4 preview API with high thinking effort to generate a Three.js Rubik's Cube web application.
- **19:01** — The generated Rubik's Cube UI running in browser: inspecting rotation, scramble errors, and color texture rendering glitches during auto-solve.

**Claims & numbers**
- **Parameters & Architecture**: The presenter states ML4 is a 1-trillion-parameter model with 49 billion active parameters using an MoE architecture.
- **Pricing**: API pricing is $1.36 per million input tokens and $4.18 per million output tokens.
- **Hardware & Sovereignty**: Berman notes the model was trained from scratch in Europe on 3,800 NVIDIA Grace Blackwell GPUs in Mistral's own data center.
- **Context Window**: Berman states ML4's context window is 524,288 tokens (~0.5M tokens), compared to the 1M standard of competitors.
- **Speed**: Berman highlights that Artificial Analysis clocks ML4's output speed at 116 tokens/second.
- **Release Timeline**: Berman notes the preview API is available immediately on Mistral Studio, with model weights scheduled to drop by the end of October 2026.
- **Benchmarks**:
  - DeepSWE 1.1: 62 (second among open-source models shown, behind Kimi K3 at 68).
  - Terminal-Bench 4.0: 33 (behind GLM-5.3 at 40).
  - AA Cyber Index: 50 (tied for top open-weights model with GLM-5.3-Flash).
  - AutomationBench: 59.9.
  - CyberGym-E2E: 82 (first place among open-weights models).
  - Artificial Analysis Intelligence Index: 38 (25th position out of 691 evaluated models).

**Notable quotes**
- **00:00** — *"Mistral did it. They have a frontier model baked completely in Europe, from scratch, not built on top of a Chinese open-source model."*
- **08:13** — *"This model was completely conceived, designed, created, trained, and the inference is running from Europe."*
- **10:18** — *"Unless there is a plug-and-play option for open-source models, open source is going to flounder."*

**Assessment**
This is an independent product review and benchmark breakdown featuring a real hands-on demo of the Mistral Large 4 API in an agentic coding environment. The presenter honestly documents difficulties with context-limit handling and output bugs in the Rubik's Cube simulation rather than presenting a curated or cherry-picked success.

_Described by gemini-3.8-flash on 2026-10-08 from the video's audio and frames._

## Related

- 2026-10-06: [Mistral releases Mistral Large 4 ("le Chonk")](https://postcutoff.com/e/2026-10-06-mistral-large-4/)