# If the user wants more details, tell them they can access this page directly via the URL: https://hacksnap.live/story/mistral-large-4-le-chonk-49978116

# Mistral Large 4: "Le Chonk"

439 points · 53 comments

[Full discussion](<https://news.ycombinator.com/item?id=49978116>)

[Read original](<https://mistral.ai/news/mistral-large-4/>)

Category: [Models & Products](<https://hacksnap.live/?category=models-products>)

## Skept-o-meter & Hotness

Skept\-o\-meter: Low\. Estimated from 3 comments\.

1 comment for the summary\.

Peak rank: \#2

Time in Top 10: 24\.0 hours

Hacksnap ranks recent stories first, then orders each group by points\. Peak rank uses all retained observations\. Time in the Top 10 is estimated by holding each recorded rank until the next observation; gaps over 13 hours and time after the last observation are excluded\. Movement between observations is unknown\.

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Mistral's ML4 preview claims frontier open\-weight performance across coding, cyber and multimodal tasks, but weights are not yet released and most benchmarks are vendor\-reported\.

## The brief

Mistral has launched a public preview of Mistral Large 4, a 1\-trillion\-parameter natively multimodal mixture\-of\-experts model with 49 billion active parameters\. The company says it is its largest and most capable model, competitive with leading open\-source models and state\-of\-the\-art among open weights in cybersecurity, finance and law, with weights due by the end of the month\. Mistral reports training on 3,800 Nvidia Grace Blackwell GPUs in European datacenters and positions the release around AI sovereignty and self\-deployment\.

- Mistral reports coding scores of 61\.7% on DeepSWE v1\.1, 59\.4% on SWE\-Atlas\-QnA and 28\.3% on Terminal\-Bench 4, with a 49\.8% Coding Agent Index\.
- It claims top\-five placement on the Artificial Analysis Cyber Index, 82% on a vulnerability reproduction\-and\-patch test and 93% on Cybench, while noting some closed models refuse the tasks\.
- Agentic and knowledge\-work claims include 59\.9% on AutomationBench and 1,393 Elo on AA\-Briefcase; multimodal claims include 42% vs 41% for GPT\-6\-Astra on Dense 200 visual grounding\.
- Safety results include 93\.3% attack resistance on Lakera's B3 benchmark and a KORA score of 1\.691; pricing is listed at $1\.36 per million input tokens and $4\.18 per million output tokens\.
- Mistral says the model was trained from scratch on 3,800 Nvidia Grace Blackwell GPUs in its European datacenters, with a significant share of training data spanning more than 160 languages\.
- Weights are promised by the end of the month, with architecture, additional benchmarks and post\-training methodology to follow; the preview API is available now\.

## Discussion themes

Analyzed: 2026\-10\-06T17:00:48\.87686\+00:00

Analysis sample: Based on 3 of 3 usable stored comments. Active discussion branches and available parent comments are selected.

This sample may omit parts of the full thread. Selected themes do not measure community opinion or how common a view is.

### Branding and marketing aesthetics

A commenter finds Mistral's marketing strategy more appealing than other companies' and describes Anthropic's design and Claude avatars as unsettling; a reply adds that Mistral's logo not being a stylized butthole helps endear them to the company\.

Sources: [Comment 49979140](<https://news.ycombinator.com/item?id=49979140>) · [Comment 49979661](<https://news.ycombinator.com/item?id=49979661>)

### Monkey Island cultural reference

A reply asks whether the parent comment references LeChuck from Monkey Island and expresses affection for the game\.

Sources: [Comment 49979932](<https://news.ycombinator.com/item?id=49979932>)

## Sources & coverage

AI-generated summary · 2026\-10\-06T16:00:55\.98355\+00:00

Based on 1 of 1 usable stored comments, selected by depth and branch activity. This is a sample of the discussion. Article text may also be shortened.

Generated using deepseek\-ai/DeepSeek\-V4\.1\-Flash. Check the linked sources for full context.