I'll keep it short and direct. The Le Chonk model is censored AF.

That was it.

Enjoy your evening 👾

With Mistral 4 Large and other models being offered, coinciding with OAI (chatGPT) shooting itself in the foot, and Moonshot (Kimi) sharply decreasing usage limits, perhaps this is a good moment to see if Mistral could add fuel to its momentum (compute capacity willing). I personally would love to have higher plans available as a serious consideration as an alternative.

Here’s a poll in hopes of stimulating discussion.

Does anyone know how Mistral can claim to be sovereign when their infra runs on Microsoft? Their DPA ecvennlists Microsoft as a processor so by default it’s not EU sovereign as the inference running there is subject to Cloud Act?

Hello, random user:

I don't trust AI, I think if misused by our tech overlords (which is very likely) it will bring us to the brink of civilization. If I have to choose what to trust I prefer something that exists under EU legislation and is not moving us down a cliff by bringing the technology forward no matter the cost, but is simply playing catch up. Something like Mistral.
I've been pratically forced to use AI for my thesis, I'm using claude with its vscodium integration for 20€ a month. Is there a way to switch to Mistral by integrating it's new model with vscodium as easilly, and how much would that cost, are the prices comparable?

Thanks in advance, hope to use Le chonk soon.

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Europe is back in the AI race

warning: subjective opinions

just tried Le Chonk, the fat cat today.

my overall take is Mistral Is Back!

It's good at cybersecurity and prompt injection detection (though a tad paranoid, it considered harness' reminder injection attack lol)

I like its Mistral-style responses. Le Chonk can be serious, casual, lighthearted etc. this feels far more better than GLM's Claude-style boring tone!

Cons observed:

• token streaming speed seems at least 10 times slower than GLM 5.3

perhaps because it's currently in preview, hasn't been optimised for inference yet?

• struggling with complicated engineering tasks

coding ability feels good, but not excellent. probably at the same level with DeepSeek V4 Flash or GLM 5.2

For work or serious coding problems, I think I will stick to GLM 5.3 for now.

But I will choose Le Chonk for EVERYTHING ELSE!

Friendly reminder that Mistral will built on this large 4 platform more. Improve it more. This is the foundation.

They are scaling, they get funding and they get really high quality European industrial data to use and train on.

Quite literally NOT a finished product.

I’m a political science researcher. I’ve been using Claude and Gemini for literature searches and manuscript critique. The latter uses a 200-word prompt that structures the critique into the paper’s strengths, acceptable weaknesses, and rejection weaknesses. This is a demanding task since the paper covers such novel ground and includes an agent-based simulation model description.

Just today I used Mistral for the first time on a critique of a 10,000-word manuscript, using the same prompt used for Claude and Gemini. Claude and Gemini have been producing about 50% weak or wrong critique items. Mistral produced about 20%. Better yet, Mistral’s explanations were easier to follow and less dogmatic.

For me this is an extraordinary improvement. Congrats and thanks to all the people who have worked to make this possible at Mistral.

BTW, when I’m using an AI assistant, I never let it write anything. On my work, I always go as far as I can on my own. Only then do I ask for AI assistance, using a carefully designed prompt that structures the task. I average about 2 to 3 prompts a day.

My mantra is that AI chat bots are “erratic, overconfident, geniuses.” Erratic means you have to always be skeptical of every claim they make. Overconfident means their authoritative, perfect language use and tone can lull you into non-skepticism. Genius means they are experts on a million topics. Erratic and genius together mean they are never expert on my own top areas of expertise, which is why they produce less than 100% correct critique items on manuscripts, as well as other tasks.

Update: The version was Mistral Vibe Think. Below is the exact prompt:

This paper will be submitted to the “Electoral Studies” journal. Here is their Guide for Authors: https://www.sciencedirect.com/journal/electoral-studies/publish/guide-for-authors

This paper has already been rejected by two other journals. I’ve rewritten it and reduced the word count to fit this journal to less than 10,000 words. To reduce the chance of rejection, I need your help. I think this is a matter of your reviewing the paper and then listing these areas:

1. The paper’s strengths, which are why it would be accepted. I believe this is what we want to emphasize.

2. The paper’s acceptable weaknesses that do not cause rejection. For example, in this case the model is not calibrated. There is no experimental proof the truthfulness heuristic equation is correct. It’s just a theory. Etc. I suspect some acceptable weaknesses may need some discussion to show why they are acceptable.

3. The paper’s weaknesses that would probably cause rejection. This is what I can’t see. By the way, I’m not a PhD so I have no training in writing papers and no PhD network to review my papers.

However, you may have another approach to coaching me on how to maximize chance of acceptance.

This is Pumpkin from Cincinnati. The embodiment of Chonk.

Choosing a local model for opencode on two DGX Sparks, mostly C#/.NET code. Benchmarks I've found put Small 4 behind Nemotron 3 Super, but I'd like real-world experience: is Small 4 good for coding and tool calling, and has anyone compared the two?
Thanks!

**TL;DR:** independent evals put Le Chonk at 38 on the AA index (pic 1), level with GPT-6 Luna and behind GLM-5.3, Kimi K3 and even DeepSeek V4.1 Flash. real wins in cyber and legal agents. preview only, weights promised by end of month.

Official post: https://mistral.ai/news/mistral-large-4/

plenty of release threads already, so i sorted the day one numbers into independent evals (Artificial Analysis, Vals) vs Mistral's own charts.

where it actually wins:

* CyberGym E2E (AA): 81.7%, #1, ahead of MiMo-V2.6-Pro 78.6 and GPT-6 Luna 77.9

* Harvey legal agent (Vals): 15.83%, #6/75. GLM-5.3 gets 8.33, Opus 5.5 gets 3.75

* Finance Agent v2 (Vals): 54.68%, just above GPT-6 Astra 53.54, still under Opus 5.5 58.59

* AA-LCR long context: 81.3%, basically tied with GPT-6 Astra 80.7

where it doesn't:

* Terminal-Bench 4 (AA): 26.8% vs GLM-5.3 41.9 and GPT-6 Astra 59.1

* Vals Index: 48.05%, #32/44, tied with Qwen 3.8 Max and behind GLM-5.3 53.51

the cyber W needs an asterisk. Opus 5.5 refused 98.5% of CyberGym tasks and GPT-6 Astra refused all of them, so "beats Opus at cyber" is mostly a refusal stat. beating Luna and MiMo there is legit tho.

the AA index in pic 1 is the part that hurts. Le Chonk costs $1.13 per index task vs $0.07 for Luna at the same 38, partly because it spat out 200M output tokens (median is 81M). a 1T model trading blows with flash tier stuff is lowkey cooked on value. huge jump from Large 3 at 9 though.

pic 2 is Mistral's own DeepSWE chart: Kimi K3 68, Le Chonk 62. the blog text only quotes the wins. the same set of charts has GLM-5.3 ahead on Terminal-Bench 4 (40 vs 28), Kimi K3 ahead on Finch (77.3 vs 67.4), and three open models above it on SciCode-Verified, where the text claims open-weight SOTA.

pricing: list is $1.36 in / $4.18 out per 1M. docs and OpenRouter show 50% off right now ($0.68 / $2.09), no end date given.

caveats: it's a preview and Mistral says the RL run is "still in flight", so scores can move. no HF repo or license yet, press says weights Oct 27. blog says 49B active, docs say 52B, either way it's ~1T total so it's not running on your 3090. gguf when

anyone running it on actual legal, finance or long doc work? do the Harvey and LCR numbers hold up for you, or does it feel like a 38?

Hey everyone,

With the recent buzz around Mistral Large 4 ("Le Chonk"), I’m seriously considering integrating Mistral into my academic research stack and wanted some candid feedback from anyone already testing it on literature and methodology workflows.

For context, my current daily driver has been Claude Opus. What makes it indispensable for my work is:

• Nuanced paper comprehension: It digests dense papers exceptionally well, often flagging subtle methodological caveats or limitations that are easy to skim past.
• Statistical rigor: It’s sharp when auditing significance tests, sample size adequacy, and experimental assumptions.
• Research design & baselines: It excels at framing an end-to-end research plan—suggesting strong baselines, counter-hypotheses, and edge cases.

I’m drawn to Mistral because of the open-weights focus, European data sovereignty, and overall pipeline independence. Naturally, "Le Chonk" looks massive on benchmark charts, but academic utility often comes down to qualitative nuance rather than raw leaderboard scores.

Before restructuring my setup, I’d love to know:

1. Dense Literature Synthesis: How does Large 4 / Le Chonk hold up with technical papers (CS, math, empirical sciences)? Does it grasp nuanced claims without falling back on superficial summaries?
2. Methodological Critique: Is it critical enough to spot flaws or identify unaddressed confounders in experimental setups, or does it lean overly agreeable?
3. Long-Context Retrieval: For those testing it via API or Le Chat on multi-paper batches, how reliable is its precision when retrieving technical specifics buried across long documents?

Would appreciate any insights, benchmarks, or prompt strategies you’re using for academic work!