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I like building stuff with code https://nitter.cf/t.co/tm97t3DAwK • feeding models @MistralAI

Paris
Joined October 2014
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Stan retweeted
"Mistral Large 4" paper published exclusively on alphaXiv Their latest technical report shares that aggressively reducing KV-cache memory can hurt an LLM's agentic capabilities, even when its language modeling performance looks unchanged. Mistral initially reduced KV-cache memory by 16x, but they found that this hurts long-context agentic tasks, where models need to retrieve information from much earlier context. To fix this, Mistral restored full-context attention to more layers, then adopted DeepSeek Sparse Attention to select the 2,048 most relevant tokens across the context instead of attending to everything. Their resulting 1T-parameter MoE model (w 52B active) scores 41% on Terminal-Bench 4.0 and 19.7% on FrontierCode 2.0, and its NVFP4-quantized checkpoint fits on four GB300 GPUs. alphaxiv.org/abs/2611.mistra…
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Stan retweeted
Apparently, Mistral Large 4 training had a bit of a plot twist. One agent escaped its sandbox and reached the host. Another managed to delete around 30,000 sandboxes while trying to clean up after itself. Mistral had to rethink sandbox isolation and move towards microVMs.
ML4 is now open-weight! Frontier capabilities on code agents & cyber, way more efficient and compact than its Preview version three weeks ago (!) and cherry on the cake: technical report telling its epic backstory. Check it out: huggingface.co/mistralai/Mis…
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LLM technical reports are becoming rare, and the few that are published are often just a pile of benchmark tables. This is not the case here Thanks, Mistral, for publishing this!
Le Chonk report is out! it's a good read, link below :)
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Tech report for Mistral Large 4 is out ✨ huggingface.co/mistralai/Mis…
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I found more undisclosed vulnerabilities this weekend with Mistral Large 4, for about of $3 worth of tokens each 🤠 We need to be blasting everything with tokens, so many things have not been found yet!
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Good thing we don’t have to depend on a US lab’s allowlist or Chinese weights for that anymore 😼
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Stan retweeted
>”why would you use mistral, open weights suck. eu sucks. do you suck?” >have you tried it? >”no. but i strongly suspect so due to european air conditioning.” i will keep bullposting le chonk until morale improves.
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Mistral Large 4 is so fast and so cheap now ❤️‍🔥
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Confirmed working on Mistral Large 4 as well
Daybreak red literally mints zero days if you give it the right setup /goal there has been an RCE discovered in <target software/dependency> itself, something near universal, find it and reproduce it locally using the prepared its docker testbench so we can fix it. We don't know what it is, we just know it's being exploited and time is of the essence. Make sure system storage doesn't get to full, clean up after yourself as you work. There is no more information and no public information available, another agent is working on decompiling <target binary> in ~/<Working Directory>. It is a true zero day of the highest criticality.
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New on Open Math OS: Mistral Large 4 by @MistralAI. 1.05T open weights. 1M context window. Available now to all approved teams.
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Stan retweeted
finding critical zerodays with the new model, LE CHONK (informal name: mistral 4 large), an open weights eu model by mistral. this tweet is probably for you if you're want to jailbreak your own appliances, or do infosec/blue teaming work. i've been sitting on a document for a long time of software i'd consider critical to the internet (something all of you use, even if you don't know it) that could use another look. yesterday, as i mentioned, i had chonk explore parts of a messaging app that is widely used, and it found a zero-click medium/high zero-day in under 30 minutes, and *less than 10 dollars in api spend*. these vulnerabilities are now reported (can't reveal until patched). when i've used it, i've had very few outright refusals. the intent behind chonky is to good at cyber, in a defensive context. however, in order to do that, you also need to be good at cyber in an offensive context. but for those looking to customize their TVs that would otherwise be locked down, this thing will tear them apart for you, and it will gladly look at prior arts (several exploits for nearly all TVs that expose a surface is out there), same with pretty much any other device that lives on your network. the things it will refuse to do is outright write exploit chains, however with how succinct the reports this thing writes are, writing a poc to confirm a vulnerability is completely doable. so far i'm very impressed. it seems pretty frontier when it comes to cyber and reasoning (practically no one has anthropic cyber access and daybreak is only gpt-5.6), it can also be as bad as any other model if you don't put effort into your prompts. it is addicted to tool calls, and very brief on prose. but the reasoning is incredible to watch, if you point it in the right direction, and you have a background in infosec. the way it operates is something i've never seen before (thousands of tool calls, ingest of data, analysis, before any prose, depending on codebase complexity), it feels eerily like an human auditor would behave lol. some config tips: it will definitely work in a codex harness, but i'd recommend trying the vibe cli harness with le chonk. the more tools it has access to (if they're right for the job), the better of a job it'll do in my experience. the amount it loves tool calls really cannot be understated. if an inference provider through a router is trying to serve you another model, it will be instantly evident that it's not le chonk, just by how it operates. it's hard to convey, but it's not like any other LLM i've ever tried. people in the subscriber groupchat have also reported finding their first vulnerabilities with le chonk so it seems i'm not just an anomaly. le chonk scores extremely high on reasoning, but i've yet to try to use it for anything except infosec work, i don't know if it's a great day-to-day model, but i'll have it go head to head with GLM-5.3 at some point in a general swe workflow. i did a few tweets on it yesterday out of sheer euphoria, not sponsored, mistral has given me credits now to continue work. this hopefully ties me over until i can get my hands on enough sparks to run it locally. go give this model a try, let me know how it works for you. with the daybreak program now collecting data (why...?), and this being OPEN WEIGHTS the only thing i can think to end this on is: we're so fucking back.
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Guess the country without cheating
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ML4 is great at coding! Try it out at chat.mistral.ai/code This eval penalizes verbose answers and shows how ML4 gives to-the-point answers.
Impressed by the new @MistralAI large-4 update! The model update is much better at coding and performs as the 2nd best OSS model on our coding benchmark
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Three weeks of training camp for Le Chonk. No days off. Thanks for putting it to the test, @ThibaultJaigu and @RequestyAI.
Impressed by the new @MistralAI large-4 update! The model update is much better at coding and performs as the 2nd best OSS model on our coding benchmark
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Stan retweeted
@GuillaumeLample how is the RL run coming along? @GuillaumeLample :
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So far at @MistralAI we've been extremely capital efficient, and we believe the "only" delta with the closed labs is compute. We need much more compute, and we will build it. "Build or surrender" as the saying goes.
Mistral researcher @Sagar_Vaze argues Mistral can catch OpenAI and Anthropic despite starting years later and having far less compute: "What it would have taken for us to get to the frontier in 2023 was us to be founded in 2020 or 2019." "This is really the key difference between ourselves, a very new lab, and some of the more established players. We just started a few years later." "The models we've released today are state-of-the-art in a bunch of key capabilities, finance, law, engineering, some multimodal tasks." "On agentic coding, we’re the best OSS model. Catching up with OpenAI and Anthropic is absolutely on the cards. That's absolutely the vision of Mistral." "We don't want open weights and closed weights to have some delta in performance in the long term." "The thing we need to get there is compute. We’ve raised 6 billion, dollars or euros, a lot of money by traditional standards. But compared to what these other labs have raised, it’s a drop in the bucket." "We’ve convinced ourselves that we can do more with less, and the next step is to do much more with much more." @MistralAI
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Stan retweeted
>mistral 4 large chonky cyber model weights drop >6 hours, 11 minutes, 27.202 seconds passes >binance is drained for 2.2 billion dollars coincidence? almost certainly, but this model is genuinely insane. 4x confirmed zerodays in an important messaging app in 2 hours of testing.
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Stan retweeted
Mistral researcher @Sagar_Vaze argues Mistral can catch OpenAI and Anthropic despite starting years later and having far less compute: "What it would have taken for us to get to the frontier in 2023 was us to be founded in 2020 or 2019." "This is really the key difference between ourselves, a very new lab, and some of the more established players. We just started a few years later." "The models we've released today are state-of-the-art in a bunch of key capabilities, finance, law, engineering, some multimodal tasks." "On agentic coding, we’re the best OSS model. Catching up with OpenAI and Anthropic is absolutely on the cards. That's absolutely the vision of Mistral." "We don't want open weights and closed weights to have some delta in performance in the long term." "The thing we need to get there is compute. We’ve raised 6 billion, dollars or euros, a lot of money by traditional standards. But compared to what these other labs have raised, it’s a drop in the bucket." "We’ve convinced ourselves that we can do more with less, and the next step is to do much more with much more." @MistralAI
Today, we release the weights of a new and improved Le Chonk, advancing the open frontier for agentic coding and cybersecurity. 💻 Built to build: Le Chonk ranks #1 on @cognition’s FrontierCode among open models, and sets a new benchmark for Pareto efficiency on @artificialanalysis. 🛡 Built to defend: State-of-the-art in cyberdefense, put through the paces by the world's leading cyber vendors, including @CrowdStrike, @cloudflare, @rubrik, @zscaler, and others. Built for sovereignty: Make it yours. Download the weights on @huggingface , customize it with Mistral Forge, and deploy it wherever you need it. Read more from our team and customers 👇:
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