Mistral Large 4

Surprisingly it only supports reasoning "none" or reasoning "high".That setting didn't seem to make any real difference - it added a tiny bit of thinking trace and high actually produced less output tokens than none.The high bicycle frame is better then the none one though.Pelicans: https://tools.simonwillison.net/markdown-svg-renderer?url=ht...(Definitely the best I've seen from any Mistral model: https://simonwillison.net/tags/pelican-riding-a-bicycle+mist... ) reply: This is such a pristine pelican. Let me say it here first folks, AGI is here.

I have a question, and perhaps some of the AI/ML infrastructure experts here could answer: Mistral says, "ML4 was trained from scratch on 3,800 NVIDIA Grace Blackwell GPUs in Mistral’s own datacenters in Europe."If a 1T params model trained on ~4k NVIDIA GB GPUs could almost match the performance of Kimi's K3 (which is on par with top closed source models of OpenAI/Anthropic) while beating/exceeding other leading SOTA models from top Chinese labs, what are we (in the US) even building these super massive data centers for? Just to churn through more backpropagation reps more quickly?SpaceXAI's Colossus supercluster in Memphis and Colossus 2 in Memphis/Mississippi (Southaven) are supposed to run into hundreds of thousands to a million GPUs. MSFT's Fairwater GPUs are supposed to have hundreds of thousands as well. So, 3800 GB GPUs are an absolute drop in the bucket. I don't understand the strategy of hyperscalers here, especially with edge inference hardware only getting better from here on (Apple, and all).Distillation explains some of the advances, but doesn't that mean hyperscalers have a ton of deadweight wrt GPUs sitting on their balance sheets?... reply:...

Impressive vision benchmarking. If the vision model is truly as good as astra, that would make it best in the world.Also strong on cyber benchmarks (better than all chinese models), so this is a good defender model.Lots of people shitting of Mistral for no reason imo. These are pretty good numbers across the board. Definitely good enough to use as a daily driver over other llms, if you have moral qualms with the others. For certain use cases, like cyber security, this may be the go to model.I like to make fun of europe, but there's lots for mistral to be proud about in this release imo. reply: I'm still embedded in the OpenAI ecosystem, but man, when I try out Mistral, it is snappy! Like basically instant responses that seem faster and better in quality than ChatGPT on instant mode. I'm impressed.

Even if it's not the best model, it can be really important step in UE sovereignty. Trained in EU, inference in EU. I guess it will matter for some companies. Hope Mistral won't disappear for the next half year. reply: For sure, as an EU based company we will only use US suppliers for coding but never for fuctions in our own products. Mistral knows this.

A strong competitor in cybersecurity as an alternative to GLM-5.3 (Mistral reports 82% on CyberGym-E2E). Visual grounding is also impressive (42% on Dense 200 vs. 41% for GPT-6 Astra).Otherwise, behind on the broader Pareto frontier, but not by much (Vals Index: 48.05% vs. GLM-5.3’s 53.51%; $13.78 vs. $7.25 per test). Many companies will prefer it over Chinease models. reply: That was about my conclusion as well: Slightly less than GLM 5.3 performance but made in Europe. So, maybe it answers Tiananmen Square questions correctly, and in French. All in all, a reasonable model, but not frontier.

Just ran this through our data analytics benchmark (I work at Plotly).It's 10x cheaper than Mistral Medium 3.5 from April and goes from 58% to 74% correct. Definitely a generational shift.It's not on the Pareto curve yet, but it's good enough for data analytics, and at this rate I suspect it'll be excellent in another few months.Full write up: https://plotly.com/blog/mistral-large-4-plotly-data-analytic... reply: If I'm reading that plot correctly, qwen3.8-27b beats in accuracy and price?

Its quite interesting to see that at least the early days of AI so far have not been a winner-take-all runaway acceleration game where catchup is impossible.I certainly wouldnt have predicted that 10 years ago.Very glad to see Mistral still in the game even after some big stumbles with Large 3. I deeply hope that this model is 'good enough' that it becomes the European go-to, giving them the resources to keep the pace up.I'm excited to try this out today. reply: I think a big part of that is the Chinese publishing the solution for everywhere hurdle in the road they've encountered in the form of a paper.Deepseek essentially releases instruction manuals in paper form.

Man, a lot of this discussion sounds like people cheering for the last kid crossing the finish line.Surely we want competition and Europe involved in that, but at this point I have grown used to either American labs smashing the frontier remarkably fast, or Chinese labs getting way, way closer than you would expect them to.Mistral’s progress, regrettably, feels much slower. This model doesn’t knock anybody’s socks off. The model is (and I hate to be this harsh) mediocre, and this mediocrity has also arrived months late.This is a pretty grim prognosis for European AI. reply: > Man, a lot of this discussion sounds like people cheering for the last kid crossing the finish line.Sometimes it's ok to cheer for the last kid crossing the finish line because they're actually running a totally different race, and winning might look completely different.When I look at what Mistral does vs other organizations I'm impressed:https://isaiprofitable.com/They aren't profitable yet, but they're a lot closer than most and they're doing a hell of a lot with very little.Pointless racing story:I was in high school track with a really tough guy who was just not a runner....

It's a bit below DeepSeek 4.1 Flash at about twice the size. For a model that was supposed to come out a few months ago this is pretty good. Mistral catching up to the chinese open-weight models is great news. Excited to see how they will build on that!

The blog post https://mistral.ai/news/mistral-large-4/

Very nice progress. Also I respect putting Kimi on those charts. Regardless of if they are beating the Pareto frontier (not now), model diversity is a good thing for humanity — I’m hopeful for the team to keep increasing their gains.

Open weight, European, competes with GLM-5.3 on cybersecurity. What's not to like?

Europe needs a lot of these. Quickly. Way to go, Mistral! Keep 'em coming.

This is awesome, one of the coolest Pokemon ever too for those that don't follow that universe :)https://bulbapedia.bulbagarden.net/wiki/Lechonk_(Pok%C3%A9mo...

The main thing I always get away from the comparison tables of these "big" models, is how well Deepseek v4.1 Flash performs. While still being the cheapest model by a long shot.

-5 on omniscience? https://artificialanalysis.ai/evaluations/omniscienceThat's not particularly great.That said I love that they don't seem to restrict cyber capabilities to any degree and even lean into it.If the best model for cyber attacks is open for everyone to use it just makes us all safer I think. Of course you then also HAVE to use it or otherwise you're vulnerable, which is a great distribution play.

I'm at a loss as to what to do now. I've been wanting to support Mistral for so long. I struggled on with Mistral Medium 3.5 for longer than I should have (although I also think it taught me some valuable process lessons).Recently I switched to the Mistral hosted GLM-5.3, this worked very well and powered through a tonne of work. Unfortunately, I also completely maxed out two subscriptions within the space of six days this month. One can't stack subscriptions with Mistral, so I'd have to register a third account for another subscription, which will be annoying with changing API keys all the time. Sure I can switch to pay-as-you-go API, but that adds up really fast. The Mistral dashboard shows that a Vibe CLI monthly subscription for €18.44 actually provides €255 worth of API use (apparently, and I tried to check this with Support but it seems like they were intentionally vague).After maxing out my Mistral subs this morning, I dropped $10 on Xiaomi to try MiMo-2.6. So far so good, seem to have done a lot of work for the $2.85 I've spent, and Xiaomi prices are still much better than the Mistral introductory offer for Le Chonk.Not sure where to jump.Edit:...

I created a brand new programming language to test LLM models on[2]. It looks like Mistral Large 4 fares about as well as Kimi K3 and Claude Sonnet 5.5[1]. Though it is much slower than Sonnet 5.5 and more expensive than both of those.1 - https://bench.killswitch-lang.org2 - https://killswitch-lang.org

Seems hard to get customers at that price range when you're competing with open source models that are 1/2 - 1/3 the price but with similar capabilities.People usually buy the cheapest, like Deepseek or GLM or they spend on Anthropic/OpenAI subs.. Are these models in the middle getting any users?On a side note, I wonder if this was the popular free Space Bunny model that left openrouter yesterday.

Excited to hear this!I barely use anything outside of cheap Chinese models on OpenRouter anymore. They are simply (more than) good enough for most of the things I do.This model looks reasonably cheap. Though not deepseek levels.Going to test it with Hermes, wondering where it will land in term of capability.Bon chance, Mistral!

I've been dreaming of this for a simple reason: the french prose combined with GLM 5.3 reasoning capabilities.GLM 5.3 is incredible because for the first time with an open-source model, it feels.. enough. I don't need much anymore, this model is great in everything. Except a thing : speaking french.If the benchmarks are true, I'd be glad to switch entirely to Mistral.

Refreshing to see this.The pricing ($.68 in/$.07 cached/$2.09 out) makes it much cheaper than Kimi K3, GLM 5.3, and Meta Muse Spark 1.3. That's great!But also much more expensive than GLM 5.3-flash and Spark 1.3 Contributor (the Meta-takes-your-data pricing of Spark 1.3).So, I think it would have to be significantly better than GLM 5.3-flash to be worth it. GLM 5.3-flash is already very good.

One step closer to Le Chaton Fat.

Excited to try this. The low costs v. benchmarks alone here are worth a serious test. K3 has been my daily driver for a month or two now and it's dramatically reduced token spend (while not having much of a negative impact on productivity).This was the era of the AI race I was waiting for.

Curious that it scores higher than opus 5.5 in cybersecurity because the closed models refuse to comply. I wonder if that means it's more susceptible to offensive uses.

How could I resist switching to a model named after my cat!?

Nice! Once they make it available through their API I will be happy to support them. My local Qwen3.8 27B is serving me well, but I miss the speed and concurrency that comes with subscriptions, and I am not currently paying for any.Tais-toi et prends mon argent!

Well, with this and Beam people are going to have to stop saying that western open models are dead. This is great news. Anthropic and OpenAI may have a bit more knowledge, talent and compute but they don't have a monopoly.Investors looking for them to make monopoly profits are going to be disappointed The premium they can extract from consumers for their models will be capped. Tokens are likely to remain close to the cost of compute, a cost which is high but falling fast.Also, yay Europe! Although the comparison between this and mimo 2.6 is not flattering...