announcement of preview release → preview release → release → final release → final release v2

I was confused and thought it&#x27;s a new model... but no, it&#x27;s the same. What&#x27;s the purpose of reposting this as a new story? Am I missing something?

Previous discussion: <a href="https:&#x2F;&#x2F;news.ycombinator.com&#x2F;item?id=49977979">https:&#x2F;&#x2F;news.ycombinator.com&#x2F;item?id=49977979</a> (2044 points 15 days ago, 1254 comments)

Are they still in competition? Or are they a government sanctioned monopoly? I’m not sure that will be competitive. There are no market forces to do so but pride.

It&#x27;s gonna be interesting to see the final version, since this is a preview model still. I&#x27;m not completely sure as to how this works, but from what I gathered it will be further trained during this period, so it might improve a bit.Since cost is so much higher than deepseek though it really seems like the biggest reason to use it would be &quot;in the mistral subscription&quot; for personal use or &quot;because you need to abide by european data privacy laws&quot; for companies.Mostly I&#x27;m happy they&#x27;re still in competition, as I mentioned already elsewhere.

Given how well LLMs do with code, I&#x27;ve been shocked and how poorly they do with sheet music.

Have you set up some sort of benchmark to see if there is actually anything useful or they are complete trash on the task?

I&#x27;m still waiting for a model that can do sheet music transcription for &lt; $0.10 per page.Shouldn&#x27;t be that difficult as there are huge datasets. But somehow, the AI labs cannot do it.

I think that this link is more helpful: <a href="https:&#x2F;&#x2F;mistral.ai&#x2F;news&#x2F;mistral-large-4&#x2F;" rel="nofollow">https:&#x2F;&#x2F;mistral.ai&#x2F;news&#x2F;mistral-large-4&#x2F;</a>Seems like it&#x27;s multimodal and visual intelligence capabilities are better than Deepseek 4.1 Flash, even though coding is not equal to DS 4.1 1017. The cost is about 10x of Deepseek though comparing on OpenRouter.Weights will be here <a href="https:&#x2F;&#x2F;huggingface.co&#x2F;mistralai&#x2F;Mistral-Large-4-1T-A52B" rel="nofollow">https:&#x2F;&#x2F;huggingface.co&#x2F;mistralai&#x2F;Mistral-Large-4-1T-A52B</a> at the end of the month.

i’ve seen it and Cyrillic in the 5x family

I saw it in earlier Claude models, but I haven&#x27;t seen it for a least 7-8 months.

Opus 4.8 and Fable 5 did the same; they would output random Chinese characters and then say “I made a typo, let me fix it” and it’s just them removing that stay Chinese characters. Haven’t seen this since 5.1 family of models.

It doesn&#x27;t, even remotely. It&#x27;s a typical failure mode in all models, some do this more often. It&#x27;s not related to samplers either. Not necessarily Chinese characters either, they likely just occupy a large portion of vocabularies. For example Gemini 2.0 used to glitch in Telugu and Ukrainian, Claude 2.x and 3.0 used to switch between random languages for a few words when writing in non-English, once in a blue moon.

I interpret it to mean there is a lot of Chinese in the training corpus.

Does this suggest Mistral is distilling Deepseek et. al.? Can distillation keep passing through with high fidelity, or is it lossy like copying a VHS tape?

I&#x27;ve seen this quite a bit on bad quants as well. I ran an early and very lossy quant of one of the Gemma3 models, and it loved to just stick Chinese and Korean characters into the text, sometimes without seeming to realize it had mixed languages.

Ha, ha. Just like when Tilly Norwood switched to Chinese while interviewing with Piers Morgan [1].On a serious note, I remember getting spooked when I saw a bunch of Chinese characters dumped out into some of my server logs (pre-LLM era). When I pasted the Chinese content from my log into google translate, it was just complete nonsense. Later I found a bug that was dumping raw binary data into my log file which interpreted as Chinese characters when viewed in a text editor. Perhaps similar bugs existed in the models.[1] <a href="https:&#x2F;&#x2F;www.youtube.com&#x2F;watch?v=8VOfGcTqqq8" rel="nofollow">https:&#x2F;&#x2F;www.youtube.com&#x2F;watch?v=8VOfGcTqqq8</a>

Sorry it was not one time but it happened easily 5-10x during the past few days on different topics and prompts and I didn&#x27;t store them. It felt like when pushing the model on some specifics in some niche topics in German (I have been prompting in German and the model responded in German). It was the acutal output and not the thinking traces and it was through the API. I jokingly asked it what the character meant and it didn&#x27;t even make sense in the context so it seemed like random chinese characters.

Can you please share: 
- what specific input you see this issue with?
- which interface you used (API, playground, specific sdk version or a wrapper app)?
- whether the chinese chars appear in the thinking section or the output?Thanks.

happened to me with Gemini too, actually. it sometimes just randomly decides to answer in a different language.

Havn&#x27;t seen it for months with other frontier models.

you can see the same thing with any models

The weird thing about this model is that it spits out Chinese characters just like the chinese models when chatting with it in German. Happened to me quite often during the past few days.

In Vibe CLI app, when you load Mistral Large 4 it swaps out the cat in the header with a larger oneIt&#x27;s a nice little detail

Still 9 days before the release of weights apparently.

Some details and benchmarks were announced but the weights didn’t get released

Le Chaton Fat is a lovely name. I predict the next Michelin restaurant with will have an LLM as the Chef...

I see Le Chaton Fat and press upvote, sure. But why? Wasn&#x27;t it released already 2 weeks ago?

I say this with a slight tongue in cheek, but Mistral does have a better option: they recently added GLM 5.3 to their platform (and it works rather well). Not trained in the EU, though, of course ;-)

Everything feels very European.Unproductive company that is 3rd place (assuming we are lumping 4 better US models and 3 Chinese models).The only thing that gives it value is that it&#x27;s generated in some region in Europe, like random cheese or wine.But I still can&#x27;t imagine a usecase outside patriotism.Privacy can be solved by self hosting a US or Chinese model.Seems performative.

Well, Models are hard to just distill down to a single benchmark number, or even a few across multiple different benchmark - they all behave and work slightly differently and you can find some who work or feel better for you and your use-case specifically.But you&#x27;re right, Mistral Large 4 seems a much more expensive but overall weaker model across the board.The reason to use them would be, well, because they&#x27;re...Mistral. They&#x27;re in EU, france is large in nuclear energy so the energy cost of training has much less of an environmental impact, the data protection laws are very good in EU. This can really be a deciding factor for many EU companies, and I think it&#x27;s good that they now have something which is at least semi-usable. (apparently)It&#x27;s also good that other options exist, even if they are worse. Deepseek is kind of like a large specter looming over every model release, because it&#x27;s just SO cheap and good. That doesn&#x27;t mean we should abandon every other model.Still, the model is not really competitive on price and abilities alone. I&#x27;m mostly happy to see Mistral still in the fight.

The organization I work for uses GPT and Claude models hosted in EU. I don&#x27;t see any practical reason to use models made in Europe. Even if you don&#x27;t want to work with American big tech there are plenty of companies in the EU serving Chinese open-weight models.

Interestingly Mistral has been selling GLM5.3 inference hosted in Europe on their own hardware, for those who are OK using a &quot;not developed in Europe&quot; model but run on hardware located in and controlled by a European corporation.<a href="https:&#x2F;&#x2F;docs.mistral.ai&#x2F;models&#x2F;zai-glm-5-3" rel="nofollow">https:&#x2F;&#x2F;docs.mistral.ai&#x2F;models&#x2F;zai-glm-5-3</a>

If you&#x27;re required to use a European model

The TAM is already covered by local models. 10% of a health society could use better models.We are in the 70s era of gas guzzling before the gas embargo and cheap foreign imports and economy cars reign.

Maybe not now, but in a few years, we might not need frontier models anymore. Open-weight models are only half a year behind the frontier, and both categories are advancing really fast.Gemini 2.0 Flash, released just 1.5 years ago, could not even repeat a few paragraphs unchanged. Now, we have Mistral, DeepSeek, and MiMo.

if you care about data-retention policy or you have to use european models ...

Well when your greatest ally acts like an adversary, who can blame them for the increased sovereign focus?

compared to Deepseek 1017, I&#x27;m not sure why I&#x27;d use this?<a href="https:&#x2F;&#x2F;openrouter.ai&#x2F;compare&#x2F;mistralai&#x2F;mistral-large-4-0&#x2F;deepseek&#x2F;deepseek-v4.1-flash-1017" rel="nofollow">https:&#x2F;&#x2F;openrouter.ai&#x2F;compare&#x2F;mistralai&#x2F;mistral-large-4-0&#x2F;de...</a>

Has anything changed? I already ran it on my adversarial programming language benchmark[1] a few weeks ago and it performed pretty poorly (on par with Kimi K3).1 - <a href="https:&#x2F;&#x2F;bench.killswitch-lang.org&#x2F;" rel="nofollow">https:&#x2F;&#x2F;bench.killswitch-lang.org&#x2F;</a>

What&#x27;s more impressive is the datacenter used for training. It shows you can get &quot;sub-SOTA&quot; for one tenth of the resources of SOTA.

Impressive if these benches hold up to real world use. Not groundbreaking, but certainly in the game.Would love to see them release some smaller open-weight models.

qwen-3.8-flash-next is suprisingly good, I find it overthinks, but it&#x27;s quite usable.

52B active parameters and my qwen-3.8-flash-next model running at home is supposedly competitive? maybe I gotta test it to believe it. <a href="https:&#x2F;&#x2F;artificialanalysis.ai&#x2F;models&#x2F;comparisons&#x2F;mistral-large-4-vs-qwen3-8-flash-next" rel="nofollow">https:&#x2F;&#x2F;artificialanalysis.ai&#x2F;models&#x2F;comparisons&#x2F;mistral-lar...</a>

&quot;Covered by GDPR&quot; is a very big plus for me. Also very simple training opt-out.

Yeah I did this one two weeks ago when it was first released. Not sure why it&#x27;s on the HN homepage today.

NVM. Looks like it already exists:<a href="https:&#x2F;&#x2F;tools.simonwillison.net&#x2F;markdown-svg-renderer?url=https%3A%2F%2Fgist.github.com%2Fsimonw%2F0db8b8196e57711b352f6c5857cf5b35" rel="nofollow">https:&#x2F;&#x2F;tools.simonwillison.net&#x2F;markdown-svg-renderer?url=ht...</a>

waiting for pelican bench results (the only benchmark that matters)