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'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're...Mistral. They'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's good that they now have something which is at least semi-usable. (apparently)
It'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's just SO cheap and good. That doesn't mean we should abandon every other model.
Still, the model is not really competitive on price and abilities alone. I'm mostly happy to see Mistral still in the fight.
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 ;-)
Interestingly Mistral has been selling GLM5.3 inference hosted in Europe on their own hardware, for those who are OK using a "not developed in Europe" model but run on hardware located in and controlled by a European corporation.
> for those who are OK using a "not developed in Europe" model but run on hardware located in and controlled by a European corporation.
For the record this is also possible in Europe on Privatemode[1] which also gives you enclave-based processing. And access to GLM-5.3 Flash as well as GLM-5.3
Indeed, I see no difference between a chinese model hosted in Europe vs a Europe-grown model.
You could argue that potentially you can train a backdoor into a foreign-grown model (that has been demonstrated to be possible in toy examples), but I would highly doubt that this would be the case today, given the potential reputational damage if discovered.
Remember Stuxnet? An american worm that was completely benign, travelled on usb drives in hiding, and only activated when it guessed that it's on Iranian plants, and iirc changed very subtly some very specific parameters of operations.
Here, with a chinese model, it would be even simpler - you finetune a model to activate certain operations when it discovers certain environments, but to behave completely normal otherwise. Kind of like a sleeper agent. And with current tech we wouldn't even be able to spot this.
It's not like they didn't sandbox it or limit the access. There were reasonable measures, but not really serious. They made a kodoku pit and didn't watch it.
The agents started hacking because they were sandboxed.
It was filtered, but had a bug, and multiple frontier agents, givent enough time first figured out how to communicate and coordinate with each other and then how to break out of it.
For some benchmarks agents need some sort of a limited internet access - otherwise the benchmarks would be pointless. Good luck implementing a good filter on the internet - if you have a way to do it reliably please share, I'm sure multiple people here would love to hear it.
> you could argue that potentially you can train a backdoor into a foreign-grown model
If we're wearing tinfoil hats then do we know the NSA have not planted stuff in Anthropic and OpenAI anyway ? ;)
I think the Chinese would be happy enough to see OpenAI and Anthropic suffer because people discover open-weights can get you most of the way there for a fraction of the cost.
You don't have to target the end users. Economic impact on the US providers would be sufficient.
The Chinese are also no doubt seeking to play the long game of hoping to (eventually) rebalance the US-centric world order in an Easterly direction.
I recall seeing something about that. I don't know of anyone still using such an ancient model when much newer Qwen 3.5-120, 3.6-35B-A3B, 3.8-27B and 3.8-flash-next all exist and score much higher on all conceivable metrics. I would like to see if anyone can replicate the same thing on a 'current' Qwen released in the past 6 months.
DS4 hosted by deepseek used through the API doesn't have a problem talking about tiananmen square and Uyghurs, but it thinks it's Claude when it speaks about it. I'm not sure what it all means. That was on v4-pro before the 4.1 switch, so may or may not be reproducable.
Add: I was surprised by the fact that the LLM itself doesn't know it's identity unless the system prompt mentions it.
Yeah ... I, erm... can't take that blog post remotely seriously.
Engineering a situation and calling it a vulnerability ?
What a joke. I'm sure you could find a way of engineering a fake vulnerability with Anthropic or OpenAI too and then write a fancy blog post about it.
AND ANYWAY ... whether you are using US or Chinese models, what stupid idiot does not review the slop written by LLMs anyway ? Especially if you are writing for a security-sensitive environment ?
The organization I work for uses GPT and Claude models hosted in EU. I don't see any practical reason to use models made in Europe. Even if you don't want to work with American big tech there are plenty of companies in the EU serving Chinese open-weight models.
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.
https://openrouter.ai/compare/mistralai/mistral-large-4-0/de...