I think, devstral-latest should be it, no? So I write to support and get an answer 12 hours later that says oh, no, devstral 2 is definetely called devstral 2 and then a page of instructions on how to set it up in Intellij... generated with AI. The screens it is refering to don't exist and never did.
devstral-2512 devstral-latest and devstral-medium-latest are all devstral 2 https://docs.mistral.ai/models/devstral-2-25-12
labs-devstral-small-2512 and devstral-small-latest are devstral small 2
devstral-medium-2507 is devstral 1.0
and devstral-small-2507 is devstral small 1.1
Or it can just be a Google like problem where a big company one part doesn't talk to the other.
How? The largest providers that are trying to win devs are locked in a competition to get the devs to continue using the models for free!
The best way to win B2B contracts is to solve the problems that plague business, not those that plague devs. The devs are fickle, have no stickiness and will jump providers to the next free provider, to self-hosted, etc.
Selling to business using Mistral's approach is, I feel, just a good business plan.
"Giving away some credits for free, then making a loss on subscribers" is an absolutely terrible business plan.
I feel we are way less protectionist than most other Economic Regions. Including the USA, which are very protectionist but always claim otherwise
They get more than 50% of their income from subsidies, are quite well off, but always find a reason to complain.
I was thinking more about stuff like "Buy American"-Regulations for public tenders. Stuff like that doesn't exist here
In a certain sense it’s a way for EU to clawback at least a small slice of all that money flowing to the US.
It's what keeps markets alive
Basically all economic regions get highly protectionist when it comes to key areas like agriculture, banking, steel production, energy, automotive manufacturing, etc.
On tariffs, the US is now higher, but tariffs are a tax that passes through overwhelmingly onto the consumer (by like 95%+). Given there's essentially no fully domestic US manufacturing supply chains and the US imports everything, it's a defacto VAT from the perspective of the consumer. The EU has VAT levels that are still much higher than the average US tariff level, which is a essentially a dampener on consumption.
As if that’s not true in the US (not just government contracts but VC in general as well)…
It's not like b2b sales is more technical merit based, individual contributor led, elsewhere.
It's always the same, depending on the field individual contributors can have some flexibility on picking tools (so a developer in a mid sized company would be able to pick whatever, an accountant probably would be more constrained, meanwhile a developer at a big bank would not have any choice). But for strategic software choices, that impact the whole company, where standardisation makes sense or is even mandatory to get actual value out of it, you need to sell to high level decision makers, not individual contributors. A CTO or a VP of X can decide to buy and mandate the implementation of something as impactful, workflow changing and potentially time and money saving as a company wide AI platform. A dev can't.
This is really why Mistral has any support.
The models are bottom barrel, but its the best Europe has...
Although you could use Chinese models on European servers.
Not everyone is obsessed with code generation. There is a whole world out there.
The problem they have is that this is not a moat - their approach is easily reproducible.
If they can pull ahead in having the most number of pre-trained models (one for this ERP, one for that CRM, etc) and then being able to close sales to companies using these products and sell them on post-trained (give us your specific ERP customisations and we'll give you access to a model that is tailored to your business), then THAT is a moat.
But they need to do this without fanfare. Just close sales, and keep closing, basically. After all, even if other AI providers copy the process, the moat would already have been established for Mistral.
My 2ct: Currently the moat may be that they are not US-American which is not reproducible by any of the US alternatives.
I hope you are right (I am in the process of finalising a product and one of the top-5 selling points contains "outside the jurisdiction of the US"), but in my experience, companies only pay lip service to ethics unless it hits their bottom line.
Sure, Mistral AI is certainly not the market leader and probably never will be but we're not talking about being a market leader but about having a moat.
I instantly believe you when you tell me that many companies do not care. On the other hand there are companies that do. At least partially: ASML, Stellantis, AXA, BNP Paribas, the French ministry of defense, Helsing, SNCF, ... are all Mistral AI customers.
Companies will use US ai models without issues in a few years.
Hang on, where are you getting the numbers from? I looked and I couldn't find any numbers on enterprises who opened their wallets for custom-trained models.
I looked, and because I believed that it might be a good business opportunity to explore, I did spend a bit of time trying to find numbers. I came away with the feeling that the winner in the AI space is going to be whoever successfully whitelabels their offering.
Right now that is Mistral, I think.
How do you measure "usage" in an enterprise/commercial context where no data on usage is available to you? I don't expect Mistral AI to make it's money on OpenRouter.
If you are in Iran, you don't want to give your data to your government.
If you are in France, you don't want to give your data to your government.
etc
If you are in France, and you host your e-mails in a datacenter in Hong-Kong, well good luck for the authorities to get it.
If you host it in "secure France", on the paper you will have more privacy and laws behind you, but in reality you are jumping into the mouth of the shark.
This is why governments are promoting: "yes yes, host here don't worry, we will protect you"
"We want your data on X, here;'s a warrant."
"No."
"You are now under arrest for contempt of court."
People have some oddly silly views on what government can and can't do to people living in their territories.
And companies really really don't care if the government has their data.
> host your e-mails in a datacenter in Hong-Kong
Now China has it, gives it a competitor in China and your market share drops like a stone. Congrats! Great choice!
The trick is to host your data in a country with a strong rule of law, and avoid illegal / geopolitical lines. If you're an American company hosting stuff in Russia, you can bet the GRU/SVR would be very happy to abuse it. If you're running a torrent site in Ukraine, you can bet the US would be very happy to claim extraterritorial magic jurisdiction and get you extradited from Poland.
As a French company, you're already beholden to French law and French legal decisions. "Data is hosted in Hong Kong" doesn't matter in the slightest, it only exposes you to more risk.
I have not seen that, actually. I still see most companies who want to jump into AI for the business sort of try RAG, but more often they just buy Chat accounts for their users.
The only place that harnesses appear to be used is in software development, but most companies aren't doing that either.
Isn't the entire deal with LLMs that they are trained as megaliths? How can bespoke modelling overcome the treasure trove of knowledge that megaliths can generically bring in, even in bespoke scenarios?
When generating images most services will have a small agent that rewrites your request and hands it off to the generative image model.
So from the treasure trove point of view, optimized agents have their place. From companies building pipelines, they also have their place.
Right, but this was done to value-optimize the product, i.e. try to always give you the shittiest (cheapest) model you can bear, because otherwise people would always choose the smartest (most expensive) model for any query.
Taking away the model choice from the user introduces a lot of ways to cut down costs, but one thing it does not do is make the product give users better/more reliable answers.
Think of it as a base model (the megalith) which then has the weights adjusted towards a specific use-case (SAP, for example).
My University also migrated to OpenExchange
And they should. Because the US is not behaving rationally at all.
https://nltimes.nl/2026/02/10/rabobank-ing-abn-amro-seek-eur...
https://www.theregister.com/2025/11/13/gartner_cio_cloud_sov...
https://www.independent.co.uk/news/world/europe/europe-zoom-...
https://www.theglobeandmail.com/business/commentary/article-...
https://sherwood.news/tech/europe-wants-to-break-up-with-us-...
Well I have even more personal experience that contradicts yours, and this isn't true at all. Everyone uses Claude / Gemini / OpenAI. Mistral isn't even on the table.
And you can Google for "We use Mistral" to find thousands of usecases by startups and other companies.
Having an option at the back of your mind is all it takes right now, until push comes to shove of course.
Proof: Most big EU companies use Claude or Gemini or OpenAI, not Mistral. That choice was made recently.
Things have changed in the loud echo chambers of the internet, maybe (but not really, since people were saying that EU data sovereignty was happening any time now since 2016).
Of course, it will be slow and painful and Europeans will need to use their own services for them to grow and mature.
IS a statement with no supporting facts considered "proof"? Just the public list of Mistral customers (https://mistral.ai/customers) is proof alone that quite a few big EU companies are _not_ in fact using Open AI or Claude or Gemini at the strategic level.
Contrast with Antrhopic's Europe based customers, the majority of which are small companies (only big one I can identify from a skim is L'Oreal): https://claude.com/customers?f80ce999_sort_date=desc&f80ce99...
Or OpenAI's customers, of which the only big European ones I can spot are Scania and Philips: https://openai.com/stories/
Note: I'm talking about strategic enterprise AI deployments for the company or at least a division, not individual developers being allowed to use Claude Code etc. The moat and the money will be in the former, not latter.
Their API is consistently among the most used on OpenRouter. While I can’t vouch for it myself, I think this is a decent proxy for capability. You can definitely see glimmers of greatness in their chat interface, it just feels like the system prompts are focused on something that doesn’t interest me.
Grok is nice for asking morally gray questions. ChatGPT will lie in these cases.
My other complaint is that ChatGPT ends every response with a teaser to ask more questions.
Are you really that oblivious to the painfully cringy manipulation tactics by the man who partied at Epstein's island? https://www.theguardian.com/technology/2025/nov/21/elon-musk...
Its just not good. Its bottom floor for LLMs.
What? That's just demonstrably false. The market doesn't consist of 5 providers.
Free Chinese models are better than it.
I have been finding Voxtral useful though.
https://generativehistory.substack.com/p/gemini-3-solves-han...
Which one's the best?
next, it sounds like it's going to be .eu
but what about ai.eu
oh, .. why?
> Post-training methods allow teams to refine model behavior for specific tasks and environments.
How do you suppose this works? They say "pretraining" but I'm certain that the amount of clean data available in proper dataset format is not nearly enough to make a "foundation model". Do you suppose what they are calling "pretraining" is actually SFT and then "post-training" is ... more SFT?
There's no way they mean "start from scratch". Maybe they do something like generate a heckin bunch of synthetic data seeded from company data using one of their SOA models -- which is basically equivalent to low resolution distillation, I would imagine. Hmm.
Post-training means everything else: SFT, DPO, RL, etc. Anything that involves things like prompt/response pairs, reward models, or benefits from human feedback of any kind.
Pre-training: refining the weights in an existing model using more training data.
Post-training: Adding some training data to the prompt (RAG, basically).
I like a lot what they are doing and I'll be watching them a lot more closely. I'd love to work for them btw!
https://denverite.com/2026/03/12/ai-recycling-facility-comme...
You could take a model like the one referenced in the article, retool it with Forge for oh I don't know, compost, and use it to flag batches that contain too much paper for instance.
These kinds of applications would work across industries, basically anywhere where you have a documented process and can stand to have automated oversight.
Even with the coding use-case you would still likely want to build a similarity search engine because searching through plain symbols isn't enough to build a contextual understanding of higher-level concepts in the code.
>you would still likely want to build a similarity search engine
In practice tools like Claude Code, Codex, Gemini, Kimi Code, etc are getting away with searching for code with grep / find and understanding code by loading a sufficient amount of code into the context window. It is sufficient to understand higher level concepts in the code. The extra complexity of maintaining vector database top of this is not free and requires extra complexity.
But seriously, RAG/retrieval is thriving. It'll be part of the mix alongside long context, reranking, and tool-based context assembly for the forseeable future.
Being able to just train a model on all of our domain knowledge would, I imagine, produce much better results.
But the OP's blog is more about ZK than about NFTs, and crypto is the only place funding work on ZK. It's kind of a devil's bargain, but I've taken crypto money to work on privacy preserving tech before and would again.
> Of course you would have to set a temperature of 0 to prevent abuse from the operator, and also assume that an operator has access to the pre-prompt
Doesn't the fact that LLM's are still non-deterministic with a 0 temperature render all of this moot? And why was I compelled to read a random blog post on the unsolved issue of validating natural language? It's a SQL injection except without a predetermined syntax to validate against, and thus a NP problem we've yet to solve.
So it'd be alive in the making decisions sense, not in a "the technology is thriving" sense.
It's certainly different data, but one could argue that real humans have been trained on 3.5 billion years of evolution data.