I like Mistral, it hits the exact sweet spot between cost and my data staying in the EU, withouth a significant drop in quality, but man are their model naming conventions confusing af. They mention they have a model called Devstral 2, which is neither Codestral nor Devestral. I want to use it, but the api only lists devstral-2512, devstral-latest, devstral-medium-latest, devstral-medium-2507, devstral-small, devstral-small-2507.
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.
IanCal 2 hours ago [-]
I got really lost on their site, but to help a bit according to their model page
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
kioleanu 2 hours ago [-]
wow, thank you, this is great. I was thinking they should have a page like this, but I couldn't find myself.
butILoveLife 8 minutes ago [-]
>data staying in the EU
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.
Manfred 3 hours ago [-]
I had the same experience. It's even more confusing when you want to create an API key because they are separated by product, maybe?
kioleanu 3 hours ago [-]
no, the key is actually universal, you can't choose a specific product
lis 1 hours ago [-]
It depends. The key for their vibe-cli is actually different. You need to get a separate key if you have a subscription and don't want to pay API usage prices.
newswasboring 3 hours ago [-]
I have a general impression they are not interested too much in individual devs and making it suite their workflow. They want to be a B2B company and deliver a custom workflow per company.
Or it can just be a Google like problem where a big company one part doesn't talk to the other.
soco 2 hours ago [-]
But wouldn't winning devs be a neat helping point in winning b2b contacts? Or they think golf courts are enough for success? Okay they might be right here, but still they make it so confusing for no obvious reason.
philipallstar 2 minutes ago [-]
Also EU protectionism itself might be enough.
MidnightRider39 53 minutes ago [-]
In my experience devs rarely have anything to say in B2B contracts. At best they can recommend a solution to the decision maker, but in almost all deals i was a part of they didn’t have any influence on the final decision.
I wish it were otherwise but alas
newswasboring 2 hours ago [-]
To me it's obvious because the size of companies they are targeting (ASML being an obvious one). I think golf course marketing works well in the EU context when decisions are being made not purely on tech reasons.
kioleanu 2 hours ago [-]
you might be correct. for example, they have an intellij plugin that allows integration without the AI Assistant, but it is only available for Enterprise customers
ogou 7 hours ago [-]
Don't sleep on Mistral. Highly underrated as a general service LLM. Cheaper, too. Their emphasis on bespoke modelling over generalized megaliths will pay off. There are all kinds of specialized datasets and restricted access stores that can benefit from their approach. Especially in highly regulated EU.
Not everyone is obsessed with code generation. There is a whole world out there.
lelanthran 5 hours ago [-]
I also think that this is the best approach for businesses wanting to adopt AI to automate, streamline, etc their business.
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.
Lapel2742 3 hours ago [-]
> The problem they have is that this is not a moat - their approach is easily reproducible.
My 2ct: Currently the moat may be that they are not US-American which is not reproducible by any of the US alternatives.
lelanthran 2 hours ago [-]
> 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.
Lapel2742 2 hours ago [-]
> 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.
soco 2 hours ago [-]
Mistral is still hosted on US providers, their EU centers are only in planning. Data access aside, if AWS or Azure (or Cloudflare) are ordered to pull the plug, it's still goodbye Mistral. Unless you use a third party hoster that is, or do it yourself of course - already possible.
amonith 1 hours ago [-]
To extend on that a little bit: they use data centers located in EU, but owned by US cloud providers.
They can still pull the plug ofc, so it's only a small difference, but still
drstewart 3 hours ago [-]
This moat doesn't seem to be much of a moat considering a non-US model doesn't even crack the top 5 by usage - except DeepSeek, which would be a strange choice for Europeans looking for data sovereignty.
lelanthran 2 hours ago [-]
> This moat doesn't seem to be much of a moat considering a non-US model doesn't even crack the top 5 by usage - except DeepSeek, which would be a strange choice for Europeans looking for data sovereignty.
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.
Lapel2742 2 hours ago [-]
> considering a non-US model doesn't even crack the top 5 by usage
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.
erispoe 47 minutes ago [-]
Except the evidence today rather points to SOTA model + harness than fine tuned models.
lelanthran 38 minutes ago [-]
> Except the evidence today rather points to SOTA model + harness than fine tuned models.
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.
srivmo 6 hours ago [-]
> Their emphasis on bespoke modelling over generalized megaliths will pay off.
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?
wodenokoto 3 hours ago [-]
ChatGPT is already a small agent that receives your message and decides which agent needs to respond. Within those, agents can have sub agents (like when it does research).
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.
TeMPOraL 3 hours ago [-]
> ChatGPT is already a small agent that receives your message and decides which agent needs to respond.
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.
lelanthran 5 hours ago [-]
> 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?
Think of it as a base model (the megalith) which then has the weights adjusted towards a specific use-case (SAP, for example).
butILoveLife 6 minutes ago [-]
If you couldn't use the words Europe to describe why you'd chose Mistral, you'd have no good reasons to choose Mistral.
Its just not good. Its bottom floor for LLMs.
Stromgren 5 hours ago [-]
Agreed. I’ve used their platform to train smaller, specialized models. Something I could have done in Codelab or some other tool, but their platform allows me to just upload a training set and as soon as it finishes I have a hosted model available at an endpoint. It obviously has some constraints compared to running the training yourself, but it also opens up the opportunity to way more people.
isodev 6 hours ago [-]
Indeed, but even for coding use cases, Vibe is more of a focused “refactor/ write this function” aid than “write me an app” and it can work locally. For me that’s a lot more valuable as an accelerator to my workflow where the developer stays in control and fully involved in the process.
spiderfarmer 5 hours ago [-]
Yes, since it's not American, it will be the de-facto choice for most big European companies.
jstummbillig 5 hours ago [-]
Why would that be? Most big EU companies use ms teams or google workspace, for example.
schubidubiduba 5 hours ago [-]
They use those because the decision to use them was made years ago. Things have changed since then
utopiah 4 hours ago [-]
I want to believe... but I also need proofs of that "trend", any reference I could read on please?
AdamN 4 hours ago [-]
It's definitely a topic of conversation in Reddit, etc... However I agree that the push to reduce US dependence by EU companies (and countries) is hampered by the fact that US stuff is already embedded (Microsoft but also Google, etc...) and that many of these companies are transnational anyway (very few European companies are solely inside the EU) and finally and most importantly just about every company will choose the option that does the job best for the right price (sovereignty is a distant second for most decision makers).
spiderfarmer 4 hours ago [-]
While few companies announce this publicly, I know from personal experience with corporate clients that many companies are preparing for Trump to use Big Tech as a bargaining chip.
And they should. Because the US is not behaving rationally at all.
>While few companies announce this publicly, I know from personal experience with corporate clients
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.
input_sh 1 hours ago [-]
Come on, compared to Google Workspace / Microsoft's whatever-it's-called-these-days, the cost of switching from one LLM provider to another is pretty much zero.
Having an option at the back of your mind is all it takes right now, until push comes to shove of course.
hk__2 3 hours ago [-]
Not at all. We continue taking that decision today.
drstewart 2 hours ago [-]
No they haven't.
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).
saulapremium 30 minutes ago [-]
I consult for various companies and have definitely seen a trend. It's not quite the rupture that some expect but clearly not nothing either. Until very recently, the risk assessment of using US providers was considered very hypothetical. Today it still doesn't feel imminent, but it does feel very real.
Of course, it will be slow and painful and Europeans will need to use their own services for them to grow and mature.
sisve 2 hours ago [-]
My _feeling_ is that a lot of EU/European politicians has talked a lot more about the need to be independent from the US after Trump threaten Greenland. At least in the nordic countries. Not only concerning data & privacy, but defence, communications, space etc. All areas. The wheel has started to turn. You will not see it if you look around. But in 10 years time, maybe more, Europe will have stopped depending on the US. And that will hit US hard. We pay a lot of money in services to the US.
Aerroon 53 minutes ago [-]
The politicians can talk, but they needed to set up an environment that would've let a European company have a decent shot at competing with the best AI models. But they didn't. Should've thought of that before being proud of setting up those strict tech regulations.
sunaookami 3 hours ago [-]
No they haven't. Every company just buys ChatGPT Enterprise.
haraldooo 6 hours ago [-]
I agree. Just started using it. Can you give some examples of fields you maybe even prefer Mistral?
umeridrisi 5 hours ago [-]
Is this the best Grok alternative?
spiderfarmer 5 hours ago [-]
Any model is.
grosswait 29 minutes ago [-]
This sounds like an ideology based reply. Grok is underrated and I think has a better chance of long term success than most. The current growth strategy means (for me) their chat harness is not up to par for serious work.
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.
butILoveLife 4 minutes ago [-]
Grok is not SOTA, but its so obviously better than Mistral. Mistral is just some European patriotism or something.
Grok is nice for asking morally gray questions. ChatGPT will lie in these cases.
losvedir 19 minutes ago [-]
> Forge enables enterprises to build models that internalize their domain knowledge. Organizations can train models on large volumes of internal documentation, codebases, structured data, and operational records. During training, the model learns the vocabulary, reasoning patterns, and constraints that define that environment.
I'm probably really out of date at this point, but my impression was that fine tuning never really worked that well for knowledge acquisition, and that don't variety of RAG is the way to go here. Fine tuning can affect the "voice", but not really the knowledge.
upghost 9 hours ago [-]
> Pre-training allows organizations to build domain-aware models by learning from large internal datasets.
> 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.
qntty 30 minutes ago [-]
Pre-training mean exposing an already-trained model to more raw text like PDF extracts etc (aka continued pre-training). You wouldn't be starting from scratch, but it's still pre-training because the objective is just next token prediction of the text you expose it to.
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.
losvedir 22 minutes ago [-]
Er, then what is the "already trained" model? I thought pre-training was the gradient descent through the internet part of building foundational models.
mirekrusin 7 hours ago [-]
Probably marketing speak for full fine-tuning vs PEFT/LoRA.
lelanthran 5 hours ago [-]
I would guess:
Pre-training: refining the weights in an existing model using more training data.
Post-training: Adding some training data to the prompt (RAG, basically).
anon373839 9 hours ago [-]
I think they are referring to “continued pretraining”.
gunalx 5 hours ago [-]
Probably just means SFT fine-tuning a base model, vs behavioural dpo and/or SFT fine-tuning a instruction model.
stingraycharles 9 hours ago [-]
I can imagine that, as usual, you start with a few examples and then instruct an LLM to synthesize more examples out of that, and train using that. Sounds horrible, but actually works fairly well in practice.
mark_l_watson 12 hours ago [-]
I am rooting for Mistral with their different approach: not really competing on the largest and advanced models, instead doing custom engineering for customers and generally serving the needs of EU customers.
ChrisGreenHeur 7 hours ago [-]
I found it to be the best model if you want to talk about topics philosophical. It has no problems going deep and technical while other models tend to be afraid of overshooting the comprehension of the reader.
jerrygoyal 10 hours ago [-]
their ocr model is goated
SyneRyder 5 hours ago [-]
Did they make significant improvements in OCR 3? The quality I was getting from Mistral OCR 2 was nowhere near as good as what I could get from just sending the same files to Claude Sonnet via an API call.
I have been finding Voxtral useful though.
oakpond 5 hours ago [-]
probably yes. considering that even some of their non-ocr models can recognize my shitty handwritten math
stavros 9 hours ago [-]
Better than Qwen? I guess the best overall is Gemini, right?
Mistral is doing some really great stuff lately. Sure, it's hard to compete with OpenAI and Anthropic and their models, but they are taking up some interesting takes and designing their product in unique ways.
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!
roxolotl 13 hours ago [-]
Mistral has been releasing some cool stuff. Definitively behind on frontier models but they are working a different angle. Was just talking at work about how hard model training is for a small company so we’d probably never do it. But with tools like this, and the new unsloth release, training feels more in reach.
ryeguy_24 11 hours ago [-]
How many proprietary use cases truly need pre-training or even fine-tuning as opposed to RAG approach? And at what point does it make sense to pre-train/fine tune? Curious.
mirekrusin 7 hours ago [-]
You can fine tune small, very fast and cheap to run specialized models ie. to react to logs, tool use and domain knowledge, possibly removing network llm comms altogether etc.
Shitty-kitty 8 hours ago [-]
rag basically gives the llm a bunch of documents to search thru for the answer.
What it doesn't do is make the algorithm any better. pre-training and fine-tunning improve the llm abaility to reason about your task.
baby 11 hours ago [-]
RAG is dead
charcircuit 11 hours ago [-]
Using tools and skills to retrieve data or files is anything but dead.
nathanappere 5 hours ago [-]
I think people just mean "using vector databases to enable RAG".
menaerus 4 hours ago [-]
Even that doesn't make sense. Why would you not build a vector database to complement your RAG engine?
charcircuit 4 hours ago [-]
For coding use cases you may want a way to search for symbols themselves or do a plain text exact match for the name of a symbol to find the relevant documents to include. There is more to searching than building a basic similarity search.
menaerus 3 hours ago [-]
Sorry but who mentioned coding as a use-case? My comment was general and not specific to the coding use-case, and I don't understand where did you get the idea from that I am arguing that building a similarity search engine would be a substitute to the symbol-search engine or that symbol-search is inferior to the similarity-search? Please don't put words into my mouth. My question was genuine without making any presumptions.
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.
CharlesW 10 hours ago [-]
And yet your blog says you think NFTs are alive. Curious.
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.
prophesi 4 hours ago [-]
Not OP, but...
> 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.
nl 8 hours ago [-]
I don't think RAG is dead, and I don't think NFTs have any use and think that they are completely dead.
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.
elicash 9 hours ago [-]
I have no interest in anything crypto, but they are making a proposal about NFTs tied to AI (LLMs and verifiable machine learning) so they can make ownership decisions.
So it'd be alive in the making decisions sense, not in a "the technology is thriving" sense.
strongly-typed 10 hours ago [-]
Wait, what does NFTs have to do with RAG?
panarky 10 hours ago [-]
I, for one, find NFT-shilling to be a strong signal that I should downgrade my trust in everything else a person says.
LoganDark 10 hours ago [-]
Nothing, I think they're just pointing out a seeming lack of awareness of what really is or isn't dead.
bigyabai 10 hours ago [-]
In what, X's hype circles? Embeddings are used in production constantly.
loeg 10 hours ago [-]
Is it??
dash2 6 hours ago [-]
I think it’s interesting what this approach suggests about who will profit from AI. I’m sceptical that having huge numbers of GPUs is a moat. After all, real humans – even geniuses – are trained on much much less data than the whole Internet. But proprietary and specialised data could very well be a moat. It’s hard to train a scientist/lawyer/analyst without reading a lot of science/law/finance. Companies’ proprietary data might encode a great deal of irreplaceable knowledge. Seems as if Mistral is taking this bet.
copirate 3 hours ago [-]
> After all, real humans – even geniuses – are trained on much much less data than the whole Internet.
It's certainly different data, but one could argue that real humans have been trained on 3.5 billion years of evolution data.
dmix 11 hours ago [-]
This is definitely the smart path for making $$ in AI. I noticed MongoDB is also going into this market with https://www.voyageai.com/ targeting business RAG applications and offering consulting for company-specific models.
csunoser 13 hours ago [-]
Huh. I initially thought this is just another finetuning end point. But apparently they are partnering up with customers on the pretraining side as well. But RL as well? Jeez RL env are really hard to get right. Best wishes I guess.
jbverschoor 5 hours ago [-]
ASML and ESA as clients means something. I dont expect to see the first name somewhere else on the logo list
todteera 3 hours ago [-]
Interesting how Mistral is investing into training models for industry specific use cases. With the commoditization of intelligence by base models, they're probably looking to creating value from specialized verticals.
tho23i42342397 2 hours ago [-]
Interesting. Does this actually scale though ? I've never seen enterprises which have "internal knowledge" in proper readable form - it's often in code, and more importantly in people who wrote them.
I recall that even at Google - with its own search engine and so on - the best way to understand anything was to read code or to reach out to those who wrote them. I don't know how it works in places that work with the "real world" like ASML.
Often the issue is not even about documentation - it's just that it's extremely hard to include all the nuances in text and still have it be readable (code-documentation comes to mind).
Interestingly, I strongly feel that this also where LLMs (and some of our more textually-obsessed academics) fail.
thecopy 3 hours ago [-]
Looks interesting. But how to explore or test or use? The product page (https://mistral.ai/products/forge) also does not contain anything useful. Just "Contact us"
Dissapointing.
andai 11 hours ago [-]
They mention pretraining too, which surprises me. I thought that was prohibitively expensive?
It's feasible for small models but, I thought small models were not reliable for factual information?
Note that any supervised fine-tuning following the Pretraining stage is just swapping the dataset and maybe tweaking some of the optimiser settings. Presumably they're talking about this kind of pre-RL fine-tuning instead of post-RL fine-tuning, and not about swapping out the Pretraining stage entirely.
apexalpha 2 hours ago [-]
This looks good but how much money are we talking here? Are we 'retraining' an entire model but adding enterprise data to the public data set?
zby 6 hours ago [-]
My bet is that the solution to continuous learning is with external storage. There is a lot of talk about context engineering - but I have not seen anyone taking context as the main bottleneck and building a system around that.
This would show that even context engineering is kind of wrong term - because context does not enter the llm in some mysterious way - it goes through prompt and the whole model of passing chat history back and forth is not the most efficient way of using the prompt limitation.
mhl47 5 hours ago [-]
"External Storage" whatever that is can not be the same as continous learning as it does not have the strong connections/capture the interdepencies of knowledge.
That said I think we will see more efforts also on the business side to have models that can help you build a knowledge base in some kind of standardized way that the model is trained to read. Or synthesize some sort on instructions how to navigate your knowledge base.
Currently e.g. Copilot tries to navigate a hot mess of a MS knowledge graph that is very different for each company. And due to its amnesia it has to repeat the discovery in every session. No wonder that does not work. We have to either standardize or store somewhere (model, instructions) how to find information efficiently.
zby 4 hours ago [-]
The key to make Copilot useful is to take the limited context problem seriously enough. There are many dimensions to it: https://zby.github.io/commonplace/notes/context-efficiency-i... and it should be the starting point for designing the systems that extensively use llms.
Centigonal 6 hours ago [-]
What do you mean when you say "external storage?"
zby 4 hours ago [-]
A knowledge base - something where the LLM knows how to find the knowledge it needs for a given task. I am working on this idea in https://zby.github.io/commonplace/
ithkuil 4 hours ago [-]
A form of context engineering
Aldipower 4 hours ago [-]
I cannot keep up with their products, model names and releases.
What is what for? Their marketing texts do not make sense for me.
Is there a nice overview somewhere?
I am a simple stupid Le Chat user with a small mind and the Tredict MCP Server connected to it (to Le Chat, not my mind), which works ok-ish. :-)
speedgoose 6 hours ago [-]
I was enthusiastic but it’s "contact us" priced for now. I was expecting a classic cloud LLM forge with a public pricing.
hermit_dev 9 hours ago [-]
The future of AI is specialization, not just achieving benevolent knowledge as fast as we can at the expense of everything and everyone along the way. I appreciate and applaud this approach. I am looking into a similar product myself. Good stuff.
reverius42 7 hours ago [-]
Ironically that was also the past of AI. In 2016 it was all about specialized models (not just training data, everything including architecture and model class/type) for specific tasks and that's the way things had been for a long time.
Are you suggesting that it's an aberration that from ~2019 to ~2026 the AI field has been working on general intelligence (I assume this is what you mean by "achieving benevolent knowledge")?
Personally I think it's remarkable how much a simple transformer model can do when scaled up in size. LLMs are an incredible feat of generalization. I don't see why the trajectory should change back towards specialization now.
holoduke 7 hours ago [-]
I don't think that's true. Nothing points to specialized LLMs being better. General purpose LLMs are just much more useful in daily work.
rorylawless 11 hours ago [-]
The fine tuning endpoint is deprecated according to the API docs. Is this the replacement?
Interesting to see. I thought they were promoting fine tuning
krinne 4 hours ago [-]
I wasnt able to find a way to access this - is this something accessible only to enterprises ?
Would love to take it for a spin, if that is even possible.
spacesh1psoda 3 hours ago [-]
Go EU!
whatever1 6 hours ago [-]
I thought that for pretraining to work and reasoning to emerge you need internet scale data. How can forge achieve it with just internal company data (unless the said company is AT&T or something) ?
dragochat 33 minutes ago [-]
where sample notebook/script? where github? where signup?
...learn a thing or two from NVIDIA or gtfo
aavci 10 hours ago [-]
How does this compare to fine tuning?
Otterly99 4 hours ago [-]
It seems to me that it is broadly the same thing, except they give you the resources to do it and expert knowledge.
burgerquizz 3 hours ago [-]
can i use mistral to read my source code and teach it so i don't need to inject the whole doc every single time and consume token every single time?
supernes 7 hours ago [-]
> Code agents are becoming the primary users of developer tools, so we built Forge for them first, not
... for humans.
bsjshshsb 11 hours ago [-]
Id training or FT > context? Anyone have experience.
Is it possible to retrain daily or hourly as info changes?
maxothex 2 hours ago [-]
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gpubridge 9 hours ago [-]
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Rendered at 12:27:51 GMT+0000 (Coordinated Universal Time) with Vercel.
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
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.
Or it can just be a Google like problem where a big company one part doesn't talk to the other.
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.
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.
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).
Its just not good. Its bottom floor for LLMs.
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.
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.
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.
I'm probably really out of date at this point, but my impression was that fine tuning never really worked that well for knowledge acquisition, and that don't variety of RAG is the way to go here. Fine tuning can affect the "voice", but not really the knowledge.
> 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 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?
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!
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.
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.
> 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.
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.
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.
I recall that even at Google - with its own search engine and so on - the best way to understand anything was to read code or to reach out to those who wrote them. I don't know how it works in places that work with the "real world" like ASML.
Often the issue is not even about documentation - it's just that it's extremely hard to include all the nuances in text and still have it be readable (code-documentation comes to mind).
Interestingly, I strongly feel that this also where LLMs (and some of our more textually-obsessed academics) fail.
Dissapointing.
It's feasible for small models but, I thought small models were not reliable for factual information?
Foundational:
- Pretraining - Mid/post-training (SFT) - RLHF or alignment post-training (RL)
And sometimes...
- Some more customer-specific fine-tuning.
Note that any supervised fine-tuning following the Pretraining stage is just swapping the dataset and maybe tweaking some of the optimiser settings. Presumably they're talking about this kind of pre-RL fine-tuning instead of post-RL fine-tuning, and not about swapping out the Pretraining stage entirely.
That said I think we will see more efforts also on the business side to have models that can help you build a knowledge base in some kind of standardized way that the model is trained to read. Or synthesize some sort on instructions how to navigate your knowledge base.
Currently e.g. Copilot tries to navigate a hot mess of a MS knowledge graph that is very different for each company. And due to its amnesia it has to repeat the discovery in every session. No wonder that does not work. We have to either standardize or store somewhere (model, instructions) how to find information efficiently.
I am a simple stupid Le Chat user with a small mind and the Tredict MCP Server connected to it (to Le Chat, not my mind), which works ok-ish. :-)
Are you suggesting that it's an aberration that from ~2019 to ~2026 the AI field has been working on general intelligence (I assume this is what you mean by "achieving benevolent knowledge")?
Personally I think it's remarkable how much a simple transformer model can do when scaled up in size. LLMs are an incredible feat of generalization. I don't see why the trajectory should change back towards specialization now.
https://docs.mistral.ai/api/endpoint/deprecated/fine-tuning
Would love to take it for a spin, if that is even possible.
...learn a thing or two from NVIDIA or gtfo
... for humans.
Is it possible to retrain daily or hourly as info changes?