Notes from the Mistral AI Now Summit

Notes from the Mistral AI Now Summit(koenvangilst.nl)

466 points by vnglst 91 days ago | 214 comments

OK, I'm 100% rooting for both Mistral and task focused small models.

But Mistral has fall really far behind since 2025Q3. It seems they can't get good reasoning models working at even medium context sizes, which is necessary to be at the table right now.

Gemma4 and Qwen3.6 are currently best in the small size; Mistral's "small" model has ~4x the parameter count at 120B and isn't even competing with models a quarter its size.

Back one year ago with Mistral Small 3.1 they were keeping up, but they've fallen into irrelevancy right now.

If Mistral seriously wants to play the on-prem and small task-specific model game, a decent proxy would be to build models that get the r/localLlama crowd excited

ar0 90 days ago | |

I agree. I am a paying Le Chat Pro user, really rooting for a European alternative. But the quality difference between Mistral and the frontier labs is growing too big to ignore. It’s worrying to me that they didn’t talk much about new models at the conference, because that is really where their focus should be IMHO.

I am wondering what is keeping them back, though: Money? Compute? Skills? Training data? My fear is that you are really only getting really good models by training on very dubious data (outputs from the frontier models etc) and that Mistral is too European and too enterprisey to take those risks.

mattnewton 90 days ago | | |

My theory with no insider information: it’s a little of all of the above, but mostly money. To some extent, you can dig yourself out of a data hole with RL and a lot of compute. And you can buy a lot of compute and some data with a lot of money. Big labs have been operating in this regime for a while and it’s one of the drivers behind their costs beyond just scaling the weights and doing the actual training. Mistral just doesn’t have access to this level of compute or the money to try and muscle their way in.

teiferer 90 days ago | | |

> I am wondering what is keeping them back, though: Money? Compute? Skills? Training data?

Not ruthless enough and no backing by a corrupt govt administration that has no morals but focuses on self-enrichment instead.

Might sound drastic but I think that's actually closer to the truth thn everbody likes to admit.

> My fear is that you are really only getting really good models by training on very dubious data (outputs from the frontier models etc) and that Mistral is too European and too enterprisey to take those risks.

Exactly.

pembrook 90 days ago | | |

> what is keeping them back, though: Money? Compute? Skills? Training data?

All of the above and more. Everything holding Mistral back is the same thing that has held Europe back from competing in the entire digital revolution. See this 1991 article lamenting the loss of any viable European PC manufacturer: https://www.nytimes.com/1991/04/22/business/europe-stumbles-...

Mistral being in Europe is disadvantaged with:

1. Money: less diverse private pension fund environment = less LPs to invest in VC funds = less VC dollars to invest in new ventures. European money is vacuumed out of the private sector into state pension funds and dumped into low yielding government bonds. This starves the private sector of capital while inflating the % of GDP driven by government spending every year (government pension funds buying government bonds in circular fashion enable runaway deficit spending...just like circular AI infrastructure spending).

2. Talent & compute: due to #1, Silicon Valley can outbid Europe for the best talent and hardware. Watch an OpenAI launch video and listen to all the European accents.

3. Local market fragmentation: Europe is a collection of countries that pretend to work together while not even having a unified capital market. The average EU citizen can barely communicate with their neighbor in a common language beyond the level of a toddler (english fluency is massively overstated by Americans who only experience tourist capitals).

4. Regulatory disadvantages: In everything from company regs, employee regs, unions, privacy regs, data portability regs, etc.

It's not "culture" or Europeans being "lazy" as most people would claim. There's currently thousands of young french people working 80 hour weeks creating dumb consulting powerpoints or legacy investment banking deal memos as we speak. Ambitious people exist everywhere in equal proportion, they're just working on the wrong things.

Europe can't compete in the digital revolution the same way they could compete in the industrial revolution due to various system design choices. Culture is simply the aesthetically observed byproducts of system design.

sofixa 90 days ago | | |

> I am wondering what is keeping them back, though: Money? Compute? Skills? Training data?

Considering all their talk about new DCs and compute, and a few offhand comments, it sounded to me that compute is a big limitation.

miki123211 90 days ago | | |

Should it, though?

I think an European company, taking Chinese models, perhaps doing its own post-training on them and training the Chinese-ness out, with a great chat service, enterprise API and coding agent, could be pretty valuable in itself.

greyskull 90 days ago | |

> task focused small models

This is tangential: and forgive my ignorance here, but is there an inherent reason why there aren't smaller, focused models from the frontier model providers?

I'm thinking something like a software-specific subset of Opus that is the default for use in Claude Code. Smaller, cheaper to deploy and consume, maybe faster.

pavpanchekha 90 days ago | | |

OpenAI used to make Codex-specific models, but they stopped. What I've gathered from interviews and similar is that training two models isn't worth the (small) lift from having a coding-specific model. You're pre-training on everything anyway, and coding RL is reasonably useful for general-purpose models too.

baq 90 days ago | |

agreed, the next price increase from frontier labs (and the inevitable limits decrease in subscription tiers) will have people thinking real hard about their model providers and that's when mistral should be ready. however, given their recent performance, I realistically don't have my hopes high up.

amunozo 90 days ago | | |

DeepSeek is both cheaper and better than Mistral.

djvdq 90 days ago | | |

Also, new Medium 3.5 is far more expensive than previous Mistral models, and much more expensive than e.g. Deepseek

echelon 90 days ago | |

Nobody trying to compete with Google, OpenAI, and Anthropic should be playing the small models / local models game.

Foundation model labs should be building very large reasoning models, then leaving it to the community to distill them down.

You can't scale a small model up, but you can scale a small model down.

I'm convinced the only way we'll have a seat at the table in the future and avoid total runaway takeoff is if there are very large models within 80% of the capabilities of the frontier models. Tiny RTX models do diddly squat to remain competitive.

Build open weights models for running on H200s. I'll spin them up on RunPod or Lambda.

farley13 90 days ago | | |

I do think there's a chance open weight models have a bit of a moment with the costs of frontier models growing on business balance sheets. It's unfortunate from my "privacy loving" PoV that it's mostly Chinese models filling the gap. ( the top models on openrouter for instance ).

I have used Mistral models out of pure ideology for web agents and the like which aren't doing a lot of heavy lifting.

ahnick 90 days ago | | |

I thought distillation meant small models don't have to compete with the big models and can always eventually achieve close parity, but it's just a matter of time to do the distillation? (i.e. how much lag do you want to live with) Am I oversimplifying?

raincole 90 days ago | |

> they've fallen into irrelevancy right now

It's a very charitable take, as Mistral has never really left the realm of irrelevancy.

It's only a matter of time before EU falls back to hosting Chinese models in EU datacenters.

lettergram 90 days ago | |

We actually found the Mistral Small 4, quantized to 4bit was comparable to Qwen 3.6 27B and is roughly the same size. At least from our experience on our use cases, the quantization of the Mistral model worked far better than trying to quantize the Qwen family.

Fully agree to your point though, Mistral in general is far behind where I'd expect and Qwen in particular is crushing it at the smaller sizes.

Personally, I'd consider anything 20B params and above a "medium" model. Small being <20B and large >100B. I think obviously we can get to the huge 1-2T param models, but frankly the margin of accuracy improvement for the speed hit is kinda insane (1-2% for many metrics).

rhdunn 90 days ago | | |

It's all relative. For local use I'd classify it by hardware (VRAM size) using FP8 or Q6 quantization:

1. tiny <2-3B -- easily runnable on lower-spec hardware

2. small 4-8B -- runnable on 8GB GPUs

3. medium 9-12B -- runnable on 12GB GPUs

4. large 13-24B -- runnable on 16GB (for the lower end models) and 24GB GPUs

5. very large 25-32GB -- runnable on 32GB GPUs

6. huge >32GB -- not easily runnable on consumer GPUs without compromising performance (offloading layers to the CPU/RAM), quality (heavy quantization, esp. at <= Q4), or price (investing in multi-GPU setups and/or server-grade hardware).

You could possibly split huge down further, as 70GB models (e.g. llama 3) are easier to get working than >120GB models and 1TB models are completely intractable.

barrell 89 days ago | |

I think it really depends on what you’re doing. I use mistral for many tasks in https://phrasing.app and they blow models many times their size out of the water.

None of my tasks use reasoning though (reasoning actually kills the performance) so perhaps that’s why. Still, I just had to rewrite my pipeline, and mistral was both faster, cheaper, and substantially better than any alternative

rhdunn 90 days ago | |

Yeah. I run LLM models locally and for me 22B-32B is the largest I'm willing to invest in trying out.

Even though Mistral 4 has 6B active parameters per token (allowing 3-3.5 per token parameters to be loaded on a 4090), the ~240GB download + storage is pushing the limits of being able to try this out locally, especially if you are downloading and evaluating multiple models.

It also makes it harder for other people to make downstream finetunes like with what happened with the older Mistral/Magistral models.

wolttam 90 days ago | | |

I think machines like the DGX Spark are about to become a lot more common/popular. It’s big enough to run sparse 150-250B MoEs with enough throughout for a single user. Deepseek v4 Flash is #1 (in terms of usage) on OpenRouter because it’s good enough to be useful. You can run it on a Spark (though it runs better across 2, which is getting up there in cost)

chartpath 90 days ago | |

I find Mistral Medium 3.5 with OpenCode is perfectly fine if you're willing to talk to it in a more fine-grained way about actual code. For me that's fine because even with huge frontier models I don't like trying to vibe prompt like a product manager.

coredev_ 90 days ago | |

I don't agree that they are falling behind. Using both chat and cli I get what I need and it's comparable to "sota" when I compare.

arkh 90 days ago | |

Mistral is entering the "let's extract has much money from EU taxpayers as we can" phase of European tech company which did not get bought by a US one.

They'll end like Dailymotion, just a zombie company.

thatsadude 90 days ago | |

Nawh, they trained on test since Llama 2, no wonder.

dyauspitr 90 days ago | |

Mistral is bad bad. For its use cases I feel like India’s Sarvam is doing better.

ctrlkctrls 90 days ago | | |

channeling Rocky (extraterrestrial) there I see :)

kergonath 90 days ago | |

> a decent proxy would be to build models that get the r/localLlama crowd excited

I don’t really disagree with your post, but this is not exactly right. That subreddit seems to go from hype train to hype train every week, I haven’t found anything really insightful in it for quite a while now.

antirez 90 days ago |

I really want Europe to be part of the AI development and research. And I strongly cheered for Mistral. But they are accumulating too much technological delay. This needs to be fixed, otherwise it will turn into yet another proof we are not able to run large tech with good results. Basically any Chinese lab is doing much better. It's not Mistral that created I don't want to say DeepSeek, but MiMo 2.5, Minimax 2.7, and so forth. There are only weaker and/or larger and slower (no MoE) models. Not good.

simonw 91 days ago |

> BNP Paribas runs Mistral models on-prem for KYC in Belgium, with sensitive data staying within the bank's walls. Abanca is using agent orchestration to handle sensitive customer information at a huge scale (2 million customers in their app). For European companies in regulated industries, this is a good alternative to relying on US hyperscalers.

Mistral leaning into on-prem and European-hosted models is very smart.

tnolet 90 days ago |

Regardless of the business. Their website design is :chefs-kiss https://mistral.ai/

Waterluvian 90 days ago | |

What specifically is good about it? I scrolled it on my phone and it seems pretty standard corporate website?

davey48016 90 days ago | |

I love everything about Mistral's branding.

ilja 90 days ago | | |

Le Chat was great, the rebrand to Vibe is meh

akkad33 90 days ago | |

It looks very crowded and the paragraphs are off

Eldodi 91 days ago |

I was at the event, and was impressed by the attendance, all the leaders from the major european listed companies were there.

Also interesting to note the number of partners they invited. Going from Microsoft, Accenture and EY to startups like alpic.ai or lingo.dev . Seems like they are ramping up their M&A game too

zuzululu 90 days ago |

Wasn't even aware Mistral was around and I think that just shows you how irrelevant it has become and not a very good sign for EU in general when the best talent are working for American AI companies.

I saw Tibo's tweet a while back and it was basically a legitimate complaint about the extreme taxation he faced back in EU (France I think) and its pretty obvious how much of a hinderance top down centralized regulation is to innovation.

While I welcome competition and independence, nobody can argue with American innovation and its ability to attract the best of the best. Once it takes seat of the AI reigns there is very little chance for other countries to compete, very much similar to semiconductor field and how only a few select countries have the talent and monopoly over its particular supply chain.

It's clear to anyone looking in that whatever EU is doing is not working (not just AI) and will not work as they do not seem flexible or humble enough to steer itself.

gregorygoc 90 days ago | |

Big tech has remote offices in every major European economy, and they pay well above top 90th percentile of market rate. It basically has a talent sucking effect on the entire economy.

LucidLynx 91 days ago |

As an European: 100x YES!

I really like the direction and the transparency of Mistral, among those players.

dtang2718 90 days ago | |

Even as a non European, it's great to see some competition from Europe against the US/Chinese models.

tomaskafka 90 days ago |

I have been on a lecture from great government IT person, they are evaluating models and are very unhappy about the situation, because they’d love to use Mistral, in some cases it’s the only EU based model they can use … and they know it’s really bad and falling more behind.

It is well possible that Mistral can make a profitable business by being bad, but still the only possible model for EU uses. Sad story, sad to witness.

petcat 91 days ago |

> Abanca is using agent orchestration to handle sensitive customer information at a huge scale (2 million customers in their app).

Maybe my perspective is skewed on what "huge scale" means, but 2 million users? That's like a few hundred megabytes of data? Or a couple GBs if there's a lot of per-user data?

vnglst 91 days ago | |

Maybe, but using state-of-the-art large language models to solve customer support queries with agentic can quickly use a lot of tokens. What I understood from the talk is that they used agents with limited responsibility and (assumption from me) smaller models, to the make sure the answers were quick, reliable and not too costly.

hadlock 90 days ago | | |

There are several payments processing companies that are already largely using AI for customer support queries. They still have an escape hatch to a human but at least one of those companies (on the smaller side) is reporting a ~99% success rate, they are down to a handful of human customer service employees now for cases where the customer can't find/produce the transaction ID.

fidotron 91 days ago | |

European consumer focused businesses do not scale easily the same way US ones do, which is a major contributor to their problems developing tech businesses generally.

OTOH such things can be quite defensible, they just rarely become anything like as profitable.

maxdo 90 days ago |

Oh most prominent eu ai company . Without reading an article predict next, will update after :

1. They give up on building competitive models. It’s time to drink wine not to struggle with competition

2. Because of #1 they will talk a bit about something around llms maybe coding agents , and after start talking about sovereignty.

lejalv 90 days ago | |

Unlike you, who drank the wine before writing the comment.

FinnKuhn 90 days ago | |

3. They are going to start focusing on B2B implementation and deployment.

See what happened to Aleph Alpha...

Oras 90 days ago |

Sounds like they don’t have a moat at all. It’s like software consultancy with a data centre. And then the article mentions many customers using these models on prem (so data centre is not really a plus).

What’s stopping any country backed startup from fine-tuning small open source models?

whiplash451 90 days ago | |

Maybe because distilling small models from bigger ones that you control gives you better small models than fine-tuning from bigger models you don't control?

(I am not claiming it is the case, but stating this as an assumption)

vb-8448 90 days ago | |

No one in Europe will buy from a random startup, the consultancy part is a MUST to do businesses with big corps, banks, finances, insurances, governs, public administration ...

Oras 89 days ago | | |

Mistral is a startup that happened to raise 100M. I said in my comment a “country backed startup”, which mistral is

rldjbpin 87 days ago | |

their moat is where they are based from and that they are making their own models. they have been before the distillation era in the open-weights model.

their model's efficacy for the mainstream comparisons may not be up to the task, but they are pivoting to their own lane for it. but the scope beyond the local market, it is yet to be seen.

kioleanu 90 days ago |

I just got an email from them saying that they’re retiring some (most?) of the dedicated models like devstral gradually through August and one should now use the general model. Cost grows exponentially

Devstral 2 (devstral-2512 and devstral-latest) → We recommend transitioning to Mistral Medium 3.5 (mistral-medium-3-5 with reasoning_effort set to "high"), a stronger model, priced $1.5/$7.5 per million input/output tokens (change from the previous $0.4/$2).

phillc73 90 days ago | |

I received the same email, although couldn’t quite figure out which retiring model I was still using, as I thought I’d already transitioned to Mistral-Medium-3.5 for everything. Anyway, after receiving the email, my hope was that it meant they were also planning on releasing some new, improved models in the next months.

sbinnee 90 days ago |

I believe that Mistral team is doing the best they can do. I like the directions they push; open models for various tasks, on-prem has a lot of potential. Sure, I use Claude code mostly for coding. But there are so many tasks other than just coding. Even for coding, eventually, I am certain they will catch up and Vibe becomes tolerable soon.

ramstar3000 90 days ago |

Excited to see more about their partnership they with Alexa+. In agentic and tool-calling, Mistral’s model architecture excels at the exact structured JSON output Alexa needs to trigger APIs and smart home routines without breaking.

rvz 90 days ago |

We should be supporting and using local models that allow you to run whatever model you want.

ogou 90 days ago |

I've said it before that Mistral is underrated. They are looking at real world use of LLMs and tooling. Bespoke models are very appealing to lots of non-tech centered companies and state agencies. Also, Mistral's actual platform is useful. While others are watching performance leaderboards like this is some eSports stream, they are building real world uses.

stephantul 90 days ago |

I was also at the event and was pretty disappointed. Most of the talks were pretty low on information. I was at the “build” stage, which supposedly was the technical stage, but the talks there didn’t really go into technical specifics.

The papyrus talk was awesome though.

Imbiss 90 days ago |

Really hope there is going to be some competition from Europe in the AI Space.

OhioMan2943 90 days ago |

Not to be confused with the fantastic AI Now institute, run by Meredith Whittaker of Signal among others.

https://ainowinstitute.org/

Almost feels like name squatting

gameshot911 90 days ago |

Does anyone else always read "Mistrial" instead of "Mistral"? Always think I'm about to read a juicy gossip piece, and let down when it's just a standard update on an AI company.

edit A lot of AI company names are really strange, actually. "Claude" is really the best a trillion+ dollar company could come up with? It sounds like the name of a grandpa or something.

wiether 90 days ago | |

https://en.wikipedia.org/wiki/Mistral_(wind)