Notes from the Mistral AI Now Summit
31–40 of 230 posts
Re: Notes from the Mistral AI Now Summit
#32As an European: 100x YES! I really like the direction and the transparency of Mistral, among those players.
Re: Notes from the Mistral AI Now Summit
#33OK, 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 i…
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.
Re: Notes from the Mistral AI Now Summit
#34I 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, an…
Europe shot itself in the dick with this hastily implemented at the height of mass hysteria bullshit and now no sane company will build anything there. an AI startup in the US or China can be a boy and his computer. in Europe, the boy needs a dozen lawyers.
Mistral's sinking into irrelevancy despite the head start they had, the very promising early models they released, and the funding they receive, might very well be the consequence of trying to comply with all that crap.
Re: Notes from the Mistral AI Now Summit
#35Earlier quoted context omitted.
>weird training biases that were required by the Chinese government What is "weird training biases" to us might not be weird to them and vice versa. Just ask the Chinese what they think about LGBTQ+, Japanese, pride parades, Islam and colored minorities. Every nation has its own biases injected in its domestic LLMs at this point. Otherwise they risk getting in trouble for hate speech/disinformation in the jurisdictio…
> What is "weird training biases" to us might not be weird to them and vice versa. I agree. That's why I think European companies might prefer a European model.
Mistral is mostly French and tends to have mostly French speaking customers, like BNP PAribas in Belgium. Germany will want its own domestic AI champions, maybe in partnership with Switzerland and Austria, similar to how Denmark already has invested in LLMs focused on the Nordic languages with money from Norway.
The biggest mistake is treating Europe like a single homogenous country/market.
Re: Notes from the Mistral AI Now Summit
#36Earlier quoted context omitted.
Yeah but why use mistral on premises instead of Qwen?
We're talking about enterprise customers. The trivial answer is Mistral has sales teams and consultants from the same company that builds the models and from the EU.
Re: Notes from the Mistral AI Now Summit
#37Earlier quoted context omitted.
> What is "weird training biases" to us might not be weird to them and vice versa. I agree. That's why I think European companies might prefer a European model.
Except there's no such thing as the "European model" similar how Europe is not a country. Mistral is mostly French and tends to have mostly French speaking customers, like BNP PAribas in Belgium. Germany will want its own domestic AI champions, maybe in partnership with Switzerland and Austria, similar to how Denmark already has invested in LLMs focused on the Nordic languages with money from Norway. The biggest mist…
I for one would love to see more country-specific models. There was a story here the other day about Norway’s National Library developing a LLM specialized in Norwegian: https://news.ycombinator.com/item?id=48270770
Re: Notes from the Mistral AI Now Summit
#38OK, 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 i…
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.
Re: Notes from the Mistral AI Now Summit
#39I 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, an…
https://en.wikipedia.org/wiki/Artificial_Intelligence_Act#Pe... Europe shot itself in the dick with this hastily implemented at the height of mass hysteria bullshit and now no sane company will build anything there. an AI startup in the US or China can be a boy and his computer. in Europe, the boy needs a dozen lawyers. Mistral's sinking into irrelevancy despite the head start they had, the very promising early model…
And yet another time they will be thinking aloud in few year "what happened that we are fully dependent on USA?"
Re: Notes from the Mistral AI Now Summit
#40OK, 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 i…
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.