I think catering to people who won't use the best version of an emerging tech is a losing strategy, but I guess we'll see.

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epolanski 6 days ago | parent [-]

No, the problem is that HN is blind to the fact that there are multiple definitions of "best".

It isn't just about "more powerful", it's also about "cheaper" or "faster".

Mistral models are faster than anything out of US (bar Gemini Flash) and are cost competitive with them.

For me, having to produce financial news in a short time span for tens of thousands of users speed and cost are important, and the fact that Opus 4.1 is "more intelligent" is worthless.

That's like telling me that a Ryzen Threadripper with 64 cores is faster than than my raspberry pi for controlling the appliances in my kitchen. It's irrelevant when it's much more expensive and energy hungry.

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BoorishBears 6 days ago | parent [-]

Pretty far off the mark.

I've spent the last year building an AI product in a situation with really cut throat margins: I've post-trained every model Mistral has released in that time frame that was either open-weights or supported fine-tuning via Le Platforme (so I've gotten them at their absolute best case)

Mistral's models are not competitive anymore, and haven't been for most of that time. Gemma 27b has better world knowledge, Deepseek obsoleted their dense models, Gemini Flash is faster and their models are not even close to cost competitive with it (shocking claim otherwise tbh).

Mistral's platform is not fast (Mistral Medium is slower than Sonnet 4, which is just straight up insane!). Cerebras is fast, but there are both competitors offering similar speeds (Samba Nova and Groq), and other models that are faster on Cerebras (people really sleep on gpt-oss after the launch jitters)

You're inventing a snowman with your analogy: their models are just irrelevant, and that's informed by using everything from dots.llm to Minimax-Text to Jamba (which is really underestimated btw, and not Chinese if sinophobia has a grip on your org) to Seed-OSS, in production.

tl;dr: the only way to justify Mistral's models is in fact to reject the best solutions in any dimension that can be described as model performance.

If you're still using them and it really isn't for non-performance reasons, I assume you're overindexing on benchmarks or behind on the last year or so of open-weight progress and would recommend actually trying some other offerings.

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epolanski 6 days ago | parent [-]

And I have spent the last year building multiple ones.

While I can't claim to have tested everything, especially as we aren't going to change our stack every single week as something releases, I can speak for my recent knowledge of comparing Mistral small and Medium (their summer releases) with offerings from Google, OAI and Anthropic.

For our use cases, where little thinking is required and its mostly about gathering and transforming data Mistral offered the lowest cost per $. There is no single cloud out there that could compete on the cost per token or speed, bar Gemini flash.

We'll re evaluate and test in the future, but we're very satisfied in a way that only Gemini flash did for us before.

Plus, they are from EU and we're very glad to sustain an European business, we'll only consider alternatives if we need them or the current offering isn't competitive anymore, that's still not the case.

	▲	BoorishBears 6 days ago \| parent [-]
		This just goes back to my original point: you don't feel pressure to keep up with all the solutions out there, and are ok taking what's good enough. Mistral can bank on others doing the same, and I have no doubt they'll be able to get along doing so. They're not in the most competitive home market either, so I do think they'll stay at the front of "EU-native" foundation models. But last week a Chinese delivery app casually chucked a model that's stronger than anything Mistral has ever released on HF (with an MIT license). When that's the competition, their current strategy is rough to say the least.