Mistral really became what all the other over-hyped EU AI start-ups / collectives (Stability, Eleuther, Aleph Alpha, Nyonic, possibly Black Forest Labs, government-funded collaborations, ...) failed to achieve, although many of them existed way before Mistral. Congrats to them, great work.
I think this is a game changer, because data privacy is a legitimate concern for many enterprise users.<p>Btw, you can also run Mistral locally within the Docker model runner on a Mac.
This is so fast it took me by surprise. I'm used to wait for ages until the response is finished on Gemini and ChatGPT, but this is instantaneous.
Not quite following. It seems to talk about features common associated with local servers but then ends with available on gcp<p>Is this an API point? A model enterprises deploy locally? A piece of software plus a local model?<p>There is so much corporate synergy speak there I can’t tell what they’re selling
This announcement accompanies the new and proprietary Mistral Medium 3, being discussed at <a href="https://news.ycombinator.com/item?id=43915995">https://news.ycombinator.com/item?id=43915995</a>
While I am rooting for Mistral, having access to a diverse set of models is the killer app IMHO. Sometimes you want to code. Sometimes you want to write. Not all models are made equal.
Another new model ( Medium 3) of Mistral is great too.
Link: <a href="https://newscvg.com/r/yGbLTWqQ" rel="nofollow">https://newscvg.com/r/yGbLTWqQ</a>
Mistral models though are not interesting as models. Context handling is weak, language is dry, coding mediocre; not sure why would anyone chose it over Chinese (Qwen, GLM, Deepseek) or American models (Gemma, Command A, Llama).