It's the number of attempts at answering the question.

I use mistral-medium-3.1 for a lot of random daily tasks, along with the vibe cli. I'd state from my personal opinion that mistral is my preferred 'model vendor' by far at this point. They're extremely consistent between releases while each of them just feels better. I also have a strong personal preference to the output.

I actively use gemini-3.1-pro-preview, claude-4.6-opus-high, and gpt-5.3-codex as well. I prefer them all for different reasons, however I usually _start_ with mistral if it's an option.

Mistral hasn't been in the running for SOTA for quite awhile now

Not at the moment, but a release of Mistral 4 seems close which likely bridges the gap.

Isn't their latest speech to text model SOTA? When I tested it on jargon, it was amazing.

https://news.ycombinator.com/item?id=46886735

I used Ministral for data cleaning.

I was surprised: even tho it was the cheapest option (against other small models from Anthropic) it performed the best in my benchmarks.

Pretty much all of my LLM usage has been using Mistral's open source models running on my PC. I do not do full agentic coding as when i tried it with Devstral Small 2 it was a bit too slow (though if i could get 2-3 times the speed of my PC from a second computer it'd be be a different story and AFAIK that is doable if i was willing to spend $2-3k on it). However i've used Mistral's models for spelling and grammar checks[0], translations[1][2], summaries[3] and trying to figure out if common email SPAM avoidance tricks are pointless in the LLM age :-P [4]. FWIW that tool you can see in the shots is a Tcl/Tk script calling a llama.cpp-based command-line utility i threw together some time ago when experimenting with llama.cpp.

I've also used Devstral Small to make a simple raytracer[5][6] (it was made using the "classic" chat by copy/pasting code, not any agentic approach and i did fix bits of it in the process) and a quick-and-dirty "games database" in Python+Flask+Sqlite for my own use (mainly a game backlog DB :-P).

I also use it to make various small snippets, have it generate some boilerplate stuff (e.g. i have an enum in C and want to write a function that prints names for each enum value or have it match a string i read from a json file with the appropriate enum value), "translate" between languages (i had it recently convert some matrix code that i had written in Pascal into C), etc.

[0] https://i.imgur.com/f4OrNI5.png

[1] https://i.imgur.com/Zac3P4t.png

[2] https://i.imgur.com/jPYYKCd.png

[3] https://i.imgur.com/WZGfCdq.png

[4] https://i.imgur.com/ytYkyQW.png

[5] https://i.imgur.com/FevOm0o.png (screenshot)

[6] https://app.filen.io/#/d/e05ae468-6741-453c-a18d-e83dcc3de92... (C code)

[7] https://i.imgur.com/BzK8JtT.png

That's likely because they're chasing enterprise - see deals with HSBC, ASML, AXA, BNP Paribas etc... Given swelling anti-US sentiment and their status as a French 'national champion', Mistral are probably in a strong position for now regardless of model performance, research quality or consumer uptake.

I'm building a knowledge graph on personal data (emails, files) with Ministral 3:3b. I try with Qwen 3.5:4b as well but mostly Ministral.

Works really well. Extracts companies you have dealt with, people, topics, events, locations, financial transactions, bills, etc.

Me neither, they're not ready for prime imo. I have a yearly sub and the product is just orders of magnitude behind Anthropic's offering. I use Code for real world stuff and I am happy with the result, Mistral is just not something I can trust right now.

I use them solely.

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