what I like in MistralOCR is that they have simple pricing $1/1k pages and API hosted on their servers. With other OCR is hard to compare pricing because are token based and you don't know how many tokens is the image unless you run your own test.

E.g. with Gemini 3.0 flash you might seem that model pricing increased only slightly comparing to Gemini 2.5 flash until you test it and will see that what used to be 258 per 384x384 input tokens now is around 3x more.

Someone posted a project here about a month ago where they compare models in head-to-head matchups similar to llmarena

https://www.ocrarena.ai/leaderboard

Hasn't been updated for Mistral but so far gemeni seems to top the leaderboard.

I spent like three hours trying to get one of these running and then gave up. I think the paddleOCR one.

It took an hour and a half to install 12 gigabytes of pytorch dependencies that can't even run on my device, and then it told me it had some sort of versioning conflict. (I think I was supposed to use UV, but I had run out of steam by that point.)

Maybe I should have asked Claude to install it for me. I gave Claude root on a $3 VPS, and it seems to enjoy the sysadmin stuff a lot more than I do...

Incidentally I had a similar experience installing open web UI... It installed 12 GB of pytorch crap.. I rage quit and deleted the whole thing, and replicated the functionality I actually needed in 100 lines of HTML.... Too bad I can't do that with OCR ;)

I've worked on document extraction a lot and while the tweet is too flippant for my taste, it's not wrong. Mistral is comparing itself to non-VLM computer vision services. While not necessarily what everyone needs, they are a very different beasts compared to VLM based extraction because it gives you precise bounding boxes, usually at the cost of larger "document understanding".

Its failure mode are also vastly different. VLM-based extraction can misread entire sentences or miss entire paragraphs. Sonnet 3 had that issue. Computer vision models instead will make in-word typos.

I think there is a lot of broad support, but they're just kind of hamstrung by EU regulation on AI development at this stage. I think the end game will ultimately be getting acquired by an American company, and then relocating.

Form processing is vastly more useful than meme generation. When people need to do real work this is the sort of tool they are going to reach for.

>It seems like EU in general should be heavily invested

Maybe, i think it will be to our benefit when the bubble pops that we are not heavily invested, no harm investing a little.

I guess it's better to do the same stuff everyone else is doing?

Following the leaders too closely seems like a bad move, at least until a profitable business model for an AI model training company is discovered. Mistral’s models are pretty good, right? I mean they don’t have all the scaffolding around them that something like chatGPT does, but building all that scaffolding could be wasted effort until a profitable business model is shown.

Until then, they seem to be able to keep enough talent in the EU to train reasonably good models. The kernel is there, which seems like the attainable goal.

(I work at OpenRouter) If you send a PDF to our API we will:

1. Use native PDF parsing if the model supports it

2. Use this Mistral OCR model (we updated to this version yesterday)

3. UNLESS you override the "engine" param to use an alternate. We support a JS-based (non-LLM) parser as well [0]

So yes, in practice a lot of OCR jobs go to Mistral, but not all of them.

Would love to hear requests for other parsers if folks have them!

[0] https://openrouter.ai/docs/guides/overview/multimodal/pdfs#p...

You might want to mention that you are a competitor.

Oh god, I'm sure I wouldn't come close to 50%; that's so hard to read

Where are you seeing 79% accuracy? 79% only occurs on the page as a win rate, not an accuracy