A tangential observation: the video on the linked page wasn't what I expected. I thought Mistral was a european AI company, so I didnt expect the video to be filmed in San Francisco featuring three people who don't seem to be european.

I'm not against them being a global organization, that's wonderful. I was just surprised. I expected a parisian office and european accents.

I’ve always thought the US Postal Service is such a technological marvel. They somehow manage to identify and route billions of pieces of mail and I have to imagine their tech is significantly more primitive than this. Not only that but US addresses are absurdly non-standardized, you can often write the same address multiple ways and have it deliver to the same location. I’m sure there’s plenty of published knowledge in this area, but whenever I see announcements about OCR it feels like this should be a solved problem if it’s been accomplished at the scale of USPS for many years.

All AI labs really need to stop using truncated y-axes for benchmark bar charts...

https://mistral.ai/_astro/cm-engish_ZhlvoT.webp?dpl=6a3a94bd...

It's cheap at $4/1k, but I'm hesitant to even benchmark this one again since the previous versions were all "98% accurate based on internal benchmarks of 4 pdfs" and ended up falling short of almost everything else on the market [1].

Even in this one, they just report that OlmOCRBench and OmniDocBench have "known limitations" and that's why they report flagship numbers from their internal benchmark.

https://getomni.ai/blog/benchmarking-open-source-models-for-...

Tested with Malayalam, normal handwriting got accurate but a slight different style got detected as kannada. Have samples if required, which sarvam got done with 99% accuracy leaving one text error.

It'll be interesting to see how this ranks against https://github.com/baidu/Unlimited-OCR

Mistral keeps reminding us that doesn´t just brew great coffee they can build great AI too. Hats off to the team. Mistral O.C.R. (Only Cool Results)

The comparisons rank it against GPT and Gemini but not Claude. Is Claude's vision support simply not competitive when it comes to OCR tasks?

"A note on out-of-scope use. OCR 4 is a document-understanding model, not a decision-maker. It is not intended for medical diagnosis, legal advice or judgment, high-stakes financial decisions, safety-critical systems, real-time/latency-sensitive processing, or non-document inputs (raw audio, video, etc.). "

Can't wait for the "oh so innovative" manager who will suggest during the next meeting "Ok... but what if WE used it for high-stakes financial decisions on non-document inputs like a photo from my phone?"

I guarantee you somebody on HN is going to comment about this "idea" next week.

Little on differences other than bounding boxes and double the price compared to their previous OCR v3 model from December - https://mistral.ai/news/mistral-ocr-3/ - other benchmarks were used back then.