Mistral OCR:
- 72.2% accuracy
- $1/1000 pages
- 5.42s / page
Which is pretty far cry from the 95% accuracy they were advertising from their private benchmark. The biggest thing I noticed is how it skips anything it classifies as an image/figure. So charts, infographics, some tables, etc. all get lifted out and returned as [image](image_002). Compared to the other VLMs that are able to interpret those images into a text representation.
https://github.com/getomni-ai/benchmark
The 95% from their benchmark: "we evaluate them on our internal “text-only” test-set containing various publication papers, and PDFs from the web; below:"
Text only.
I realize it’s not a common business case, came across it testing how well LLMs can solve simple games. On a side note, if you bypass OCR and give models a text layout of a board standard LLMs cannot solve Scrabble boards but the thinking models usually can.
I'll give it a try, but I'm not holding my breath. I'm a huge AI Enthusiast and I've yet to be impressed with anything they've put out.