Mistral OCR 4

To the best of my knowledge, most of the founding team started their careers in the US ( meta,etc..) and their primary investors are US VCs. In that regard, they smartly benefit on both side : US funding and European brains

~Any borderline-large European tech company will have an office on the US west coast, for sales if nothing else. And probably sales engineering. The timezone difference is eight to ten hours; there is really no way around it.

(I did work for one which had an office in Vancouver, instead; same tz.)

Unfortunately Europeans are terrible customers for making money. They ask a lot of questions and they're very stingy with their wallets. Americans on the other hand ...

Why would anybody do that you would simply get terrible results compared to dozens of other more capable models. It's for converting to text not answering questions. Just seems like you need some sort of weird angle to bring out an anti AI stance

“I delegated critical financial decisions to my OCR software, and you won’t believe what happened next.”

Right, just announced https://x.com/BaiduAI_News/status/2069322806748410291

I used Abbyy Finereader for several years. I loved it. I completed some large projects with it. Modern VLMs put classic FineReader to shame for processing low-resolution/degraded/non-standard text.

I'm personally using the small Qwen 3.5 models. If you have an OCR problem, Mistral OCR 4 is probably great. Open weights models that you can run on a laptop may also work great.

Opposite advice. It's very useful to me for dev and general tasks.

Been using Claude in parallele, it's better not not that much, just 10x (or 100x ?) more expensive.

Mistral's coding models aren't on par with current SOTA US and Chinese models if that's what you're referring to, but I rather like their OCR models.

> After paying for Mistral and using it for a while I genuinely hated it

For OCR?

Same, I got a refund 3 days later. It is unusable.

what did you use it for and when?

Opus is very good at OCR. Way better than the small 1-4B VLMs. If Opus failed, most likely those smaller models will fail as well.

I do not believe this story.

Opus 4.8 scanned hundreds of PDFs for me recently with the worst handwriting imaginable. 100% successful, other than one record where even I could not figure out what was written.

I read that as "languages under-represented in the training set".

Looks good to me on both brave (on android) and firefox (on windows 11). Lets see what ssl labs says (it is running now)

https://www.ssllabs.com/ssltest/analyze.html?d=mistral.ai&la...

Looks good so far, A+ on ipv4 as well as ipv6

Edit: I also asked Gemini 3.1 Pro to analyze the certificate and it looks good

It looks like you have shared an `about:certificate` URL containing a chain of three Base64-encoded X.509 TLS/SSL certificates. This specific chain is used to secure connections to *mistral.ai*.

Here is the decoded breakdown of the certificate chain you provided:

## Certificate Chain Overview

This is a standard three-tier certificate chain issued by Google Trust Services for the Mistral AI domain.

---

### 1. Leaf Certificate (End-Entity)

This is the specific certificate issued to the website to verify its identity and encrypt traffic.

* *Subject (Common Name):* `mistral.ai` * *Subject Alternative Names (SANs):* `mistral.ai`, `workers.mistral.ai` * *Issuer:* WE1 (Google Trust Services) * *Valid From:* June 13, 2026 * *Valid To:* September 11, 2026 * *Key Type:* Elliptic Curve (ECDSA)

### 2. Intermediate Certificate

This certificate acts as a bridge between the website's certificate and the trusted Root CA.

* *Subject:* WE1 (Google Trust Services) * *Issuer:* GTS Root R4 (Google Trust Services LLC) * *Valid From:* December 13, 2023 * *Valid To:* February 20, 2029 * *Key Type:* Elliptic Curve (ECDSA)

### 3. Root Certificate

This is the foundational trust anchor pre-installed in browsers and operating systems.

* *Subject:* GTS Root R4 (Google Trust Services LLC) * *Issuer:* GTS Root R4 (Self-signed) * *Valid From:* June 22, 2016 * *Valid To:* June 22, 2036 * *Key Type:* Elliptic Curve (ECDSA)

I was just using infinity parser 2 (flash, to be fair) for pennies self-hosted to run through thousands of pages of documents with remarkable confidence. I decided to use https://huggingface.co/datasets/allenai/olmOCR-bench to determine what was the best OCR tool, yesterday, but I've got no idea what the best is now. What is the dominant OCR eval right now? Between Baidu and Mistral this morning, I wonder if there's a new tool to switch to..

(jerry from llamaindex here) we're gonna benchmark on ParseBench and report the results!

Or Apples local OCR/Vision models?

In the sense that you can get similarity scores for individual characters referenced against a known database of characters written by various individuals. You can get stylometry scores out of small LLMs that do demographic segmentation based on writing style using the same methods.

They won't have the capacity to be fed an image of handwritten text and say "Ahh, this is a note written by Winston Churchill!". You could very easily use these models and your agent framework of choice, like Hermes, the Segment Anything models, and other foss tooling to build a dedicated, specialist handwriting recognition system. Or facial recognition, or fingerprint recognition, etc - these sorts of things can be done very procedurally, without a lot of interpretive AI.

Yes, we have successfully used Mistral OCR for digitizing handwritten forms. You always have low percentage that need human review and adjustment, but overall Mistral has been highly accurate (their price is amazing, too).

If you mean handwriting to text then yes