I've got a scan from a book that I OCR with new releases. Ligatures, critical sigla, Fraktur letterforms, subscripts, superscripts, etc.
Nothing special about this model for overly-detailed work like mine.
It's been a while since I last tested (and discontinued my subscription), but the "pro" models from OpenAI dominate. Not surprising, given the price difference, but it would be nice if an OCR-specific model could perform better. It's worth mentioning that even the highest-end models do a pretty poor job with intricate text like mine.
While I haven't tried OpenAI for OCR, I've put my small scale OCR work through both Claude and Mistral OCR. Claude is absolutely better - even in OCR work I did last week and compared with Mistral OCR 4.0.
Mistral's one advantage is that Anthropic now flags OCR, because they don't allow anything that could be considered "reproduction", even of work for which you own the copyright. So my new workflow is Mistral OCR for the actual OCR, followed by a proofreading pass by Claude (which is allowed). Claude is obviously more expensive, but it caught entirely hallucinated sentences created by Mistral OCR 4.0, so I was glad for the backup check.
Same company that OCRed millions of books, the irony.
I feel Anthropic is destroying itself with all these restriction. They got away because their models were the best for coding, but that is not an advantage anymore as OpenAI and other open source are already better.
Yep, my understanding is that many guardrails like this are actually the result of government legislation (eg the Fable bans) or terms of settling copyright lawsuits over reproducing copyrighted text and lyrics.
I mentioned it in a sibling reply, but here's Anthropic's support document about not using Claude to reproduce content verbatim that already exists, regardless of copyright.
> Claude is obviously more expensive, but it caught entirely hallucinated sentences created by Mistral OCR 4.0, so I was glad for the backup check.
What does this entail? What does Claude do to decide that the text it was provided was hallucinated? Are you telling Claude that the source was OCR'd by another LLM?
I'm basically doing the OCR twice, except in the Claude proofreading pass, it is not being asked to transcribe the document to a Markdown file. I'm pointing it to the same image input files, and to the Mistral OCR transcript Markdown file (it knows it's a Mistral OCR output), and ask Claude to check that the text is correct and point out the errors - and then make the necessary edits.
I can't speak for Mistral OCR 4.1, but the hallucinations in 4.0 were so egregious (just completely making up new sentences in the middle of a page) that I knew I can't trust Mistral OCR on its own.
How bad is the first OCR pass allowed to be to still count as proofreading? Can you let Claude compare the images with /dev/random and make the necessary edits to correct differences?
Hmm, that's an interesting idea. But it's the classifier that is triggering, and it triggers specifically on Claude's output. So I think the /dev/random case wouldn't work, because that gets Claude into the state of just reproducing the entire text from the original again.
It doesn't always get flagged. Single pages are almost always okay. Running a program that sequentially runs single pages through the API is often not okay - I wrote a program in the early 4.x days before the rule came in, that's how I hit it first. But I've also had entire articles go through just fine recently in a Claude Code session (I'd forgotten about Anthropic's rules!), and then others where I get classifier errors by page 4.
The Mistral OCR errors were small in size. Single sentences, formatting errors, paragraphs with newlines. So this was a genuine proofreading job with small changes. For the most part Mistral is actually good, but I can't have it just inventing sentences in the middle of a document. That's where the Claude proofreading pass was most helpful.
I read that as “hallucinated” in the practical sense: the OCR output contained text that wasn't actually present in the scan, rather than just misreading a character.
Not the person you’re responding to, but I’ve had Claude refuse to OCR pages from in-copyright books. I was sometimes (but not always) able to get around that by changing models, by telling it that I was doing the text conversion only for personal use, or by first telling it to use Tesseract or another OCR engine to do the initial pass and then having a Claude subagent proofread and clean up the OCR output.
I’ve also had it refuse to OCR public-domain books that included content that it didn’t like, such as references to prostitution in 19th-century books about Japan.
I had one session where Claude refused to continue after it hit some kind of guiderail restriction. I couldn’t see what the trigger was, so I started a new session, gave Claude the link to the previous session, and asked it to diagnose the problem. This new Claude said it couldn’t view the exact guardrail issue, but it did suggest a workaround that turned out to be effective.
"it refuse to OCR public-domain books that included content that it didn’t like, such as references to prostitution in 19th-century books about Japan."
Thoughtcrime -like territory and self-sensorship. The AI safety lobby is such a vile influence on the freedom of expression and communication via technology (since AI is starting to eat up rest of technology).
I guess the main problem is positioning AI tools as "human-equivalent" creators by the big AI corps. If they were positioned simply as "better OCR and proofreading" people would attribute to them as much responsibility as they would to a - say - typewriter and we would not need to have this nonsense.
I do realize most of the valuation comes from the positioning of "our TAM is the global salary base of 50 trilion and we aim to supesede human workers in the near future" which implies they need to position this technology as "human equivalent" or that valuation is no longer as credible.
Are you familiar with the Thesaurus Linguae Graecae? It's a project that may meet your needs. Paywalled, unfortunately, but I assume it would be a one-time expense.
This is too funny considering they did that themselves. I’m pretty tired of these companies deciding what we can and can’t do while they act with impunity.
I also saw this when I made a (personal use only) book translation (agent learns how book 1-3 of a row is translated, then translates book 4 the same way because it is not available in my language).
Claude happily translated most of the book, except a few chapters which it denied. Tried several times always the same results (with no other context about the rest of the book).
The response / conversation gets blocked by the guardrails with "API Error: 400 Output blocked by content filtering policy"
At first I thought it was something in the scanned content that was being flagged, but it was the attempt to transcribe that was itself being flagged. Anthropic mention it on their pages:
"Anthropic takes these steps because Claude’s purpose is to generate new content and ideas, not to reproduce content that already exists."
Side note - Claude itself is not aware of this policy, and is unable to see the API responses - the turn just ends. Which turned into a really bizarre failure state where Claude thought I was gaslighting it and kept insisting it could do the work and even had the entire text in memory. Every time it would go to show me and prove it, it would hit API Error 400. I was only able to convince Claude by showing screenshots of my Claude Code screen output so it could see that I was seeing API errors. I've never seen Claude get into that angry & snarky state before, and I hope it doesn't happen again.
I have been quite happy with Mistral OCR for the documents I needed to process (typeset, but old, with questionable scan quality, sometimes elaborate typesetting or, much worse, typewriter-and-handwriting approximations of it). I do not test every new model when they are released, but I did a review shortly after Mistral OCR 3 was released and it was a very good compromise: cheap, fast, and good results without further processing. I found generalist models to be way too much faf to get them to avoid unnecessary modifications to the text and report accurate bounding boxes for figures and tables.
That said, models have sometimes surprising weaknesses and a model could be terrible overall but magically work for one type of document.
I got the opposite experience very recently : tried to OCR a bunch of handwritten emails addresses with chatGPT and I had to make so many corrections that I gave up. Whereas Mistral nailed it on first pass.
> the "pro" models from OpenAI dominate. Not surprising considering the price difference, but it would ne nice if an OCR-specific model could do better.
I haven't been impressed with any of Mistral's models. They obviously realized that they couldn't compete at the frontier so they decided to go for smaller focused models but even those have not been that good.
We moved away from Cursor but I was looking for a model that would help with FIM (fill-in-middle) multiline autocompletion and people were recommending Mistral's Codestral. We gave it a shot and it was lackluster at best.. Even Google's Gemini did a significantly better job than Codestral.
Ultimately Opus-class models got good enough and I don't do much manual coding anymore.
Yet: how is pricing?
Evaluating contents and routing appropriately isn't a new challenge in OCR, one of the oldest fields of applications in ML.
Thus, how do the smaller open models perform in tandem with relatively pricy $/pg models & APIs?
Your use case is remarkably rare relative to the volume and price sensitivity of enterprise data warehouse ops.
The big difference is traditional OCR used basic pattern matching to find text, whereas models like Mistral OCR (and GPT, etc) use computer vision instead and deep learning to parse text, math equations, and apparently in some cases extract images too.
I'd love to see some advancements in traditional OCR based on ideas and concepts we've learned from newer "OCR-like" models since traditional OCR is drastically cheaper.
I made a benchmark for handwriting recognition for a project while keeping line breaks and errors (grammar, spelling). Sonnet absolutely dominates it since a good half a year. 5.6 did not change that for me. This should also translate to better ocr.
You're the second person to mention handwriting. I think it might be handwriting-specific; perhaps Anthropic has a better corpus for this.
I really wouldn't know, though. Anthropic models barf out copyright issues for my use case, so I'm unable even to benchmark them. It's a common problem when you're scanning public domain books. Mine are reference texts often cited.
So is Mistral OCR the best one? Have any other OCR models caught some of what you describe? I've been kind of interested in how "OCR" type models work compared to old school OCR.
My use case isn't in the realm of old-school OCR, so it's not a good comparison, but anyway:
As another user pointed out, it's surprisingly random (task-specific). Llama Scout outperformed Gemini Flash 2.5 on a benchmark I built at the time. I didn't include an OCR models.
Mistral might indeed be the best OCR-specific model for my task, now that you ask. Funny. It's so bad at my work that I didn't register it might be the best in its category. This is just based on vibes from my single scan.
It's very common. PaddleOCR is enough to get extremely well-done bounding boxes, and it runs very fast on a $150.00 GPU.
There's always room for improvement, though. I suspect a tool will emerge for highly detailed OCR that implements a nested bounding-box-based multi-scale approach, effectively OCRing small sections at a time and then gradually compiling them by expanding the surface area using the bounding boxes.
I've thought a lot about implementing it anyway.
edit: I see you're asking about the block labels. Leaving the comment in case someone finds it interesting.
I think people misunderstand the utility of Mistral's OCR. It's not going to beat SOTA models for extraction on edge-case docs, but it's MUCH cheaper and faster and does an excellent job on simple ones. I've been working on converting PDFs to EPUBs and Mistral has been making steady improvements. On a chapter of Bleak House it was able to extract and tag the header, titles, and references at the bottom every time. The only thing it struggled on was line numbers in the right margin which it correctly tagged as "aside text" 3/5 times, but always separated from the core text each time. The important thing to keep in mind is that there's no prompting needed, just upload the PDF and voila!. There's even a batch mode with a 50% discount.
> The important thing to keep in mind is that there's no prompting needed, just upload the PDF and voila!
I'm sorry, noob here. I have a special book that I bought which I can open only inside the Kindle app (Windows/mobile). I have been meaning to screenshot the pages and convert them into a document/PDF. What do I have to do to make it fast? Just upload all the screenshots one by one and tell Mistral "Chat" to OCR them?
At this point I lost all hope for Europe playing any significant role in the AI race. If that’s a good or a bad thing I don’t know, but it seems to me like that’s the reality.
Much like spaceflight, aerospace or nuclear engineering, you need to retain local talent for national defense purposes. Being 70% as good is still way better than being 100% dependent and heavily leveraged by your opponents.
If your employees are only 70% as good, as skilled, as knowledgeable while not knowing it, then you can absolutely kill a reactor just like Chernobyl. The overconfidence is what allowed them to put the reactor in the state they did and not realize the implications.
Far worse than 70%. Chernobyl was a catastrophically flawed reactor design. Operators ran a badly planned safety test in which operators intentionally caused a dangerous state while key protections were disabled or bypassed.
? It absolutely is a race. Whether thats a positive thing or not is debatable but every lab is definitely in a race. What prize do you win? Imagine a world where only one country has AGI/ASI. Or a world where Europe only gets access to frontier models 6 months later. Far from ideal.
> Imagine a world where only one country has AGI/ASI.
You've fallen for the hype. The only way these AI companies can justify their bullshit worth and burn of capital is by promising a magical AGI. It's just marketing and it is still unclear if LLM's will ever be profitable.
If they won't - then EU is doing the right thing laying low.
> Or a world where Europe only gets access to frontier models 6 months later.
> If they won't - then EU is doing the right thing laying low.
Exactly. People seem to forget that US is burning money at an accelerating rate without any promise of returns. This is a high risk situation. Time will tell.
> What prize do you win? Imagine a world where only one country has AGI/ASI.
"Winning the race" doesn't give you that in any meaningful capacity. It gives you, at the absolute most, a temporary window where that's the case. See: nuclear weapons.
Citing nuclear weapons isn't the flex you think it is. There are only 9 countries that have nuclear weapons and they absolutely flex this power over non-nuclear powers (see Ukraine, Germany, SE Asia etc..)
Europe already has an innovation problem that's already causing structural economic instabilities which Germany has been struggling (and lately failing) to prop up.
As much as it pains me to say this, AI is already a tech revolution and it seems like Europe is just ignoring it. There's more innovation in 3 blocks in downtown San Francisco than the entire continent of Europe.
> There are only 9 countries that have nuclear weapons
QED being first didn't grant exclusivity.
The premise wasn't that AGI isn't useful. There are actually layers to the metaphor where first-mover advantage of AGI is even less meaningful than it was for nuclear weapons, but I leave those as an exercise to the reader to discover.
Lots of tech money is demonstrably wrong, at least in the general case. Microsoft's whole business model is to be the second mover. Patents exist because first mover advantage alone isn't enough to hold onto a win.
If AI is different, the onus is on AI folk to explain why, not the reverse.
The frontier labs are racing as hard as they can right now because they know as well as anyone else that they're at best 6 months, probably closer to 3 months, ahead of the generic commodity AI market. The majority of the tech money currently boosting them is a bet that either they can stay that far ahead without tripping, or that a moat can be engineered. It's not at all clear that it can - or, if it can, that it will before the money runs out.
> You should at least try to explain your contrarian position
FWIW I explained it fully and provided the argument, along with explicit grounding examples. Can you tell me what exactly you don't understand?
To quote your other post:
> This is the weakest part of the argument. Absolutely no reason to believe the second inventor will be 10x faster.
Nor is there any reason to believe that it will work at any usable speed. An LLM capable of AGI running at 0.000001tok/s isn't a very good head start. Nor is it a good moat if it can't actually realize anything material fast enough. Lord knows America has labor problems.
I'm demonstrating the context is a lot more complicated than "be first". There are innumerable specific examples. They're not guaranteed to happen, that's not the point being made.
Also "no reason" is curious when it's the explicit pattern this exact industry has exhibited. Chinese models lag a few months, and come in swinging with an order of magnitude more efficiency. Does it apply? Who can say. It's certainly on the table. Pretending like it's any more ridiculous than AGI itself is irrational.
> Does this ever happen? Even in traditional manufacturing, the second “inventor” starts behind and has to improve their own process to surpass the first.
To use the industry itself once again: Japan was the first to deep learning. Performance issues prevented them from capitalizing on it. Now they're barely even a player on the field. Interestingly and adjacent to the industry, Japan was second to symbolic AI, and the FGCS was lightyears ahead of America's crufty Lisp ecosystem. For a more modern example, Google was the first to transformers. Much of this revolution is thanks to them. A shame that Gemini is third-rate at best.
It's not unique to this industry. That second inventor more often than not does a better job than the original one. It's a pretty common pattern. Otherwise, we wouldn't need patents.
My "contrarian position" is anything but. There is nothing new under the sun. You don't just need to be first, you need to maintain exclusivity.
A reductive equation, economics isn't thermodynamics. Money is fictional and value is subjective and unstable. Within this context, being first to AGI means nothing if the second invention of it comes 2 months later and works an order of magnitude faster than what the first iteration had self-improved to at that point in time. First mover advantage isn't decisive, you have to actually be able to capitalize on it in a robust way.
This is the weakest part of the argument. Absolutely no reason to believe the second inventor will be 10x faster.
Does this ever happen? Even in traditional manufacturing, the second “inventor” starts behind and has to improve their own process to surpass the first.
This presupposes AGI or ASI are a real, reachable thing. My read is they might be, but not as LLMs. Until there's a fundamental rearchitecture, I'm AGI-agnostic and given that view, it doesn't seem rational to bet the house on it.
True, but that isn't EU/US/China, it's OpenAI/Anthropic/Grok/ …/DeepMind (based in UK)/… DeepSeek
With a lot of Chinese nationals in American companies, and an American corporation owning DeepMind, and a lot of people very upset with all of them at the same time, this is very messy.
The non-doom scenarios include "utopia for all", and "power flows to investors, not citizens of whichever nation the winning model's corp. was registered in".
Independently, "oh look all the investors went bankrupt" can happen in both "doom" and "normal technology" timelines.
And US lost the significant role in chip/pc manufacturing. But if a product becomes commodity or utility (which at least for now it seems is the direction), with little lockin, it's not a big deal.
I hope we (EU) don't waste money trying to train local models (which at least some people in Poland try to do), and tries to build our own chips - AI chips have different architecture than regular processor/GPU, and TSMC doesn't need to be winner in this new race.
And if not this, then smaller labs, harnesses and actual application.
GigaChat is russo-centric and is completely useless outside Russia itself, performs absolutely worse than Mistral once you start using English. Reminder that Russia's economy is smaller than Italy's alone. They are not winning over Europe in any single metric, let alone against the poorest country in Europe, which is not even part of NATO nor the EU, on the battlefield.
I've heard that before though. I don't think you can compare economies like that since Russia has "near infinite potential" in natural resources, people, etc which Italy doesn't.
They can kick into a true war economy and then become significantly more of a threat. They haven't done so yet because the middle-class population likes their luxury lifestyle (for now).
I highly doubt Ukraine will "win" - in fact I believe Russia is ramping up to find an excuse at some point to attack a NATO country, but in a way and during a point in time it's difficult for NATO to retaliate.
Yeah? And here I've been a happy Transkribus customer for some time now. If there are better models or interfaces out there for analyzing historical handwriting, I'll definitely take a look.
For anyone interested, I have an ocr pipeline running on rented GPUs, doing around 1000pages for 0.05-01 usd with around 0.8 seconds per page with full bounding boxes support for grounding.
If you’re interested you can find contact to me via this profile.
Accuracy is truly what people die for in the OCR game. Price isn't the primary function here.. it's an equation of price, accuracy, speed, and in mayn cases regulation.
Tbf even with tesseract you already get shit ton of accuracy and you can probably do these 1000 pages for way less than 3.5€. For 3.5€ you can spin up a cloud instance with 8vCPU+32gb on gcloud for 11 hours (or 11 instances for an hour) which can do way more than 1000 pages per hour on tesseract. It takes you around 6 second per page +-4 seconds start/stop depending on what you are doing on that instance size without too much optimization (you can probably even run multiple processes on a single node)
Google documentai costs 1.5$ per 1000 which is probably better in quality and speed.
Tesseract is not a substitute for these models, which understand complex layouts and also extract bounding boxes for things like tables and pictures. They are also much better at making sense of cursive scripts.
I’ve been there, implementing a way to linearise text from a document with pages with 1, 2 or 3 columns, some of them in landscape is a nightmare. And that’s not even considering equations.
In the end it’s way easier to use a specialised model, trained by other people to do exactly what I need.
It‘s also cheaper than the murican ones from Google, Amazon, …. And tesseract was an example. Heck you can go xberg and use paddleocr. Most often layout is less of a problem for ocr. Most often you need high accuracy, which tools like these are often worse in the 95 percentile.
Most use cases dont need that kind of accuracy, just doesnt justify the 3-4usd range. I build for that exact case (tender documents, we’re processing north of 100k pages per day), it doesnt need to recognize scanned written text from 1930s, its usually pdf/docs/scanned printed pages.
The accuracy is great, bounding boxes are must have for proper grounding for building answers by LLMs. Tesseract was too slow and not enough in some cases (for example tables or images which we also recognize and describe)
If you're getting inaccurate results from OCR what's the purpose of even doing it? Inaccuracy of text of any kind seems like a completely obvious failure of the entire purpose of scanning text into a computer.
It depends on what you need. For example a while ago I scanned and OCR'ed a bunch of receipts to get a timeline of my salary. I only cared about the gross and net figures, and nothing else mattered. Tesseract's output had a bunch of errors and misdetections, but the main figures always came out OK, and a local LLM was able to pick them out from the noise every time.
There's a big gulf between "it's as if a human being had transcribed it and reconstructed the original document" and "so completely broken it can't be used for anything".
Accuracy can have different dimensions, depends on what you can tolerate and whether you can detect it to apply more powerful methods.
Imagine you have a cheap and 99% accurate ocr. The other 1% you can detect and apply more powerful (more accurate but slower and more expensive) ocr method.
What would you use? At scale these things add up.
Can it produce accessible PDF files that will pass accessibility tests? Someone who can do that will make a killing laundering PDFs for academia: by April 26, every PDF, syllabus, and academic document needs to comply with WCAG 2.1 Level AA, which means structural tagging, alt text, and lots of other checklist items that AI could probably generate.
The VLM's are so good at complex document understanding now. But you just can't trust them not to invisibly censor sensitive clinical/legal docs, even at the maximally permissive settings.
And the deep learning OCR-only models won't censor, but can and do hallucinate. I've yet to see a 'scan with different approaches and reconcile and say you're not sure if they don't agree' system just work for generic complex documents.
The way my harness set it up is going through 2 or 3 providers, and cross-checking through them, also with plain text extracted if available.
I think we also had a layer that for any quote extracted tested it back if it exists within the original.
If you wanted 100% accuracy, I think it wouldn't be too difficult nowadays to guess the font&size&other text settings, and re render the crucial parts.
Does anyone know a site that lets you browse examples of input / output pairs?, particularly with layout analysis (bounding boxes of figures, tables, etc).
How does this compare to Baidu Unlimited OCR. I've been very impressed with Baidu and it's essentially free to run on a decent computer, other than electricity costs.
I've been experimenting with using NuExtract this week on locally OCRing bank statements that don't have a predefined document structure. It's way better than Tesseract or a generic vision-enabled model. It runs great on a single RTX 4090 at the modest throughput I need.
Their hosted, API-based service is something like a third of the cost of this model.
Nothing special about this model for overly-detailed work like mine.
It's been a while since I last tested (and discontinued my subscription), but the "pro" models from OpenAI dominate. Not surprising, given the price difference, but it would be nice if an OCR-specific model could perform better. It's worth mentioning that even the highest-end models do a pretty poor job with intricate text like mine.
Mistral's one advantage is that Anthropic now flags OCR, because they don't allow anything that could be considered "reproduction", even of work for which you own the copyright. So my new workflow is Mistral OCR for the actual OCR, followed by a proofreading pass by Claude (which is allowed). Claude is obviously more expensive, but it caught entirely hallucinated sentences created by Mistral OCR 4.0, so I was glad for the backup check.
I feel Anthropic is destroying itself with all these restriction. They got away because their models were the best for coding, but that is not an advantage anymore as OpenAI and other open source are already better.
I mentioned it in a sibling reply, but here's Anthropic's support document about not using Claude to reproduce content verbatim that already exists, regardless of copyright.
https://privacy.claude.com/en/articles/10023638-why-am-i-rec...
What does this entail? What does Claude do to decide that the text it was provided was hallucinated? Are you telling Claude that the source was OCR'd by another LLM?
I can't speak for Mistral OCR 4.1, but the hallucinations in 4.0 were so egregious (just completely making up new sentences in the middle of a page) that I knew I can't trust Mistral OCR on its own.
It doesn't always get flagged. Single pages are almost always okay. Running a program that sequentially runs single pages through the API is often not okay - I wrote a program in the early 4.x days before the rule came in, that's how I hit it first. But I've also had entire articles go through just fine recently in a Claude Code session (I'd forgotten about Anthropic's rules!), and then others where I get classifier errors by page 4.
The Mistral OCR errors were small in size. Single sentences, formatting errors, paragraphs with newlines. So this was a genuine proofreading job with small changes. For the most part Mistral is actually good, but I can't have it just inventing sentences in the middle of a document. That's where the Claude proofreading pass was most helpful.
I haven't seen any difference in my ocr workflows, what do you mean by this?
I’ve also had it refuse to OCR public-domain books that included content that it didn’t like, such as references to prostitution in 19th-century books about Japan.
I had one session where Claude refused to continue after it hit some kind of guiderail restriction. I couldn’t see what the trigger was, so I started a new session, gave Claude the link to the previous session, and asked it to diagnose the problem. This new Claude said it couldn’t view the exact guardrail issue, but it did suggest a workaround that turned out to be effective.
Thoughtcrime -like territory and self-sensorship. The AI safety lobby is such a vile influence on the freedom of expression and communication via technology (since AI is starting to eat up rest of technology).
I guess the main problem is positioning AI tools as "human-equivalent" creators by the big AI corps. If they were positioned simply as "better OCR and proofreading" people would attribute to them as much responsibility as they would to a - say - typewriter and we would not need to have this nonsense.
I do realize most of the valuation comes from the positioning of "our TAM is the global salary base of 50 trilion and we aim to supesede human workers in the near future" which implies they need to position this technology as "human equivalent" or that valuation is no longer as credible.
At first I thought it was something in the scanned content that was being flagged, but it was the attempt to transcribe that was itself being flagged. Anthropic mention it on their pages:
"Anthropic takes these steps because Claude’s purpose is to generate new content and ideas, not to reproduce content that already exists."
https://privacy.claude.com/en/articles/10023638-why-am-i-rec...
Side note - Claude itself is not aware of this policy, and is unable to see the API responses - the turn just ends. Which turned into a really bizarre failure state where Claude thought I was gaslighting it and kept insisting it could do the work and even had the entire text in memory. Every time it would go to show me and prove it, it would hit API Error 400. I was only able to convince Claude by showing screenshots of my Claude Code screen output so it could see that I was seeing API errors. I've never seen Claude get into that angry & snarky state before, and I hope it doesn't happen again.
That said, models have sometimes surprising weaknesses and a model could be terrible overall but magically work for one type of document.
I haven't been impressed with any of Mistral's models. They obviously realized that they couldn't compete at the frontier so they decided to go for smaller focused models but even those have not been that good.
We moved away from Cursor but I was looking for a model that would help with FIM (fill-in-middle) multiline autocompletion and people were recommending Mistral's Codestral. We gave it a shot and it was lackluster at best.. Even Google's Gemini did a significantly better job than Codestral.
Ultimately Opus-class models got good enough and I don't do much manual coding anymore.
I'd love to see some advancements in traditional OCR based on ideas and concepts we've learned from newer "OCR-like" models since traditional OCR is drastically cheaper.
I really wouldn't know, though. Anthropic models barf out copyright issues for my use case, so I'm unable even to benchmark them. It's a common problem when you're scanning public domain books. Mine are reference texts often cited.
As another user pointed out, it's surprisingly random (task-specific). Llama Scout outperformed Gemini Flash 2.5 on a benchmark I built at the time. I didn't include an OCR models.
Mistral might indeed be the best OCR-specific model for my task, now that you ask. Funny. It's so bad at my work that I didn't register it might be the best in its category. This is just based on vibes from my single scan.
There's always room for improvement, though. I suspect a tool will emerge for highly detailed OCR that implements a nested bounding-box-based multi-scale approach, effectively OCRing small sections at a time and then gradually compiling them by expanding the surface area using the bounding boxes.
I've thought a lot about implementing it anyway.
edit: I see you're asking about the block labels. Leaving the comment in case someone finds it interesting.
I'm sorry, noob here. I have a special book that I bought which I can open only inside the Kindle app (Windows/mobile). I have been meaning to screenshot the pages and convert them into a document/PDF. What do I have to do to make it fast? Just upload all the screenshots one by one and tell Mistral "Chat" to OCR them?
You've fallen for the hype. The only way these AI companies can justify their bullshit worth and burn of capital is by promising a magical AGI. It's just marketing and it is still unclear if LLM's will ever be profitable.
If they won't - then EU is doing the right thing laying low.
> Or a world where Europe only gets access to frontier models 6 months later.
What a calamity!
Exactly. People seem to forget that US is burning money at an accelerating rate without any promise of returns. This is a high risk situation. Time will tell.
"Winning the race" doesn't give you that in any meaningful capacity. It gives you, at the absolute most, a temporary window where that's the case. See: nuclear weapons.
Europe already has an innovation problem that's already causing structural economic instabilities which Germany has been struggling (and lately failing) to prop up.
As much as it pains me to say this, AI is already a tech revolution and it seems like Europe is just ignoring it. There's more innovation in 3 blocks in downtown San Francisco than the entire continent of Europe.
QED being first didn't grant exclusivity.
The premise wasn't that AGI isn't useful. There are actually layers to the metaphor where first-mover advantage of AGI is even less meaningful than it was for nuclear weapons, but I leave those as an exercise to the reader to discover.
This is your opinion. Lots of tech money appears to disagree.
You should at least try to explain your contrarian position, or cite your favorite source that makes an argument that you believe.
If AI is different, the onus is on AI folk to explain why, not the reverse.
The frontier labs are racing as hard as they can right now because they know as well as anyone else that they're at best 6 months, probably closer to 3 months, ahead of the generic commodity AI market. The majority of the tech money currently boosting them is a bet that either they can stay that far ahead without tripping, or that a moat can be engineered. It's not at all clear that it can - or, if it can, that it will before the money runs out.
FWIW I explained it fully and provided the argument, along with explicit grounding examples. Can you tell me what exactly you don't understand?
To quote your other post:
> This is the weakest part of the argument. Absolutely no reason to believe the second inventor will be 10x faster.
Nor is there any reason to believe that it will work at any usable speed. An LLM capable of AGI running at 0.000001tok/s isn't a very good head start. Nor is it a good moat if it can't actually realize anything material fast enough. Lord knows America has labor problems.
I'm demonstrating the context is a lot more complicated than "be first". There are innumerable specific examples. They're not guaranteed to happen, that's not the point being made.
Also "no reason" is curious when it's the explicit pattern this exact industry has exhibited. Chinese models lag a few months, and come in swinging with an order of magnitude more efficiency. Does it apply? Who can say. It's certainly on the table. Pretending like it's any more ridiculous than AGI itself is irrational.
> Does this ever happen? Even in traditional manufacturing, the second “inventor” starts behind and has to improve their own process to surpass the first.
To use the industry itself once again: Japan was the first to deep learning. Performance issues prevented them from capitalizing on it. Now they're barely even a player on the field. Interestingly and adjacent to the industry, Japan was second to symbolic AI, and the FGCS was lightyears ahead of America's crufty Lisp ecosystem. For a more modern example, Google was the first to transformers. Much of this revolution is thanks to them. A shame that Gemini is third-rate at best.
It's not unique to this industry. That second inventor more often than not does a better job than the original one. It's a pretty common pattern. Otherwise, we wouldn't need patents.
My "contrarian position" is anything but. There is nothing new under the sun. You don't just need to be first, you need to maintain exclusivity.
And I quote:
> Imagine a world where only one country has AGI/ASI.
This is the weakest part of the argument. Absolutely no reason to believe the second inventor will be 10x faster.
Does this ever happen? Even in traditional manufacturing, the second “inventor” starts behind and has to improve their own process to surpass the first.
We'll see.
With a lot of Chinese nationals in American companies, and an American corporation owning DeepMind, and a lot of people very upset with all of them at the same time, this is very messy.
Race dynamics increases p(doom) for everyone.
The non-doom scenarios include "utopia for all", and "power flows to investors, not citizens of whichever nation the winning model's corp. was registered in".
Independently, "oh look all the investors went bankrupt" can happen in both "doom" and "normal technology" timelines.
I hope we (EU) don't waste money trying to train local models (which at least some people in Poland try to do), and tries to build our own chips - AI chips have different architecture than regular processor/GPU, and TSMC doesn't need to be winner in this new race.
And if not this, then smaller labs, harnesses and actual application.
They can kick into a true war economy and then become significantly more of a threat. They haven't done so yet because the middle-class population likes their luxury lifestyle (for now).
I highly doubt Ukraine will "win" - in fact I believe Russia is ramping up to find an excuse at some point to attack a NATO country, but in a way and during a point in time it's difficult for NATO to retaliate.
I’m glad Mistral is working on useful solutions.
OpenAI/Anthropic is like a retarded little sibling chasing “AGI” and giving up on rich media and other modalities.
OpenAI/Anthropic is the worst of the mainstream AI.
It goes:
1. Gemini
2. Vidu
3. Le Chat (Mistral)
4. DeepAI
5. [insert MiniMax provider]
If you’re interested you can find contact to me via this profile.
3.5 usd/1000 pages is just too expensive…
Google documentai costs 1.5$ per 1000 which is probably better in quality and speed.
I’ve been there, implementing a way to linearise text from a document with pages with 1, 2 or 3 columns, some of them in landscape is a nightmare. And that’s not even considering equations.
In the end it’s way easier to use a specialised model, trained by other people to do exactly what I need.
So, maybe it can be tuned for your usecase but with that kind of investment €3.5 for 1000 pages is a bargain...
There's a big gulf between "it's as if a human being had transcribed it and reconstructed the original document" and "so completely broken it can't be used for anything".
Imagine you have a cheap and 99% accurate ocr. The other 1% you can detect and apply more powerful (more accurate but slower and more expensive) ocr method. What would you use? At scale these things add up.
Because I'm assuming that's why they get to charge more for the right type of customer.
And the deep learning OCR-only models won't censor, but can and do hallucinate. I've yet to see a 'scan with different approaches and reconcile and say you're not sure if they don't agree' system just work for generic complex documents.
I think we also had a layer that for any quote extracted tested it back if it exists within the original.
If you wanted 100% accuracy, I think it wouldn't be too difficult nowadays to guess the font&size&other text settings, and re render the crucial parts.
What's an example of this?
Their hosted, API-based service is something like a third of the cost of this model.
After stuff like Chat Control I think they're obviously seeing a big demand for this kind of "internet safety" technology in Europe.