Show HN: Qwen-2.5-32B is now the best open source OCR model
50 comments
·April 1, 2025jauntywundrkind
The 32b sounds like it has some useful small tweakers. Tweaks to make output more human friendly, better mathematical reasoning, better fine-grained understanding. https://qwenlm.github.io/blog/qwen2.5-vl-32b/ https://news.ycombinator.com/item?id=43464068
Qwen2.5-VL-72b was released two months ago (to little fanfare in submissions, i think, but some very enthusiastic comments such as rabid enthusiasm for handwriting recognition) already very interesting. Its actually one of the releases that kind of turned me on to AI, that broke through some of my skepticism & grumpiness. There's pretty good release notes detailing capabilities here; well done blog post. https://qwenlm.github.io/blog/qwen2.5-vl/
One thing that really piqued my interest was Qwen HTML output, where it can provide bounding boxes in HTML format for its output. That really closes the loop interestingly to me, makes the output something I can imagine quickly building useful visual feedback around, or using the structured data from easily. I can't imagine an easier to use output format.
ks2048
I suppose none of these models can output bounding box coordinates for extracted text? That seems to be a big advantage of traditional OCR over LLMs.
For applications I'm interested in, until we can get to 95+% accuracy, it will require human double-checking / corrections, which seems unfeasible w/o bounding boxes to quickly check for errors.
michaelt
qwen2.5-vl-72b-instruct seems perfectly happy outputting bounding boxes in my testing.
There's also a paper https://arxiv.org/pdf/2409.12191 where they explicitly say some of their training included bounding boxes and coordinates.
themanmaran
We're also looking to test qwen and other for the bounding box support. Simon Willison had a great demo page where he used Gemini 2.5 to draw bounding boxes, and the results were pretty impressive. It would probably be pretty easy to drop qwen into the same UI.
chpatrick
Actually qwen 2.5 is trained to provide bounding boxes
deepsquirrelnet
Yep, this is true. I was poking around on their github and they have examples in their “cookbooks” section. Eg:
https://github.com/QwenLM/Qwen2.5-VL/blob/main/cookbooks/ocr...
kapitalx
If you're limited to open source models, that's very true. But for larger models and depending on your document needs, we're definitely seeing very high accuracy (95%-99%) for direct to json extraction (no markdown in between step) with our solution at https://doctly.ai.
kapitalx
In addition, gemini Pro 2.5 does really well with bounding boxes, but yeah not open source :(
jsight
I'd guess that it wouldn't be a huge effort to fine tune them to produce bounding boxes.
I haven't done it with OCR tasks, but I have fine tuned other models to produce them instead of merely producing descriptive text. I'm not sure if there are datasets for this already, but creating one shouldn't be very difficult.
pmarreck
Downloading the MLX version of "Qwen2.5-VL-32b-Instruct -8bit" via LM Studio right now since it's not yet available on Ollama and I can run it locally... I have an OCR side project for it to work on, want to see how performant it is on my M4... will report back
hdjjhhvvhga
I'm very curious about the results - I've been using mistral-ocr for the last 2 weeks and it worked really well.
pmarreck
Its errors are interesting (averaging around one per paragraph). Semantically-correct, but wrong on precision (simple example, the English word "ardour" is transcripted as "ardor", and a foreign word like "palazzo" which is intended to remain so, is translated to "palace"). I'm still messing with temp/presence/frequency/top-p/top-k/prompting to see if I can squeeze some more precision out of it, but I'm running out of time.
Not sure if it matters but I exported a PDF page as a PNG with 200dpi resolution, and used that.
It seems like it's reading the text but getting the details wrong.
I would not be comfortable using this in an official capacity without more accuracy. I could see using this for words that another OCR system is uncertain about, though, as a fallback.
daemonologist
You mention that you measured cost and latency in addition to accuracy - would you be willing to share those results as well? (I understand that for these open models they would vary between providers, but it would be useful to have an approximate baseline.)
themanmaran
Yes, I'll add that to the writeup! You're right, initially excluded it because it was really dependent on the providers, so lots of variance. Especially with the Qwen models.
High level results were:
- Qwen 32b => $0.33/1000 pages => 53s/page
- Qwen 72b => $0.71/1000 pages => 51s/page
- Llama 90b => $8.50/1000 pages => 44s/page
- Llama 11b => $0.21/1000 pages => 08s/page
- Gemma 27b => $0.25/1000 pages => 22s/page
- Mistral => $1.00/1000 pages => 03s/page
dylan604
One of these things is not like the others. $8.50/1000?? Any chance that's a typo? Otherwise, for someone that has no experience with LLM pricing models, why is Llama 90b so expensive?
int_19h
It's not uncommon when using brokers to see outliers like this. What happens basically is that some models are very popular and have many different providers, and are priced "close to the metal" since the routing will normally pick the cheapest option with the specified requirements (like context size). But then other models - typically more specialized ones - are only hosted by a single provider, and said provider can then price it much higher than raw compute cost.
E.g. if you look at https://openrouter.ai/models?order=pricing-high-to-low, you'll see that there are some 7B and 8B models that are more expensive than Claude Sonnet 3.7.
themanmaran
That was the cost when we ran Llama 90b using TogetherAI. But it's quite hard to standardize, since it depends a lot on who is hosting the model (i.e. together, openrouter, grok, etc.)
I think in order to run a proper cost comparison, we would need to run each model on an AWS gpu instance and compare the runtime required.
esafak
A 2d plot would be great
fpgaminer
I've been consistently surprised by Gemini's OCR capabilities. And yeah, Qwen is climbing the vision ladder _fast_.
In my workflows I often have multiple models competing side-by-side, so I get to compare the same task executed on, say, 4o, Gemini, and Qwen. And I deal with a very wide range of vision related tasks. The newest Qwen models are not only overall better than their previous release by a good margin, but also much more stable (less prone to glitching) and easier to finetune. I'm not at all surprised they're topping the OCR benchmark.
What bugs me though is OpenAI. Outside of OCR, 4o is still king in terms of overall understanding of images. But 4o is now almost a year old, and in all that time they have neither improved the vision performance in any newer releases, nor have they improved OCR. OpenAI's OCR has been bad for a long time, and it's both odd and annoying.
Taken with a grain of salt since again I've only had it in my workflow for about a week or two, but I'd say Qwen 2.5 VL 72b beats Gemini for general vision. That lands it in second place for me. And it can be run _locally_. That's nuts. I'm going to laugh if Qwen drops another iteration in a couple months that beats 4o.
AndrewDucker
Tesseract can manage 99% accuracy on anything other than handwriting. Without being an LLM.
Is there an advantage of using an LLM here?
jauntywundrkind
I'm really curious about this too! I don't know!
There's some comments I've run across saying Qwen2.5-VL's really good at handwriting recognition.
It'd also be interesting to see how Tesseract compares when trying to OCR more mixed text+graphic media. Some possible examples: high-design magazines with color backgrounds, TikTok posts, maps, cardboard hold-up signs at political gatherings.
ks2048
I've been doing some experiments with the OCR API on macOS lately and wonder how it compares to these LLMs.
Overall, it's very impressive, but makes some mistakes (on easy images - i.e. obviously wrong) that require human intervention.
I would like to compare it to these models, but this benchmark is beyond OCR - extracted structured JSON.
CSMastermind
I've been very impressed with Qwen in my testing, I think people are underestimating it
stavros
I wrote a small, client-side-JS-only app that does OCR and TTS on board game cards, so my friends and I can listen to someone read the cards' flavor text. On a few pages of text in total so far, Qwen has made zero mistakes. It's very impressive.
WillAdams
How does one configure an LLM interface using this to process multiple files with a single prompt?
jumploops
Do you mean you want to process multiple files with a single LLM call or process multiple files using the same prompt across multiple LLM calls?
(I would recommend the latter)
WillAdams
Multiple files with a single LLM call.
I have a prompt which works for a single file in Copilot, but it's slower than opening the file and looking at it to find one specific piece of information and re-saving it manually and then running a .bat file to rename with more of the information, then filling out the last two bits when entering things.
codybontecou
Nice work Tyler and team!
ianhawes
Is there a reason Surya isn’t included?
sandreas
What about mini cpm v2.6?
Last week was big for open source LLMs. We got:
- Qwen 2.5 VL (72b and 32b)
- Gemma-3 (27b)
- DeepSeek-v3-0324
And a couple weeks ago we got the new mistral-ocr model. We updated our OCR benchmark to include the new models.
We evaluated 1,000 documents for JSON extraction accuracy. Major takeaways:
- Qwen 2.5 VL (72b and 32b) are by far the most impressive. Both landed right around 75% accuracy (equivalent to GPT-4o’s performance). Qwen 72b was only 0.4% above 32b. Within the margin of error.
- Both Qwen models passed mistral-ocr (72.2%), which is specifically trained for OCR.
- Gemma-3 (27B) only scored 42.9%. Particularly surprising given that it's architecture is based on Gemini 2.0 which still tops the accuracy chart.
The data set and benchmark runner is fully open source. You can check out the code and reproduction steps here:
- https://getomni.ai/blog/benchmarking-open-source-models-for-...
- https://github.com/getomni-ai/benchmark
- https://huggingface.co/datasets/getomni-ai/ocr-benchmark