- 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-...
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.
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.