Putting this here because a lot of people in this sub are processing documents locally Paperless-ngx, Tesseract pipelines, Surya, marker, etc.  and the benchmark comparison is relevant even if the model itself isn't self-hostable.

Nanonets just released OCR-3. Quick specs:

* 35B total parameters, A3B active (Mixture-of-Experts)
* 93.1 on olmOCR benchmark (GPT-5.4 gets 81.0, Gemini 3.1 Pro gets 79.6)
* 90.5 on OmniDocBench
* Proprietary, API-only: not open weights

The architecture is interesting even if you can't run it locally. It's an MoE that only activates 2–3 expert sub-networks per token, so inference is 2x faster than a dense model of equivalent quality. They trained 75% on low-res images because document structure (tables, headers, columns) can be learned without full pixel detail. Only 25% of training used full resolution for character-level accuracy. At inference, token count caps at 1280 per image — predictable cost per request.

**Where it actually outperforms the models you can run locally:**

Tables are the biggest gap. OCR-3 scores 94.2 on the tables category vs GPT-5.4 at 91.1. It outputs simple tables as markdown pipes and complex tables (merged cells, colspan/rowspan, nested) as HTML — doesn't try to flatten everything.

Multi-column layouts: 87.6 vs GPT-5.4 at 83.7. Reading order stays correct on two-column court filings, academic papers, etc.

Old degraded scans: 49.6, still bad, but GPT-5.4 gets 43.9. Nobody's cracked this well yet.

**Features that local pipelines don't have (yet):**

* Confidence scores on every extracted element. You can route by certainty — >90% to DB, 60–90% to a second model, <60% to human review.
* Bounding boxes on every element. Trace any value back to exact page coordinates.
* Five endpoints from one model: parse, extract (schema-based), split (classify documents), chunk (RAG-optimized), VQA.

**The honest tradeoff:** it's not local and but you get 10K pages for free. If you need privacy, air-gapped processing, or zero API costs, this doesn't work for you. But if you're already calling any external API in your document pipeline and accuracy matters more than self-hosting, the benchmarks speak for themselves.

They also found something worth knowing about olmOCR: 437 of 864 "failed" tests were evaluator brittleness, not actual model errors. After correction, the weighted average goes from 87.4 to 93.1. If you're benchmarking local models on olmOCR, that evaluator noise might be affecting your numbers too.

Research page: [https://nanonets.com/research/nanonets-ocr-3](https://nanonets.com/research/nanonets-ocr-3)

For people running local OCR — what's your current stack and where does it fall apart? Curious how Surya/marker/Tesseract compare on the same edge cases (merged tables, multi-column, degraded scans).

Nanonets OCR-3: 35B (A3B active) MoE model scoring 93.1 on olmOCR beats GPT-5.4 and Gemini on document parsing, but it's API-only (not local)