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# Baidu Open-Sources Unlimited OCR 2 for Hundred-Page Document Parsing

**[Machine Learning News](https://daily.dev/sources/mlnews)** · 5 min read · 1 upvotes · 0 comments

## Summary

Baidu has open-sourced Unlimited OCR 2, a 3.34B parameter vision-language model for parsing hundred-page documents in a single request. Its SR-SWA attention scheme caps attention at a fixed 512-token reference set, keeping per-step decoding cost nearly flat as documents grow. It scores 77.1 on MarathonDocBench, well ahead of the next model Kimi-K3 at 33.7, while rival Qwen3.8-Max drops from 78.3 to 22.9 on long documents. Heading hierarchy is its weakest area at 48.2. Weights are MIT-licensed, runs on a single CUDA GPU in BF16, and all benchmark numbers are self-reported by Baidu.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://www.marktechpost.com/2026/10/26/baidu-unlimited-ocr-2-open-source-long-document-ocr>

## Questions this post answers

### How many parameters does Baidu's Unlimited OCR 2 model have and what license is it under?

Unlimited OCR 2 has 3.34 billion parameters and ships under the MIT license, with weights on Hugging Face and code on GitHub. Active parameter count for the mixture-of-experts decoder is not disclosed. It runs on a single CUDA GPU in BF16, with Baidu testing on an NVIDIA A800-80GB; BF16 weights total about 6.7 GB.

_daily.dev surfaces open-source OCR releases like this for teams evaluating document parsing stacks._

### What is SR-SWA attention in Baidu's Unlimited OCR 2 model?

SR-SWA, or Sparse Reference Sliding Window Attention, compresses text-visual attention into a fixed set of working references, with 512 tokens given as the example size. It pairs a 128-token local window with top-16 selection over 32-token blocks (16 blocks of 32 tokens). This keeps per-step decoding latency and memory nearly constant as document length grows, unlike full attention which scales quadratically.

_developers scaling OCR to long documents can track attention-efficiency techniques like this on daily.dev._

### How does Unlimited OCR 2 compare to other models on long document OCR benchmarks?

Unlimited OCR 2 scores 77.1 overall on MarathonDocBench, a benchmark of hundred-page documents, versus 33.7 for the next-best model Kimi-K3 and 28.6 for Gemini-3.8-Flash. Qwen3.8-Max, which leads on the shorter MPDocBench at 78.3, collapses to 22.9 on MarathonDocBench, while Unlimited OCR 2 drops only 7.5 points from its 84.6 MPDocBench score. All figures are self-reported by Baidu.

_teams choosing an OCR model for lengthy documents can weigh benchmark trade-offs like these via daily.dev._

## Similar posts on daily.dev

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- [Qwen3-VL can scan two-hour videos and pinpoint nearly every detail](https://daily.dev/posts/qwen3-vl-can-scan-two-hour-videos-and-pinpoint-nearly-every-detail-5dvyilfvf) · Hacker News · 1 upvotes · 0 comments
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---

Tags: [#machine-learning](https://daily.dev/tags/machine-learning), [#open-source](https://daily.dev/tags/open-source), [#rag](https://daily.dev/tags/rag)

[View this post on daily.dev](https://daily.dev/posts/baidu-open-sources-unlimited-ocr-2-for-hundred-page-document-parsing-aofbwyhhm)

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