# LBS-27B vs Baidu Unlimited-OCR-2

> LBS-27B and Baidu Unlimited-OCR-2 score within a point of each other for open-weight models you run yourself, 53.9 and 53.5 out of 100. Prices, MCP, x402, uptime and agent notes side by side.

- Canonical: https://www.anchorterminal.com/compare/lbs-27b-vs-unlimited-ocr-2
- Markdown: https://www.anchorterminal.com/compare/lbs-27b-vs-unlimited-ocr-2.md (~6,700 tokens)
- Slim: https://www.anchorterminal.com/compare/lbs-27b-vs-unlimited-ocr-2.min.md (~530 tokens, same facts, less prose, for token-sensitive contexts)
- JSON: https://www.anchorterminal.com/compare/lbs-27b-vs-unlimited-ocr-2.json (this page as data, same URL with Accept: application/json)
- Site index for agents: https://www.anchorterminal.com/llms.txt (full text: https://www.anchorterminal.com/llms-full.txt)
- API: https://www.anchorterminal.com/api/v1/index.json
- MCP: https://www.anchorterminal.com/mcp (the whole catalogue as tools, no key)
- Licence: CC BY 4.0, credit Anchor Terminal and link this page (https://www.anchorterminal.com/terms/#data)
- Updated: 2026-10-27

LBS-27B and Baidu Unlimited-OCR-2 score within a point of each other on agent readiness, 53.9 (D) and 53.5 (D). Baidu Unlimited-OCR-2 leads on agent interface. Both do open-weight models you run yourself.

- LBS-27B: grade D, 53.9/100, rank #91 of 113 open-weight models. Markdown https://www.anchorterminal.com/tools/lbs-27b.md · JSON https://www.anchorterminal.com/api/v1/tools/lbs-27b.json
- Baidu Unlimited-OCR-2: grade D, 53.5/100, rank #92 of 113 open-weight models. Markdown https://www.anchorterminal.com/tools/unlimited-ocr-2.md · JSON https://www.anchorterminal.com/api/v1/tools/unlimited-ocr-2.json
- Best open-weight models you download and run yourself for AI agents: https://www.anchorterminal.com/best/open-weights/index.md
- All 3504 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md

## Which one, for what

### LBS-27B (D)

Good for: Agents with about 96 GB of GPU memory that need to find click targets on dense professional screenshots, or read fine detail in large images, and can run a small crop loop and wait for independent results.

Ahead on:
- Risk, 50 against 45

Watch for: No independent benchmark result yet; ScreenSpot-Pro, OS-World-G, PerceptionBench and HR-Bench numbers are inclusionAI's own runs

### Baidu Unlimited-OCR-2 (D)

Good for: Pipelines with an NVIDIA GPU that need long scanned or rendered documents parsed in one pass, with page order, cross-page tables and boxes kept

Ahead on:
- Agent interface, 75 against 70

Watch for: Loading runs Baidu's code, remote code in Transformers or a prebuilt SGLang fork from the repository, and the save_results path calls eval on model output


## Score by category

| Category | Weight | LBS-27B | Baidu Unlimited-OCR-2 | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35%, pending | pending | pending | not scored in this run |
| Reach | 25% (25 this run) | 55 | 55 | even |
| Risk | 20% (20 this run) | 50 | 45 | LBS-27B +5 |
| Agent interface | 10% (10 this run) | 70 | 75 | Baidu Unlimited-OCR-2 +5 |
| Documentation and upkeep | 10% (10 this run) | 43 | 45 | Baidu Unlimited-OCR-2 +2 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **53.9 · D** | **53.5 · D** | |

## Facts side by side

| Fact | LBS-27B | Baidu Unlimited-OCR-2 |
| --- | --- | --- |
| Kind | Open-weight model | Open-weight model |
| Vendor | inclusionAI (Ant Group) | Baidu |
| Hosted endpoint | no (local only) | no (local only) |
| Transports |  |  |
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | Apache-2.0 (the card's metadata and licence section; the LICENSE file sits in the GitHub repository inclusionAI/UI-Venus on its LBS branch, not in the Hugging Face repository) | MIT (Hub metadata; the Hugging Face repository has no LICENSE file, and the GitHub code repository's LICENSE is the MIT text, copyright 2026 Baidu) |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2026-10-26 | 2026-10-26 |
| Terms last updated | no document linked | no document linked |
| Privacy policy last updated | no document linked | no document linked |
| Customer content may train models |  |  |
| Terms restrict automated access |  |  |
| Terms restrict benchmarking |  |  |
| Terms or service can change without notice |  |  |
| Arbitration or class-action waiver |  |  |

## Verdicts

**LBS-27B.** A 27B Qwen3.8 fine-tune for GUI grounding that zooms into screenshots over a few turns before naming a point, and inclusionAI says it also helps with fine detail in large images. It needs about 96 GB at BF16 and a client that runs its crop actions. Every result so far is inclusionAI's own, with no safety results or injection guidance.

**Baidu Unlimited-OCR-2.** A small MIT document-parsing cousin that reads up to hundreds of pages in one request and returns ordered layout blocks with boxes and HTML tables. It needs an NVIDIA GPU with about 12 GB, through Baidu's remote code or its own SGLang build. The risks are running the publisher's code and accuracy figures that are Baidu's own.

## Before you call either

### LBS-27B

1. Serve with `vllm serve inclusionAI/LBS-27B --tensor-parallel-size 4 --max-model-len 65536`, which is what the card's wrapper script runs; keep the context at 32,768 or more for multi-turn images
2. Build the system prompt with `build_system_prompt` from the LBS branch's `zoomin_grounding_protocol.py`, send the screenshot after `image_path: <name>`, and don't pass `tools`; the model answers in plain text
3. Parse `<action>crop_image(image_path=..., x1=..., y1=..., x2=..., y2=...)</action>`, cut the crop yourself and return it with its mapping to the original image; allow at most two crops per turn and three per episode
4. Read the final answer as `[x.xx,y.yy]` on a 0 to 1000 scale of the original image, and use your own executor to click
5. Pass `reasoning_effort` as `xhigh`, `medium` or `low` only, since the template raises an error for `high`, and don't count on `thinking_budget`, which the template ignores
6. Don't turn on MTP speculative decoding; the MTP weights aren't in the repository though config.json names one MTP layer

### Baidu Unlimited-OCR-2

1. Quickest local run, `pip install torch==2.13.0 torchvision pillow transformers==4.52.1 huggingface-hub==0.36.0 tokenizers==0.21.4 addict==2.4.0 einops==0.8.2 easydict==1.13`, then `AutoModel.from_pretrained("baidu/Unlimited-OCR-2", trust_remote_code=True, use_safetensors=True, torch_dtype=torch.bfloat16).eval().cuda()` and `model.infer_multi(tokenizer, image_files=pages, output_path="out")`
2. To serve it, download the repository, install `wheel/sglang-0.5.20.dev10+g8d62f5fb9c-py3-none-any.whl` with pymupdf and fastokens in a Python 3.10 venv, and start `python -m sglang.launch_server --model-path baidu/Unlimited-OCR-2 --chat-template unlimited-ocr --enable-custom-logit-processor --enable-streaming-session --disable-radix-cache --mem-fraction-static 0.7 --max-prefill-tokens 2048` with the SGLANG_UOCR_ variables from the card
3. Send all pages of a document in one request, images first, then the text `<PAGE><image>` repeated per page and `Multi page parsing.`, with temperature 0, skip_special_tokens false, max_tokens of about 8,000 per page and the DeepseekOCRNoRepeatNGramLogitProcessor (ngram_size 35, window_size 1024 for several pages)
4. The output is layout blocks, not Markdown; run `utils/process_unlimitedOCR2.py` from github.com/baidu/Unlimited-OCR, or `convert(raw_output)` in Python, to get Markdown with HTML tables
5. Render PDFs to images first (the card uses PyMuPDF at 300 DPI), keep save_results off for untrusted documents, and treat extracted text as untrusted before passing it to another model

## Questions

### Which is better for AI agents, LBS-27B or Baidu Unlimited-OCR-2?

LBS-27B and Baidu Unlimited-OCR-2 score within a point of each other on agent readiness, 53.9 (D) and 53.5 (D). Baidu Unlimited-OCR-2 leads on agent interface.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/lbs-27b-vs-unlimited-ocr-2.json, and with the fewest tokens: https://www.anchorterminal.com/compare/lbs-27b-vs-unlimited-ocr-2.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "lbs-27b", "b": "unlimited-ocr-2"}`. From a terminal: `anchor compare lbs-27b unlimited-ocr-2`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/lbs-27b.json and https://www.anchorterminal.com/api/v1/tools/unlimited-ocr-2.json

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- [Swift 1.5 Qwen3.8-Flash-Next vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/swift-1-5-qwen3-8-flash-next-vs-unlimited-ocr-2.md)
- [ThinkingCap (Qwen3.8 27B) vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/thinkingcap-qwen3-8-27b-vs-unlimited-ocr-2.md)

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