Head to head · Open-weight models you run yourself · October 2026 research run
LongCat-2.5 vs Baidu Unlimited-OCR-2
Baidu Unlimited-OCR-2 scores 54.2 (C) on agent readiness against LongCat-2.5's 50.5 (D), and leads in 2 of 4 scored categories. LongCat-2.5 leads on risk. Both do open-weight models you run yourself.
Best open-weight models you download and run yourself for AI agents · All 6359 open weights comparisons
Which one, for what
Best for Operators with NPU clusters, and anyone tracking what the open flagships can do
Ahead on
- Risk, 60 against 45
Watch for
3,562 GB in BF16 and 1,336 GB at 4 bits
Best 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
- Reach, 55 against 35
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
Why the edge
Each category where one leads by five points or more, with the reason behind each side's score from its assessment. The numbers are in the table below; this is what they stand for.
Reach Baidu Unlimited-OCR-2 +20
LongCat-2.5 35
Memory 0, the smallest build is Meituan's W4A4 repository, 1,336 GB, and 1,336 GB × 1.2 = 1,603 GB, far above 256 GB. The BF16 files are 3,562 GB. Runtimes 0, none of the six runs it in a released version: vLLM 0.33.0, SGLang 0.5.23, Transformers 5.22.2 and llama.cpp b12242 have no LongcatCausalLM or pro_v code, and the model code config.json names isn't in the repository. Builds 10, one quantised build from Meituan, the W4A4 repository (4-bit experts, 1,336 GB). Terms 15, MIT in the metadata, the LICENSE file and the card, with nothing added. Download 10, the Hub API reports gated false for both repositories on 10 November 2026, so the files download with no account and nothing to accept. Hosted 0, Hugging Face maps no inference provider to it. 0 + 0 + 10 + 15 + 10 + 0 = 35.
Baidu Unlimited-OCR-2 55
Memory earns the full 30, since the only build is 6.69 GB of BF16 weights that need about 8 GB with the fifth added. Runtimes score 0. Released vLLM 0.33.0, SGLang 0.5.23 and Transformers 5.22.1 know the first Unlimited-OCR but not the sparse attention this one adds, Baidu's own routes are remote code in Transformers and a customised SGLang wheel, llama.cpp b12111 has nothing for it, so Ollama has nothing either, and LM Studio couldn't be checked. Builds score 0, as Baidu publishes no quantised build (16 exist, all by others). Terms get 15 for MIT with nothing added. Download gets 10 for an ungated repository, and hosted too 0, with no inference provider mapped and the card's Baidu Cloud line unconfirmed.
Risk LongCat-2.5 +15
LongCat-2.5 60
File safety 25, safetensors only, no .bin, .pt or .pth files and no Python in the repository. Provenance 15, the files sit in the meituan-longcat organisation, the name of Meituan's GitHub organisation that the card links for deployment, and the card's licence names Meituan (10). Safety evaluation 0, no safety results on the card. Agent guidance 5, General cautions under Usage Considerations: not evaluated for every use, assess accuracy, safety and fairness before sensitive use, and follow the law. Licence risk 15, MIT with no use policy, no threshold and no right to change the terms. Security programme 0, no way to report a flaw on the card beyond a general contact, and Meituan's own security pages weren't looked up, so this scores as absent and is an open question. 25 + 15 + 0 + 5 + 15 + 0 = 60.
Baidu Unlimited-OCR-2 45
File safety gets 15, safetensors only but loaded with trust_remote_code or through a prebuilt SGLang wheel from the repository. Provenance gets 15 for files in Baidu's verified organisation, linked from its GitHub organisation, with hashes for every file. Licence risk keeps 15, since MIT has none of the deductions. Safety evaluation, agent guidance and the security programme score 0, with no safety results, no cautions about use and no security contact found.
Score by category
| Category | Weight this run | LongCat-2.5 | Baidu Unlimited-OCR-2 | Edge |
|---|---|---|---|---|
| Capability | 35%pending | pending | pending | not scored in this run |
| Reach | 25%25 | 35 | 55 | Baidu Unlimited-OCR-2 +20 |
| Risk | 20%20 | 60 | 45 | LongCat-2.5 +15 |
| Agent interface | 10%10 | 75 | 75 | even |
| Documentation and upkeep | 10%10 | 46 | 50 | Baidu Unlimited-OCR-2 +4 |
| Negative events | ≤15 | 0 | 0 | |
| Total | D 50.5/100 | C 54.2/100 |
Facts side by side
| Fact | LongCat-2.5 | Baidu Unlimited-OCR-2 |
|---|---|---|
| Kind | Open-weight model | Open-weight model |
| Vendor | Meituan (LongCat) | Baidu |
| Hosted endpoint | no (local only) | no (local only) |
| Transports | ||
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | MIT | 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 | 2026-11-10 | 2026-10-26 |
| Terms last updated | not read yet | no document linked |
| Privacy policy last updated | no document linked | no document linked |
How the scores have moved
LongCat-2.5
No score change since it was listed.
Verdicts
LongCat-2.5
Meituan's flagship is MIT-licensed and documented for tool calling and switchable reasoning, with strong numbers of its own on agentic coding. Almost nobody can run it: 3,562 GB of weights, a 1,336 GB 4-bit build for NPU accelerators, and no released runtime with its architecture. A model to watch until a runtime or a provider picks it up.
Baidu Unlimited-OCR-2
An MIT document parser of 3.3B parameters that reads up to hundreds of pages in one request and returns ordered layout blocks with boxes and HTML tables. ParseBench's independent run puts content faithfulness at 89.16 and charts at 1.06. It needs an NVIDIA GPU with about 12 GB and runs only through Baidu's remote code or its own SGLang build.
Before you call either
LongCat-2.5
- You can't run this on a workstation or a single GPU server. Plan for a provider, and none serves it yet
- If an operator serves it, call it as an OpenAI-compatible chat API with tools in the function format
- Pass
chat_template_kwargswithenable_thinkingtrue or false - With thinking on, keep earlier reasoning in the history (
save_history_reasoning_contenttrue), as the card recommends - The card's results use temperature 1.0 and top_p 0.95
- Its benchmark table is Meituan's own. The unmarked scores are in-house
Baidu Unlimited-OCR-2
- 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, thenAutoModel.from_pretrained("baidu/Unlimited-OCR-2", trust_remote_code=True, use_safetensors=True, torch_dtype=torch.bfloat16).eval().cuda()andmodel.infer_multi(tokenizer, image_files=pages, output_path="out") - To serve it, download the repository, install
wheel/sglang-0.5.20.dev13+g75b9b06b02-py3-none-any.whlwith pymupdf and fastokens in a Python 3.10 venv, and startpython -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 2048with the SGLANG_UOCR_ variables from the card - Send all pages of a document in one request, images first, then the text
<PAGE><image>repeated per page andMulti 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) - The output is layout blocks, not Markdown; run
utils/process_unlimitedOCR2.pyfrom github.com/baidu/Unlimited-OCR, orconvert(raw_output)in Python, to get Markdown with HTML tables - 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, LongCat-2.5 or Baidu Unlimited-OCR-2?
Baidu Unlimited-OCR-2 scores 54.2 (C) on agent readiness against LongCat-2.5's 50.5 (D), and leads in 2 of 4 scored categories. LongCat-2.5 leads on risk.
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- Qwen3-VL-32B-Instruct vs Baidu Unlimited-OCR-2
- Qwen3-VL-4B-Instruct vs Baidu Unlimited-OCR-2
- Qwen3-VL-8B-Instruct vs Baidu Unlimited-OCR-2
- Qwopus3.8-27B-Flash-V2 vs Baidu Unlimited-OCR-2
- Recursive Flint vs Baidu Unlimited-OCR-2
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- Spark-X2.6-4B vs Baidu Unlimited-OCR-2
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- Baidu Unlimited-OCR-2 vs ZAYA1-8B
- Chandra OCR 2 vs Baidu Unlimited-OCR-2
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- HunyuanOCR 1.5 vs Baidu Unlimited-OCR-2
- LightOnOCR-3-4B vs Baidu Unlimited-OCR-2
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Machine-readable
- This page as Markdown
/compare/longcat-2-5-vs-unlimited-ocr-2.md· slim.min.md· JSON.json(or sendAccept: text/markdown) - Each listing in full
/api/v1/tools/longcat-2-5.json·/api/v1/tools/unlimited-ocr-2.json - From a terminal
anchor compare longcat-2-5 unlimited-ocr-2(the CLI) - Over MCP
compare_tools {"a": "longcat-2-5", "b": "unlimited-ocr-2"}at/mcp, no key