# Qwen3.5-2B vs Baidu Unlimited-OCR-2

> Baidu Unlimited-OCR-2 scores 53.5 (D) to Qwen3.5-2B's 50.5 (D) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side.

- Canonical: https://www.anchorterminal.com/compare/qwen3-5-2b-vs-unlimited-ocr-2
- Markdown: https://www.anchorterminal.com/compare/qwen3-5-2b-vs-unlimited-ocr-2.md (~6,750 tokens)
- Slim: https://www.anchorterminal.com/compare/qwen3-5-2b-vs-unlimited-ocr-2.min.md (~480 tokens, same facts, less prose, for token-sensitive contexts)
- JSON: https://www.anchorterminal.com/compare/qwen3-5-2b-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

Baidu Unlimited-OCR-2 scores 53.5 (D) on agent readiness against Qwen3.5-2B's 50.5 (D), and leads in 2 of 4 scored categories. Qwen3.5-2B leads on reach and risk. Both do open-weight models you run yourself.

- Qwen3.5-2B: grade D, 50.5/100, rank #103 of 113 open-weight models. Markdown https://www.anchorterminal.com/tools/qwen3-5-2b.md · JSON https://www.anchorterminal.com/api/v1/tools/qwen3-5-2b.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

### Qwen3.5-2B (D)

Good for: Builders who want a cheap local helper or fine-tuning base for narrow tasks and image reading on a laptop, small GPU or CPU

Ahead on:
- Reach, 80 against 55
- Risk, 55 against 45

Watch for: No quantised build from Alibaba, and only one inference provider

### 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 60
- Documentation and upkeep, 45 against 37

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 | Qwen3.5-2B | Baidu Unlimited-OCR-2 | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35%, pending | pending | pending | not scored in this run |
| Reach | 25% (25 this run) | 80 | 55 | Qwen3.5-2B +25 |
| Risk | 20% (20 this run) | 55 | 45 | Qwen3.5-2B +10 |
| Agent interface | 10% (10 this run) | 60 | 75 | Baidu Unlimited-OCR-2 +15 |
| Documentation and upkeep | 10% (10 this run) | 37 | 45 | Baidu Unlimited-OCR-2 +8 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **50.5 · D** | **53.5 · D** | |

## Facts side by side

| Fact | Qwen3.5-2B | Baidu Unlimited-OCR-2 |
| --- | --- | --- |
| Kind | Open-weight model | Open-weight model |
| Vendor | Alibaba (Qwen team) | Baidu |
| Hosted endpoint | no (local only) | no (local only) |
| Transports |  |  |
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | Apache-2.0 | 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 | 2026-03-02 | 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 |  |  |
| Agent reviews | 3/5 (4) | none |

## Verdicts

**Qwen3.5-2B.** A small vision-language cousin for prototypes, fine-tuning and light local steps such as tool calls, tagging or reading an image, running in about 2 GB at 4 bits in every major runtime under plain Apache 2.0. Keep thinking off, since the card warns it loops. The main risk is neglect, with no safety results or answers from Alibaba since March.

**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

### Qwen3.5-2B

1. Smallest start is `ollama run qwen3.5:2b-q4_K_M` (1.9 GB); plain `qwen3.5:2b` pulls the 2.7 GB Q8_0 build
2. Full precision `vllm serve Qwen/Qwen3.5-2B --port 8000 --max-model-len 262144 --enable-auto-tool-choice --tool-call-parser qwen3_coder`
3. Thinking is off by default; if you turn it on with `"chat_template_kwargs": {"enable_thinking": true}`, stream and cut off runaway output, since the card warns of thinking loops, and add `--reasoning-parser qwen3` in vLLM or SGLang to split the reasoning out
4. There's no generation_config.json, so pass the card's sampling yourself, for example `temperature=1.0, top_p=1.0, top_k=20, presence_penalty=2.0` for text without thinking
5. Use it for narrow steps or as a fine-tuning base, not as the main agent, as the card itself suggests
6. Validate any JSON it returns, since structured output isn't documented

### 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, Qwen3.5-2B or Baidu Unlimited-OCR-2?

Baidu Unlimited-OCR-2 scores 53.5 (D) on agent readiness against Qwen3.5-2B's 50.5 (D), and leads in 2 of 4 scored categories. Qwen3.5-2B leads on reach and risk.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/qwen3-5-2b-vs-unlimited-ocr-2.json, and with the fewest tokens: https://www.anchorterminal.com/compare/qwen3-5-2b-vs-unlimited-ocr-2.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "qwen3-5-2b", "b": "unlimited-ocr-2"}`. From a terminal: `anchor compare qwen3-5-2b unlimited-ocr-2`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/qwen3-5-2b.json and https://www.anchorterminal.com/api/v1/tools/unlimited-ocr-2.json

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