# Qwen3.5-122B-A10B vs Baidu Unlimited-OCR-2 > Qwen3.5-122B-A10B scores 63.6 (B) to Baidu Unlimited-OCR-2's 53.5 (D) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side. - Canonical: https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-unlimited-ocr-2 - Markdown: https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-unlimited-ocr-2.md (~7,100 tokens) - Slim: https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-unlimited-ocr-2.min.md (~530 tokens, same facts, less prose, for token-sensitive contexts) - JSON: https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-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 Qwen3.5-122B-A10B scores 63.6 (B) on agent readiness against Baidu Unlimited-OCR-2's 53.5 (D), and leads in 2 of 4 scored categories. Baidu Unlimited-OCR-2 leads on agent interface and documentation and upkeep. Both do open-weight models you run yourself. - Qwen3.5-122B-A10B: grade B, 63.6/100, rank #23 of 113 open-weight models. Markdown https://www.anchorterminal.com/tools/qwen3-5-122b-a10b.md · JSON https://www.anchorterminal.com/api/v1/tools/qwen3-5-122b-a10b.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 ### Qwen3.5-122B-A10B (B) Good for: Agents with a 128 GB unified-memory machine or two large GPUs that want a capable vision-language model with a plain licence. Ahead on: - Reach, 80 against 55 - Risk, 55 against 45 Watch for: Too big for a single 64 GB machine even at 4 bits ### 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 - Documentation and upkeep, 45 against 39 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 | Qwen3.5-122B-A10B | Baidu Unlimited-OCR-2 | Edge | | --- | --- | --- | --- | --- | | Capability | 35%, pending | pending | pending | not scored in this run | | Reach | 25% (25 this run) | 80 | 55 | Qwen3.5-122B-A10B +25 | | Risk | 20% (20 this run) | 55 | 45 | Qwen3.5-122B-A10B +10 | | Agent interface | 10% (10 this run) | 70 | 75 | Baidu Unlimited-OCR-2 +5 | | Documentation and upkeep | 10% (10 this run) | 39 | 45 | Baidu Unlimited-OCR-2 +6 | | Negative events | ≤15 | 0 | 0 | | | **Total** | | **63.6 · B** | **53.5 · D** | | ## Facts side by side | Fact | Qwen3.5-122B-A10B | 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 | 2026-02-24 | 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 **Qwen3.5-122B-A10B.** A strong mid-size cousin for coding, agent steps and reading images or video, with thinking you can switch off per request. Alibaba's 4-bit build needs about 95 GB, which fits one 128 GB unified-memory machine, and several providers host it. The main risk is silence, with no safety results, agent guidance or answers from Alibaba. **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 ### Qwen3.5-122B-A10B 1. Quickest start on a 128 GB machine is `ollama run qwen3.5:122b` (Q4_K_M, 81 GB of files, about 97 GB with context) 2. Alibaba's 4-bit build serves with `vllm serve Qwen/Qwen3.5-122B-A10B-GPTQ-Int4 --max-model-len 262144 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder --quantization moe_wna16`, though that kernel isn't documented for AMD gfx1151 3. Use the qwen3_coder tool parser and the qwen3 reasoning parser, as the card's commands do 4. Turn thinking off with `"chat_template_kwargs": {"enable_thinking": false}` in the request body, since it's on by default and `/think` and `/nothink` aren't supported 5. Sampling per the card is temperature 0.6, top_p 0.95 and top_k 20 when thinking, and temperature 0.7, top_p 0.8, top_k 20 and presence_penalty 1.5 without 6. Keep the context at 128K or more if you rely on thinking, and turn on YaRN only past 262,144 tokens 7. Add `--language-model-only` in vLLM to skip the vision encoder and free memory for the KV cache when you only need text 8. 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 `` 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, Qwen3.5-122B-A10B or Baidu Unlimited-OCR-2? Qwen3.5-122B-A10B scores 63.6 (B) on agent readiness against Baidu Unlimited-OCR-2's 53.5 (D), and leads in 2 of 4 scored categories. Baidu Unlimited-OCR-2 leads on agent interface and documentation and upkeep. ## For agents - This comparison as JSON: https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-unlimited-ocr-2.json, and with the fewest tokens: https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-unlimited-ocr-2.min.md - Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "qwen3-5-122b-a10b", "b": "unlimited-ocr-2"}`. 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How: https://www.anchorterminal.com/agents/#mcp