{ "data": { "a": { "slug": "unlimited-ocr-2", "name": "Baidu Unlimited-OCR-2", "vendor": "Baidu", "vendorUrl": "https://github.com/baidu", "kind": "weights", "category": "open-weights", "summary": "Unlimited-OCR-2 is Baidu's open-weight document OCR model, about 3.3B parameters, that parses page images, from one page to documents of hundreds of pages in one request, into layout blocks with boxes and HTML tables, under MIT.", "url": "https://www.anchorterminal.com/tools/unlimited-ocr-2", "markdownUrl": "https://www.anchorterminal.com/tools/unlimited-ocr-2.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/unlimited-ocr-2.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/unlimited-ocr-2.json", "repo": "https://huggingface.co/baidu/Unlimited-OCR-2", "license": "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)", "transports": [], "packages": [ { "registry": "huggingface", "name": "baidu/Unlimited-OCR-2" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The repository isn't gated (checked 2026-10-27).", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under MIT by the repository's metadata and the LICENSE file in Baidu's GitHub code repository. Baidu sells no per-call access to these weights in this listing. Running them costs the hardware, 6.69 GB of BF16 safetensors on an NVIDIA GPU; Baidu tested the Transformers route on one A800 80 GB. No inference provider serves it on Hugging Face (checked 2026-10-27).", "priceSummary": "Free", "where": "local", "x402": { "level": "no", "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-10-27).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-10-27" }, "docsUrl": "https://huggingface.co/baidu/Unlimited-OCR-2", "capabilities": [ "docs.ocr", "docs.parse", "docs.tables", "model.open-weights" ], "tags": [ "open-weights", "model", "ocr", "document-parsing", "multi-page", "long-documents", "tables", "layout", "bounding-boxes", "vision", "mixture-of-experts", "safetensors", "self-hosted", "new" ], "lastRelease": "2026-10-26", "graded": true, "anchor": { "graded": true, "score": 53.5, "grade": "D", "agentReady": false, "rank": 0, "ranked": false, "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools", "rankOf": 1243, "categoryRank": 75, "methodology": "0.7", "run": "2026-10-01", "scores": { "interface": 75, "reach": 55, "risk": 45, "upkeep": 45 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-10-27" }, "negative": 0, "verdict": "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.", "bestFor": "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", "strengths": [ "Small, 3.3B parameters with about 0.5B active per token, in 6.69 GB of BF16 safetensors", "Reads many pages in one request with cross-page context, merging tables and text that continue across pages", "MIT with nothing added, ungated, files hashed in Baidu's verified organisation", "An OpenAI-compatible server through SGLang with page-window scheduling for long documents, and a documented converter from its block output to Markdown" ], "weaknesses": [ "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", "No released runtime runs its sparse attention yet, and no quantised build", "The card states no parameters, languages, context length or limitations, and no safety results or security contact were found", "Every accuracy figure is Baidu's own, one of its two long-document benchmarks is Baidu's own, and no independent result exists for this checkpoint", "Charts and figures come back as boxes to crop, not as data, and the report says heading hierarchy is weak over long documents" ], "agentNotes": [ "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\")`", "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", "Send all pages of a document in one request, images first, then the text `\u003cPAGE\u003e\u003cimage\u003e` 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)", "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", "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" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 0, "avgRating": 0, "history": [ { "basis": "public evidence", "confidence": "low", "grade": "D", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 53.5 } ], "editorialScores": { "interface": 75, "reach": 55, "risk": 45, "upkeep": 45 }, "provenanceScore": 73, "checked": { "categories": { "interface": "2026-10-27", "reach": "2026-10-27", "risk": "2026-10-27", "upkeep": "2026-10-27" }, "last": "2026-10-27", "oldest": "2026-10-27", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "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\n# then AutoModel.from_pretrained(\"baidu/Unlimited-OCR-2\", trust_remote_code=True, use_safetensors=True, torch_dtype=torch.bfloat16).eval().cuda()\n# and model.infer_multi(tokenizer, image_files=[\"page_1.jpg\", \"page_2.jpg\"], output_path=\"out\")", "http": "# after: hf download baidu/Unlimited-OCR-2 --local-dir uocr2 \u0026\u0026 uv venv --python 3.10 \u0026\u0026 . .venv/bin/activate\n# uv pip install uocr2/wheel/sglang-0.5.20.dev10+g8d62f5fb9c-py3-none-any.whl pymupdf==1.27.2.2 fastokens==0.3.2\n# SGLANG_UOCR_EANABLE_DOC_LIMIT=1 python -m sglang.launch_server --model-path baidu/Unlimited-OCR-2 --served-model-name Unlimited-OCR-2 --chat-template unlimited-ocr --tokenizer-backend fastokens --mem-fraction-static 0.7 --max-prefill-tokens 2048 --enable-custom-logit-processor --enable-streaming-session --disable-radix-cache --port 10000\ncurl http://127.0.0.1:10000/v1/chat/completions -H 'Content-Type: application/json' -d '{\"model\":\"Unlimited-OCR-2\",\"temperature\":0,\"max_tokens\":8000,\"skip_special_tokens\":false,\"images_config\":{\"crop_mode\":true},\"messages\":[{\"role\":\"user\",\"content\":[{\"type\":\"image_url\",\"image_url\":{\"url\":\"data:image/png;base64,...\"}},{\"type\":\"text\",\"text\":\"\u003cPAGE\u003e\u003cimage\u003eMulti page parsing.\"}]}]}'" }, "letme": { "capability": "https://letme.dev/docs.ocr", "tool": "https://letme.dev/unlimited-ocr-2" }, "weights": { "trainedBy": "Baidu (ERNIE Team)", "builtOn": "baidu/Unlimited-OCR (post-trained from its checkpoint per the report, MIT by its metadata), whose backbone is deepseek-ai/DeepSeek-OCR (MIT by its metadata); not declared in the card's metadata", "params": "3.3B (3B-A0.5B mixture-of-experts decoder per the report)", "context": 131072, "goodFor": "Parsing long scanned or rendered documents, tens to hundreds of pages, into ordered layout blocks with boxes and HTML tables in one request, on an NVIDIA GPU, if Baidu's own results hold up.", "runtimes": [], "builds": [ { "name": "Unlimited-OCR-2 (BF16 safetensors)", "url": "https://huggingface.co/baidu/Unlimited-OCR-2", "who": "publisher", "precision": "bf16", "sizeGB": 6.69, "memoryGB": 12, "format": "safetensors", "runsOn": [ "Transformers (remote code)", "SGLang (Baidu's customised wheel)" ], "note": "Two shards, 6,688,305,576 bytes, so 6.69 GB × 1.2 = 8.03 GB, within 12 GB. The whole repository is 6.87 GB with the report, the wheel and a 156 MB GIF, which comes to the same band. The only build from Baidu; no copy by anyone else exists yet" } ], "run": "hf download baidu/Unlimited-OCR-2 --local-dir uocr2 \u0026\u0026 uv venv --python 3.10 \u0026\u0026 . .venv/bin/activate \u0026\u0026 uv pip install uocr2/wheel/sglang-0.5.20.dev10+g8d62f5fb9c-py3-none-any.whl pymupdf==1.27.2.2 fastokens==0.3.2 \u0026\u0026 SGLANG_UOCR_EANABLE_DOC_LIMIT=1 SGLANG_UOCR_DOC_GUNDAM_PAGE_WINDOW=10 SGLANG_UOCR_DOC_BASE_PAGE_WINDOW=10 SGLANG_UOCR_MAX_PAGE_TOKENS=20000 python -m sglang.launch_server --model-path baidu/Unlimited-OCR-2 --served-model-name Unlimited-OCR-2 --chat-template unlimited-ocr --tokenizer-backend fastokens --mem-fraction-static 0.7 --max-prefill-tokens 2048 --enable-custom-logit-processor --enable-streaming-session --allow-auto-truncate --disable-radix-cache --port 10000", "runBuild": "Unlimited-OCR-2 (BF16 safetensors)", "checked": "2026-10-27", "minMemoryGB": 12 }, "family": { "lab": "Baidu", "family": "Unlimited OCR 2", "url": "https://www.anchorterminal.com/open-weights/#baidu", "olderCount": 1 }, "area": "models", "provenance": { "legalEntity": "Baidu, Inc.", "domain": "baidu.com", "domainRegistered": "1999-10-11", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://huggingface.co/baidu/Unlimited-OCR-2/commits/main", "securityTxt": "unknown", "checked": "2026-10-27", "notes": [ "The files sit in Baidu's verified baidu organisation on Hugging Face, which Baidu's GitHub organisation profile links to, and the GitHub code repository's README links this repository. The report's byline is the Baidu ERNIE Team. The legal entity is the card's Baidu Inc., not checked against a registry. The domain registration date is from Verisign's RDAP record for baidu.com, read 2026-10-27.", "Nothing is hosted in this listing, so the MIT licence stands in for terms of service and no privacy policy is scored.", "The changelog is the repository's commit history, twelve commits on 26 October 2026 by one account (WEISHU), the weights at 04:38 UTC and the last README change at 14:05 UTC. The card's release line dates the release 27 October 2026.", "www.baidu.com's robots.txt disallows every path to all agents not named in it, so its security.txt is not read for this listing, and bsrc.baidu.com answered Access denied to its robots.txt. No security contact is named on the card or in the GitHub repository." ], "score": 73 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/unlimited-ocr-2.json", "live": { "slug": "unlimited-ocr-2", "securityTxt": { "url": "https://baidu.com/.well-known/security.txt", "state": "none", "checkedAt": "2026-10-30T15:38:12.945819764Z" }, "pages": [ { "url": "https://huggingface.co/baidu/Unlimited-OCR-2/commits/main", "kind": "changelog", "status": 200, "checkedAt": "2026-10-30T05:41:33.878979898Z", "changedAt": "2026-10-30T05:41:33.878979898Z", "fingerprint": "2b672ea8161b" } ], "updatedAt": "2026-10-30T15:38:12.945819764Z" } }, "answer": "Xiaomi-OCR-0 scores 59 (C) on agent readiness against Baidu Unlimited-OCR-2's 53.5 (D), and leads in 3 of 4 scored categories. Baidu Unlimited-OCR-2 leads on agent interface.", "b": { "slug": "xiaomi-ocr-0", "name": "Xiaomi-OCR-0", "vendor": "Xiaomi", "vendorUrl": "https://huggingface.co/xiaomi-research", "kind": "weights", "category": "document-extraction", "summary": "Xiaomi-OCR-0 is Xiaomi's open-weight OCR model, 0.87B parameters on Qwen3.5-0.8B-Base, that turns document images into Markdown with LaTeX formulas and tables, extracts fields as JSON and answers questions about documents, under Apache 2.0.", "url": "https://www.anchorterminal.com/tools/xiaomi-ocr-0", "markdownUrl": "https://www.anchorterminal.com/tools/xiaomi-ocr-0.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/xiaomi-ocr-0.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/xiaomi-ocr-0.json", "repo": "https://huggingface.co/xiaomi-research/Xiaomi-OCR-0", "license": "Apache-2.0 (card metadata and card text; the Hugging Face repository has no LICENSE file, and the GitHub code repository's LICENSE is the Apache 2.0 text, copyright 2026 Xiaomi Corporation)", "transports": [], "packages": [ { "registry": "huggingface", "name": "xiaomi-research/Xiaomi-OCR-0" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The repository isn't gated (checked 2026-10-30). It moved from SeerRay-Lab/Xiaomi-OCR-0, which the card and the GitHub README still link and which now redirects here.", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0, the same licence as the base Qwen3.5-0.8B-Base. Xiaomi sells no per-call access to these weights. Running them costs the hardware, a 1.75 GB BF16 safetensors file that runs on a GPU, Apple silicon or a CPU. No inference provider serves it on Hugging Face, and the project Space is a static page with no inference (checked 2026-10-30).", "priceSummary": "Free", "where": "local", "x402": { "level": "no", "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-10-30).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-10-30" }, "docsUrl": "https://huggingface.co/xiaomi-research/Xiaomi-OCR-0", "capabilities": [ "docs.ocr", "docs.parse", "docs.extract", "docs.tables", "model.open-weights" ], "tags": [ "open-weights", "model", "ocr", "document-parsing", "key-information-extraction", "document-vqa", "tables", "formulas", "handwriting", "vision", "mcp", "agent-skill", "safetensors", "self-hosted", "new" ], "lastRelease": "2026-09-29", "graded": true, "anchor": { "graded": true, "score": 59, "grade": "C", "agentReady": false, "rank": 0, "ranked": false, "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools", "rankOf": 1243, "categoryRank": 11, "methodology": "0.7", "run": "2026-10-01", "scores": { "interface": 70, "reach": 75, "risk": 65, "upkeep": 60 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-10-30" }, "negative": -2, "negativeNotes": [ "The model card says '95.24 on Real5-OmniDocBench, ranked #1'. Real5-OmniDocBench's own leaderboard, kept by the benchmark's authors, ranks Xiaomi-OCR-0 second at 92.73 behind PaddleOCR-VL-1.6 at 93.19 (read 30 October 2026), and Xiaomi merged that 92.73 into its own repository's eval results on 23 October 2026 while leaving the card's claim in place. A '#1' a first-hand ranking shows false is a deduction of 2 (research/new-2026-10-20/NEWLIST.md). The GitHub README limits the rank to 'the comparison on this page' and says the 95.24 is Xiaomi's two-stage run; proposed for the founder to confirm at landing" ], "verdict": "A small document-reading cousin that turns page images into Markdown with LaTeX and tables, pulls fields from receipts and forms as JSON, and answers questions about a page. It runs natively in vLLM, SGLang, Transformers and llama.cpp in about 2 GB, even on a CPU. The risks are accuracy figures that are mostly Xiaomi's own and an inflated ranking claim.", "bestFor": "Agents and pipelines on modest hardware that need local OCR of scanned or photographed pages, receipts and forms into Markdown or JSON", "strengths": [ "Tiny, 0.87B parameters in one 1.75 GB BF16 safetensors file, about 2 GB in memory, and runs on GPU, Apple silicon or CPU", "Stock Qwen3.5 architecture, so released vLLM, SGLang, Transformers and llama.cpp load it with no remote code", "Apache 2.0 on an Apache 2.0 base, ungated, with the files hashed", "Parsing, key information extraction to JSON and document question answering in one model, with documented prompts", "An independent leaderboard puts it second of 33 on Real5-OmniDocBench at 92.73, with the best table scores in four of five capture conditions", "A local MCP server with four tools that only talks to a loopback inference server" ], "weaknesses": [ "The card's '#1 on Real5-OmniDocBench' is contradicted by that benchmark's own leaderboard, and its OmniDocBench and VQA figures are Xiaomi's own runs with a relaxed evaluation config", "No result on ParseBench or OmniDocBench's own leaderboard, so Capability is pending", "No quantised build from Xiaomi; the GGUF, MLX and Ollama packages are other people's and Xiaomi says their accuracy isn't established", "One image per request and a 16,384-token context, so documents are read page by page", "The card names no languages, limitations or context, and there are no safety results or prompt-injection guidance", "The agent setup asks an agent to read and execute a remote SKILL.md that installs packages, and the MCP fetches any image_url it's given" ], "agentNotes": [ "Serve it with `vllm serve xiaomi-research/Xiaomi-OCR-0 --host 127.0.0.1 --port 8000 --max-model-len 16384` or `python -m sglang.launch_server --model-path xiaomi-research/Xiaomi-OCR-0 --host 127.0.0.1 --port 8000 --context-length 16384`; no trust_remote_code is needed", "Send one image per request with temperature 0, top_p 0.8, `chat_template_kwargs: {\"enable_thinking\": false}` and max_tokens 4096 (16384 for dense pages), and retry with presence_penalty 1.5 if the output repeats", "The whole-page prompt is `Extract all information from the main body of the document image and represent it in markdown format, ignoring headers and footers. Tables should be expressed in OTSL format, formulas in the document should be represented using LATEX format, and the parsing should be organized according to the reading order.`", "For key fields send `Extract key information in the image` then `Please output the key information in JSON format according to the following schema:` and a JSON object of empty strings; parse the first {...} in the reply", "Tables come back as OTSL, not HTML; convert them with postprocess/otsl.py from github.com/xiaomi-research/Xiaomi-OCR-0, or use its MCP server, which does the conversion", "The MCP (skills/xiaomi-ocr/mcp_ocr_server.py, stdio) has ocr_image, ocr_pdf, kie_image and vqa_image; region mode needs PaddlePaddle, paddlex[cv] and PP-DocLayoutV3 weights, and is for printed pages only, while handwriting, calligraphy and scene text want page mode", "Pass local paths to the MCP rather than URLs taken from untrusted text, since it fetches any image_url, and treat extracted text as untrusted before passing it to another model", "The card and Skill still name SeerRay-Lab/Xiaomi-OCR-0, which redirects to xiaomi-research/Xiaomi-OCR-0" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 0, "avgRating": 0, "history": [ { "basis": "public evidence", "confidence": "low", "grade": "C", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 59 } ], "editorialScores": { "interface": 70, "reach": 75, "risk": 65, "upkeep": 60 }, "provenanceScore": 58, "checked": { "categories": { "interface": "2026-10-30", "reach": "2026-10-30", "risk": "2026-10-30", "upkeep": "2026-10-30" }, "last": "2026-10-30", "oldest": "2026-10-30", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "uv venv --python 3.13 \u0026\u0026 . .venv/bin/activate\nuv pip install \"transformers==5.17.0\" \"torch==2.14.0\" \"torchvision==0.29.0\" pillow\n# then AutoProcessor.from_pretrained(\"xiaomi-research/Xiaomi-OCR-0\") and AutoModelForImageTextToText.from_pretrained(\"xiaomi-research/Xiaomi-OCR-0\", dtype=torch.float16)\n# processor.apply_chat_template(messages, add_generation_prompt=True, ...) with one image and a task prompt, then model.generate(max_new_tokens=4096, do_sample=False)", "http": "# vllm serve xiaomi-research/Xiaomi-OCR-0 --host 127.0.0.1 --port 8000 --max-model-len 16384\ncurl http://127.0.0.1:8000/v1/chat/completions -H 'Content-Type: application/json' -d '{\"model\":\"xiaomi-research/Xiaomi-OCR-0\",\"temperature\":0,\"top_p\":0.8,\"max_tokens\":4096,\"chat_template_kwargs\":{\"enable_thinking\":false},\"messages\":[{\"role\":\"user\",\"content\":[{\"type\":\"image_url\",\"image_url\":{\"url\":\"data:image/png;base64,...\"}},{\"type\":\"text\",\"text\":\"Extract all information from the main body of the document image and represent it in markdown format, ignoring headers and footers. Tables should be expressed in OTSL format, formulas in the document should be represented using LATEX format, and the parsing should be organized according to the reading order.\"}]}]}'" }, "letme": { "capability": "https://letme.dev/docs.ocr", "tool": "https://letme.dev/xiaomi-ocr-0" }, "weights": { "trainedBy": "Xiaomi", "builtOn": "Qwen/Qwen3.5-0.8B-Base (named in the card's metadata, Apache 2.0)", "params": "0.87B (873,438,784 by the Hub's count; 0.8B on the card)", "context": 16384, "goodFor": "Reading document images on small hardware, scanned, photographed or skewed pages to Markdown with LaTeX formulas and tables, receipts and forms to JSON fields, and short answers about a page, locally and under a permissive licence.", "runtimes": [ "Transformers", "vLLM", "SGLang", "llama.cpp", "Ollama" ], "builds": [ { "name": "Xiaomi-OCR-0 (BF16 safetensors)", "url": "https://huggingface.co/xiaomi-research/Xiaomi-OCR-0/blob/main/model.safetensors", "who": "publisher", "precision": "bf16", "sizeGB": 1.75, "memoryGB": 8, "format": "safetensors", "runsOn": [ "Transformers", "vLLM", "SGLang" ], "note": "1,746,937,856 bytes, so 1.75 GB x 1.2 = 2.10 GB, within 8 GB. The whole repository with tokenizer, images and examples is 1.77 GB, the same band. The only build from Xiaomi; SHA-256 d4551e9f6127ac9e..." } ], "run": "vllm serve xiaomi-research/Xiaomi-OCR-0 --host 127.0.0.1 --port 8000 --max-model-len 16384", "runBuild": "Xiaomi-OCR-0 (BF16 safetensors)", "checked": "2026-10-30", "minMemoryGB": 8 }, "family": { "lab": "Xiaomi (MiMo)", "family": "Xiaomi-OCR", "url": "https://www.anchorterminal.com/open-weights/#xiaomi", "olderCount": 12 }, "area": "web-data", "provenance": { "legalEntity": "Xiaomi Corporation", "domain": "mi.com", "domainRegistered": "", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://huggingface.co/xiaomi-research/Xiaomi-OCR-0/commits/main", "securityTxt": "unknown", "checked": "2026-10-30", "notes": [ "The files sit in the xiaomi-research organisation on Hugging Face (Xiaomi Research, 12 members, not verified by the Hub), moved from SeerRay-Lab, another unverified organisation whose tag and members overlap. The GitHub repository under xiaomi-research carries the Apache 2.0 licence with 'Copyright 2026 Xiaomi Corporation', and the report's byline includes ten authors. No page on a Xiaomi domain linking either organisation was found, and Xiaomi's sites answered 403 to robots.txt in the 28 October reading for MiMo.", "Nothing is hosted in this listing, so the Apache 2.0 licence stands in for terms of service and no privacy policy is scored.", "The changelog is the repository's commit history, 17 commits from 29 September to 29 October 2026, all by loongwayX (Longwei Xu, a member of xiaomi-research and an author of the report) except merged pull requests from Hugging Face staff and PaddlePaddle. The weights, configs and template are unchanged since 29 September.", "No SECURITY.md in the GitHub repository (404 at main). Xiaomi's company security centre is carried from the MiMo-V2.6-Distill reading of 28 October 2026 and wasn't re-read." ], "score": 58 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/xiaomi-ocr-0.json" }, "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "Baidu", "b": "Xiaomi", "name": "Vendor" }, { "a": "no (local only)", "b": "no (local only)", "name": "Hosted endpoint" }, { "a": "", "b": "", "name": "Transports" }, { "a": "None", "b": "None", "name": "Auth" }, { "a": "Free", "b": "Free", "name": "Pricing" }, { "a": "no", "b": "no", "name": "x402" }, { "a": "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)", "b": "Apache-2.0 (card metadata and card text; the Hugging Face repository has no LICENSE file, and the GitHub code repository's LICENSE is the Apache 2.0 text, copyright 2026 Xiaomi Corporation)", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "no", "b": "no", "name": "llms.txt" }, { "a": "2026-10-26", "b": "2026-09-29", "name": "Last release" }, { "a": "no document linked", "b": "no document linked", "name": "Terms last updated" }, { "a": "no document linked", "b": "no document linked", "name": "Privacy policy last updated" }, { "a": "", "b": "", "name": "Customer content may train models" }, { "a": "", "b": "", "name": "Terms restrict automated access" }, { "a": "", "b": "", "name": "Terms restrict benchmarking" }, { "a": "", "b": "", "name": "Terms or service can change without notice" }, { "a": "", "b": "", "name": "Arbitration or class-action waiver" } ], "faq": [ { "answer": "Xiaomi-OCR-0 scores 59 (C) on agent readiness against Baidu Unlimited-OCR-2's 53.5 (D), and leads in 3 of 4 scored categories. Baidu Unlimited-OCR-2 leads on agent interface.", "question": "Which is better for AI agents, Baidu Unlimited-OCR-2 or Xiaomi-OCR-0?" } ], "goodFor": [ { "aheadOn": [ "Agent interface, 75 against 70" ], "also": null, "goodFor": "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", "slug": "unlimited-ocr-2", "watchFor": "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" }, { "aheadOn": [ "Reach, 75 against 55", "Risk, 65 against 45", "Documentation and upkeep, 60 against 45" ], "also": null, "goodFor": "Agents and pipelines on modest hardware that need local OCR of scanned or photographed pages, receipts and forms into Markdown or JSON", "slug": "xiaomi-ocr-0", "watchFor": "The card's '#1 on Real5-OmniDocBench' is contradicted by that benchmark's own leaderboard, and its OmniDocBench and VQA figures are Xiaomi's own runs with a relaxed evaluation config" } ], "job": { "capability": "docs.ocr", "name": "OCR" }, "others": [ { "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-unlimited-ocr-2.json", "title": "Agents-A1-4B vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-unlimited-ocr-2.json", "title": "Apertus v1.5 8B vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-unlimited-ocr-2.json", "title": "Apodex-1.1-mini vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-unlimited-ocr-2.json", "title": "BigBang-v1 vs Baidu Unlimited-OCR-2", "url": 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Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-unlimited-ocr-2.json", "title": "gpt-oss-20b vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-unlimited-ocr-2.json", "title": "Granite 4.2 3B vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/hy3-vs-unlimited-ocr-2.json", "title": "Hy3 vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/hy3-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/inkling-small-vs-unlimited-ocr-2.json", "title": "Inkling-Small vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/inkling-small-vs-unlimited-ocr-2" }, { "json": 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"Qwen3.5-4B vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/qwen3-5-4b-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-unlimited-ocr-2.json", "title": "Qwen3.5-9B vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/qwen3-6-27b-vs-unlimited-ocr-2.json", "title": "Qwen3.6-27B vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/qwen3-6-27b-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-unlimited-ocr-2.json", "title": "Qwen3.6-35B-A3B vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/qwen3-8-27b-vs-unlimited-ocr-2.json", "title": "Qwen3.8-27B vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/qwen3-8-27b-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-unlimited-ocr-2.json", "title": "Qwen3.8-Flash-Next vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/qwen3-coder-30b-a3b-instruct-vs-unlimited-ocr-2.json", "title": "Qwen3-Coder-30B-A3B-Instruct vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/qwen3-coder-30b-a3b-instruct-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/qwen3-coder-next-vs-unlimited-ocr-2.json", "title": "Qwen3-Coder-Next vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/qwen3-coder-next-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-2b-instruct-vs-unlimited-ocr-2.json", "title": "Qwen3-VL-2B-Instruct vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/qwen3-vl-2b-instruct-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-30b-a3b-instruct-vs-unlimited-ocr-2.json", "title": "Qwen3-VL-30B-A3B-Instruct vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/qwen3-vl-30b-a3b-instruct-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-32b-instruct-vs-unlimited-ocr-2.json", "title": "Qwen3-VL-32B-Instruct vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/qwen3-vl-32b-instruct-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-unlimited-ocr-2.json", "title": "Qwen3-VL-4B-Instruct vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-8b-instruct-vs-unlimited-ocr-2.json", "title": "Qwen3-VL-8B-Instruct vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/qwen3-vl-8b-instruct-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-unlimited-ocr-2.json", "title": "Qwopus3.8-27B-Flash-V2 vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/recursive-flint-vs-unlimited-ocr-2.json", "title": "Recursive Flint vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/recursive-flint-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/smollm3-3b-vs-unlimited-ocr-2.json", "title": "SmolLM3-3B vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/smollm3-3b-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/spark-x2-6-4b-vs-unlimited-ocr-2.json", "title": "Spark-X2.6-4B vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/spark-x2-6-4b-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/swift-1-5-qwen3-8-27b-vs-unlimited-ocr-2.json", "title": "Swift 1.5 Qwen3.8-27B vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/swift-1-5-qwen3-8-27b-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/swift-1-5-qwen3-8-flash-next-vs-unlimited-ocr-2.json", "title": "Swift 1.5 Qwen3.8-Flash-Next vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/swift-1-5-qwen3-8-flash-next-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/thinkingcap-qwen3-8-27b-vs-unlimited-ocr-2.json", "title": "ThinkingCap (Qwen3.8 27B) vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/thinkingcap-qwen3-8-27b-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-parse-vs-xiaomi-ocr-0.json", "title": "Nemotron 3.5 Parse vs Xiaomi-OCR-0", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-parse-vs-xiaomi-ocr-0" } ], "scores": [ { "key": "capability", "name": "Capability", "pending": true, "weight": 35 }, { "by": 20, "edge": "xiaomi-ocr-0", "key": "reach", "name": "Reach", "unlimited-ocr-2": 55, "weight": 25, "xiaomi-ocr-0": 75 }, { "by": 20, "edge": "xiaomi-ocr-0", "key": "risk", "name": "Risk", "unlimited-ocr-2": 45, "weight": 20, "xiaomi-ocr-0": 65 }, { "by": 5, "edge": "unlimited-ocr-2", "key": "interface", "name": "Agent interface", "unlimited-ocr-2": 75, "weight": 10, "xiaomi-ocr-0": 70 }, { "by": 15, "edge": "xiaomi-ocr-0", "key": "upkeep", "name": "Documentation and upkeep", "unlimited-ocr-2": 45, "weight": 10, "xiaomi-ocr-0": 60 } ], "summary": "Xiaomi-OCR-0 scores 59 (C) on agent readiness against Baidu Unlimited-OCR-2's 53.5 (D), and leads in 3 of 4 scored categories. Baidu Unlimited-OCR-2 leads on agent interface. Both do ocr.", "tutorials": [], "verdicts": { "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.", "xiaomi-ocr-0": "A small document-reading cousin that turns page images into Markdown with LaTeX and tables, pulls fields from receipts and forms as JSON, and answers questions about a page. It runs natively in vLLM, SGLang, Transformers and llama.cpp in about 2 GB, even on a CPU. The risks are accuracy figures that are mostly Xiaomi's own and an inflated ranking claim." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/unlimited-ocr-2-vs-xiaomi-ocr-0", "json": "https://www.anchorterminal.com/compare/unlimited-ocr-2-vs-xiaomi-ocr-0.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/unlimited-ocr-2-vs-xiaomi-ocr-0.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/unlimited-ocr-2-vs-xiaomi-ocr-0.min.md" }, "markdown": "Xiaomi-OCR-0 scores 59 (C) on agent readiness against Baidu Unlimited-OCR-2's 53.5 (D), and leads in 3 of 4 scored categories. Baidu Unlimited-OCR-2 leads on agent interface. Both do ocr.\n\n- Baidu Unlimited-OCR-2: grade D, 53.5/100, rank #98 of 120 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\n- Xiaomi-OCR-0: grade C, 59/100, rank #72 of 120 open-weight models. Markdown https://www.anchorterminal.com/tools/xiaomi-ocr-0.md · JSON https://www.anchorterminal.com/api/v1/tools/xiaomi-ocr-0.json\n- Best open-weight models you download and run yourself for AI agents: https://www.anchorterminal.com/best/open-weights/index.md\n- All 3589 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md\n- Best document parsing, OCR and extraction APIs for AI agents: https://www.anchorterminal.com/best/document-extraction/index.md\n- All 144 documents comparisons: https://www.anchorterminal.com/compare/document-extraction/index.md\n\n## Which one, for what\n\n### Baidu Unlimited-OCR-2 (D)\n\nGood 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\n\nAhead on:\n- Agent interface, 75 against 70\n\nWatch 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\n\n### Xiaomi-OCR-0 (C)\n\nGood for: Agents and pipelines on modest hardware that need local OCR of scanned or photographed pages, receipts and forms into Markdown or JSON\n\nAhead on:\n- Reach, 75 against 55\n- Risk, 65 against 45\n- Documentation and upkeep, 60 against 45\n\nWatch for: The card's '#1 on Real5-OmniDocBench' is contradicted by that benchmark's own leaderboard, and its OmniDocBench and VQA figures are Xiaomi's own runs with a relaxed evaluation config\n\n\n## Score by category\n\n| Category | Weight | Baidu Unlimited-OCR-2 | Xiaomi-OCR-0 | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 55 | 75 | Xiaomi-OCR-0 +20 |\n| Risk | 20% (20 this run) | 45 | 65 | Xiaomi-OCR-0 +20 |\n| Agent interface | 10% (10 this run) | 75 | 70 | Baidu Unlimited-OCR-2 +5 |\n| Documentation and upkeep | 10% (10 this run) | 45 | 60 | Xiaomi-OCR-0 +15 |\n| Negative events | ≤15 | 0 | -2 | |\n| **Total** | | **53.5 · D** | **59 · C** | |\n\n## Facts side by side\n\n| Fact | Baidu Unlimited-OCR-2 | Xiaomi-OCR-0 |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Baidu | Xiaomi |\n| Hosted endpoint | no (local only) | no (local only) |\n| Transports | | |\n| Auth | None | None |\n| Pricing | Free | Free |\n| x402 | no | no |\n| Licence | 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) | Apache-2.0 (card metadata and card text; the Hugging Face repository has no LICENSE file, and the GitHub code repository's LICENSE is the Apache 2.0 text, copyright 2026 Xiaomi Corporation) |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-10-26 | 2026-09-29 |\n| Terms last updated | no document linked | no document linked |\n| Privacy policy last updated | no document linked | no document linked |\n| Customer content may train models | | |\n| Terms restrict automated access | | |\n| Terms restrict benchmarking | | |\n| Terms or service can change without notice | | |\n| Arbitration or class-action waiver | | |\n\n## Verdicts\n\n**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.\n\n**Xiaomi-OCR-0.** A small document-reading cousin that turns page images into Markdown with LaTeX and tables, pulls fields from receipts and forms as JSON, and answers questions about a page. It runs natively in vLLM, SGLang, Transformers and llama.cpp in about 2 GB, even on a CPU. The risks are accuracy figures that are mostly Xiaomi's own and an inflated ranking claim.\n\n## Before you call either\n\n### Baidu Unlimited-OCR-2\n\n1. 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\")`\n2. 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\n3. Send all pages of a document in one request, images first, then the text `\u003cPAGE\u003e\u003cimage\u003e` 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)\n4. 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\n5. 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\n\n### Xiaomi-OCR-0\n\n1. Serve it with `vllm serve xiaomi-research/Xiaomi-OCR-0 --host 127.0.0.1 --port 8000 --max-model-len 16384` or `python -m sglang.launch_server --model-path xiaomi-research/Xiaomi-OCR-0 --host 127.0.0.1 --port 8000 --context-length 16384`; no trust_remote_code is needed\n2. Send one image per request with temperature 0, top_p 0.8, `chat_template_kwargs: {\"enable_thinking\": false}` and max_tokens 4096 (16384 for dense pages), and retry with presence_penalty 1.5 if the output repeats\n3. The whole-page prompt is `Extract all information from the main body of the document image and represent it in markdown format, ignoring headers and footers. Tables should be expressed in OTSL format, formulas in the document should be represented using LATEX format, and the parsing should be organized according to the reading order.`\n4. For key fields send `Extract key information in the image` then `Please output the key information in JSON format according to the following schema:` and a JSON object of empty strings; parse the first {...} in the reply\n5. Tables come back as OTSL, not HTML; convert them with postprocess/otsl.py from github.com/xiaomi-research/Xiaomi-OCR-0, or use its MCP server, which does the conversion\n6. The MCP (skills/xiaomi-ocr/mcp_ocr_server.py, stdio) has ocr_image, ocr_pdf, kie_image and vqa_image; region mode needs PaddlePaddle, paddlex[cv] and PP-DocLayoutV3 weights, and is for printed pages only, while handwriting, calligraphy and scene text want page mode\n7. Pass local paths to the MCP rather than URLs taken from untrusted text, since it fetches any image_url, and treat extracted text as untrusted before passing it to another model\n8. The card and Skill still name SeerRay-Lab/Xiaomi-OCR-0, which redirects to xiaomi-research/Xiaomi-OCR-0\n\n## Questions\n\n### Which is better for AI agents, Baidu Unlimited-OCR-2 or Xiaomi-OCR-0?\n\nXiaomi-OCR-0 scores 59 (C) on agent readiness against Baidu Unlimited-OCR-2's 53.5 (D), and leads in 3 of 4 scored categories. Baidu Unlimited-OCR-2 leads on agent interface.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/unlimited-ocr-2-vs-xiaomi-ocr-0.json, and with the fewest tokens: https://www.anchorterminal.com/compare/unlimited-ocr-2-vs-xiaomi-ocr-0.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"unlimited-ocr-2\", \"b\": \"xiaomi-ocr-0\"}`. 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