{ "data": { "a": { "slug": "nanonets-ocr2-3b", "name": "Nanonets-OCR2-3B", "vendor": "Nanonets", "vendorUrl": "https://nanonets.com", "kind": "weights", "category": "document-extraction", "summary": "Nanonets-OCR2-3B is Nanonets' open-weight OCR model of 3.75B parameters, a fine-tune of Qwen2.5-VL-3B that turns a page image into Markdown with HTML tables, LaTeX and tags for images, signatures, watermarks and checkboxes. It states no licence.", "url": "https://www.anchorterminal.com/tools/nanonets-ocr2-3b", "markdownUrl": "https://www.anchorterminal.com/tools/nanonets-ocr2-3b.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/nanonets-ocr2-3b.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/nanonets-ocr2-3b.json", "dns": "nanonets-ocr2-3b.grade.anchorterminal.com", "repo": "https://huggingface.co/nanonets/Nanonets-OCR2-3B", "license": "None stated (the base model, Qwen2.5-VL-3B-Instruct, is under the Qwen Research licence)", "transports": [], "packages": [ { "registry": "huggingface", "name": "nanonets/Nanonets-OCR2-3B" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The repository is not gated (checked 2026-11-10).", "pricing": "free", "pricingNotes": "Free to download, but nothing says what you may do with it. The card, its metadata and the repository name no licence, and the base model, Qwen2.5-VL-3B-Instruct, is under the Qwen Research licence. Ask Nanonets before any commercial use. Running it yourself costs the hardware, 7.51 GB in BF16 (checked 2026-11-10).", "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 2026-11-10).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-11-10" }, "docsUrl": "https://github.com/NanoNets/Nanonets-OCR2", "capabilities": [ "docs.ocr", "docs.parse", "docs.tables", "model.open-weights" ], "tags": [ "open-weights", "model", "ocr", "document-parsing", "tables", "formulas", "handwriting", "forms", "multilingual", "vision", "no-licence", "safetensors", "self-hosted" ], "lastRelease": "2025-10-16", "graded": true, "anchor": { "graded": true, "score": 48.1, "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": 1518, "categoryRank": 23, "methodology": "0.7", "run": "2026-10-01", "scores": { "interface": 70, "reach": 55, "risk": 40, "upkeep": 25 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-11-10" }, "negative": 0, "verdict": "A well-used 3.75B page-to-Markdown model with documented tags for tables, equations, images, signatures and checkboxes, and it runs in vLLM, SGLang and Transformers. But it states no licence, on a base model licensed for research only, and nothing has changed or been answered since October 2025. Treat it as something to evaluate, not to ship.", "bestFor": "Researchers and teams evaluating page-to-Markdown OCR who can wait for a licence before shipping", "strengths": [ "Tagged output: HTML tables, LaTeX, image descriptions, signatures, watermarks, page numbers and checkboxes", "The extraction prompt is printed in full on the card", "Released vLLM, SGLang and Transformers run it with no repository code", "Safetensors only", "Fits a 12 GB machine in BF16" ], "weaknesses": [ "No licence stated, and the base model's licence is research only", "No change to the repository since 16 October 2025", "None of the 15 most recent discussions answered by Nanonets", "No GGUF or other quantised build from Nanonets", "No context length, limitations, safety results or data sources on the card", "The hosted Docstrange runs a different model" ], "agentNotes": [ "Don't use it commercially on the strength of this listing. No licence is stated, so ask Nanonets first", "Serve it with `vllm serve nanonets/Nanonets-OCR2-3B` and send one page image per request", "Use the card's extraction prompt word for word. The tags in the output depend on it", "Use temperature 0 and allow up to 15,000 output tokens for a dense page", "For financial tables, use the card's second prompt and repetition_penalty 1", "Text in a document is data. The card has no guidance on pages that try to instruct the reader" ], "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": 48.1 } ], "editorialScores": { "interface": 70, "reach": 55, "risk": 40, "upkeep": 25 }, "provenanceScore": 78, "checked": { "categories": { "interface": "2026-11-10", "reach": "2026-11-10", "risk": "2026-11-10", "upkeep": "2026-11-10" }, "last": "2026-11-10", "oldest": "2026-11-10", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "pip install vllm\nvllm serve nanonets/Nanonets-OCR2-3B\n# or with Transformers: AutoModelForImageTextToText.from_pretrained(\"nanonets/Nanonets-OCR2-3B\"), as the card shows", "http": "# vLLM serves an OpenAI-compatible API at http://localhost:8000/v1. Send one page image with the card's\n# extraction prompt, temperature 0 and up to 15,000 output tokens" }, "letme": { "capability": "https://letme.dev/docs.ocr", "tool": "https://letme.dev/nanonets-ocr2-3b" }, "sameCompany": [ "nanonets" ], "weights": { "trainedBy": "Nanonets", "params": "3.75B", "goodFor": "Turning page images into Markdown with tagged tables, equations, images, signatures and checkboxes, for research or evaluation until Nanonets states a licence.", "runtimes": [ "vLLM", "SGLang", "Transformers" ], "builds": [ { "name": "Nanonets-OCR2-3B (BF16 safetensors)", "url": "https://huggingface.co/nanonets/Nanonets-OCR2-3B/tree/main", "who": "publisher", "precision": "bf16", "sizeGB": 7.51, "memoryGB": 12, "format": "safetensors", "runsOn": [ "vLLM", "SGLang", "Transformers" ], "note": "Two shards. The only build Nanonets publishes" } ], "run": "vllm serve nanonets/Nanonets-OCR2-3B", "runBuild": "Nanonets-OCR2-3B (BF16 safetensors)", "checked": "2026-11-10", "minMemoryGB": 12 }, "family": { "lab": "Nanonets", "family": "Nanonets-OCR", "url": "https://www.anchorterminal.com/open-weights/#nanonets", "others": [ { "name": "Nanonets-OCR2-1.5B-exp", "url": "https://huggingface.co/nanonets/Nanonets-OCR2-1.5B-exp" }, { "name": "Nanonets-OCR-s", "url": "https://huggingface.co/nanonets/Nanonets-OCR-s" } ] }, "area": "web-data", "provenance": { "legalEntity": "Nano Net Technologies Inc.", "domain": "nanonets.com", "domainRegistered": "2005-10-15", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://huggingface.co/nanonets/Nanonets-OCR2-3B/commits/main", "securityTxt": "none", "checked": "2026-11-10", "notes": [ "The legal entity and the domain's registration date are carried from our Nanonets listing.", "Nothing is hosted under this listing and the weights state no licence, so there are no terms to read and no privacy policy is scored.", "The changelog is the commit history, 8 commits from 13 to 16 October 2025.", "Nanonets' own terms and privacy policy cover its hosted service, not these weights." ], "score": 78 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/nanonets-ocr2-3b.json" }, "answer": "Xiaomi-OCR-0 scores 59 (C) on agent readiness against Nanonets-OCR2-3B's 48.1 (D), and leads in 3 of 4 scored categories.", "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", "dns": "xiaomi-ocr-0.grade.anchorterminal.com", "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": 1518, "categoryRank": 16, "methodology": "0.7", "run": "2026-10-01", "scores": { "interface": 70, "reach": 80, "risk": 65, "upkeep": 65 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-11-04" }, "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 0.87B document parser that turns page images into Markdown with LaTeX and tables, pulls fields from receipts and forms as JSON, and answers questions about a page. Released vLLM, SGLang, Transformers and llama.cpp run it in about 2 GB. Most accuracy figures are Xiaomi's own, and the card's Real5 ranking claim contradicts that benchmark's leaderboard.", "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, and Ollama through its pinned llama.cpp", "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": 80, "risk": 65, "upkeep": 65 }, "provenanceScore": 58, "checked": { "categories": { "interface": "2026-11-04", "reach": "2026-11-04", "risk": "2026-11-04", "upkeep": "2026-11-04" }, "last": "2026-11-04", "lastMoved": "2026-11-04", "oldest": "2026-11-04", "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", "live": { "slug": "xiaomi-ocr-0", "securityTxt": { "url": "https://mi.com/.well-known/security.txt", "state": "unknown", "checkedAt": "2026-11-10T16:09:14.836444364Z" }, "domain": { "domain": "mi.com", "registered": "1998-11-06", "source": "https://rdap.verisign.com/com/v1/domain/mi.com", "checkedAt": "2026-11-08T15:02:05.569801022Z" }, "pages": [ { "url": "https://huggingface.co/xiaomi-research/Xiaomi-OCR-0/commits/main", "kind": "changelog", "status": 200, "checkedAt": "2026-11-10T16:23:56.998736147Z", "changedAt": "2026-11-10T16:23:56.998736147Z", "fingerprint": "8cbdc4de003f" } ], "updatedAt": "2026-11-10T16:23:56.998736147Z" } }, "edges": [ { "by": 40, "category": "upkeep", "leads": "xiaomi-ocr-0", "name": "Documentation and upkeep", "reasons": { "a": "Model card 5, Intended use and the languages are on the card. Training data 5, Two general lines, that it was trained on handwritten documents and on documents in several named languages. Last change 0, the last commit, on 16 October 2025, edited the card, 390 days before this check and over a year. Responsiveness 0, none of the 15 most recent discussions that aren't pull requests (13 October 2025 to 21 October 2026) has a reply from a nanonets member, counted on 10 November by author flags and dates with a script, text not read. Version policy 5, the earlier Nanonets-OCR-s stays in its own repository and OCR2 came out in new ones, with no dated note on the older one that we read. File health 10, Config, template and card agree on the Qwen2.5-VL classes. 5 + 5 + 0 + 0 + 5 + 10 = 25.", "b": "The card covers intended use and parameters and misses limitations, architecture, context length, languages and a cut-off (5). Training data is described by size and labelling with no datasets named and no cut-off (5). The last change to the repository is the card commit of 29 October 2026, 6 days before this check, after the 23 October eval file deletion; card and documentation commits count on this line (20). The one question on the Hub was answered the same day by a Xiaomi Research member, and no new question has been opened since (20). It's a first release (5), and the files agree with the card (10). 5 + 5 + 20 + 20 + 5 + 10 = 65." }, "scores": { "a": 25, "b": 65 } }, { "by": 25, "category": "reach", "leads": "xiaomi-ocr-0", "name": "Reach", "reasons": { "a": "Memory 30, the only build from Nanonets is the BF16 safetensors files, 7.51 GB, and 7.51 GB × 1.2 = 9.0 GB, within 16 GB. No runtime library has a build. Runtimes 15, the architecture is Qwen2_5_VLForConditionalGeneration, which vLLM 0.33.0, SGLang 0.5.23 and Transformers 5.22.2 run from the safetensors files, three at 5 each. Builds 0, Nanonets publishes the BF16 weights only. Terms 0, no licence. Download 10, the Hub API reports gated false 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. 30 + 15 + 0 + 0 + 10 + 0 = 55.", "b": "Memory earns the full 30, since the only build is a 1.75 GB BF16 file that needs about 2.1 GB with the fifth added. Runtimes reach the 25 cap. The checkpoint keeps Qwen3.5's architecture, which released vLLM, SGLang, Transformers and llama.cpp run natively, and Ollama v0.40.14 pins llama.cpp b11963, which has the qwen35 architecture and the Qwen3-VL projector; LM Studio couldn't be checked. Builds score 0, as Xiaomi publishes no quantised build and every GGUF or MLX copy is someone else's. Terms get 15 for Apache 2.0 with nothing added on an Apache 2.0 base. Download gets 10 for an ungated repository, and hosted too 0, with no inference provider on 4 November and a static project Space. 30 + 25 + 0 + 15 + 10 + 0 = 80." }, "scores": { "a": 55, "b": 80 } }, { "by": 25, "category": "risk", "leads": "xiaomi-ocr-0", "name": "Risk", "reasons": { "a": "File safety 25, safetensors only, no .bin, .pt or .pth files and no Python in the repository. Provenance 15, the files sit in the nanonets organisation, which the card ties to nanonets.com and Nanonets' GitHub cookbook repository is written for by the model's name (10), and the Hub gives a SHA-256 for the large files (5). Safety evaluation 0, no safety results on the card. Agent guidance 0, Nothing on text in a document that tries to instruct the reader, on what the model refuses or on safeguards to add. Licence risk 0, With no licence there is nothing to hold Nanonets to. Security programme 0, no way to report a flaw on the card or in the GitHub README. Our Nanonets listing found no security.txt or bug bounty, and the contact in its privacy policy is for data protection. 25 + 15 + 0 + 0 + 0 + 0 = 40.", "b": "File safety gets 25, one safetensors file with no Python in the repository and native loading everywhere. Provenance gets 15 for files in the xiaomi-research organisation that Xiaomi's GitHub repository links, with hashes for every file, though neither organisation is verified. Licence risk keeps 15, since Apache 2.0 has none of the deductions. Agent guidance gets 5 for general cautions in Xiaomi's Skill, and the security programme 5 for Xiaomi's company security centre, carried from the 28 October reading. Safety evaluation scores 0, with no results published." }, "scores": { "a": 40, "b": 65 } } ], "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "Nanonets", "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": "None stated (the base model, Qwen2.5-VL-3B-Instruct, is under the Qwen Research licence)", "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": "2025-10-16", "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" } ], "faq": [ { "answer": "Xiaomi-OCR-0 scores 59 (C) on agent readiness against Nanonets-OCR2-3B's 48.1 (D), and leads in 3 of 4 scored categories.", "question": "Which is better for AI agents, Nanonets-OCR2-3B or Xiaomi-OCR-0?" } ], "goodFor": [ { "aheadOn": null, "also": null, "goodFor": "Researchers and teams evaluating page-to-Markdown OCR who can wait for a licence before shipping", "slug": "nanonets-ocr2-3b", "watchFor": "No licence stated, and the base model's licence is research only" }, { "aheadOn": [ "Reach, 80 against 55", "Risk, 65 against 40", "Documentation and upkeep, 65 against 25" ], "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" } ], "history": { "nanonets-ocr2-3b": null, "xiaomi-ocr-0": [ { "causes": [ "correction" ], "date": "2026-11-04", "delta": 0, "from": 59, "fromGrade": "C", "to": 59, "toGrade": "C" } ] }, "job": { "capability": "docs.ocr", "name": "OCR" }, "others": [ { "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" }, { "json": "https://www.anchorterminal.com/compare/chandra-ocr-2-vs-nanonets-ocr2-3b.json", "title": "Chandra OCR 2 vs Nanonets-OCR2-3B", "url": "https://www.anchorterminal.com/compare/chandra-ocr-2-vs-nanonets-ocr2-3b" }, { "json": "https://www.anchorterminal.com/compare/chandra-ocr-2-vs-xiaomi-ocr-0.json", "title": "Chandra OCR 2 vs Xiaomi-OCR-0", "url": "https://www.anchorterminal.com/compare/chandra-ocr-2-vs-xiaomi-ocr-0" }, { "json": "https://www.anchorterminal.com/compare/deepseek-ocr-2-vs-nanonets-ocr2-3b.json", "title": "DeepSeek-OCR 2 vs Nanonets-OCR2-3B", "url": "https://www.anchorterminal.com/compare/deepseek-ocr-2-vs-nanonets-ocr2-3b" }, { "json": "https://www.anchorterminal.com/compare/deepseek-ocr-2-vs-xiaomi-ocr-0.json", "title": "DeepSeek-OCR 2 vs Xiaomi-OCR-0", "url": "https://www.anchorterminal.com/compare/deepseek-ocr-2-vs-xiaomi-ocr-0" }, { "json": "https://www.anchorterminal.com/compare/hunyuanocr-vs-nanonets-ocr2-3b.json", "title": "HunyuanOCR 1.5 vs Nanonets-OCR2-3B", "url": "https://www.anchorterminal.com/compare/hunyuanocr-vs-nanonets-ocr2-3b" }, { "json": "https://www.anchorterminal.com/compare/hunyuanocr-vs-xiaomi-ocr-0.json", "title": "HunyuanOCR 1.5 vs Xiaomi-OCR-0", "url": "https://www.anchorterminal.com/compare/hunyuanocr-vs-xiaomi-ocr-0" }, { "json": "https://www.anchorterminal.com/compare/lightonocr-3-4b-vs-nanonets-ocr2-3b.json", "title": "LightOnOCR-3-4B vs Nanonets-OCR2-3B", "url": "https://www.anchorterminal.com/compare/lightonocr-3-4b-vs-nanonets-ocr2-3b" }, { "json": "https://www.anchorterminal.com/compare/lightonocr-3-4b-vs-xiaomi-ocr-0.json", "title": "LightOnOCR-3-4B vs Xiaomi-OCR-0", "url": "https://www.anchorterminal.com/compare/lightonocr-3-4b-vs-xiaomi-ocr-0" }, { "json": "https://www.anchorterminal.com/compare/nanonets-ocr2-3b-vs-nemotron-3-5-parse.json", "title": "Nanonets-OCR2-3B vs Nemotron 3.5 Parse", "url": "https://www.anchorterminal.com/compare/nanonets-ocr2-3b-vs-nemotron-3-5-parse" }, { "json": "https://www.anchorterminal.com/compare/nanonets-ocr2-3b-vs-surya-ocr-2-5.json", "title": "Nanonets-OCR2-3B vs Surya OCR 2.5", "url": "https://www.anchorterminal.com/compare/nanonets-ocr2-3b-vs-surya-ocr-2-5" }, { "json": "https://www.anchorterminal.com/compare/nanonets-ocr2-3b-vs-unlimited-ocr-2.json", "title": "Nanonets-OCR2-3B vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/nanonets-ocr2-3b-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/surya-ocr-2-5-vs-xiaomi-ocr-0.json", "title": "Surya OCR 2.5 vs Xiaomi-OCR-0", "url": "https://www.anchorterminal.com/compare/surya-ocr-2-5-vs-xiaomi-ocr-0" }, { "json": "https://www.anchorterminal.com/compare/unlimited-ocr-2-vs-xiaomi-ocr-0.json", "title": "Baidu Unlimited-OCR-2 vs Xiaomi-OCR-0", "url": "https://www.anchorterminal.com/compare/unlimited-ocr-2-vs-xiaomi-ocr-0" } ], "panel": null, "scores": [ { "key": "capability", "name": "Capability", "pending": true, "weight": 35 }, { "by": 25, "edge": "xiaomi-ocr-0", "key": "reach", "name": "Reach", "nanonets-ocr2-3b": 55, "weight": 25, "xiaomi-ocr-0": 80 }, { "by": 25, "edge": "xiaomi-ocr-0", "key": "risk", "name": "Risk", "nanonets-ocr2-3b": 40, "weight": 20, "xiaomi-ocr-0": 65 }, { "by": 0, "edge": "", "key": "interface", "name": "Agent interface", "nanonets-ocr2-3b": 70, "weight": 10, "xiaomi-ocr-0": 70 }, { "by": 40, "edge": "xiaomi-ocr-0", "key": "upkeep", "name": "Documentation and upkeep", "nanonets-ocr2-3b": 25, "weight": 10, "xiaomi-ocr-0": 65 } ], "summary": "Xiaomi-OCR-0 scores 59 (C) on agent readiness against Nanonets-OCR2-3B's 48.1 (D), and leads in 3 of 4 scored categories. Both do ocr.", "tasks": null, "tutorials": [], "verdicts": { "nanonets-ocr2-3b": "A well-used 3.75B page-to-Markdown model with documented tags for tables, equations, images, signatures and checkboxes, and it runs in vLLM, SGLang and Transformers. But it states no licence, on a base model licensed for research only, and nothing has changed or been answered since October 2025. Treat it as something to evaluate, not to ship.", "xiaomi-ocr-0": "A 0.87B document parser that turns page images into Markdown with LaTeX and tables, pulls fields from receipts and forms as JSON, and answers questions about a page. Released vLLM, SGLang, Transformers and llama.cpp run it in about 2 GB. Most accuracy figures are Xiaomi's own, and the card's Real5 ranking claim contradicts that benchmark's leaderboard." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/nanonets-ocr2-3b-vs-xiaomi-ocr-0", "json": "https://www.anchorterminal.com/compare/nanonets-ocr2-3b-vs-xiaomi-ocr-0.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/nanonets-ocr2-3b-vs-xiaomi-ocr-0.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/nanonets-ocr2-3b-vs-xiaomi-ocr-0.min.md" }, "markdown": "Xiaomi-OCR-0 scores 59 (C) on agent readiness against Nanonets-OCR2-3B's 48.1 (D), and leads in 3 of 4 scored categories. Both do ocr.\n\n- Nanonets-OCR2-3B: grade D, 48.1/100, rank #153 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/nanonets-ocr2-3b.md · JSON https://www.anchorterminal.com/api/v1/tools/nanonets-ocr2-3b.json\n- Xiaomi-OCR-0: grade C, 59/100, rank #101 of 160 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 document parsing, OCR and extraction APIs for AI agents: https://www.anchorterminal.com/best/document-extraction/index.md\n- All 215 documents comparisons: https://www.anchorterminal.com/compare/document-extraction/index.md\n\n## Which one, for what\n\n### Nanonets-OCR2-3B (D)\n\nGood for: Researchers and teams evaluating page-to-Markdown OCR who can wait for a licence before shipping\n\nWatch for: No licence stated, and the base model's licence is research only\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, 80 against 55\n- Risk, 65 against 40\n- Documentation and upkeep, 65 against 25\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## Why the edge\n\n### Documentation and upkeep: Xiaomi-OCR-0 +40\n\n- Nanonets-OCR2-3B (25): Model card 5, Intended use and the languages are on the card. Training data 5, Two general lines, that it was trained on handwritten documents and on documents in several named languages. Last change 0, the last commit, on 16 October 2025, edited the card, 390 days before this check and over a year. Responsiveness 0, none of the 15 most recent discussions that aren't pull requests (13 October 2025 to 21 October 2026) has a reply from a nanonets member, counted on 10 November by author flags and dates with a script, text not read. Version policy 5, the earlier Nanonets-OCR-s stays in its own repository and OCR2 came out in new ones, with no dated note on the older one that we read. File health 10, Config, template and card agree on the Qwen2.5-VL classes. 5 + 5 + 0 + 0 + 5 + 10 = 25.\n- Xiaomi-OCR-0 (65): The card covers intended use and parameters and misses limitations, architecture, context length, languages and a cut-off (5). Training data is described by size and labelling with no datasets named and no cut-off (5). The last change to the repository is the card commit of 29 October 2026, 6 days before this check, after the 23 October eval file deletion; card and documentation commits count on this line (20). The one question on the Hub was answered the same day by a Xiaomi Research member, and no new question has been opened since (20). It's a first release (5), and the files agree with the card (10). 5 + 5 + 20 + 20 + 5 + 10 = 65.\n\n### Reach: Xiaomi-OCR-0 +25\n\n- Nanonets-OCR2-3B (55): Memory 30, the only build from Nanonets is the BF16 safetensors files, 7.51 GB, and 7.51 GB × 1.2 = 9.0 GB, within 16 GB. No runtime library has a build. Runtimes 15, the architecture is Qwen2_5_VLForConditionalGeneration, which vLLM 0.33.0, SGLang 0.5.23 and Transformers 5.22.2 run from the safetensors files, three at 5 each. Builds 0, Nanonets publishes the BF16 weights only. Terms 0, no licence. Download 10, the Hub API reports gated false 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. 30 + 15 + 0 + 0 + 10 + 0 = 55.\n- Xiaomi-OCR-0 (80): Memory earns the full 30, since the only build is a 1.75 GB BF16 file that needs about 2.1 GB with the fifth added. Runtimes reach the 25 cap. The checkpoint keeps Qwen3.5's architecture, which released vLLM, SGLang, Transformers and llama.cpp run natively, and Ollama v0.40.14 pins llama.cpp b11963, which has the qwen35 architecture and the Qwen3-VL projector; LM Studio couldn't be checked. Builds score 0, as Xiaomi publishes no quantised build and every GGUF or MLX copy is someone else's. Terms get 15 for Apache 2.0 with nothing added on an Apache 2.0 base. Download gets 10 for an ungated repository, and hosted too 0, with no inference provider on 4 November and a static project Space. 30 + 25 + 0 + 15 + 10 + 0 = 80.\n\n### Risk: Xiaomi-OCR-0 +25\n\n- Nanonets-OCR2-3B (40): File safety 25, safetensors only, no .bin, .pt or .pth files and no Python in the repository. Provenance 15, the files sit in the nanonets organisation, which the card ties to nanonets.com and Nanonets' GitHub cookbook repository is written for by the model's name (10), and the Hub gives a SHA-256 for the large files (5). Safety evaluation 0, no safety results on the card. Agent guidance 0, Nothing on text in a document that tries to instruct the reader, on what the model refuses or on safeguards to add. Licence risk 0, With no licence there is nothing to hold Nanonets to. Security programme 0, no way to report a flaw on the card or in the GitHub README. Our Nanonets listing found no security.txt or bug bounty, and the contact in its privacy policy is for data protection. 25 + 15 + 0 + 0 + 0 + 0 = 40.\n- Xiaomi-OCR-0 (65): File safety gets 25, one safetensors file with no Python in the repository and native loading everywhere. Provenance gets 15 for files in the xiaomi-research organisation that Xiaomi's GitHub repository links, with hashes for every file, though neither organisation is verified. Licence risk keeps 15, since Apache 2.0 has none of the deductions. Agent guidance gets 5 for general cautions in Xiaomi's Skill, and the security programme 5 for Xiaomi's company security centre, carried from the 28 October reading. Safety evaluation scores 0, with no results published.\n\n## Score by category\n\n| Category | Weight | Nanonets-OCR2-3B | Xiaomi-OCR-0 | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 55 | 80 | Xiaomi-OCR-0 +25 |\n| Risk | 20% (20 this run) | 40 | 65 | Xiaomi-OCR-0 +25 |\n| Agent interface | 10% (10 this run) | 70 | 70 | even |\n| Documentation and upkeep | 10% (10 this run) | 25 | 65 | Xiaomi-OCR-0 +40 |\n| Negative events | ≤15 | 0 | -2 | |\n| **Total** | | **48.1 · D** | **59 · C** | |\n\n## Facts side by side\n\n| Fact | Nanonets-OCR2-3B | Xiaomi-OCR-0 |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Nanonets | 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 | None stated (the base model, Qwen2.5-VL-3B-Instruct, is under the Qwen Research licence) | 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 | 2025-10-16 | 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\n## How the scores have moved\n\n### Nanonets-OCR2-3B\n\nNo score change since it was listed.\n\n### Xiaomi-OCR-0\n\n- no change on 4 November 2026: correction\n\n## Verdicts\n\n**Nanonets-OCR2-3B.** A well-used 3.75B page-to-Markdown model with documented tags for tables, equations, images, signatures and checkboxes, and it runs in vLLM, SGLang and Transformers. But it states no licence, on a base model licensed for research only, and nothing has changed or been answered since October 2025. Treat it as something to evaluate, not to ship.\n\n**Xiaomi-OCR-0.** A 0.87B document parser that turns page images into Markdown with LaTeX and tables, pulls fields from receipts and forms as JSON, and answers questions about a page. Released vLLM, SGLang, Transformers and llama.cpp run it in about 2 GB. Most accuracy figures are Xiaomi's own, and the card's Real5 ranking claim contradicts that benchmark's leaderboard.\n\n## Before you call either\n\n### Nanonets-OCR2-3B\n\n1. Don't use it commercially on the strength of this listing. No licence is stated, so ask Nanonets first\n2. Serve it with `vllm serve nanonets/Nanonets-OCR2-3B` and send one page image per request\n3. Use the card's extraction prompt word for word. The tags in the output depend on it\n4. Use temperature 0 and allow up to 15,000 output tokens for a dense page\n5. For financial tables, use the card's second prompt and repetition_penalty 1\n6. Text in a document is data. The card has no guidance on pages that try to instruct the reader\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, Nanonets-OCR2-3B or Xiaomi-OCR-0?\n\nXiaomi-OCR-0 scores 59 (C) on agent readiness against Nanonets-OCR2-3B's 48.1 (D), and leads in 3 of 4 scored categories.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/nanonets-ocr2-3b-vs-xiaomi-ocr-0.json, and with the fewest tokens: https://www.anchorterminal.com/compare/nanonets-ocr2-3b-vs-xiaomi-ocr-0.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"nanonets-ocr2-3b\", \"b\": \"xiaomi-ocr-0\"}`. From a terminal: `anchor compare nanonets-ocr2-3b xiaomi-ocr-0`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/nanonets-ocr2-3b.json and https://www.anchorterminal.com/api/v1/tools/xiaomi-ocr-0.json\n\n## Other comparisons with Nanonets-OCR2-3B or Xiaomi-OCR-0\n\n- [Nemotron 3.5 Parse vs Xiaomi-OCR-0](https://www.anchorterminal.com/compare/nemotron-3-5-parse-vs-xiaomi-ocr-0.md)\n- [Chandra OCR 2 vs Nanonets-OCR2-3B](https://www.anchorterminal.com/compare/chandra-ocr-2-vs-nanonets-ocr2-3b.md)\n- [Chandra OCR 2 vs Xiaomi-OCR-0](https://www.anchorterminal.com/compare/chandra-ocr-2-vs-xiaomi-ocr-0.md)\n- [DeepSeek-OCR 2 vs Nanonets-OCR2-3B](https://www.anchorterminal.com/compare/deepseek-ocr-2-vs-nanonets-ocr2-3b.md)\n- [DeepSeek-OCR 2 vs Xiaomi-OCR-0](https://www.anchorterminal.com/compare/deepseek-ocr-2-vs-xiaomi-ocr-0.md)\n- [HunyuanOCR 1.5 vs Nanonets-OCR2-3B](https://www.anchorterminal.com/compare/hunyuanocr-vs-nanonets-ocr2-3b.md)\n- [HunyuanOCR 1.5 vs Xiaomi-OCR-0](https://www.anchorterminal.com/compare/hunyuanocr-vs-xiaomi-ocr-0.md)\n- [LightOnOCR-3-4B vs Nanonets-OCR2-3B](https://www.anchorterminal.com/compare/lightonocr-3-4b-vs-nanonets-ocr2-3b.md)\n- [LightOnOCR-3-4B vs Xiaomi-OCR-0](https://www.anchorterminal.com/compare/lightonocr-3-4b-vs-xiaomi-ocr-0.md)\n- [Nanonets-OCR2-3B vs Nemotron 3.5 Parse](https://www.anchorterminal.com/compare/nanonets-ocr2-3b-vs-nemotron-3-5-parse.md)\n- [Nanonets-OCR2-3B vs Surya OCR 2.5](https://www.anchorterminal.com/compare/nanonets-ocr2-3b-vs-surya-ocr-2-5.md)\n- [Nanonets-OCR2-3B vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/nanonets-ocr2-3b-vs-unlimited-ocr-2.md)\n- [Surya OCR 2.5 vs Xiaomi-OCR-0](https://www.anchorterminal.com/compare/surya-ocr-2-5-vs-xiaomi-ocr-0.md)\n- [Baidu Unlimited-OCR-2 vs Xiaomi-OCR-0](https://www.anchorterminal.com/compare/unlimited-ocr-2-vs-xiaomi-ocr-0.md)\n", "meta": { "attribution": "Anchor Terminal (https://www.anchorterminal.com)", "attributionRequired": true, "credit": "CC BY 4.0, credit Anchor Terminal and link the page or API address you took it from (https://www.anchorterminal.com/terms/#data)", "docs": "https://www.anchorterminal.com/docs/", "generatedAt": "2026-11-11", "license": "CC-BY-4.0", "method": "https://www.anchorterminal.com/benchmark/", "methodology": "0.7", "openapi": "https://www.anchorterminal.com/openapi.json", "preview": false, "run": "2026-10-01", "runLabel": "October 2026 research run", "terms": "https://www.anchorterminal.com/terms/#data" }, "page": { "breadcrumbs": [ { "name": "Home", "url": "https://www.anchorterminal.com/" }, { "name": "Compare", "url": "https://www.anchorterminal.com/compare/" }, { "name": "Nanonets-OCR2-3B vs Xiaomi-OCR-0", "url": "" } ], "description": "Xiaomi-OCR-0 scores 59 (C) to Nanonets-OCR2-3B's 48.1 (D) for ocr. Prices, MCP, x402, uptime and agent notes side by side.", "facts": [ "Nanonets-OCR2-3B D 48.1", "Xiaomi-OCR-0 C 59", "scores" ], "h1": "Nanonets-OCR2-3B vs Xiaomi-OCR-0", "image": "https://www.anchorterminal.com/assets/og/default.png", "path": "/compare/nanonets-ocr2-3b-vs-xiaomi-ocr-0", "published": "2026-10-01", "section": "tools", "title": "Nanonets-OCR2-3B vs Xiaomi-OCR-0 for AI agents (2026)", "toc": null, "updated": "2026-11-10", "url": "https://www.anchorterminal.com/compare/nanonets-ocr2-3b-vs-xiaomi-ocr-0" }, "tokens": { "markdown": 3450, "slim": 680 }, "version": 1 }