{ "data": { "a": { "slug": "hunyuanocr", "name": "HunyuanOCR 1.5", "vendor": "Tencent (Hy team)", "vendorUrl": "https://aistudio.tencent.com", "kind": "weights", "category": "document-extraction", "summary": "HunyuanOCR 1.5 is Tencent's open-weight OCR model of 1.1B parameters for document parsing, text spotting, information extraction and translating text in images, with a 128K context, under a licence that doesn't apply in the EU, the UK or South Korea.", "url": "https://www.anchorterminal.com/tools/hunyuanocr", "markdownUrl": "https://www.anchorterminal.com/tools/hunyuanocr.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/hunyuanocr.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/hunyuanocr.json", "dns": "hunyuanocr.grade.anchorterminal.com", "repo": "https://huggingface.co/tencent/HunyuanOCR", "license": "Tencent Hunyuan Community License (not licensed in the EU, the UK or South Korea)", "transports": [], "packages": [ { "registry": "huggingface", "name": "tencent/HunyuanOCR" } ], "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. The Tencent Hunyuan Community License allows commercial use under Tencent's acceptable use policy, except for anyone with more than 100 million monthly active users, and it doesn't apply in the European Union, the United Kingdom or South Korea, where it grants no rights. Running it yourself costs little hardware, 2.24 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/Tencent-Hunyuan/HunyuanOCR", "capabilities": [ "docs.ocr", "docs.parse", "docs.extract", "docs.tables", "model.open-weights" ], "tags": [ "open-weights", "model", "ocr", "document-parsing", "text-spotting", "information-extraction", "translation", "vision", "custom-licence", "territory-limit", "safetensors", "self-hosted" ], "lastRelease": "2026-08-28", "graded": true, "anchor": { "graded": true, "score": 56.6, "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": 19, "methodology": "0.7", "run": "2026-10-01", "scores": { "interface": 80, "reach": 60, "risk": 45, "upkeep": 48 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-11-10" }, "negative": 0, "verdict": "A small OCR model that does documents, text spotting, extraction and translation in one, with a 128K context and code in Transformers, vLLM and llama.cpp. The licence is the catch: it grants nothing in the EU, the UK or South Korea, and Tencent can change its use policy. No quantised build, safety results or data sources.", "bestFor": "Teams outside the EU, the UK and South Korea that want one small model for OCR, extraction and image translation", "strengths": [ "Four OCR jobs in one 1.1B model, with several images in a request", "A 128K context, trained for it per the card", "Released Transformers, vLLM and llama.cpp have the architecture", "Fits any machine: 2.24 GB in BF16", "The card documents prompts, settings and three ways to run it" ], "weaknesses": [ "The licence doesn't apply in the EU, the UK or South Korea", "Tencent can update the acceptable use policy, and a 100 million user threshold applies", "No GGUF or other quantised build from Tencent", "Version 1.5 replaced 1.0 in the same repository", "A few of the questions on the repository answered", "No limitations, safety results or data sources on the card" ], "agentNotes": [ "Check where you and your users are first. The licence grants no rights in the European Union, the United Kingdom or South Korea", "Download with `--exclude \"v1.0/*\"` so the old version isn't fetched", "Serve it with vLLM 0.25.1 or later at `--max-model-len 131072`, or use Tencent's scripts on GitHub for its sampling and post-processing", "Use temperature 0, top_p 1 and repetition_penalty 1.08", "The default document prompt is in Chinese and is on the card. Other tasks take their own instruction as the prompt", "For llama.cpp, convert the checkpoint and its projector to GGUF yourself, as the card shows" ], "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": 56.6 } ], "editorialScores": { "interface": 80, "reach": 60, "risk": 45, "upkeep": 48 }, "provenanceScore": 68, "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\u003e=0.25.1\"\nhf download tencent/HunyuanOCR --local-dir ./HunyuanOCR --exclude \"v1.0/*\"\nvllm serve ./HunyuanOCR --served-model-name tencent/HunyuanOCR --max-model-len 131072\n# Tencent's own scripts, with its sampling and post-processing, are in github.com/Tencent-Hunyuan/HunyuanOCR (inference/vLLM/serve.sh)", "http": "# vLLM serves an OpenAI-compatible API at http://localhost:8000/v1. Send one image with a task prompt,\n# temperature 0, top_p 1 and repetition_penalty 1.08, per the card" }, "letme": { "capability": "https://letme.dev/docs.ocr", "tool": "https://letme.dev/hunyuanocr" }, "sameCompany": [ "hy-memory", "tencentdb-agent-memory", "hy-memory-30b-a3b", "hy3", "wevisone-vl-2b", "wevisone-vl-4b" ], "weights": { "trainedBy": "Tencent Hunyuan", "params": "1.1B", "context": 131072, "goodFor": "Small, fast OCR for documents, text in photos, field extraction and translating text in images, where the licence applies. It doesn't in the EU, the UK or South Korea.", "runtimes": [ "llama.cpp", "vLLM", "Transformers" ], "builds": [ { "name": "HunyuanOCR 1.5 (BF16 safetensors)", "url": "https://huggingface.co/tencent/HunyuanOCR/blob/main/model.safetensors", "who": "publisher", "precision": "bf16", "sizeGB": 2.24, "memoryGB": 8, "format": "safetensors", "runsOn": [ "vLLM", "Transformers" ], "note": "The root of the repository. Leave out the v1.0 folder when downloading, as the card says" } ], "run": "vllm serve tencent/HunyuanOCR --max-model-len 131072", "runBuild": "HunyuanOCR 1.5 (BF16 safetensors)", "checked": "2026-11-10", "minMemoryGB": 8 }, "family": { "lab": "Tencent (Hunyuan / Hy)", "family": "HunyuanOCR", "url": "https://www.anchorterminal.com/open-weights/#tencent", "olderCount": 22 }, "area": "web-data", "provenance": { "legalEntity": "Tencent (named in the LICENSE file's copyright line)", "domain": "tencent.com", "domainRegistered": "1998-09-14", "endpointOnVendorDomain": null, "terms": "https://huggingface.co/tencent/HunyuanOCR/raw/main/LICENSE", "privacy": "", "statusPage": "", "changelog": "https://huggingface.co/tencent/HunyuanOCR/commits/main", "securityTxt": "none", "checked": "2026-11-10", "notes": [ "The licence names Tencent. The domain's registration date is carried from our other Tencent listings.", "Nothing is hosted under this listing, so the model licence stands in for terms of service and no privacy policy is scored.", "The changelog is the commit history, 50 commits or more since 18 November 2025, the last two on 26 and 28 August 2026 changing config.json.", "Tencent's Security Response Center takes reports company-wide, per the Hy3 listing; nothing on the card links it." ], "score": 68 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/hunyuanocr.json" }, "answer": "Xiaomi-OCR-0 scores 59 (C) on agent readiness against HunyuanOCR 1.5's 56.6 (C), and leads in 3 of 4 scored categories. HunyuanOCR 1.5 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", "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": 20, "category": "reach", "leads": "xiaomi-ocr-0", "name": "Reach", "reasons": { "a": "Memory 30, the only build from Tencent is the BF16 safetensors file at the root, 2.24 GB, and 2.24 GB × 1.2 = 2.7 GB, within 16 GB. ggml-org's GGUF (Q8_0 0.58 GB with a 0.73 GB projector) dates from 21 May 2026 and wasn't matched to version 1.5. Runtimes 15, Released Transformers 5.22.2 has the hunyuan_vl model, vLLM 0.33.0 registers HunYuanVLForConditionalGeneration, and llama.cpp b12242 has the architecture, which the card says the community build runs after conversion. Builds 0, Tencent publishes the BF16 weights only and tells users to convert to GGUF themselves. Terms 5, A custom licence that allows free commercial use under a use policy and below 100 million monthly active users, but not for anyone: it doesn't apply in the European Union, the United Kingdom or South Korea. 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, and the card names no Tencent API that serves it. 30 + 15 + 0 + 5 + 10 + 0 = 60.", "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": 60, "b": 80 } }, { "by": 20, "category": "risk", "leads": "xiaomi-ocr-0", "name": "Risk", "reasons": { "a": "File safety 25, safetensors only, no .bin, .pt or .pth files, and no Python at the root. Provenance 15, the files sit in the tencent organisation, which Tencent's GitHub repository for the model links to (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, the card has no limitations or use section, and nothing on text in an image that tries to instruct the reader. Licence risk 0, 15 less 5 for limits beyond the law (an acceptable use policy and three territories where the licence doesn't apply), 5 for terms the licensor can change (Tencent may update the policy from time to time) and 5 for the 100 million monthly active user threshold. Security programme 5, Tencent's Security Response Center takes vulnerability reports company-wide, as the Hy3 listing records (5). 25 + 15 + 0 + 0 + 0 + 5 = 45.", "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": 45, "b": 65 } }, { "by": 17, "category": "upkeep", "leads": "xiaomi-ocr-0", "name": "Documentation and upkeep", "reasons": { "a": "Model card 8, Intended use, the architecture by reference to 1.0's unchanged backbone, and the context (128K) are on the card. Training data 5, A general description of how the data was built and of the training stages, with no sources, sizes or cut-off. Last change 15, the last commit, on 28 August 2026, changed config.json, 74 days before this check and within 90. Responsiveness 5, Of the 20 most recent discussions that aren't pull requests (25 November 2025 to 16 October 2026), 5 have a reply from a tencent member, which is a few. Version policy 5, Version 1.5 replaced 1.0 at the root of the same repository. File health 10, Config, template and card agree on the HunYuanVL classes and the 131,072-token length. 8 + 5 + 15 + 5 + 5 + 10 = 48.", "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": 48, "b": 65 } }, { "by": 10, "category": "interface", "leads": "hunyuanocr", "name": "Agent interface", "reasons": { "a": "Chat template 20, chat_template.jinja in the repository (930 characters). Tool-calling format 25, Read for a document parser as what it takes and returns. Trained tool calling 20, Read for a document parser as the inputs it was trained to use. Structured output 5, Read for a document parser as typed outputs. Reasoning control 0, A document parser with no reasoning. Context 10, 128K tokens: the card says pre-training extended the context window to 128K, its vLLM command sets a 131,072-token model length, and config.json gives the language model 131,072 positions. 20 + 25 + 20 + 5 + 0 + 10 = 80.", "b": "Read for a document parser, the tool-calling, trained tool calling and structured-output lines stand for what it takes and returns. Xiaomi documents the image, the five task prompts and the Markdown, OTSL, LaTeX and JSON outputs, and ships the converters and an MCP server (25). It reads one image per request (10). JSON for key fields and Markdown with tables are documented with how to ask for them (15). The Jinja template is in the repository (20). Reasoning control scores 0, as thinking is only switched off, and context 0, at the 16,384 tokens Xiaomi documents." }, "scores": { "a": 80, "b": 70 } } ], "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "Tencent (Hy team)", "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": "Tencent Hunyuan Community License (not licensed in the EU, the UK or South Korea)", "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": "2026-08-28", "b": "2026-09-29", "name": "Last release" }, { "a": "not read yet", "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 HunyuanOCR 1.5's 56.6 (C), and leads in 3 of 4 scored categories. HunyuanOCR 1.5 leads on agent interface.", "question": "Which is better for AI agents, HunyuanOCR 1.5 or Xiaomi-OCR-0?" } ], "goodFor": [ { "aheadOn": [ "Agent interface, 80 against 70" ], "also": null, "goodFor": "Teams outside the EU, the UK and South Korea that want one small model for OCR, extraction and image translation", "slug": "hunyuanocr", "watchFor": "The licence doesn't apply in the EU, the UK or South Korea" }, { "aheadOn": [ "Reach, 80 against 60", "Risk, 65 against 45", "Documentation and upkeep, 65 against 48" ], "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": { "hunyuanocr": 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-hunyuanocr.json", "title": "Chandra OCR 2 vs HunyuanOCR 1.5", "url": "https://www.anchorterminal.com/compare/chandra-ocr-2-vs-hunyuanocr" }, { "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-hunyuanocr.json", "title": "DeepSeek-OCR 2 vs HunyuanOCR 1.5", "url": "https://www.anchorterminal.com/compare/deepseek-ocr-2-vs-hunyuanocr" }, { "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-lightonocr-3-4b.json", "title": "HunyuanOCR 1.5 vs LightOnOCR-3-4B", "url": "https://www.anchorterminal.com/compare/hunyuanocr-vs-lightonocr-3-4b" }, { "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-nemotron-3-5-parse.json", "title": "HunyuanOCR 1.5 vs Nemotron 3.5 Parse", "url": "https://www.anchorterminal.com/compare/hunyuanocr-vs-nemotron-3-5-parse" }, { "json": "https://www.anchorterminal.com/compare/hunyuanocr-vs-surya-ocr-2-5.json", "title": "HunyuanOCR 1.5 vs Surya OCR 2.5", "url": "https://www.anchorterminal.com/compare/hunyuanocr-vs-surya-ocr-2-5" }, { "json": "https://www.anchorterminal.com/compare/hunyuanocr-vs-unlimited-ocr-2.json", "title": "HunyuanOCR 1.5 vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/hunyuanocr-vs-unlimited-ocr-2" }, { "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-xiaomi-ocr-0.json", "title": "Nanonets-OCR2-3B vs Xiaomi-OCR-0", "url": "https://www.anchorterminal.com/compare/nanonets-ocr2-3b-vs-xiaomi-ocr-0" }, { "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": 20, "edge": "xiaomi-ocr-0", "hunyuanocr": 60, "key": "reach", "name": "Reach", "weight": 25, "xiaomi-ocr-0": 80 }, { "by": 20, "edge": "xiaomi-ocr-0", "hunyuanocr": 45, "key": "risk", "name": "Risk", "weight": 20, "xiaomi-ocr-0": 65 }, { "by": 10, "edge": "hunyuanocr", "hunyuanocr": 80, "key": "interface", "name": "Agent interface", "weight": 10, "xiaomi-ocr-0": 70 }, { "by": 17, "edge": "xiaomi-ocr-0", "hunyuanocr": 48, "key": "upkeep", "name": "Documentation and upkeep", "weight": 10, "xiaomi-ocr-0": 65 } ], "summary": "Xiaomi-OCR-0 scores 59 (C) on agent readiness against HunyuanOCR 1.5's 56.6 (C), and leads in 3 of 4 scored categories. HunyuanOCR 1.5 leads on agent interface. Both do ocr.", "tasks": null, "tutorials": [], "verdicts": { "hunyuanocr": "A small OCR model that does documents, text spotting, extraction and translation in one, with a 128K context and code in Transformers, vLLM and llama.cpp. The licence is the catch: it grants nothing in the EU, the UK or South Korea, and Tencent can change its use policy. No quantised build, safety results or data sources.", "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/hunyuanocr-vs-xiaomi-ocr-0", "json": "https://www.anchorterminal.com/compare/hunyuanocr-vs-xiaomi-ocr-0.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/hunyuanocr-vs-xiaomi-ocr-0.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/hunyuanocr-vs-xiaomi-ocr-0.min.md" }, "markdown": "Xiaomi-OCR-0 scores 59 (C) on agent readiness against HunyuanOCR 1.5's 56.6 (C), and leads in 3 of 4 scored categories. HunyuanOCR 1.5 leads on agent interface. Both do ocr.\n\n- HunyuanOCR 1.5: grade C, 56.6/100, rank #114 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/hunyuanocr.md · JSON https://www.anchorterminal.com/api/v1/tools/hunyuanocr.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### HunyuanOCR 1.5 (C)\n\nGood for: Teams outside the EU, the UK and South Korea that want one small model for OCR, extraction and image translation\n\nAhead on:\n- Agent interface, 80 against 70\n\nWatch for: The licence doesn't apply in the EU, the UK or South Korea\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 60\n- Risk, 65 against 45\n- Documentation and upkeep, 65 against 48\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### Reach: Xiaomi-OCR-0 +20\n\n- HunyuanOCR 1.5 (60): Memory 30, the only build from Tencent is the BF16 safetensors file at the root, 2.24 GB, and 2.24 GB × 1.2 = 2.7 GB, within 16 GB. ggml-org's GGUF (Q8_0 0.58 GB with a 0.73 GB projector) dates from 21 May 2026 and wasn't matched to version 1.5. Runtimes 15, Released Transformers 5.22.2 has the hunyuan_vl model, vLLM 0.33.0 registers HunYuanVLForConditionalGeneration, and llama.cpp b12242 has the architecture, which the card says the community build runs after conversion. Builds 0, Tencent publishes the BF16 weights only and tells users to convert to GGUF themselves. Terms 5, A custom licence that allows free commercial use under a use policy and below 100 million monthly active users, but not for anyone: it doesn't apply in the European Union, the United Kingdom or South Korea. 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, and the card names no Tencent API that serves it. 30 + 15 + 0 + 5 + 10 + 0 = 60.\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 +20\n\n- HunyuanOCR 1.5 (45): File safety 25, safetensors only, no .bin, .pt or .pth files, and no Python at the root. Provenance 15, the files sit in the tencent organisation, which Tencent's GitHub repository for the model links to (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, the card has no limitations or use section, and nothing on text in an image that tries to instruct the reader. Licence risk 0, 15 less 5 for limits beyond the law (an acceptable use policy and three territories where the licence doesn't apply), 5 for terms the licensor can change (Tencent may update the policy from time to time) and 5 for the 100 million monthly active user threshold. Security programme 5, Tencent's Security Response Center takes vulnerability reports company-wide, as the Hy3 listing records (5). 25 + 15 + 0 + 0 + 0 + 5 = 45.\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### Documentation and upkeep: Xiaomi-OCR-0 +17\n\n- HunyuanOCR 1.5 (48): Model card 8, Intended use, the architecture by reference to 1.0's unchanged backbone, and the context (128K) are on the card. Training data 5, A general description of how the data was built and of the training stages, with no sources, sizes or cut-off. Last change 15, the last commit, on 28 August 2026, changed config.json, 74 days before this check and within 90. Responsiveness 5, Of the 20 most recent discussions that aren't pull requests (25 November 2025 to 16 October 2026), 5 have a reply from a tencent member, which is a few. Version policy 5, Version 1.5 replaced 1.0 at the root of the same repository. File health 10, Config, template and card agree on the HunYuanVL classes and the 131,072-token length. 8 + 5 + 15 + 5 + 5 + 10 = 48.\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### Agent interface: HunyuanOCR 1.5 +10\n\n- HunyuanOCR 1.5 (80): Chat template 20, chat_template.jinja in the repository (930 characters). Tool-calling format 25, Read for a document parser as what it takes and returns. Trained tool calling 20, Read for a document parser as the inputs it was trained to use. Structured output 5, Read for a document parser as typed outputs. Reasoning control 0, A document parser with no reasoning. Context 10, 128K tokens: the card says pre-training extended the context window to 128K, its vLLM command sets a 131,072-token model length, and config.json gives the language model 131,072 positions. 20 + 25 + 20 + 5 + 0 + 10 = 80.\n- Xiaomi-OCR-0 (70): Read for a document parser, the tool-calling, trained tool calling and structured-output lines stand for what it takes and returns. Xiaomi documents the image, the five task prompts and the Markdown, OTSL, LaTeX and JSON outputs, and ships the converters and an MCP server (25). It reads one image per request (10). JSON for key fields and Markdown with tables are documented with how to ask for them (15). The Jinja template is in the repository (20). Reasoning control scores 0, as thinking is only switched off, and context 0, at the 16,384 tokens Xiaomi documents.\n\n## Score by category\n\n| Category | Weight | HunyuanOCR 1.5 | Xiaomi-OCR-0 | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 60 | 80 | Xiaomi-OCR-0 +20 |\n| Risk | 20% (20 this run) | 45 | 65 | Xiaomi-OCR-0 +20 |\n| Agent interface | 10% (10 this run) | 80 | 70 | HunyuanOCR 1.5 +10 |\n| Documentation and upkeep | 10% (10 this run) | 48 | 65 | Xiaomi-OCR-0 +17 |\n| Negative events | ≤15 | 0 | -2 | |\n| **Total** | | **56.6 · C** | **59 · C** | |\n\n## Facts side by side\n\n| Fact | HunyuanOCR 1.5 | Xiaomi-OCR-0 |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Tencent (Hy team) | 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 | Tencent Hunyuan Community License (not licensed in the EU, the UK or South Korea) | 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 | 2026-08-28 | 2026-09-29 |\n| Terms last updated | not read yet | no document linked |\n| Privacy policy last updated | no document linked | no document linked |\n\n## How the scores have moved\n\n### HunyuanOCR 1.5\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**HunyuanOCR 1.5.** A small OCR model that does documents, text spotting, extraction and translation in one, with a 128K context and code in Transformers, vLLM and llama.cpp. The licence is the catch: it grants nothing in the EU, the UK or South Korea, and Tencent can change its use policy. No quantised build, safety results or data sources.\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### HunyuanOCR 1.5\n\n1. Check where you and your users are first. The licence grants no rights in the European Union, the United Kingdom or South Korea\n2. Download with `--exclude \"v1.0/*\"` so the old version isn't fetched\n3. Serve it with vLLM 0.25.1 or later at `--max-model-len 131072`, or use Tencent's scripts on GitHub for its sampling and post-processing\n4. Use temperature 0, top_p 1 and repetition_penalty 1.08\n5. The default document prompt is in Chinese and is on the card. Other tasks take their own instruction as the prompt\n6. For llama.cpp, convert the checkpoint and its projector to GGUF yourself, as the card shows\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, HunyuanOCR 1.5 or Xiaomi-OCR-0?\n\nXiaomi-OCR-0 scores 59 (C) on agent readiness against HunyuanOCR 1.5's 56.6 (C), and leads in 3 of 4 scored categories. HunyuanOCR 1.5 leads on agent interface.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/hunyuanocr-vs-xiaomi-ocr-0.json, and with the fewest tokens: https://www.anchorterminal.com/compare/hunyuanocr-vs-xiaomi-ocr-0.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"hunyuanocr\", \"b\": \"xiaomi-ocr-0\"}`. From a terminal: `anchor compare hunyuanocr xiaomi-ocr-0`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/hunyuanocr.json and https://www.anchorterminal.com/api/v1/tools/xiaomi-ocr-0.json\n\n## Other comparisons with HunyuanOCR 1.5 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 HunyuanOCR 1.5](https://www.anchorterminal.com/compare/chandra-ocr-2-vs-hunyuanocr.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 HunyuanOCR 1.5](https://www.anchorterminal.com/compare/deepseek-ocr-2-vs-hunyuanocr.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 LightOnOCR-3-4B](https://www.anchorterminal.com/compare/hunyuanocr-vs-lightonocr-3-4b.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 Nemotron 3.5 Parse](https://www.anchorterminal.com/compare/hunyuanocr-vs-nemotron-3-5-parse.md)\n- [HunyuanOCR 1.5 vs Surya OCR 2.5](https://www.anchorterminal.com/compare/hunyuanocr-vs-surya-ocr-2-5.md)\n- [HunyuanOCR 1.5 vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/hunyuanocr-vs-unlimited-ocr-2.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 Xiaomi-OCR-0](https://www.anchorterminal.com/compare/nanonets-ocr2-3b-vs-xiaomi-ocr-0.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": "HunyuanOCR 1.5 vs Xiaomi-OCR-0", "url": "" } ], "description": "Xiaomi-OCR-0 scores 59 (C) to HunyuanOCR 1.5's 56.6 (C) for ocr. Prices, MCP, x402, uptime and agent notes side by side.", "facts": [ "HunyuanOCR 1.5 C 56.6", "Xiaomi-OCR-0 C 59", "scores" ], "h1": "HunyuanOCR 1.5 vs Xiaomi-OCR-0", "image": "https://www.anchorterminal.com/assets/og/default.png", "path": "/compare/hunyuanocr-vs-xiaomi-ocr-0", "published": "2026-10-01", "section": "tools", "title": "HunyuanOCR 1.5 vs Xiaomi-OCR-0 for AI agents (2026) | Anchor Terminal", "toc": null, "updated": "2026-11-10", "url": "https://www.anchorterminal.com/compare/hunyuanocr-vs-xiaomi-ocr-0" }, "tokens": { "markdown": 3900, "slim": 830 }, "version": 1 }