{ "data": { "a": { "slug": "lbs-27b", "name": "LBS-27B", "vendor": "inclusionAI (Ant Group)", "vendorUrl": "https://www.inclusion-ai.org", "kind": "weights", "category": "open-weights", "summary": "LBS-27B is an open-weight vision-language model from inclusionAI's UI-Venus line, fine-tuned from Qwen3.8-27B to find what to click on a screenshot by zooming into regions over several turns, and applied to visual questions on large images.", "url": "https://www.anchorterminal.com/tools/lbs-27b", "markdownUrl": "https://www.anchorterminal.com/tools/lbs-27b.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/lbs-27b.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/lbs-27b.json", "repo": "https://huggingface.co/inclusionAI/LBS-27B", "license": "Apache-2.0 (the card's metadata and licence section; the LICENSE file sits in the GitHub repository inclusionAI/UI-Venus on its LBS branch, not in the Hugging Face repository)", "transports": [], "packages": [ { "registry": "huggingface", "name": "inclusionAI/LBS-27B" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The Hub reports gated false for inclusionAI/LBS-27B (checked 2026-10-27).", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0 as the card states for the weights and the code. Running it costs the hardware, 54.73 GB of BF16 files and no quantised build from the publisher. No hosted API or inference provider serves it (checked 2026-10-27).", "priceSummary": "Free", "where": "local", "x402": { "level": "no", "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-10-27).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-10-27" }, "docsUrl": "https://github.com/inclusionAI/UI-Venus/tree/LBS", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "dense", "qwen", "vision", "gui-grounding", "computer-use", "reasoning", "safetensors", "self-hosted", "new" ], "lastRelease": "2026-10-26", "graded": true, "anchor": { "graded": true, "score": 53.9, "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": 1217, "categoryRank": 73, "methodology": "0.6", "run": "2026-10-01", "scores": { "interface": 70, "reach": 55, "risk": 50, "upkeep": 43 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-10-27" }, "negative": 0, "verdict": "A 27B Qwen3.8 fine-tune for GUI grounding that zooms into screenshots over a few turns before naming a point, and inclusionAI says it also helps with fine detail in large images. It needs about 96 GB at BF16 and a client that runs its crop actions. Every result so far is inclusionAI's own, with no safety results or injection guidance.", "bestFor": "Agents with about 96 GB of GPU memory that need to find click targets on dense professional screenshots, or read fine detail in large images, and can run a small crop loop and wait for independent results.", "strengths": [ "Qwen3.8-27B's stock architecture with no repository code, so released vLLM, SGLang, Transformers and llama.cpp load it", "Apache 2.0 for the weights and the base, ungated, safetensors only", "A simple, documented plain-text crop protocol with a point answer on a 0 to 1000 scale, and the training recipe and evaluation kit published", "Up to two crops per turn, learned in GRPO, and 262,144 tokens of context from the base", "inclusionAI publishes per-item scores and selected trajectories for its own benchmark runs" ], "weaknesses": [ "No independent benchmark result yet; ScreenSpot-Pro, OS-World-G, PerceptionBench and HR-Bench numbers are inclusionAI's own runs", "BF16 only, about 66 GB to load, and no Ollama, LM Studio or quantised build", "The crop loop and system prompt come from the publisher's Python, not a runtime parser or a template", "No safety evaluation, prompt-injection guidance, training data sources or knowledge cut-off", "No hosted API or inference provider serves it" ], "agentNotes": [ "Serve with `vllm serve inclusionAI/LBS-27B --tensor-parallel-size 4 --max-model-len 65536`, which is what the card's wrapper script runs; keep the context at 32,768 or more for multi-turn images", "Build the system prompt with `build_system_prompt` from the LBS branch's `zoomin_grounding_protocol.py`, send the screenshot after `image_path: \u003cname\u003e`, and don't pass `tools`; the model answers in plain text", "Parse `\u003caction\u003ecrop_image(image_path=..., x1=..., y1=..., x2=..., y2=...)\u003c/action\u003e`, cut the crop yourself and return it with its mapping to the original image; allow at most two crops per turn and three per episode", "Read the final answer as `[x.xx,y.yy]` on a 0 to 1000 scale of the original image, and use your own executor to click", "Pass `reasoning_effort` as `xhigh`, `medium` or `low` only, since the template raises an error for `high`, and don't count on `thinking_budget`, which the template ignores", "Don't turn on MTP speculative decoding; the MTP weights aren't in the repository though config.json names one MTP layer" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 0, "avgRating": 0, "history": [ { "basis": "public evidence", "confidence": "low", "grade": "D", "methodology": "0.6", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 53.9 } ], "editorialScores": { "interface": 70, "reach": 55, "risk": 50, "upkeep": 43 }, "provenanceScore": 57, "checked": { "categories": { "interface": "2026-10-27", "reach": "2026-10-27", "risk": "2026-10-27", "upkeep": "2026-10-27" }, "last": "2026-10-27", "oldest": "2026-10-27", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "# The card's route clones the LBS branch and wraps vllm serve (four GPUs in its example)\ngit clone --branch LBS --single-branch https://github.com/inclusionAI/UI-Venus.git\ncd UI-Venus/evaluation\nbash scripts/serve_vllm.sh start inclusionAI/LBS-27B --name lbs --tp 4 --dp 1 --daemon\n# the same server without the wrapper (the script's defaults are a 65,536-token context and 64 sequences)\nvllm serve inclusionAI/LBS-27B --served-model-name lbs --tensor-parallel-size 4 --max-model-len 65536", "http": "curl http://localhost:8000/v1/chat/completions -H 'Content-Type: application/json' \\\n -d '{\"model\": \"lbs\", \"max_tokens\": 2560,\n \"messages\": [{\"role\": \"system\", \"content\": \"\u003ctask, image size, crop_image spec and answer format, as build_system_prompt in the LBS branch writes them\u003e\"},\n {\"role\": \"user\", \"content\": [{\"type\": \"text\", \"text\": \"image_path: screenshot.png\"},\n {\"type\": \"image_url\", \"image_url\": {\"url\": \"data:image/png;base64,...\"}}]}],\n \"chat_template_kwargs\": {\"reasoning_effort\": \"xhigh\"}}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/lbs-27b" }, "sameCompany": [ "ming-3-0-asr-tiny", "ming-3-0-audio-flash", "ming-3-0-audio-tiny", "ming-3-0-tts-tiny", "ling-3-0-tiny" ], "weights": { "trainedBy": "inclusionAI (Ant Group), with Wuhan University of Technology, CASIA and Zhejiang University", "builtOn": "Qwen/Qwen3.8-27B (fine-tune, named in the card's text, not in its base_model metadata; Apache 2.0, copyright Alibaba Cloud)", "params": "27B", "context": 262144, "goodFor": "A cousin that finds the point to click on a screenshot, and answers questions about fine detail in large images, by zooming in over a few turns, on a machine with about 96 GB of memory and a client that runs the crops, if inclusionAI's own results hold up.", "runtimes": [ "vLLM", "SGLang", "Transformers", "llama.cpp" ], "builds": [ { "name": "LBS-27B (BF16)", "url": "https://huggingface.co/inclusionAI/LBS-27B", "who": "publisher", "precision": "bf16", "sizeGB": 54.73, "memoryGB": 96, "format": "safetensors", "runsOn": [ "vLLM", "SGLang", "Transformers" ], "note": "Whole repository, 54.73 GB, almost all of it 12 BF16 shards. 54.73 GB × 1.2 = 65.7 GB, just over 64 GB. The only build; no Ollama, LM Studio or ggml-org build and no copy by anyone else on 27 October 2026" } ], "run": "vllm serve inclusionAI/LBS-27B --served-model-name lbs --tensor-parallel-size 4 --max-model-len 65536", "runBuild": "LBS-27B (BF16)", "checked": "2026-10-27", "minMemoryGB": 96 }, "family": { "lab": "inclusionAI (Ant Group)", "family": "UI-Venus", "url": "https://www.anchorterminal.com/open-weights/#inclusionai", "others": [ { "name": "UI-Venus-2-9B", "url": "https://huggingface.co/inclusionAI/UI-Venus-2-9B", "params": "9B" }, { "name": "UI-Venus-1.5-30B-A3B", "url": "https://huggingface.co/inclusionAI/UI-Venus-1.5-30B-A3B", "params": "30B total, 3B active" }, { "name": "UI-Venus-1.5-8B", "url": "https://huggingface.co/inclusionAI/UI-Venus-1.5-8B", "params": "8B" }, { "name": "UI-Venus-1.5-2B", "url": "https://huggingface.co/inclusionAI/UI-Venus-1.5-2B", "params": "2B" } ], "olderCount": 54 }, "area": "models", "provenance": { "legalEntity": "inclusionAI, Ant Group (named in the site footer of inclusion-ai.org); the work is credited to authors from Wuhan University of Technology, CASIA, Zhejiang University and Ant Group", "domain": "inclusion-ai.org", "domainRegistered": "2025-04-03", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://huggingface.co/inclusionAI/LBS-27B/commits/main", "securityTxt": "none", "checked": "2026-10-27", "notes": [ "inclusion-ai.org links to huggingface.co/inclusionAI and github.com/inclusionAI (read 2026-10-27). The Hub doesn't verify the organisation, which the calibration of 21 October credits as the publisher's own. The domain registration date is carried from the Ming-3.0 listings' check of 21 October 2026.", "The model card links github.com/inclusionAI/UI-Venus on its LBS branch, where the LICENSE, the NOTICE, the training recipe and the evaluation kit sit. The NOTICE reads Copyright 2026 the LBS contributors.", "Nothing is hosted, so the Apache 2.0 licence stands in for terms of service and no privacy policy is scored.", "The changelog is the Hugging Face commit history, two commits on 26 October 2026 (initial commit and one upload).", "inclusion-ai.org/.well-known/security.txt reset the connection on 27 October 2026 and answered 404 on 21 October. The LBS branch has no SECURITY.md. Ant Group runs a company-wide security response centre, AntSRC (https://security.alipay.com/, answering on 27 October 2026); nothing on the card or the branch links it." ], "score": 57 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/lbs-27b.json", "live": { "slug": "lbs-27b", "securityTxt": { "url": "https://inclusion-ai.org/.well-known/security.txt", "state": "unknown", "checkedAt": "2026-10-27T15:31:25.810013407Z" }, "updatedAt": "2026-10-27T15:31:25.810013407Z" } }, "answer": "LBS-27B and Baidu Unlimited-OCR-2 score within a point of each other on agent readiness, 53.9 (D) and 53.5 (D). Baidu Unlimited-OCR-2 leads on agent interface.", "b": { "slug": "unlimited-ocr-2", "name": "Baidu Unlimited-OCR-2", "vendor": "Baidu", "vendorUrl": "https://github.com/baidu", "kind": "weights", "category": "open-weights", "summary": "Unlimited-OCR-2 is Baidu's open-weight document OCR model, about 3.3B parameters, that parses page images, from one page to documents of hundreds of pages in one request, into layout blocks with boxes and HTML tables, under MIT.", "url": "https://www.anchorterminal.com/tools/unlimited-ocr-2", "markdownUrl": "https://www.anchorterminal.com/tools/unlimited-ocr-2.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/unlimited-ocr-2.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/unlimited-ocr-2.json", "repo": "https://huggingface.co/baidu/Unlimited-OCR-2", "license": "MIT (Hub metadata; the Hugging Face repository has no LICENSE file, and the GitHub code repository's LICENSE is the MIT text, copyright 2026 Baidu)", "transports": [], "packages": [ { "registry": "huggingface", "name": "baidu/Unlimited-OCR-2" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The repository isn't gated (checked 2026-10-27).", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under MIT by the repository's metadata and the LICENSE file in Baidu's GitHub code repository. Baidu sells no per-call access to these weights in this listing. Running them costs the hardware, 6.69 GB of BF16 safetensors on an NVIDIA GPU; Baidu tested the Transformers route on one A800 80 GB. No inference provider serves it on Hugging Face (checked 2026-10-27).", "priceSummary": "Free", "where": "local", "x402": { "level": "no", "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-10-27).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-10-27" }, "docsUrl": "https://huggingface.co/baidu/Unlimited-OCR-2", "capabilities": [ "docs.ocr", "docs.parse", "docs.tables", "model.open-weights" ], "tags": [ "open-weights", "model", "ocr", "document-parsing", "multi-page", "long-documents", "tables", "layout", "bounding-boxes", "vision", "mixture-of-experts", "safetensors", "self-hosted", "new" ], "lastRelease": "2026-10-26", "graded": true, "anchor": { "graded": true, "score": 53.5, "grade": "D", "agentReady": false, "rank": 0, "ranked": false, "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools", "rankOf": 1217, "categoryRank": 74, "methodology": "0.6", "run": "2026-10-01", "scores": { "interface": 75, "reach": 55, "risk": 45, "upkeep": 45 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-10-27" }, "negative": 0, "verdict": "A small MIT document-parsing cousin that reads up to hundreds of pages in one request and returns ordered layout blocks with boxes and HTML tables. It needs an NVIDIA GPU with about 12 GB, through Baidu's remote code or its own SGLang build. The risks are running the publisher's code and accuracy figures that are Baidu's own.", "bestFor": "Pipelines with an NVIDIA GPU that need long scanned or rendered documents parsed in one pass, with page order, cross-page tables and boxes kept", "strengths": [ "Small, 3.3B parameters with about 0.5B active per token, in 6.69 GB of BF16 safetensors", "Reads many pages in one request with cross-page context, merging tables and text that continue across pages", "MIT with nothing added, ungated, files hashed in Baidu's verified organisation", "An OpenAI-compatible server through SGLang with page-window scheduling for long documents, and a documented converter from its block output to Markdown" ], "weaknesses": [ "Loading runs Baidu's code, remote code in Transformers or a prebuilt SGLang fork from the repository, and the save_results path calls eval on model output", "No released runtime runs its sparse attention yet, and no quantised build", "The card states no parameters, languages, context length or limitations, and no safety results or security contact were found", "Every accuracy figure is Baidu's own, one of its two long-document benchmarks is Baidu's own, and no independent result exists for this checkpoint", "Charts and figures come back as boxes to crop, not as data, and the report says heading hierarchy is weak over long documents" ], "agentNotes": [ "Quickest local run, `pip install torch==2.13.0 torchvision pillow transformers==4.52.1 huggingface-hub==0.36.0 tokenizers==0.21.4 addict==2.4.0 einops==0.8.2 easydict==1.13`, then `AutoModel.from_pretrained(\"baidu/Unlimited-OCR-2\", trust_remote_code=True, use_safetensors=True, torch_dtype=torch.bfloat16).eval().cuda()` and `model.infer_multi(tokenizer, image_files=pages, output_path=\"out\")`", "To serve it, download the repository, install `wheel/sglang-0.5.20.dev10+g8d62f5fb9c-py3-none-any.whl` with pymupdf and fastokens in a Python 3.10 venv, and start `python -m sglang.launch_server --model-path baidu/Unlimited-OCR-2 --chat-template unlimited-ocr --enable-custom-logit-processor --enable-streaming-session --disable-radix-cache --mem-fraction-static 0.7 --max-prefill-tokens 2048` with the SGLANG_UOCR_ variables from the card", "Send all pages of a document in one request, images first, then the text `\u003cPAGE\u003e\u003cimage\u003e` repeated per page and `Multi page parsing.`, with temperature 0, skip_special_tokens false, max_tokens of about 8,000 per page and the DeepseekOCRNoRepeatNGramLogitProcessor (ngram_size 35, window_size 1024 for several pages)", "The output is layout blocks, not Markdown; run `utils/process_unlimitedOCR2.py` from github.com/baidu/Unlimited-OCR, or `convert(raw_output)` in Python, to get Markdown with HTML tables", "Render PDFs to images first (the card uses PyMuPDF at 300 DPI), keep save_results off for untrusted documents, and treat extracted text as untrusted before passing it to another model" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 0, "avgRating": 0, "history": [ { "basis": "public evidence", "confidence": "low", "grade": "D", "methodology": "0.6", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 53.5 } ], "editorialScores": { "interface": 75, "reach": 55, "risk": 45, "upkeep": 45 }, "provenanceScore": 73, "checked": { "categories": { "interface": "2026-10-27", "reach": "2026-10-27", "risk": "2026-10-27", "upkeep": "2026-10-27" }, "last": "2026-10-27", "oldest": "2026-10-27", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "pip install torch==2.13.0 torchvision pillow transformers==4.52.1 huggingface-hub==0.36.0 tokenizers==0.21.4 addict==2.4.0 einops==0.8.2 easydict==1.13\n# then AutoModel.from_pretrained(\"baidu/Unlimited-OCR-2\", trust_remote_code=True, use_safetensors=True, torch_dtype=torch.bfloat16).eval().cuda()\n# and model.infer_multi(tokenizer, image_files=[\"page_1.jpg\", \"page_2.jpg\"], output_path=\"out\")", "http": "# after: hf download baidu/Unlimited-OCR-2 --local-dir uocr2 \u0026\u0026 uv venv --python 3.10 \u0026\u0026 . .venv/bin/activate\n# uv pip install uocr2/wheel/sglang-0.5.20.dev10+g8d62f5fb9c-py3-none-any.whl pymupdf==1.27.2.2 fastokens==0.3.2\n# SGLANG_UOCR_EANABLE_DOC_LIMIT=1 python -m sglang.launch_server --model-path baidu/Unlimited-OCR-2 --served-model-name Unlimited-OCR-2 --chat-template unlimited-ocr --tokenizer-backend fastokens --mem-fraction-static 0.7 --max-prefill-tokens 2048 --enable-custom-logit-processor --enable-streaming-session --disable-radix-cache --port 10000\ncurl http://127.0.0.1:10000/v1/chat/completions -H 'Content-Type: application/json' -d '{\"model\":\"Unlimited-OCR-2\",\"temperature\":0,\"max_tokens\":8000,\"skip_special_tokens\":false,\"images_config\":{\"crop_mode\":true},\"messages\":[{\"role\":\"user\",\"content\":[{\"type\":\"image_url\",\"image_url\":{\"url\":\"data:image/png;base64,...\"}},{\"type\":\"text\",\"text\":\"\u003cPAGE\u003e\u003cimage\u003eMulti page parsing.\"}]}]}'" }, "letme": { "capability": "https://letme.dev/docs.ocr", "tool": "https://letme.dev/unlimited-ocr-2" }, "weights": { "trainedBy": "Baidu (ERNIE Team)", "builtOn": "baidu/Unlimited-OCR (post-trained from its checkpoint per the report, MIT by its metadata), whose backbone is deepseek-ai/DeepSeek-OCR (MIT by its metadata); not declared in the card's metadata", "params": "3.3B (3B-A0.5B mixture-of-experts decoder per the report)", "context": 131072, "goodFor": "Parsing long scanned or rendered documents, tens to hundreds of pages, into ordered layout blocks with boxes and HTML tables in one request, on an NVIDIA GPU, if Baidu's own results hold up.", "runtimes": [], "builds": [ { "name": "Unlimited-OCR-2 (BF16 safetensors)", "url": "https://huggingface.co/baidu/Unlimited-OCR-2", "who": "publisher", "precision": "bf16", "sizeGB": 6.69, "memoryGB": 12, "format": "safetensors", "runsOn": [ "Transformers (remote code)", "SGLang (Baidu's customised wheel)" ], "note": "Two shards, 6,688,305,576 bytes, so 6.69 GB × 1.2 = 8.03 GB, within 12 GB. The whole repository is 6.87 GB with the report, the wheel and a 156 MB GIF, which comes to the same band. The only build from Baidu; no copy by anyone else exists yet" } ], "run": "hf download baidu/Unlimited-OCR-2 --local-dir uocr2 \u0026\u0026 uv venv --python 3.10 \u0026\u0026 . .venv/bin/activate \u0026\u0026 uv pip install uocr2/wheel/sglang-0.5.20.dev10+g8d62f5fb9c-py3-none-any.whl pymupdf==1.27.2.2 fastokens==0.3.2 \u0026\u0026 SGLANG_UOCR_EANABLE_DOC_LIMIT=1 SGLANG_UOCR_DOC_GUNDAM_PAGE_WINDOW=10 SGLANG_UOCR_DOC_BASE_PAGE_WINDOW=10 SGLANG_UOCR_MAX_PAGE_TOKENS=20000 python -m sglang.launch_server --model-path baidu/Unlimited-OCR-2 --served-model-name Unlimited-OCR-2 --chat-template unlimited-ocr --tokenizer-backend fastokens --mem-fraction-static 0.7 --max-prefill-tokens 2048 --enable-custom-logit-processor --enable-streaming-session --allow-auto-truncate --disable-radix-cache --port 10000", "runBuild": "Unlimited-OCR-2 (BF16 safetensors)", "checked": "2026-10-27", "minMemoryGB": 12 }, "family": { "lab": "Baidu", "family": "Unlimited OCR 2", "url": "https://www.anchorterminal.com/open-weights/#baidu", "olderCount": 1 }, "area": "models", "provenance": { "legalEntity": "Baidu, Inc.", "domain": "baidu.com", "domainRegistered": "1999-10-11", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://huggingface.co/baidu/Unlimited-OCR-2/commits/main", "securityTxt": "unknown", "checked": "2026-10-27", "notes": [ "The files sit in Baidu's verified baidu organisation on Hugging Face, which Baidu's GitHub organisation profile links to, and the GitHub code repository's README links this repository. The report's byline is the Baidu ERNIE Team. The legal entity is the card's Baidu Inc., not checked against a registry. The domain registration date is from Verisign's RDAP record for baidu.com, read 2026-10-27.", "Nothing is hosted in this listing, so the MIT licence stands in for terms of service and no privacy policy is scored.", "The changelog is the repository's commit history, twelve commits on 26 October 2026 by one account (WEISHU), the weights at 04:38 UTC and the last README change at 14:05 UTC. The card's release line dates the release 27 October 2026.", "www.baidu.com's robots.txt disallows every path to all agents not named in it, so its security.txt is not read for this listing, and bsrc.baidu.com answered Access denied to its robots.txt. No security contact is named on the card or in the GitHub repository." ], "score": 73 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/unlimited-ocr-2.json", "live": { "slug": "unlimited-ocr-2", "securityTxt": { "url": "https://baidu.com/.well-known/security.txt", "state": "none", "checkedAt": "2026-10-27T15:32:20.71581076Z" }, "updatedAt": "2026-10-27T15:32:20.71581076Z" } }, "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "inclusionAI (Ant Group)", "b": "Baidu", "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": "Apache-2.0 (the card's metadata and licence section; the LICENSE file sits in the GitHub repository inclusionAI/UI-Venus on its LBS branch, not in the Hugging Face repository)", "b": "MIT (Hub metadata; the Hugging Face repository has no LICENSE file, and the GitHub code repository's LICENSE is the MIT text, copyright 2026 Baidu)", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "no", "b": "no", "name": "llms.txt" }, { "a": "2026-10-26", "b": "2026-10-26", "name": "Last release" }, { "a": "no document linked", "b": "no document linked", "name": "Terms last updated" }, { "a": "no document linked", "b": "no document linked", "name": "Privacy policy last updated" }, { "a": "", "b": "", "name": "Customer content may train models" }, { "a": "", "b": "", "name": "Terms restrict automated access" }, { "a": "", "b": "", "name": "Terms restrict benchmarking" }, { "a": "", "b": "", "name": "Terms or service can change without notice" }, { "a": "", "b": "", "name": "Arbitration or class-action waiver" } ], "faq": [ { "answer": "LBS-27B and Baidu Unlimited-OCR-2 score within a point of each other on agent readiness, 53.9 (D) and 53.5 (D). Baidu Unlimited-OCR-2 leads on agent interface.", "question": "Which is better for AI agents, LBS-27B or Baidu Unlimited-OCR-2?" } ], "goodFor": [ { "aheadOn": [ "Risk, 50 against 45" ], "also": null, "goodFor": "Agents with about 96 GB of GPU memory that need to find click targets on dense professional screenshots, or read fine detail in large images, and can run a small crop loop and wait for independent results.", "slug": "lbs-27b", "watchFor": "No independent benchmark result yet; ScreenSpot-Pro, OS-World-G, PerceptionBench and HR-Bench numbers are inclusionAI's own runs" }, { "aheadOn": [ "Agent interface, 75 against 70" ], "also": null, "goodFor": "Pipelines with an NVIDIA GPU that need long scanned or rendered documents parsed in one pass, with page order, cross-page tables and boxes kept", "slug": "unlimited-ocr-2", "watchFor": "Loading runs Baidu's code, remote code in Transformers or a prebuilt SGLang fork from the repository, and the save_results path calls eval on model output" } ], "job": { "capability": "model.open-weights", "name": "Open-weight models you run yourself" }, "others": [ { "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-lbs-27b.json", "title": "Agents-A1-4B vs LBS-27B", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-lbs-27b" }, { "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-unlimited-ocr-2.json", "title": "Agents-A1-4B vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-lbs-27b.json", "title": "Apertus v1.5 8B vs LBS-27B", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-lbs-27b" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-unlimited-ocr-2.json", "title": "Apertus v1.5 8B vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-lbs-27b.json", "title": "Apodex-1.1-mini vs LBS-27B", "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-lbs-27b" }, { "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-unlimited-ocr-2.json", "title": "Apodex-1.1-mini vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-lbs-27b.json", "title": "BigBang-v1 vs LBS-27B", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-lbs-27b" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-unlimited-ocr-2.json", "title": "BigBang-v1 vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/carbon-x-vs-lbs-27b.json", "title": "Carbon-X vs LBS-27B", "url": "https://www.anchorterminal.com/compare/carbon-x-vs-lbs-27b" }, { "json": "https://www.anchorterminal.com/compare/carbon-x-vs-unlimited-ocr-2.json", "title": "Carbon-X vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/carbon-x-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-lbs-27b.json", "title": "Cosmos-Reason2-2B vs LBS-27B", "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-lbs-27b" }, { "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-unlimited-ocr-2.json", "title": "Cosmos-Reason2-2B vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-lbs-27b.json", "title": "Darwin-36B-Opus vs LBS-27B", "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-lbs-27b" }, { "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-unlimited-ocr-2.json", "title": "Darwin-36B-Opus vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-lbs-27b.json", "title": "DeepSeek-V3.2 vs LBS-27B", "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-lbs-27b" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-unlimited-ocr-2.json", "title": "DeepSeek-V3.2 vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-lbs-27b.json", "title": "DeepSeek V4.1 Flash vs LBS-27B", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-lbs-27b" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-unlimited-ocr-2.json", "title": "DeepSeek V4.1 Flash vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-lbs-27b.json", "title": "DeepSeek-V4-Flash-0731 vs LBS-27B", "url": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-lbs-27b" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-unlimited-ocr-2.json", "title": "DeepSeek-V4-Flash-0731 vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-lbs-27b.json", "title": "DiffusionGemma 26B A4B IT vs LBS-27B", "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-lbs-27b" }, { "json": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-unlimited-ocr-2.json", "title": "DiffusionGemma 26B A4B IT vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-lbs-27b.json", "title": "Gemma 4 12B IT vs LBS-27B", "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-lbs-27b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-unlimited-ocr-2.json", "title": "Gemma 4 12B IT vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-lbs-27b.json", "title": "Gemma 4 26B A4B IT vs LBS-27B", "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-lbs-27b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-unlimited-ocr-2.json", "title": "Gemma 4 26B A4B IT vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-lbs-27b.json", "title": "Gemma 4 31B IT vs LBS-27B", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-lbs-27b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-unlimited-ocr-2.json", "title": "Gemma 4 31B IT vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-lbs-27b.json", "title": "Gemma 4 E2B IT vs LBS-27B", "url": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-lbs-27b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-unlimited-ocr-2.json", "title": "Gemma 4 E2B IT vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-lbs-27b.json", "title": "Gemma 4 E4B IT vs LBS-27B", "url": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-lbs-27b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-unlimited-ocr-2.json", "title": "Gemma 4 E4B IT vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-lbs-27b.json", "title": "GLM-4.5-Air vs LBS-27B", "url": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-lbs-27b" }, { "json": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-unlimited-ocr-2.json", "title": "GLM-4.5-Air vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-lbs-27b.json", "title": "GLM-4.7-Flash vs LBS-27B", "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-lbs-27b" }, { "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-unlimited-ocr-2.json", "title": "GLM-4.7-Flash vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-lbs-27b.json", "title": "GLM-5.3-Flash vs LBS-27B", "url": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-lbs-27b" }, { "json": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-unlimited-ocr-2.json", "title": "GLM-5.3-Flash vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/glm-5-3-vs-lbs-27b.json", "title": "GLM-5.3 vs LBS-27B", "url": "https://www.anchorterminal.com/compare/glm-5-3-vs-lbs-27b" }, { "json": "https://www.anchorterminal.com/compare/glm-5-3-vs-unlimited-ocr-2.json", "title": "GLM-5.3 vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/glm-5-3-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-lbs-27b.json", "title": "gpt-oss-120b vs LBS-27B", "url": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-lbs-27b" }, { "json": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-unlimited-ocr-2.json", "title": "gpt-oss-120b vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-lbs-27b.json", "title": "gpt-oss-20b vs LBS-27B", "url": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-lbs-27b" }, { "json": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-unlimited-ocr-2.json", "title": "gpt-oss-20b vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-lbs-27b.json", "title": "Granite 4.2 3B vs LBS-27B", "url": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-lbs-27b" }, { "json": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-unlimited-ocr-2.json", "title": "Granite 4.2 3B vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/hy3-vs-lbs-27b.json", "title": "Hy3 vs LBS-27B", "url": "https://www.anchorterminal.com/compare/hy3-vs-lbs-27b" }, { "json": "https://www.anchorterminal.com/compare/hy3-vs-unlimited-ocr-2.json", "title": "Hy3 vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/hy3-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/inkling-small-vs-lbs-27b.json", "title": "Inkling-Small vs LBS-27B", "url": "https://www.anchorterminal.com/compare/inkling-small-vs-lbs-27b" }, { "json": "https://www.anchorterminal.com/compare/inkling-small-vs-unlimited-ocr-2.json", "title": "Inkling-Small vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/inkling-small-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/inkling-vs-lbs-27b.json", "title": "Inkling vs LBS-27B", "url": "https://www.anchorterminal.com/compare/inkling-vs-lbs-27b" }, { "json": "https://www.anchorterminal.com/compare/inkling-vs-unlimited-ocr-2.json", "title": "Inkling vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/inkling-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-lbs-27b.json", "title": "InternVL3.5-GPT-OSS-20B-A4B-Preview vs LBS-27B", "url": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-lbs-27b" }, { "json": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-unlimited-ocr-2.json", "title": "InternVL3.5-GPT-OSS-20B-A4B-Preview vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-lbs-27b.json", "title": "Kimi K3 vs LBS-27B", "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-lbs-27b" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-unlimited-ocr-2.json", "title": "Kimi K3 vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-lbs-27b.json", "title": "Kolibri-1 vs LBS-27B", "url": "https://www.anchorterminal.com/compare/kolibri-1-vs-lbs-27b" }, { "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-unlimited-ocr-2.json", "title": "Kolibri-1 vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/kolibri-1-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/kronos-vs-lbs-27b.json", "title": "Kronos vs LBS-27B", "url": "https://www.anchorterminal.com/compare/kronos-vs-lbs-27b" }, { "json": "https://www.anchorterminal.com/compare/kronos-vs-unlimited-ocr-2.json", "title": "Kronos vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/kronos-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-lbs-27b.json", "title": "Laguna M.1 vs LBS-27B", "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-lbs-27b" }, { "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-unlimited-ocr-2.json", "title": "Laguna M.1 vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-lbs-27b.json", "title": "Laguna S 2.1 vs LBS-27B", "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-lbs-27b" }, { "json": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-unlimited-ocr-2.json", "title": "Laguna S 2.1 vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-lbs-27b.json", "title": "Laguna XS 2.1 vs LBS-27B", "url": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-lbs-27b" }, { "json": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-unlimited-ocr-2.json", "title": "Laguna XS 2.1 vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/lbs-27b-vs-lfm2-5-2-6b.json", "title": "LBS-27B vs LFM2.5-2.6B", "url": "https://www.anchorterminal.com/compare/lbs-27b-vs-lfm2-5-2-6b" }, { "json": "https://www.anchorterminal.com/compare/lbs-27b-vs-ling-3-0-tiny.json", "title": "LBS-27B vs Ling-3.0-tiny", "url": "https://www.anchorterminal.com/compare/lbs-27b-vs-ling-3-0-tiny" }, { "json": "https://www.anchorterminal.com/compare/lbs-27b-vs-mimo-v2-6-distill-qwen-9b.json", "title": "LBS-27B vs MiMo-V2.6 Distill Qwen 9B", "url": "https://www.anchorterminal.com/compare/lbs-27b-vs-mimo-v2-6-distill-qwen-9b" }, { "json": "https://www.anchorterminal.com/compare/lbs-27b-vs-minicpm-o-4-5.json", "title": "LBS-27B vs MiniCPM-o 4.5", "url": "https://www.anchorterminal.com/compare/lbs-27b-vs-minicpm-o-4-5" }, { "json": "https://www.anchorterminal.com/compare/lbs-27b-vs-minicpm5-2b.json", "title": "LBS-27B vs MiniCPM5-2B", "url": "https://www.anchorterminal.com/compare/lbs-27b-vs-minicpm5-2b" }, { "json": "https://www.anchorterminal.com/compare/lbs-27b-vs-minimax-m2-5.json", "title": "LBS-27B vs MiniMax-M2.5", "url": "https://www.anchorterminal.com/compare/lbs-27b-vs-minimax-m2-5" }, { "json": "https://www.anchorterminal.com/compare/lbs-27b-vs-minimax-m2-7.json", "title": "LBS-27B vs MiniMax-M2.7", "url": "https://www.anchorterminal.com/compare/lbs-27b-vs-minimax-m2-7" }, { "json": "https://www.anchorterminal.com/compare/lbs-27b-vs-minimax-m3-1-flash.json", "title": "LBS-27B vs MiniMax-M3.1-Flash", "url": "https://www.anchorterminal.com/compare/lbs-27b-vs-minimax-m3-1-flash" }, { "json": 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Glimmer 30B vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-unlimited-ocr-2.json", "title": "Nanbeige4.3-33B vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-unlimited-ocr-2.json", "title": "NVIDIA Nemotron 3.5 Bolt E4B 4GB vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-unlimited-ocr-2.json", "title": "Nemotron 3.5 Lightning 30B A3B vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-unlimited-ocr-2" }, { "json": 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"https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-unlimited-ocr-2.json", "title": "Qwen3.5-122B-A10B vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-2b-vs-unlimited-ocr-2.json", "title": "Qwen3.5-2B vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/qwen3-5-2b-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-unlimited-ocr-2.json", "title": "Qwen3.5-397B-A17B vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-4b-vs-unlimited-ocr-2.json", "title": "Qwen3.5-4B vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/qwen3-5-4b-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-unlimited-ocr-2.json", "title": "Qwen3.5-9B vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/qwen3-6-27b-vs-unlimited-ocr-2.json", "title": "Qwen3.6-27B vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/qwen3-6-27b-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-unlimited-ocr-2.json", "title": "Qwen3.6-35B-A3B vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/qwen3-8-27b-vs-unlimited-ocr-2.json", "title": "Qwen3.8-27B vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/qwen3-8-27b-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-unlimited-ocr-2.json", "title": "Qwen3.8-Flash-Next vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/qwen3-coder-30b-a3b-instruct-vs-unlimited-ocr-2.json", "title": "Qwen3-Coder-30B-A3B-Instruct vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/qwen3-coder-30b-a3b-instruct-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/qwen3-coder-next-vs-unlimited-ocr-2.json", "title": "Qwen3-Coder-Next vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/qwen3-coder-next-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-2b-instruct-vs-unlimited-ocr-2.json", "title": "Qwen3-VL-2B-Instruct vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/qwen3-vl-2b-instruct-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-30b-a3b-instruct-vs-unlimited-ocr-2.json", "title": "Qwen3-VL-30B-A3B-Instruct vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/qwen3-vl-30b-a3b-instruct-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-32b-instruct-vs-unlimited-ocr-2.json", "title": "Qwen3-VL-32B-Instruct vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/qwen3-vl-32b-instruct-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-unlimited-ocr-2.json", "title": "Qwen3-VL-4B-Instruct vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-8b-instruct-vs-unlimited-ocr-2.json", "title": "Qwen3-VL-8B-Instruct vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/qwen3-vl-8b-instruct-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-unlimited-ocr-2.json", "title": "Qwopus3.8-27B-Flash-V2 vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/recursive-flint-vs-unlimited-ocr-2.json", "title": "Recursive Flint vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/recursive-flint-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/smollm3-3b-vs-unlimited-ocr-2.json", "title": "SmolLM3-3B vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/smollm3-3b-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/spark-x2-6-4b-vs-unlimited-ocr-2.json", "title": "Spark-X2.6-4B vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/spark-x2-6-4b-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/swift-1-5-qwen3-8-27b-vs-unlimited-ocr-2.json", "title": "Swift 1.5 Qwen3.8-27B vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/swift-1-5-qwen3-8-27b-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/swift-1-5-qwen3-8-flash-next-vs-unlimited-ocr-2.json", "title": "Swift 1.5 Qwen3.8-Flash-Next vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/swift-1-5-qwen3-8-flash-next-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/thinkingcap-qwen3-8-27b-vs-unlimited-ocr-2.json", "title": "ThinkingCap (Qwen3.8 27B) vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/thinkingcap-qwen3-8-27b-vs-unlimited-ocr-2" } ], "scores": [ { "key": "capability", "name": "Capability", "pending": true, "weight": 35 }, { "by": 0, "edge": "", "key": "reach", "lbs-27b": 55, "name": "Reach", "unlimited-ocr-2": 55, "weight": 25 }, { "by": 5, "edge": "lbs-27b", "key": "risk", "lbs-27b": 50, "name": "Risk", "unlimited-ocr-2": 45, "weight": 20 }, { "by": 5, "edge": "unlimited-ocr-2", "key": "interface", "lbs-27b": 70, "name": "Agent interface", "unlimited-ocr-2": 75, "weight": 10 }, { "by": 2, "edge": "unlimited-ocr-2", "key": "upkeep", "lbs-27b": 43, "name": "Documentation and upkeep", "unlimited-ocr-2": 45, "weight": 10 } ], "summary": "LBS-27B and Baidu Unlimited-OCR-2 score within a point of each other on agent readiness, 53.9 (D) and 53.5 (D). Baidu Unlimited-OCR-2 leads on agent interface. Both do open-weight models you run yourself.", "tutorials": [], "verdicts": { "lbs-27b": "A 27B Qwen3.8 fine-tune for GUI grounding that zooms into screenshots over a few turns before naming a point, and inclusionAI says it also helps with fine detail in large images. It needs about 96 GB at BF16 and a client that runs its crop actions. Every result so far is inclusionAI's own, with no safety results or injection guidance.", "unlimited-ocr-2": "A small MIT document-parsing cousin that reads up to hundreds of pages in one request and returns ordered layout blocks with boxes and HTML tables. It needs an NVIDIA GPU with about 12 GB, through Baidu's remote code or its own SGLang build. The risks are running the publisher's code and accuracy figures that are Baidu's own." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/lbs-27b-vs-unlimited-ocr-2", "json": "https://www.anchorterminal.com/compare/lbs-27b-vs-unlimited-ocr-2.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/lbs-27b-vs-unlimited-ocr-2.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/lbs-27b-vs-unlimited-ocr-2.min.md" }, "markdown": "LBS-27B and Baidu Unlimited-OCR-2 score within a point of each other on agent readiness, 53.9 (D) and 53.5 (D). Baidu Unlimited-OCR-2 leads on agent interface. Both do open-weight models you run yourself.\n\n- LBS-27B: grade D, 53.9/100, rank #91 of 113 open-weight models. Markdown https://www.anchorterminal.com/tools/lbs-27b.md · JSON https://www.anchorterminal.com/api/v1/tools/lbs-27b.json\n- Baidu Unlimited-OCR-2: grade D, 53.5/100, rank #92 of 113 open-weight models. Markdown https://www.anchorterminal.com/tools/unlimited-ocr-2.md · JSON https://www.anchorterminal.com/api/v1/tools/unlimited-ocr-2.json\n- Best open-weight models you download and run yourself for AI agents: https://www.anchorterminal.com/best/open-weights/index.md\n- All 3504 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md\n\n## Which one, for what\n\n### LBS-27B (D)\n\nGood for: Agents with about 96 GB of GPU memory that need to find click targets on dense professional screenshots, or read fine detail in large images, and can run a small crop loop and wait for independent results.\n\nAhead on:\n- Risk, 50 against 45\n\nWatch for: No independent benchmark result yet; ScreenSpot-Pro, OS-World-G, PerceptionBench and HR-Bench numbers are inclusionAI's own runs\n\n### Baidu Unlimited-OCR-2 (D)\n\nGood for: Pipelines with an NVIDIA GPU that need long scanned or rendered documents parsed in one pass, with page order, cross-page tables and boxes kept\n\nAhead on:\n- Agent interface, 75 against 70\n\nWatch for: Loading runs Baidu's code, remote code in Transformers or a prebuilt SGLang fork from the repository, and the save_results path calls eval on model output\n\n\n## Score by category\n\n| Category | Weight | LBS-27B | Baidu Unlimited-OCR-2 | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 55 | 55 | even |\n| Risk | 20% (20 this run) | 50 | 45 | LBS-27B +5 |\n| Agent interface | 10% (10 this run) | 70 | 75 | Baidu Unlimited-OCR-2 +5 |\n| Documentation and upkeep | 10% (10 this run) | 43 | 45 | Baidu Unlimited-OCR-2 +2 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **53.9 · D** | **53.5 · D** | |\n\n## Facts side by side\n\n| Fact | LBS-27B | Baidu Unlimited-OCR-2 |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | inclusionAI (Ant Group) | Baidu |\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 | Apache-2.0 (the card's metadata and licence section; the LICENSE file sits in the GitHub repository inclusionAI/UI-Venus on its LBS branch, not in the Hugging Face repository) | MIT (Hub metadata; the Hugging Face repository has no LICENSE file, and the GitHub code repository's LICENSE is the MIT text, copyright 2026 Baidu) |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-10-26 | 2026-10-26 |\n| Terms last updated | no document linked | no document linked |\n| Privacy policy last updated | no document linked | no document linked |\n| Customer content may train models | | |\n| Terms restrict automated access | | |\n| Terms restrict benchmarking | | |\n| Terms or service can change without notice | | |\n| Arbitration or class-action waiver | | |\n\n## Verdicts\n\n**LBS-27B.** A 27B Qwen3.8 fine-tune for GUI grounding that zooms into screenshots over a few turns before naming a point, and inclusionAI says it also helps with fine detail in large images. It needs about 96 GB at BF16 and a client that runs its crop actions. Every result so far is inclusionAI's own, with no safety results or injection guidance.\n\n**Baidu Unlimited-OCR-2.** A small MIT document-parsing cousin that reads up to hundreds of pages in one request and returns ordered layout blocks with boxes and HTML tables. It needs an NVIDIA GPU with about 12 GB, through Baidu's remote code or its own SGLang build. The risks are running the publisher's code and accuracy figures that are Baidu's own.\n\n## Before you call either\n\n### LBS-27B\n\n1. Serve with `vllm serve inclusionAI/LBS-27B --tensor-parallel-size 4 --max-model-len 65536`, which is what the card's wrapper script runs; keep the context at 32,768 or more for multi-turn images\n2. Build the system prompt with `build_system_prompt` from the LBS branch's `zoomin_grounding_protocol.py`, send the screenshot after `image_path: \u003cname\u003e`, and don't pass `tools`; the model answers in plain text\n3. Parse `\u003caction\u003ecrop_image(image_path=..., x1=..., y1=..., x2=..., y2=...)\u003c/action\u003e`, cut the crop yourself and return it with its mapping to the original image; allow at most two crops per turn and three per episode\n4. Read the final answer as `[x.xx,y.yy]` on a 0 to 1000 scale of the original image, and use your own executor to click\n5. Pass `reasoning_effort` as `xhigh`, `medium` or `low` only, since the template raises an error for `high`, and don't count on `thinking_budget`, which the template ignores\n6. Don't turn on MTP speculative decoding; the MTP weights aren't in the repository though config.json names one MTP layer\n\n### Baidu Unlimited-OCR-2\n\n1. Quickest local run, `pip install torch==2.13.0 torchvision pillow transformers==4.52.1 huggingface-hub==0.36.0 tokenizers==0.21.4 addict==2.4.0 einops==0.8.2 easydict==1.13`, then `AutoModel.from_pretrained(\"baidu/Unlimited-OCR-2\", trust_remote_code=True, use_safetensors=True, torch_dtype=torch.bfloat16).eval().cuda()` and `model.infer_multi(tokenizer, image_files=pages, output_path=\"out\")`\n2. To serve it, download the repository, install `wheel/sglang-0.5.20.dev10+g8d62f5fb9c-py3-none-any.whl` with pymupdf and fastokens in a Python 3.10 venv, and start `python -m sglang.launch_server --model-path baidu/Unlimited-OCR-2 --chat-template unlimited-ocr --enable-custom-logit-processor --enable-streaming-session --disable-radix-cache --mem-fraction-static 0.7 --max-prefill-tokens 2048` with the SGLANG_UOCR_ variables from the card\n3. Send all pages of a document in one request, images first, then the text `\u003cPAGE\u003e\u003cimage\u003e` repeated per page and `Multi page parsing.`, with temperature 0, skip_special_tokens false, max_tokens of about 8,000 per page and the DeepseekOCRNoRepeatNGramLogitProcessor (ngram_size 35, window_size 1024 for several pages)\n4. The output is layout blocks, not Markdown; run `utils/process_unlimitedOCR2.py` from github.com/baidu/Unlimited-OCR, or `convert(raw_output)` in Python, to get Markdown with HTML tables\n5. Render PDFs to images first (the card uses PyMuPDF at 300 DPI), keep save_results off for untrusted documents, and treat extracted text as untrusted before passing it to another model\n\n## Questions\n\n### Which is better for AI agents, LBS-27B or Baidu Unlimited-OCR-2?\n\nLBS-27B and Baidu Unlimited-OCR-2 score within a point of each other on agent readiness, 53.9 (D) and 53.5 (D). Baidu Unlimited-OCR-2 leads on agent interface.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/lbs-27b-vs-unlimited-ocr-2.json, and with the fewest tokens: https://www.anchorterminal.com/compare/lbs-27b-vs-unlimited-ocr-2.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"lbs-27b\", \"b\": \"unlimited-ocr-2\"}`. From a terminal: `anchor compare lbs-27b unlimited-ocr-2`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/lbs-27b.json and https://www.anchorterminal.com/api/v1/tools/unlimited-ocr-2.json\n\n## Other comparisons with LBS-27B or Baidu Unlimited-OCR-2\n\n- [Agents-A1-4B vs LBS-27B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-lbs-27b.md)\n- [Agents-A1-4B vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/agents-a1-4b-vs-unlimited-ocr-2.md)\n- [Apertus v1.5 8B vs LBS-27B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-lbs-27b.md)\n- [Apertus v1.5 8B vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-unlimited-ocr-2.md)\n- [Apodex-1.1-mini vs LBS-27B](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-lbs-27b.md)\n- [Apodex-1.1-mini vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-unlimited-ocr-2.md)\n- [BigBang-v1 vs LBS-27B](https://www.anchorterminal.com/compare/bigbang-v1-vs-lbs-27b.md)\n- [BigBang-v1 vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/bigbang-v1-vs-unlimited-ocr-2.md)\n- [Carbon-X vs LBS-27B](https://www.anchorterminal.com/compare/carbon-x-vs-lbs-27b.md)\n- [Carbon-X vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/carbon-x-vs-unlimited-ocr-2.md)\n- [Cosmos-Reason2-2B vs LBS-27B](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-lbs-27b.md)\n- [Cosmos-Reason2-2B vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-unlimited-ocr-2.md)\n- [Darwin-36B-Opus vs LBS-27B](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-lbs-27b.md)\n- [Darwin-36B-Opus vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-unlimited-ocr-2.md)\n- [DeepSeek-V3.2 vs LBS-27B](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-lbs-27b.md)\n- [DeepSeek-V3.2 vs Baidu 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Unlimited-OCR-2](https://www.anchorterminal.com/compare/smollm3-3b-vs-unlimited-ocr-2.md)\n- [Spark-X2.6-4B vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/spark-x2-6-4b-vs-unlimited-ocr-2.md)\n- [Swift 1.5 Qwen3.8-27B vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/swift-1-5-qwen3-8-27b-vs-unlimited-ocr-2.md)\n- [Swift 1.5 Qwen3.8-Flash-Next vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/swift-1-5-qwen3-8-flash-next-vs-unlimited-ocr-2.md)\n- [ThinkingCap (Qwen3.8 27B) vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/thinkingcap-qwen3-8-27b-vs-unlimited-ocr-2.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": "2026-10-27", "license": "CC-BY-4.0", "method": 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