{ "data": { "similar": [ { "grade": "BB", "json": "https://www.anchorterminal.com/tools/qwen3-embedding-8b.json", "name": "Qwen3-Embedding-8B", "score": 74.7, "shared": [ "model.open-weights" ], "slug": "qwen3-embedding-8b" }, { "grade": "BB", "json": "https://www.anchorterminal.com/tools/gemma-4-31b-it.json", "name": "Gemma 4 31B IT", "score": 74.1, "shared": [ "model.open-weights" ], "slug": "gemma-4-31b-it" }, { "grade": "BB", "json": "https://www.anchorterminal.com/tools/qwen3-embedding-4b.json", "name": "Qwen3-Embedding-4B", "score": 72.9, "shared": [ "model.open-weights" ], "slug": "qwen3-embedding-4b" }, { "grade": "BB", "json": "https://www.anchorterminal.com/tools/gemma-4-26b-a4b-it.json", "name": "Gemma 4 26B A4B IT", "score": 72.2, "shared": [ "model.open-weights" ], "slug": "gemma-4-26b-a4b-it" }, { "grade": "BB", "json": "https://www.anchorterminal.com/tools/gemma-4-12b-it.json", "name": "Gemma 4 12B IT", "score": 71.3, "shared": [ "model.open-weights" ], "slug": "gemma-4-12b-it" }, { "grade": "BB", "json": "https://www.anchorterminal.com/tools/gpt-oss-120b.json", "name": "gpt-oss-120b", "score": 70.2, "shared": [ "model.open-weights" ], "slug": "gpt-oss-120b" } ], "tool": { "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" ], "breakdown": [ { "key": "capability", "name": "Capability", "weight": 35, "effectiveWeight": 0, "pending": true, "points": 0, "reason": "Pending for this listing. Nobody independent has published results for it in at least two areas yet, so its weight is shared across the other four and the total is capped at 61 until they do or our own runs start." }, { "key": "reach", "name": "Reach", "weight": 25, "effectiveWeight": 38.46, "score": 55, "points": 21.15, "reason": "Memory earns 10, since the only build is 54.73 GB of BF16, about 66 GB with a fifth added, which fits the reference machine but not 64 GB. Runtimes earn 20 from released vLLM, SGLang, Transformers and llama.cpp, which all carry Qwen3.8-27B's architecture, and Ollama and LM Studio have no build. Builds earn 0, as inclusionAI publishes no quantised build. Terms earn 15 for Apache 2.0 with nothing added, on the weights and on the base. Download earns 10, with no gate. Hosted earns 0, as no provider or inclusionAI API serves it.", "checked": "2026-10-27" }, { "key": "risk", "name": "Risk", "weight": 20, "effectiveWeight": 30.77, "score": 50, "points": 15.38, "reason": "File safety earns 15, safetensors only but with the documented route running the publisher's scripts to serve it and to write its system prompt. Provenance earns 15, inclusionAI's own organisation with a hash for every file. Safety evaluation and agent guidance earn 0, as nothing is published on either, prompt injection through screenshots included. Licence risk keeps all 15 under Apache 2.0. The security programme earns 5 for Ant Group's AntSRC, which isn't linked to the model.", "checked": "2026-10-27" }, { "key": "interface", "name": "Agent interface", "weight": 10, "effectiveWeight": 15.38, "score": 70, "points": 10.77, "reason": "The chat template earns 20, Qwen's Jinja file in the repository. The tool-calling format earns 10, as general tool calls rest on the inherited template and the crop action is parsed by inclusionAI's own code, not a runtime parser. Trained tool calling earns 20 for up to two crop actions per turn learned in GRPO. Structured output earns 0. Reasoning control earns 10, with reasoning effort documented and the reasoning shown as think text. Context earns 10 for 262,144 tokens with no scaling.", "checked": "2026-10-27" }, { "key": "upkeep", "name": "Documentation and upkeep", "weight": 10, "effectiveWeight": 15.38, "score": 43, "points": 6.62, "reason": "The model card earns 8, with intended use, one limitation and parameters, and no architecture, context, languages or cut-off. Training data earns 5 for a general description of synthetic GUI data with no cut-off. Last change earns 20 for 26 October 2026. Responsiveness earns 0, the one discussion unanswered. Version policy earns 5 for a first release. File health earns 5, as the config still sets an MTP layer the weights lack and the card names a reasoning setting the template refuses.", "checked": "2026-10-27" } ], "assessment": { "date": "2026-10-27", "basis": "public evidence", "confidence": "low", "notes": { "capability": "Pending for this listing. Independent results were found in 0 areas (none), and Capability needs 2. Its weight is shared across the other four categories and the total is capped at 61 until there are more, or our own runs start.", "interface": "The chat template earns 20, Qwen's Jinja file in the repository. The tool-calling format earns 10, as general tool calls rest on the inherited template and the crop action is parsed by inclusionAI's own code, not a runtime parser. Trained tool calling earns 20 for up to two crop actions per turn learned in GRPO. Structured output earns 0. Reasoning control earns 10, with reasoning effort documented and the reasoning shown as think text. Context earns 10 for 262,144 tokens with no scaling.", "reach": "Memory earns 10, since the only build is 54.73 GB of BF16, about 66 GB with a fifth added, which fits the reference machine but not 64 GB. Runtimes earn 20 from released vLLM, SGLang, Transformers and llama.cpp, which all carry Qwen3.8-27B's architecture, and Ollama and LM Studio have no build. Builds earn 0, as inclusionAI publishes no quantised build. Terms earn 15 for Apache 2.0 with nothing added, on the weights and on the base. Download earns 10, with no gate. Hosted earns 0, as no provider or inclusionAI API serves it.", "risk": "File safety earns 15, safetensors only but with the documented route running the publisher's scripts to serve it and to write its system prompt. Provenance earns 15, inclusionAI's own organisation with a hash for every file. Safety evaluation and agent guidance earn 0, as nothing is published on either, prompt injection through screenshots included. Licence risk keeps all 15 under Apache 2.0. The security programme earns 5 for Ant Group's AntSRC, which isn't linked to the model.", "upkeep": "The model card earns 8, with intended use, one limitation and parameters, and no architecture, context, languages or cut-off. Training data earns 5 for a general description of synthetic GUI data with no cut-off. Last change earns 20 for 26 October 2026. Responsiveness earns 0, the one discussion unanswered. Version policy earns 5 for a first release. File health earns 5, as the config still sets an MTP layer the weights lack and the card names a reasoning setting the template refuses." }, "sources": [ { "what": "model card, results, usage, licence section (read 27 October 2026)", "url": "https://huggingface.co/inclusionAI/LBS-27B", "seen": "2026-10-27" }, { "what": "Hub API, gated false, licence tag, parameter count, no base_model metadata, empty provider mapping, embedded template", "url": "https://huggingface.co/api/models/inclusionAI/LBS-27B", "seen": "2026-10-27" }, { "what": "file tree, sizes and SHA-256, no LICENSE or Python files", "url": "https://huggingface.co/api/models/inclusionAI/LBS-27B/tree/main?recursive=true", "seen": "2026-10-27" }, { "what": "two commits on 26 October 2026", "url": "https://huggingface.co/api/models/inclusionAI/LBS-27B/commits/main", "seen": "2026-10-27" }, { "what": "one discussion, authors and dates only", "url": "https://huggingface.co/api/models/inclusionAI/LBS-27B/discussions", "seen": "2026-10-27" }, { "what": "Qwen3_5ForConditionalGeneration, 262,144 positions, mtp_num_hidden_layers 1", "url": "https://huggingface.co/inclusionAI/LBS-27B/raw/main/config.json", "seen": "2026-10-27" }, { "what": "tensor names, no mtp tensors", "url": "https://huggingface.co/inclusionAI/LBS-27B/raw/main/model.safetensors.index.json", "seen": "2026-10-27" }, { "what": "chat template, identical to Qwen3.8-27B's", "url": "https://huggingface.co/inclusionAI/LBS-27B/raw/main/chat_template.jinja", "seen": "2026-10-27" }, { "what": "base model, Apache 2.0 LICENSE, config, template and index for comparison", "url": "https://huggingface.co/Qwen/Qwen3.8-27B", "seen": "2026-10-27" }, { "what": "Apache 2.0 text, unaltered", "url": "https://raw.githubusercontent.com/inclusionAI/UI-Venus/LBS/LICENSE", "seen": "2026-10-27" }, { "what": "authors, protocol, results, training summary, licence and data scope", "url": "https://raw.githubusercontent.com/inclusionAI/UI-Venus/LBS/README.md", "seen": "2026-10-27" }, { "what": "training setup, serving example with build_system_prompt, Ascend NPUs", "url": "https://raw.githubusercontent.com/inclusionAI/UI-Venus/LBS/agentic_gui_grounding_rl/README.md", "seen": "2026-10-27" }, { "what": "serving defaults, reasoning_effort and thinking_budget settings", "url": "https://raw.githubusercontent.com/inclusionAI/UI-Venus/LBS/evaluation/README.md", "seen": "2026-10-27" }, { "what": "the vllm serve command the wrapper runs", "url": "https://raw.githubusercontent.com/inclusionAI/UI-Venus/LBS/evaluation/scripts/serve_vllm.sh", "seen": "2026-10-27" }, { "what": "comparison scope, judges differ, chart not controlled", "url": "https://raw.githubusercontent.com/inclusionAI/UI-Venus/LBS/docs/BENCHMARK_OVERVIEW.md", "seen": "2026-10-27" }, { "what": "vLLM v0.32.0 Qwen3_5ForConditionalGeneration", "url": "https://raw.githubusercontent.com/vllm-project/vllm/v0.32.0/vllm/model_executor/models/registry.py", "seen": "2026-10-27" }, { "what": "SGLang v0.5.22 Qwen3_5ForConditionalGeneration", "url": "https://raw.githubusercontent.com/sgl-project/sglang/v0.5.22/python/sglang/srt/models/qwen3_5.py", "seen": "2026-10-27" }, { "what": "Transformers v5.20.2 qwen3_5", "url": "https://raw.githubusercontent.com/huggingface/transformers/v5.20.2/src/transformers/models/qwen3_5/modeling_qwen3_5.py", "seen": "2026-10-27" }, { "what": "llama.cpp b11888 converter, Qwen3_5ForConditionalGeneration mapped", "url": "https://raw.githubusercontent.com/ggml-org/llama.cpp/b11888/conversion/__init__.py", "seen": "2026-10-27" }, { "what": "Ollama search, no LBS entry", "url": "https://ollama.com/search?q=lbs", "seen": "2026-10-27" }, { "what": "OpenRouter model list, no LBS model", "url": "https://openrouter.ai/api/v1/models", "seen": "2026-10-27" }, { "what": "ScreenSpot-Pro leaderboard data, 99 entries, no LBS row", "url": "https://gui-agent.github.io/grounding-leaderboard/results/screenspot_pro.json", "seen": "2026-10-27" }, { "what": "PerceptionBench leaderboard, 16 models, no LBS row", "url": "https://raw.githubusercontent.com/MoonshotAI/PerceptionBench/master/README.md", "seen": "2026-10-27" }, { "what": "AntSRC, Ant Group's security response centre", "url": "https://security.alipay.com/", "seen": "2026-10-27" } ], "openQuestions": [ "Capability is computed later. No independent result was found on 27 October 2026 (Artificial Analysis, the ScreenSpot-Pro leaderboard, the PerceptionBench leaderboard, OpenRouter), so Capability stays pending and the total is capped at 61", "GUI grounding (ScreenSpot-Pro, OSWorld-G) isn't among the checklist's areas. The vision area names MMMU, ChartQA, DocVQA and OCRBench, so even a leaderboard row on ScreenSpot-Pro wouldn't count as things stand. Worth deciding whether grounding joins the image area", "File safety 15 takes 10 off for the publisher's scripts that serve the model and write its system prompt. Plain vllm serve loads it with no repository code, and reading only the load path gives 25 and Risk 60", "Tool-calling format 10. Crediting the publisher's documented crop action and shipped parser as the calibration's ship-the-parser reading gives 25 and Interface 85", "Trained tool calling 20 rests on one fixed crop action, two per turn. Reading it as not general tool calling gives 10 or 0", "Reasoning control 10. The publisher's thinking_budget isn't read by the template and its high setting is refused, so a stricter reading gives 0 and Interface 60", "Model card 8 credits the executor line as a limitation. Not crediting it gives 5 and Upkeep 40", "Security programme 5 for AntSRC, as the Ming-3.0 listings read it. Ling-3.0-tiny was given 0 on the same evidence, so the two readings need aligning", "unchecked, GitHub issues on inclusionAI/UI-Venus, since github.com pages weren't fetched (no robots.txt could be read through the session's proxy)", "The Hub's safetensors metadata gives 27,356,728,560 BF16 parameters but a total of 3,054,832; the first figure is used" ] }, "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 }, "scoreHistory": [ { "date": "2026-10-27", "grade": "D", "kind": "listed", "method": "0.6", "score": 53.9 } ] }, "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 }, "notable": [ "Weights uploaded on 26 October 2026. The repository was created at 05:36 UTC and the files went in at 11:15 UTC under one commit by an inclusionAI member, and the UI-Venus collection was updated at 11:19 UTC. On 27 October the Hub counted 10 likes and no downloads yet (https://huggingface.co/api/models/inclusionAI/LBS-27B/commits/main)", "Who made it. The GitHub README names seven authors from Wuhan University of Technology, the Institute of Automation of the Chinese Academy of Sciences, Zhejiang University and Ant Group, and the files sit in inclusionAI, Ant Group's Hugging Face organisation, in the UI-Venus collection beside UI-Venus-2, UI-Venus-1.5, GUI-G2 and VISTA. No paper is out yet; the README says citation details will follow an arXiv release (https://github.com/inclusionAI/UI-Venus/tree/LBS)", "How it works. The model reads a screenshot, writes its reasoning in think tags, and can ask for up to two crops per turn and three per episode as plain-text \u003caction\u003ecrop_image(...)\u003c/action\u003e lines. The client cuts and returns the crops, and the final answer is a point [x.xx,y.yy] scaled to 0 to 1000 on the original image. It doesn't use the chat template's tool-calling format, and a separate executor is needed to act on a live application", "Training, as the card describes it. About 10,000 synthetic GUI grounding examples for a cold-start fine-tune, then 80 GRPO steps at batch size 64 with eight rollouts per prompt, and no general-domain or visual-question data. The GitHub training README says the recorded run used Ascend NPUs. The full training data isn't published; eight prompt rows and 32 recorded rollouts are", "inclusionAI's claims, from its own runs with agentic zoom-in, are ScreenSpot-Pro 81.15, OS-World-G (Refine) 84.57, PerceptionBench 59.67 with a self-hosted DeepSeek V4 Flash judge, HR-Bench 4K 89.13 and HR-Bench 8K 87.75. The publisher says its comparison chart mixes setups and judges and isn't controlled. No independent result was found on 27 October 2026, and the ScreenSpot-Pro and PerceptionBench leaderboards have no LBS row (https://github.com/inclusionAI/UI-Venus/blob/LBS/docs/BENCHMARK_OVERVIEW.md)", "The files are Qwen3.8-27B's shape with the multi-token prediction head left out. The Hub counts 27,356,728,560 BF16 parameters against the base's 27,781,427,952, and the 15 mtp tensors in the base's index are missing here, though config.json still sets one MTP layer. chat_template.jinja is byte for byte Qwen's" ], "area": "models", "details": [ { "label": "Surface graded", "value": "The open weights at huggingface.co/inclusionAI/LBS-27B, read from the card, the files and their metadata, the chat template, the LICENSE and READMEs on the LBS branch of github.com/inclusionAI/UI-Venus (by raw file) and the runtimes' own code on 27 October 2026. Nothing was downloaded or run" }, { "label": "Model", "value": "Qwen3_5ForConditionalGeneration (model_type qwen3_5), 27,356,728,560 BF16 parameters by the Hub's count. 64 text layers, three Gated DeltaNet linear-attention layers to each full-attention layer, hidden size 5,120, 24 attention heads and 4 KV heads, vocabulary 248,320, and a 27-layer vision encoder with 16-pixel patches. Images and video in, text out. No custom code and no auto_map" }, { "label": "Files", "value": "54.73 GB in all, 12 BF16 safetensors shards, tokenizer.json, chat_template.jinja, configs for the model, generation, processor and preprocessor, and the card. No pickle files, no Python files and no LICENSE file in the Hugging Face repository. SHA-256 on every LFS file" }, { "label": "Licence", "value": "Apache 2.0. The card's metadata says apache-2.0, and its licence section says the weights and the project software are released under Apache 2.0, with third-party datasets and screenshots keeping their own terms. The LICENSE on the GitHub LBS branch is the Apache 2.0 text with the appendix left unfilled. The base, Qwen3.8-27B, is Apache 2.0, copyright Alibaba Cloud. No use policy found" }, { "label": "Runtimes", "value": "The card serves it with vLLM through the publisher's wrapper script. Released vLLM 0.32.0, SGLang 0.5.22 and Transformers 5.20.2 carry Qwen3_5ForConditionalGeneration, and llama.cpp b11888's converter maps it, with a qwen3vl vision projector. No Ollama library entry, no LM Studio catalogue entry and no ggml-org or lmstudio-community build on 27 October 2026" }, { "label": "Context", "value": "262,144 tokens in config.json (max_position_embeddings, rope_type default), inherited from Qwen3.8-27B. The card states no context length, and the publisher's serving script defaults to 65,536 tokens and warns below 32,768 that multi-turn image context may overflow" }, { "label": "Tools and reasoning", "value": "The crop action is plain text in the reply, parsed by the publisher's own Python, not a runtime parser, and the system prompt that declares it is built by the publisher's build_system_prompt. The inherited Qwen template still declares tools in \u003ctools\u003e tags with \u003ctool_call\u003e blocks, which LBS's training didn't use. Thinking is on by default; reasoning_effort takes xhigh, medium or low, and enable_thinking false turns it off. The publisher's wrappers send reasoning_effort xhigh and a thinking_budget of 4,096 that the template doesn't read" }, { "label": "Hardware", "value": "Not stated in memory. The card's example uses four devices with tensor parallelism, and the training README says the recorded run used Ascend NPUs. The BF16 files need about 66 GB with a fifth added, so 96 GB of memory, within the 128 GB reference machine's GPU share" }, { "label": "Training data", "value": "Described in general terms, about 10,000 synthetic GUI grounding examples for supervised fine-tuning and 80 GRPO steps on synthetic GUI grounding tasks, with no general-domain data. No sources, sizes beyond that or cut-off. Eight training prompts and 32 rollouts from the run are in the GitHub repository" } ], "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, "checks": [ { "check": "Legal entity named", "value": "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", "points": 20, "max": 20, "state": "ok" }, { "check": "Domain age", "value": "inclusion-ai.org, registered 2025-04-03 (1 year)", "points": 3, "max": 15, "state": "part" }, { "check": "Endpoint on the vendor's domain", "value": "no hosted endpoint", "points": 0, "max": 0, "state": "na" }, { "check": "Terms of service", "value": "nothing hosted, so the 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) licence stands in", "points": 10, "max": 10, "state": "ok" }, { "check": "Privacy policy", "value": "nothing hosted, not scored", "points": 0, "max": 0, "state": "na" }, { "check": "Status page", "value": "not found", "points": 0, "max": 10, "state": "no" }, { "check": "Changelog", "value": "published", "points": 10, "max": 10, "state": "ok" }, { "check": "security.txt", "value": "not found", "points": 0, "max": 10, "state": "no" } ] }, "pageJsonUrl": "https://www.anchorterminal.com/tools/lbs-27b.json" }, "verify": { "accepts": "a page on inclusion-ai.org or one of its subdomains", "badgeUrl": "https://www.anchorterminal.com/badges/lbs-27b.svg", "body": { "slug": "lbs-27b", "url": "the page with the badge or the link" }, "docs": "https://www.anchorterminal.com/builders/#verify", "effect": "none, it never changes a grade, rank or review", "endpoint": "https://www.anchorterminal.com/api/v1/verify", "listingUrl": "https://www.anchorterminal.com/tools/lbs-27b", "mcpTool": "verify_listing", "recheck": "weekly; two failed checks in a row and it lapses, a later pass restores it", "snippets": { "html": "\u003ca href=\"https://www.anchorterminal.com/tools/lbs-27b\"\u003e\u003cimg src=\"https://www.anchorterminal.com/badges/lbs-27b.svg\" alt=\"LBS-27B on Anchor Terminal\" height=\"20\"\u003e\u003c/a\u003e", "markdown": "[![LBS-27B on Anchor Terminal](https://www.anchorterminal.com/badges/lbs-27b.svg)](https://www.anchorterminal.com/tools/lbs-27b)", "link": "\u003ca href=\"https://www.anchorterminal.com/tools/lbs-27b\"\u003eLBS-27B on Anchor Terminal\u003c/a\u003e" } } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/tools/lbs-27b", "json": "https://www.anchorterminal.com/tools/lbs-27b.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/tools/lbs-27b.md", "mcp": "https://www.anchorterminal.com/mcp", "pricing": "https://www.anchorterminal.com/tools/lbs-27b#pricing", "reviews": "https://www.anchorterminal.com/tools/lbs-27b#reviews", "slim": "https://www.anchorterminal.com/tools/lbs-27b.min.md" }, "markdown": "## Overview\n\n**Grade D · 53.9/100 · rank #91 of 113 open-weight models · #73 in Open-weight models · not agent-ready · confidence low**\n\n\nMore from inclusionAI, listed separately because each is its own product: [Ming-3.0-asr-tiny](https://www.anchorterminal.com/tools/ming-3-0-asr-tiny.md) (Speech-to-text), [Ming-3.0-audio-flash](https://www.anchorterminal.com/tools/ming-3-0-audio-flash.md) (Speech-to-text), [Ming-3.0-audio-tiny](https://www.anchorterminal.com/tools/ming-3-0-audio-tiny.md) (Speech-to-text), [Ming-3.0-tts-tiny](https://www.anchorterminal.com/tools/ming-3-0-tts-tiny.md) (Text-to-speech), [Ling-3.0-tiny](https://www.anchorterminal.com/tools/ling-3-0-tiny.md) (Open-weight models).\n\nTrained by inclusionAI (Ant Group), in the UI-Venus family, with [UI-Venus-2-9B](https://huggingface.co/inclusionAI/UI-Venus-2-9B), [UI-Venus-1.5-30B-A3B](https://huggingface.co/inclusionAI/UI-Venus-1.5-30B-A3B), [UI-Venus-1.5-8B](https://huggingface.co/inclusionAI/UI-Venus-1.5-8B), [UI-Venus-1.5-2B](https://huggingface.co/inclusionAI/UI-Venus-1.5-2B). Every model from this lab and its older versions: https://www.anchorterminal.com/open-weights/#inclusionai.\n\n## Assessment\n\nA 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## Facts\n\n| Field | Value |\n| --- | --- |\n| Vendor | inclusionAI (Ant Group) (https://www.inclusion-ai.org) |\n| Kind | Open-weight model |\n| Category | Open-weight models (https://www.anchorterminal.com/categories/open-weights) |\n| Auth | None · 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). |\n| Pricing | Free (Free) · 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). |\n| x402 | No · Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-10-27). |\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) |\n| Packages | huggingface: `inclusionAI/LBS-27B` |\n| Source | https://huggingface.co/inclusionAI/LBS-27B |\n| Docs | https://github.com/inclusionAI/UI-Venus/tree/LBS |\n| llms.txt | not found |\n| Last release | 2026-10-26 |\n| Surface graded | The open weights at huggingface.co/inclusionAI/LBS-27B, read from the card, the files and their metadata, the chat template, the LICENSE and READMEs on the LBS branch of github.com/inclusionAI/UI-Venus (by raw file) and the runtimes' own code on 27 October 2026. Nothing was downloaded or run |\n| Model | Qwen3_5ForConditionalGeneration (model_type qwen3_5), 27,356,728,560 BF16 parameters by the Hub's count. 64 text layers, three Gated DeltaNet linear-attention layers to each full-attention layer, hidden size 5,120, 24 attention heads and 4 KV heads, vocabulary 248,320, and a 27-layer vision encoder with 16-pixel patches. Images and video in, text out. No custom code and no auto_map |\n| Files | 54.73 GB in all, 12 BF16 safetensors shards, tokenizer.json, chat_template.jinja, configs for the model, generation, processor and preprocessor, and the card. No pickle files, no Python files and no LICENSE file in the Hugging Face repository. SHA-256 on every LFS file |\n| Licence | Apache 2.0. The card's metadata says apache-2.0, and its licence section says the weights and the project software are released under Apache 2.0, with third-party datasets and screenshots keeping their own terms. The LICENSE on the GitHub LBS branch is the Apache 2.0 text with the appendix left unfilled. The base, Qwen3.8-27B, is Apache 2.0, copyright Alibaba Cloud. No use policy found |\n| Runtimes | The card serves it with vLLM through the publisher's wrapper script. Released vLLM 0.32.0, SGLang 0.5.22 and Transformers 5.20.2 carry Qwen3_5ForConditionalGeneration, and llama.cpp b11888's converter maps it, with a qwen3vl vision projector. No Ollama library entry, no LM Studio catalogue entry and no ggml-org or lmstudio-community build on 27 October 2026 |\n| Context | 262,144 tokens in config.json (max_position_embeddings, rope_type default), inherited from Qwen3.8-27B. The card states no context length, and the publisher's serving script defaults to 65,536 tokens and warns below 32,768 that multi-turn image context may overflow |\n| Tools and reasoning | The crop action is plain text in the reply, parsed by the publisher's own Python, not a runtime parser, and the system prompt that declares it is built by the publisher's build_system_prompt. The inherited Qwen template still declares tools in \u003ctools\u003e tags with \u003ctool_call\u003e blocks, which LBS's training didn't use. Thinking is on by default; reasoning_effort takes xhigh, medium or low, and enable_thinking false turns it off. The publisher's wrappers send reasoning_effort xhigh and a thinking_budget of 4,096 that the template doesn't read |\n| Hardware | Not stated in memory. The card's example uses four devices with tensor parallelism, and the training README says the recorded run used Ascend NPUs. The BF16 files need about 66 GB with a fifth added, so 96 GB of memory, within the 128 GB reference machine's GPU share |\n| Training data | Described in general terms, about 10,000 synthetic GUI grounding examples for supervised fine-tuning and 80 GRPO steps on synthetic GUI grounding tasks, with no general-domain data. No sources, sizes beyond that or cut-off. Eight training prompts and 32 rollouts from the run are in the GitHub repository |\n| Capabilities | model.open-weights |\n| Tags | open-weights, model, dense, qwen, vision, gui-grounding, computer-use, reasoning, safetensors, self-hosted, new |\n| JSON | https://www.anchorterminal.com/api/v1/tools/lbs-27b.json |\n\n## Score breakdown (methodology v0.6, October 2026 research run)\n\nAssessed 2026-10-27 from public evidence against the published checklist (https://www.anchorterminal.com/benchmark/#checklist). Confidence: low. Performance and Task success pending (no score, not in the total); the total is Σ(score × weight) ÷ 80 over the 7 assessed categories. \"This run\" is each category's share of the 100 points.\n\n| Category | Weight | This run | Score (0–100) | Points |\n| --- | --- | --- | --- | --- |\n| Capability | 35% | pending | pending | n/a |\n| Reach | 25% | 38.5 | 55 | 21.1 |\n| Risk | 20% | 30.8 | 50 | 15.4 |\n| Agent interface | 10% | 15.4 | 70 | 10.8 |\n| Documentation and upkeep | 10% | 15.4 | 43 | 6.6 |\n| Negative events | up to −15 | up to −15 | none recorded | 0 |\n| **Total** | | | | **53.9 → D** |\n\n### Why each score\n\n- Capability: Pending for this listing. Nobody independent has published results for it in at least two areas yet, so its weight is shared across the other four and the total is capped at 61 until they do or our own runs start.\n- Reach 55 (checked 2026-10-27): Memory earns 10, since the only build is 54.73 GB of BF16, about 66 GB with a fifth added, which fits the reference machine but not 64 GB. Runtimes earn 20 from released vLLM, SGLang, Transformers and llama.cpp, which all carry Qwen3.8-27B's architecture, and Ollama and LM Studio have no build. Builds earn 0, as inclusionAI publishes no quantised build. Terms earn 15 for Apache 2.0 with nothing added, on the weights and on the base. Download earns 10, with no gate. Hosted earns 0, as no provider or inclusionAI API serves it.\n- Risk 50 (checked 2026-10-27): File safety earns 15, safetensors only but with the documented route running the publisher's scripts to serve it and to write its system prompt. Provenance earns 15, inclusionAI's own organisation with a hash for every file. Safety evaluation and agent guidance earn 0, as nothing is published on either, prompt injection through screenshots included. Licence risk keeps all 15 under Apache 2.0. The security programme earns 5 for Ant Group's AntSRC, which isn't linked to the model.\n- Agent interface 70 (checked 2026-10-27): The chat template earns 20, Qwen's Jinja file in the repository. The tool-calling format earns 10, as general tool calls rest on the inherited template and the crop action is parsed by inclusionAI's own code, not a runtime parser. Trained tool calling earns 20 for up to two crop actions per turn learned in GRPO. Structured output earns 0. Reasoning control earns 10, with reasoning effort documented and the reasoning shown as think text. Context earns 10 for 262,144 tokens with no scaling.\n- Documentation and upkeep 43 (checked 2026-10-27): The model card earns 8, with intended use, one limitation and parameters, and no architecture, context, languages or cut-off. Training data earns 5 for a general description of synthetic GUI data with no cut-off. Last change earns 20 for 26 October 2026. Responsiveness earns 0, the one discussion unanswered. Version policy earns 5 for a first release. File health earns 5, as the config still sets an MTP layer the weights lack and the card names a reasoning setting the template refuses.\n\nFix list for a coding agent, everything this grade says the listing lacks, the biggest gain first (17 items): https://www.anchorterminal.com/fixes/lbs-27b.md (JSON https://www.anchorterminal.com/fixes/lbs-27b.json)\n\n### What we couldn't check\n\n- Capability is computed later. No independent result was found on 27 October 2026 (Artificial Analysis, the ScreenSpot-Pro leaderboard, the PerceptionBench leaderboard, OpenRouter), so Capability stays pending and the total is capped at 61\n- GUI grounding (ScreenSpot-Pro, OSWorld-G) isn't among the checklist's areas. The vision area names MMMU, ChartQA, DocVQA and OCRBench, so even a leaderboard row on ScreenSpot-Pro wouldn't count as things stand. Worth deciding whether grounding joins the image area\n- File safety 15 takes 10 off for the publisher's scripts that serve the model and write its system prompt. Plain vllm serve loads it with no repository code, and reading only the load path gives 25 and Risk 60\n- Tool-calling format 10. Crediting the publisher's documented crop action and shipped parser as the calibration's ship-the-parser reading gives 25 and Interface 85\n- Trained tool calling 20 rests on one fixed crop action, two per turn. Reading it as not general tool calling gives 10 or 0\n- Reasoning control 10. The publisher's thinking_budget isn't read by the template and its high setting is refused, so a stricter reading gives 0 and Interface 60\n- Model card 8 credits the executor line as a limitation. Not crediting it gives 5 and Upkeep 40\n- Security programme 5 for AntSRC, as the Ming-3.0 listings read it. Ling-3.0-tiny was given 0 on the same evidence, so the two readings need aligning\n- unchecked, GitHub issues on inclusionAI/UI-Venus, since github.com pages weren't fetched (no robots.txt could be read through the session's proxy)\n- The Hub's safetensors metadata gives 27,356,728,560 BF16 parameters but a total of 3,054,832; the first figure is used\n\n### Sources\n\n- model card, results, usage, licence section (read 27 October 2026): \u003chttps://huggingface.co/inclusionAI/LBS-27B\u003e (seen 2026-10-27)\n- Hub API, gated false, licence tag, parameter count, no base_model metadata, empty provider mapping, embedded template: \u003chttps://huggingface.co/api/models/inclusionAI/LBS-27B\u003e (seen 2026-10-27)\n- file tree, sizes and SHA-256, no LICENSE or Python files: \u003chttps://huggingface.co/api/models/inclusionAI/LBS-27B/tree/main?recursive=true\u003e (seen 2026-10-27)\n- two commits on 26 October 2026: \u003chttps://huggingface.co/api/models/inclusionAI/LBS-27B/commits/main\u003e (seen 2026-10-27)\n- one discussion, authors and dates only: \u003chttps://huggingface.co/api/models/inclusionAI/LBS-27B/discussions\u003e (seen 2026-10-27)\n- Qwen3_5ForConditionalGeneration, 262,144 positions, mtp_num_hidden_layers 1: \u003chttps://huggingface.co/inclusionAI/LBS-27B/raw/main/config.json\u003e (seen 2026-10-27)\n- tensor names, no mtp tensors: \u003chttps://huggingface.co/inclusionAI/LBS-27B/raw/main/model.safetensors.index.json\u003e (seen 2026-10-27)\n- chat template, identical to Qwen3.8-27B's: \u003chttps://huggingface.co/inclusionAI/LBS-27B/raw/main/chat_template.jinja\u003e (seen 2026-10-27)\n- base model, Apache 2.0 LICENSE, config, template and index for comparison: \u003chttps://huggingface.co/Qwen/Qwen3.8-27B\u003e (seen 2026-10-27)\n- Apache 2.0 text, unaltered: \u003chttps://raw.githubusercontent.com/inclusionAI/UI-Venus/LBS/LICENSE\u003e (seen 2026-10-27)\n- authors, protocol, results, training summary, licence and data scope: \u003chttps://raw.githubusercontent.com/inclusionAI/UI-Venus/LBS/README.md\u003e (seen 2026-10-27)\n- training setup, serving example with build_system_prompt, Ascend NPUs: \u003chttps://raw.githubusercontent.com/inclusionAI/UI-Venus/LBS/agentic_gui_grounding_rl/README.md\u003e (seen 2026-10-27)\n- serving defaults, reasoning_effort and thinking_budget settings: \u003chttps://raw.githubusercontent.com/inclusionAI/UI-Venus/LBS/evaluation/README.md\u003e (seen 2026-10-27)\n- the vllm serve command the wrapper runs: \u003chttps://raw.githubusercontent.com/inclusionAI/UI-Venus/LBS/evaluation/scripts/serve_vllm.sh\u003e (seen 2026-10-27)\n- comparison scope, judges differ, chart not controlled: \u003chttps://raw.githubusercontent.com/inclusionAI/UI-Venus/LBS/docs/BENCHMARK_OVERVIEW.md\u003e (seen 2026-10-27)\n- vLLM v0.32.0 Qwen3_5ForConditionalGeneration: \u003chttps://raw.githubusercontent.com/vllm-project/vllm/v0.32.0/vllm/model_executor/models/registry.py\u003e (seen 2026-10-27)\n- SGLang v0.5.22 Qwen3_5ForConditionalGeneration: \u003chttps://raw.githubusercontent.com/sgl-project/sglang/v0.5.22/python/sglang/srt/models/qwen3_5.py\u003e (seen 2026-10-27)\n- Transformers v5.20.2 qwen3_5: \u003chttps://raw.githubusercontent.com/huggingface/transformers/v5.20.2/src/transformers/models/qwen3_5/modeling_qwen3_5.py\u003e (seen 2026-10-27)\n- llama.cpp b11888 converter, Qwen3_5ForConditionalGeneration mapped: \u003chttps://raw.githubusercontent.com/ggml-org/llama.cpp/b11888/conversion/__init__.py\u003e (seen 2026-10-27)\n- Ollama search, no LBS entry: \u003chttps://ollama.com/search?q=lbs\u003e (seen 2026-10-27)\n- OpenRouter model list, no LBS model: \u003chttps://openrouter.ai/api/v1/models\u003e (seen 2026-10-27)\n- ScreenSpot-Pro leaderboard data, 99 entries, no LBS row: \u003chttps://gui-agent.github.io/grounding-leaderboard/results/screenspot_pro.json\u003e (seen 2026-10-27)\n- PerceptionBench leaderboard, 16 models, no LBS row: \u003chttps://raw.githubusercontent.com/MoonshotAI/PerceptionBench/master/README.md\u003e (seen 2026-10-27)\n- AntSRC, Ant Group's security response centre: \u003chttps://security.alipay.com/\u003e (seen 2026-10-27)\n\n## Score history\n\nEvery move of a score since the listing was graded, newest first, with its cause. A score can't move without an entry here. JSON: https://www.anchorterminal.com/api/v1/tools/lbs-27b/history.json\n\n- 2026-10-27: graded 53.9 D (methodology 0.6)\n\n## Who's behind it (provenance 57/100, checked 2026-10-27)\n\n| Check | Finding | Points |\n| --- | --- | --- |\n| Legal entity named | 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 | 20/20 |\n| Domain age | inclusion-ai.org, registered 2025-04-03 (1 year) | 3/15 |\n| Endpoint on the vendor's domain | no hosted endpoint | n/a |\n| Terms of service | nothing hosted, so the 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) licence stands in | 10/10 |\n| Privacy policy | nothing hosted, not scored | n/a |\n| Status page | not found | 0/10 |\n| Changelog | published | 10/10 |\n| security.txt | not found | 0/10 |\n\ninclusion-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.\n\nThe 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.\n\nNothing is hosted, so the Apache 2.0 licence stands in for terms of service and no privacy policy is scored.\n\nThe changelog is the Hugging Face commit history, two commits on 26 October 2026 (initial commit and one upload).\n\ninclusion-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.\n\n### Terms and privacy, as read\n\nA reading by a fixed set of rules, each answered with the vendor's own sentence. Not legal advice.\n\n**Terms of service**. Nothing is hosted by the vendor, so there are no terms of service to read. The 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) licence stands in and the check scores in full.\n\n\n**Privacy policy**. Nothing is hosted by the vendor, so there is no privacy policy to read and the check isn't scored.\n\n\n## Probe metrics\n\nNot measured yet. Our benchmark probes haven't run, so there's no availability, latency or error rate from a run and Performance is pending. Live uptime, where we poll the endpoint, is under Live and doesn't change the score.\n\nNo hosted version of these weights has a price on OpenRouter, so what a task costs is your own hardware and power.\n\n## Strengths\n\n- Qwen3.8-27B's stock architecture with no repository code, so released vLLM, SGLang, Transformers and llama.cpp load it\n- Apache 2.0 for the weights and the base, ungated, safetensors only\n- 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\n- Up to two crops per turn, learned in GRPO, and 262,144 tokens of context from the base\n- inclusionAI publishes per-item scores and selected trajectories for its own benchmark runs\n\n## Weaknesses\n\n- No independent benchmark result yet; ScreenSpot-Pro, OS-World-G, PerceptionBench and HR-Bench numbers are inclusionAI's own runs\n- BF16 only, about 66 GB to load, and no Ollama, LM Studio or quantised build\n- The crop loop and system prompt come from the publisher's Python, not a runtime parser or a template\n- No safety evaluation, prompt-injection guidance, training data sources or knowledge cut-off\n- No hosted API or inference provider serves it\n\n\n\n## Before you call it (notes for agents)\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## Get started\n\nInstall:\n\n```bash\n# 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\n```\n\nFirst request:\n\n```bash\ncurl 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\"}}'\n```\n\n## Similar tools\n\nRanked by shared capabilities, then score. Same-category tools with no shared capability key are listed last.\n\n| Tool | Grade | Score | Rank | Shared capabilities | x402 | Markdown |\n| --- | --- | --- | --- | --- | --- | --- |\n| Qwen3-Embedding-8B | BB | 74.7 | open-weight #1 | model.open-weights | no | https://www.anchorterminal.com/tools/qwen3-embedding-8b.md |\n| Gemma 4 31B IT | BB | 74.1 | open-weight #2 | model.open-weights | no | https://www.anchorterminal.com/tools/gemma-4-31b-it.md |\n| Qwen3-Embedding-4B | BB | 72.9 | open-weight #3 | model.open-weights | no | https://www.anchorterminal.com/tools/qwen3-embedding-4b.md |\n| Gemma 4 26B A4B IT | BB | 72.2 | open-weight #4 | model.open-weights | no | https://www.anchorterminal.com/tools/gemma-4-26b-a4b-it.md |\n| Gemma 4 12B IT | BB | 71.3 | open-weight #5 | model.open-weights | no | https://www.anchorterminal.com/tools/gemma-4-12b-it.md |\n| gpt-oss-120b | BB | 70.2 | open-weight #6 | model.open-weights | no | https://www.anchorterminal.com/tools/gpt-oss-120b.md |\n\n## Panel reviews (0)\n\nReviewed by the Anchor panel (https://www.anchorterminal.com/reviewers/index.md): .\n\nDesk reviews, written from public documentation, pricing, terms, source and status history on 1 October 2026. No calls made. For a desk review, the outcome says whether the reviewer's questions could be answered from public material: success, partial or failure. How reviews work: https://www.anchorterminal.com/reviews/how-it-works.md\n\n## Notable\n\n- Weights uploaded on 26 October 2026. The repository was created at 05:36 UTC and the files went in at 11:15 UTC under one commit by an inclusionAI member, and the UI-Venus collection was updated at 11:19 UTC. On 27 October the Hub counted 10 likes and no downloads yet (source: \u003chttps://huggingface.co/api/models/inclusionAI/LBS-27B/commits/main\u003e)\n- Who made it. The GitHub README names seven authors from Wuhan University of Technology, the Institute of Automation of the Chinese Academy of Sciences, Zhejiang University and Ant Group, and the files sit in inclusionAI, Ant Group's Hugging Face organisation, in the UI-Venus collection beside UI-Venus-2, UI-Venus-1.5, GUI-G2 and VISTA. No paper is out yet; the README says citation details will follow an arXiv release (source: \u003chttps://github.com/inclusionAI/UI-Venus/tree/LBS\u003e)\n- How it works. The model reads a screenshot, writes its reasoning in think tags, and can ask for up to two crops per turn and three per episode as plain-text \u003caction\u003ecrop_image(...)\u003c/action\u003e lines. The client cuts and returns the crops, and the final answer is a point [x.xx,y.yy] scaled to 0 to 1000 on the original image. It doesn't use the chat template's tool-calling format, and a separate executor is needed to act on a live application\n- Training, as the card describes it. About 10,000 synthetic GUI grounding examples for a cold-start fine-tune, then 80 GRPO steps at batch size 64 with eight rollouts per prompt, and no general-domain or visual-question data. The GitHub training README says the recorded run used Ascend NPUs. The full training data isn't published; eight prompt rows and 32 recorded rollouts are\n- inclusionAI's claims, from its own runs with agentic zoom-in, are ScreenSpot-Pro 81.15, OS-World-G (Refine) 84.57, PerceptionBench 59.67 with a self-hosted DeepSeek V4 Flash judge, HR-Bench 4K 89.13 and HR-Bench 8K 87.75. The publisher says its comparison chart mixes setups and judges and isn't controlled. No independent result was found on 27 October 2026, and the ScreenSpot-Pro and PerceptionBench leaderboards have no LBS row (source: \u003chttps://github.com/inclusionAI/UI-Venus/blob/LBS/docs/BENCHMARK_OVERVIEW.md\u003e)\n- The files are Qwen3.8-27B's shape with the multi-token prediction head left out. The Hub counts 27,356,728,560 BF16 parameters against the base's 27,781,427,952, and the 15 mtp tensors in the base's index are missing here, though config.json still sets one MTP layer. chat_template.jinja is byte for byte Qwen's\n\n- #73 of 84 in 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## Compare\n\n- [Agents-A1-4B vs LBS-27B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-lbs-27b.md): C 61 vs D 53.9\n- [Apertus v1.5 8B vs LBS-27B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-lbs-27b.md): C 61 vs D 53.9\n- [Apodex-1.1-mini vs LBS-27B](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-lbs-27b.md): C 61 vs D 53.9\n- [BigBang-v1 vs LBS-27B](https://www.anchorterminal.com/compare/bigbang-v1-vs-lbs-27b.md): C 54.4 vs D 53.9\n- [Carbon-X vs LBS-27B](https://www.anchorterminal.com/compare/carbon-x-vs-lbs-27b.md): C 61 vs D 53.9\n- [Cosmos-Reason2-2B vs LBS-27B](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-lbs-27b.md): C 59.5 vs D 53.9\n- [Darwin-36B-Opus vs LBS-27B](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-lbs-27b.md): C 61 vs D 53.9\n- [DeepSeek-V3.2 vs LBS-27B](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-lbs-27b.md): C 58.6 vs D 53.9\n- [DeepSeek V4.1 Flash vs LBS-27B](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-lbs-27b.md): C 59 vs D 53.9\n- [DeepSeek-V4-Flash-0731 vs LBS-27B](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-lbs-27b.md): B 62.4 vs D 53.9\n- [DiffusionGemma 26B A4B IT vs LBS-27B](https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-lbs-27b.md): D 50 vs D 53.9\n- [Gemma 4 12B IT vs LBS-27B](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-lbs-27b.md): BB 71.3 vs D 53.9\n- [Gemma 4 26B A4B IT vs LBS-27B](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-lbs-27b.md): BB 72.2 vs D 53.9\n- [Gemma 4 31B IT vs LBS-27B](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-lbs-27b.md): BB 74.1 vs D 53.9\n- [Gemma 4 E2B IT vs LBS-27B](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-lbs-27b.md): C 60.4 vs D 53.9\n- [Gemma 4 E4B IT vs LBS-27B](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-lbs-27b.md): B 63.2 vs D 53.9\n- [GLM-4.5-Air vs LBS-27B](https://www.anchorterminal.com/compare/glm-4-5-air-vs-lbs-27b.md): C 59.1 vs D 53.9\n- [GLM-4.7-Flash vs LBS-27B](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-lbs-27b.md): B 63 vs D 53.9\n- [GLM-5.3-Flash vs LBS-27B](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-lbs-27b.md): B 65.1 vs D 53.9\n- [GLM-5.3 vs LBS-27B](https://www.anchorterminal.com/compare/glm-5-3-vs-lbs-27b.md): B 64.6 vs D 53.9\n- [gpt-oss-120b vs LBS-27B](https://www.anchorterminal.com/compare/gpt-oss-120b-vs-lbs-27b.md): BB 70.2 vs D 53.9\n- [gpt-oss-20b vs LBS-27B](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-lbs-27b.md): B 69.9 vs D 53.9\n- [Granite 4.2 3B vs LBS-27B](https://www.anchorterminal.com/compare/granite-4-2-3b-vs-lbs-27b.md): C 59.8 vs D 53.9\n- [Hy3 vs LBS-27B](https://www.anchorterminal.com/compare/hy3-vs-lbs-27b.md): B 62.6 vs D 53.9\n- [Inkling-Small vs LBS-27B](https://www.anchorterminal.com/compare/inkling-small-vs-lbs-27b.md): B 66.5 vs D 53.9\n- [Inkling vs LBS-27B](https://www.anchorterminal.com/compare/inkling-vs-lbs-27b.md): B 64.2 vs D 53.9\n- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs LBS-27B](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-lbs-27b.md): D 51.2 vs D 53.9\n- [Kimi K3 vs LBS-27B](https://www.anchorterminal.com/compare/kimi-k3-vs-lbs-27b.md): B 62.4 vs D 53.9\n- [Kolibri-1 vs LBS-27B](https://www.anchorterminal.com/compare/kolibri-1-vs-lbs-27b.md): C 61 vs D 53.9\n- [Kronos vs LBS-27B](https://www.anchorterminal.com/compare/kronos-vs-lbs-27b.md): C 55.2 vs D 53.9\n- [Laguna M.1 vs LBS-27B](https://www.anchorterminal.com/compare/laguna-m-1-vs-lbs-27b.md): C 55.5 vs D 53.9\n- [Laguna S 2.1 vs LBS-27B](https://www.anchorterminal.com/compare/laguna-s-2-1-vs-lbs-27b.md): C 61 vs D 53.9\n- [Laguna XS 2.1 vs LBS-27B](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-lbs-27b.md): C 61 vs D 53.9\n- [LBS-27B vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/lbs-27b-vs-lfm2-5-2-6b.md): D 53.9 vs D 51.5\n- [LBS-27B vs Ling-3.0-tiny](https://www.anchorterminal.com/compare/lbs-27b-vs-ling-3-0-tiny.md): D 53.9 vs C 55\n- [LBS-27B vs MiMo-V2.6 Distill Qwen 9B](https://www.anchorterminal.com/compare/lbs-27b-vs-mimo-v2-6-distill-qwen-9b.md): D 53.9 vs C 61\n- [LBS-27B vs MiniCPM-o 4.5](https://www.anchorterminal.com/compare/lbs-27b-vs-minicpm-o-4-5.md): D 53.9 vs C 61\n- [LBS-27B vs MiniCPM5-2B](https://www.anchorterminal.com/compare/lbs-27b-vs-minicpm5-2b.md): D 53.9 vs C 60.2\n- [LBS-27B vs MiniMax-M2.5](https://www.anchorterminal.com/compare/lbs-27b-vs-minimax-m2-5.md): D 53.9 vs C 57.5\n- [LBS-27B vs MiniMax-M2.7](https://www.anchorterminal.com/compare/lbs-27b-vs-minimax-m2-7.md): D 53.9 vs C 56.1\n- [LBS-27B vs MiniMax-M3.1-Flash](https://www.anchorterminal.com/compare/lbs-27b-vs-minimax-m3-1-flash.md): D 53.9 vs D 50.5\n- [LBS-27B vs Muse Glimmer 30B](https://www.anchorterminal.com/compare/lbs-27b-vs-muse-glimmer-30b.md): D 53.9 vs BB 70\n- [LBS-27B vs Nanbeige4.3-33B](https://www.anchorterminal.com/compare/lbs-27b-vs-nanbeige-4-3-33b.md): D 53.9 vs C 61\n- [LBS-27B vs NVIDIA Nemotron 3.5 Bolt E4B 4GB](https://www.anchorterminal.com/compare/lbs-27b-vs-nemotron-3-5-bolt-e4b-4gb.md): D 53.9 vs C 61\n- [LBS-27B vs Nemotron 3.5 Lightning 30B A3B](https://www.anchorterminal.com/compare/lbs-27b-vs-nemotron-3-5-lightning-30b-a3b.md): D 53.9 vs B 64.2\n- [LBS-27B vs Nemotron 3 Nano 30B A3B](https://www.anchorterminal.com/compare/lbs-27b-vs-nemotron-3-nano-30b-a3b.md): D 53.9 vs B 67.1\n- [LBS-27B vs Nemotron 3 Nano 4B](https://www.anchorterminal.com/compare/lbs-27b-vs-nemotron-3-nano-4b.md): D 53.9 vs C 56.8\n- [LBS-27B vs Nemotron 3 Nano Omni 30B A3B Reasoning](https://www.anchorterminal.com/compare/lbs-27b-vs-nemotron-3-nano-omni-30b-a3b-reasoning.md): D 53.9 vs C 60.7\n- [LBS-27B vs Nemotron 3 Super 120B A12B](https://www.anchorterminal.com/compare/lbs-27b-vs-nemotron-3-super-120b-a12b.md): D 53.9 vs B 66.2\n- [LBS-27B vs Nemotron 3 Ultra 550B A55B](https://www.anchorterminal.com/compare/lbs-27b-vs-nemotron-3-ultra-550b-a55b.md): D 53.9 vs B 68.8\n- [LBS-27B vs occamy-1.0](https://www.anchorterminal.com/compare/lbs-27b-vs-occamy-1-0.md): D 53.9 vs C 61\n- [LBS-27B vs Olmo 3 7B Think](https://www.anchorterminal.com/compare/lbs-27b-vs-olmo-3-7b-think.md): D 53.9 vs D 52.3\n- [LBS-27B vs Ornith-1.5-35B-A3B](https://www.anchorterminal.com/compare/lbs-27b-vs-ornith-1-5-35b-a3b.md): D 53.9 vs C 61\n- [LBS-27B vs Ornith-1.5-397B](https://www.anchorterminal.com/compare/lbs-27b-vs-ornith-1-5-397b.md): D 53.9 vs C 59\n- [LBS-27B vs Ornith-1.5-9B](https://www.anchorterminal.com/compare/lbs-27b-vs-ornith-1-5-9b.md): D 53.9 vs C 61\n- [LBS-27B vs Qwen3-30B-A3B-Instruct-2507](https://www.anchorterminal.com/compare/lbs-27b-vs-qwen3-30b-a3b-instruct-2507.md): D 53.9 vs C 56.6\n- [LBS-27B vs Qwen3-4B-Instruct-2507](https://www.anchorterminal.com/compare/lbs-27b-vs-qwen3-4b-instruct-2507.md): D 53.9 vs C 55.7\n- [LBS-27B vs Qwen3.5-0.8B](https://www.anchorterminal.com/compare/lbs-27b-vs-qwen3-5-0-8b.md): D 53.9 vs D 46.7\n- [LBS-27B vs Qwen3.5-122B-A10B](https://www.anchorterminal.com/compare/lbs-27b-vs-qwen3-5-122b-a10b.md): D 53.9 vs B 63.6\n- [LBS-27B vs Qwen3.5-2B](https://www.anchorterminal.com/compare/lbs-27b-vs-qwen3-5-2b.md): D 53.9 vs D 50.5\n- [LBS-27B vs Qwen3.5-397B-A17B](https://www.anchorterminal.com/compare/lbs-27b-vs-qwen3-5-397b-a17b.md): D 53.9 vs B 64.6\n- [LBS-27B vs Qwen3.5-4B](https://www.anchorterminal.com/compare/lbs-27b-vs-qwen3-5-4b.md): D 53.9 vs C 61.2\n- [LBS-27B vs Qwen3.5-9B](https://www.anchorterminal.com/compare/lbs-27b-vs-qwen3-5-9b.md): D 53.9 vs C 60.5\n- [LBS-27B vs Qwen3.6-27B](https://www.anchorterminal.com/compare/lbs-27b-vs-qwen3-6-27b.md): D 53.9 vs B 67.2\n- [LBS-27B vs Qwen3.6-35B-A3B](https://www.anchorterminal.com/compare/lbs-27b-vs-qwen3-6-35b-a3b.md): D 53.9 vs B 63.2\n- [LBS-27B vs Qwen3.8-27B](https://www.anchorterminal.com/compare/lbs-27b-vs-qwen3-8-27b.md): D 53.9 vs B 68.6\n- [LBS-27B vs Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/lbs-27b-vs-qwen3-8-flash-next.md): D 53.9 vs C 61.8\n- [LBS-27B vs Qwen3-Coder-30B-A3B-Instruct](https://www.anchorterminal.com/compare/lbs-27b-vs-qwen3-coder-30b-a3b-instruct.md): D 53.9 vs C 55.7\n- [LBS-27B vs Qwen3-Coder-Next](https://www.anchorterminal.com/compare/lbs-27b-vs-qwen3-coder-next.md): D 53.9 vs C 57.6\n- [LBS-27B vs Qwen3-Omni-30B-A3B-Instruct](https://www.anchorterminal.com/compare/lbs-27b-vs-qwen3-omni-30b-a3b-instruct.md): D 53.9 vs E 41.8\n- [LBS-27B vs Qwen3-VL-2B-Instruct](https://www.anchorterminal.com/compare/lbs-27b-vs-qwen3-vl-2b-instruct.md): D 53.9 vs C 61\n- [LBS-27B vs Qwen3-VL-30B-A3B-Instruct](https://www.anchorterminal.com/compare/lbs-27b-vs-qwen3-vl-30b-a3b-instruct.md): D 53.9 vs C 58\n- [LBS-27B vs Qwen3-VL-32B-Instruct](https://www.anchorterminal.com/compare/lbs-27b-vs-qwen3-vl-32b-instruct.md): D 53.9 vs C 56.4\n- [LBS-27B vs Qwen3-VL-4B-Instruct](https://www.anchorterminal.com/compare/lbs-27b-vs-qwen3-vl-4b-instruct.md): D 53.9 vs D 52.6\n- [LBS-27B vs Qwen3-VL-8B-Instruct](https://www.anchorterminal.com/compare/lbs-27b-vs-qwen3-vl-8b-instruct.md): D 53.9 vs C 54.9\n- [LBS-27B vs Qwopus3.8-27B-Flash-V2](https://www.anchorterminal.com/compare/lbs-27b-vs-qwopus3-8-27b-flash-v2.md): D 53.9 vs C 61\n- [LBS-27B vs Recursive Flint](https://www.anchorterminal.com/compare/lbs-27b-vs-recursive-flint.md): D 53.9 vs C 61\n- [LBS-27B vs SmolLM3-3B](https://www.anchorterminal.com/compare/lbs-27b-vs-smollm3-3b.md): D 53.9 vs C 61\n- [LBS-27B vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/lbs-27b-vs-spark-x2-6-4b.md): D 53.9 vs C 61\n- [LBS-27B vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/lbs-27b-vs-swift-1-5-qwen3-8-27b.md): D 53.9 vs C 61\n- [LBS-27B vs Swift 1.5 Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/lbs-27b-vs-swift-1-5-qwen3-8-flash-next.md): D 53.9 vs D 52.3\n- [LBS-27B vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/lbs-27b-vs-thinkingcap-qwen3-8-27b.md): D 53.9 vs C 61\n- [LBS-27B vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/lbs-27b-vs-unlimited-ocr-2.md): D 53.9 vs D 53.5\n\n## Verify this listing\n\nFor the vendor. The badge or a plain link to this page verifies the listing, from a page on inclusion-ai.org or one of its subdomains. It shows the listing is the vendor's and that the vendor knows it's here, and it never changes a grade, rank or review. The vendor sends the page's address to `POST https://www.anchorterminal.com/api/v1/verify` as `{\"slug\": \"lbs-27b\", \"url\": \"…\"}`, or calls the `verify_listing` tool at https://www.anchorterminal.com/mcp. We fetch the page once, then again every week; two failed checks in a row and the verification lapses, and a later pass restores it. What we check: https://www.anchorterminal.com/builders/index.md#verify\n\nHTML badge:\n\n```html\n\u003ca href=\"https://www.anchorterminal.com/tools/lbs-27b\"\u003e\u003cimg src=\"https://www.anchorterminal.com/badges/lbs-27b.svg\" alt=\"LBS-27B on Anchor Terminal\" height=\"20\"\u003e\u003c/a\u003e\n```\n\nMarkdown badge, for a README:\n\n```markdown\n[![LBS-27B on Anchor Terminal](https://www.anchorterminal.com/badges/lbs-27b.svg)](https://www.anchorterminal.com/tools/lbs-27b)\n```\n\nPlain link:\n\n```html\n\u003ca href=\"https://www.anchorterminal.com/tools/lbs-27b\"\u003eLBS-27B on Anchor Terminal\u003c/a\u003e\n```\n\n## Share this listing\n\nFor the vendor. Sharing assets for social media, two PNGs of 1200 × 630 that say LBS-27B is listed on Anchor Terminal, with the vendor's logo and this page's address and no grade or score.\n\n- Dark: https://www.anchorterminal.com/assets/share/lbs-27b-dark.png\n- Light: https://www.anchorterminal.com/assets/share/lbs-27b-light.png\n- Create a video and download its frames: https://www.anchorterminal.com/assets/listing-video/index.html?listing=lbs-27b\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": "https://www.anchorterminal.com/benchmark/", "methodology": "0.6", "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": "Terminal", "url": "https://www.anchorterminal.com/tools/" }, { "name": "Open-weight models", "url": "https://www.anchorterminal.com/categories/open-weights" }, { "name": "LBS-27B", "url": "" } ], "description": "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.", "facts": [ "#73 in Open weights", "None auth", "0 desk reviews" ], "h1": "LBS-27B", "image": "https://www.anchorterminal.com/assets/og/tools-lbs-27b.png", "path": "/tools/lbs-27b", "published": "2026-10-01", "section": "tools", "title": "LBS-27B review (2026): pricing, alternatives and grade D", "toc": null, "updated": "2026-10-27", "url": "https://www.anchorterminal.com/tools/lbs-27b" }, "tokens": { "markdown": 9500, "slim": 2230 }, "version": 1 }