{ "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", "dns": "lbs-27b.grade.anchorterminal.com", "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": 55.8, "grade": "C", "agentReady": false, "rank": 0, "ranked": false, "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools", "rankOf": 1518, "categoryRank": 89, "methodology": "0.7", "run": "2026-10-01", "scores": { "interface": 70, "reach": 60, "risk": 50, "upkeep": 43 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-11-04" }, "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, llama.cpp and Ollama 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 from inclusionAI, about 66 GB to load, with GGUF copies only from bartowski and mradermacher", "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": "C", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 55.8 } ], "editorialScores": { "interface": 70, "reach": 60, "risk": 50, "upkeep": 43 }, "provenanceScore": 61, "checked": { "categories": { "interface": "2026-11-04", "reach": "2026-11-04", "risk": "2026-11-04", "upkeep": "2026-11-04" }, "last": "2026-11-04", "lastMoved": "2026-11-04", "oldest": "2026-11-04", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "# 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# or, with llama.cpp and bartowski's GGUF, a third party's conversion:\nllama-server -hf bartowski/LBS-27B-GGUF:Q4_K_M --jinja", "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", "ling-3-1-flash-1019" ], "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": "Finding the point to click on a screenshot, and answering 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", "Ollama" ], "builds": [ { "name": "bartowski LBS-27B-GGUF (Q4_K_M)", "url": "https://huggingface.co/bartowski/LBS-27B-GGUF", "who": "bartowski", "precision": "q4_k_m", "sizeGB": 18.13, "memoryGB": 24, "format": "gguf", "runsOn": [ "llama.cpp" ], "note": "From bartowski, a third party's conversion and not inclusionAI's. 17.20 GB of Q4_K_M weights plus the 0.93 GB F16 projector, from the Hub's model information on 10 November 2026. Recorded after the last grading, so Reach doesn't count it" }, { "name": "bartowski LBS-27B-GGUF (Q8_0)", "url": "https://huggingface.co/bartowski/LBS-27B-GGUF", "who": "bartowski", "precision": "q8_0", "sizeGB": 29.59, "memoryGB": 48, "format": "gguf", "runsOn": [ "llama.cpp" ], "note": "From bartowski, a third party's conversion and not inclusionAI's. 28.67 GB of Q8_0 weights plus the 0.93 GB F16 projector, from the Hub's model information on 10 November 2026. Recorded after the last grading, so Reach doesn't count it" }, { "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 from inclusionAI; no Ollama, LM Studio or ggml-org build, and GGUF copies only from bartowski and mradermacher on 4 November 2026" } ], "run": "vllm serve inclusionAI/LBS-27B --served-model-name lbs --tensor-parallel-size 4 --max-model-len 65536", "runBuild": "LBS-27B (BF16)", "checked": "2026-11-04", "minMemoryGB": 24 }, "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": 61 }, "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": "2026-11-10T16:09:19.556283743Z" }, "domain": { "domain": "inclusion-ai.org", "registered": "2025-04-03", "source": "https://rdap.publicinterestregistry.org/rdap/domain/inclusion-ai.org", "checkedAt": "2026-11-08T15:01:29.637357411Z" }, "pages": [ { "url": "https://huggingface.co/inclusionAI/LBS-27B/commits/main", "kind": "changelog", "status": 200, "checkedAt": "2026-11-10T16:22:50.667379833Z", "changedAt": "2026-11-10T16:22:50.667379833Z", "fingerprint": "afe1eb24957f" } ], "updatedAt": "2026-11-10T16:22:50.667379833Z" } }, "answer": "LBS-27B scores 55.8 (C) on agent readiness against SenseNova-Loop-9B-Preview's 46.8 (D), and leads in 2 of 4 scored categories.", "b": { "slug": "sensenova-loop-9b-preview", "name": "SenseNova-Loop-9B-Preview", "vendor": "SenseTime (SenseNova)", "vendorUrl": "https://www.sensetime.com/en", "kind": "weights", "category": "open-weights", "summary": "SenseNova-Loop-9B-Preview is SenseTime's open-weight multimodal model of about 9B parameters. It runs 24 of its 32 decoder layers twice, with text and images in, tool calling, a thinking switch and a 256K context. Apache 2.0. No released runtime loads it.", "url": "https://www.anchorterminal.com/tools/sensenova-loop-9b-preview", "markdownUrl": "https://www.anchorterminal.com/tools/sensenova-loop-9b-preview.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/sensenova-loop-9b-preview.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/sensenova-loop-9b-preview.json", "dns": "sensenova-loop-9b-preview.grade.anchorterminal.com", "repo": "https://huggingface.co/sensenova/SenseNova-Loop-9B-Preview", "license": "Apache-2.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "sensenova/SenseNova-Loop-9B-Preview" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The repository is not gated (checked 2026-11-10).", "pricing": "free", "pricingNotes": "Free to download and use under Apache 2.0. Running it yourself costs the hardware: the files are 38.1 GB because most weights are stored in 32-bit floats, and the card's example loads them as BF16 (checked 2026-11-10).", "priceSummary": "Free", "where": "local", "x402": { "level": "no", "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-11-10).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-11-10" }, "docsUrl": "https://github.com/OpenSenseNova/SenseNova-Loop", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "multimodal", "vision", "looped-transformer", "tool-calling", "reasoning", "long-context", "preview", "apache-2.0", "safetensors", "custom-code", "self-hosted" ], "lastRelease": "2026-11-02", "graded": true, "anchor": { "graded": true, "score": 46.8, "grade": "D", "agentReady": false, "rank": 0, "ranked": false, "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools", "rankOf": 1518, "categoryRank": 110, "methodology": "0.7", "run": "2026-10-01", "scores": { "interface": 60, "reach": 40, "risk": 50, "upkeep": 44 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-11-10" }, "negative": 0, "verdict": "An unusual architecture under Apache 2.0, with a 256K training context, a thinking switch and tool calling in the template. It's a preview in practice too: no released runtime runs it, loading it runs the repository's code, the files are twice the size the card implies, and nobody from SenseNova has answered a question yet.", "bestFor": "Researchers who want to study or fine-tune a looped transformer, more than agents that need a model to call today", "strengths": [ "Apache 2.0, ungated", "A 256K training context, stated on the card", "Thinking can be switched per request", "The architecture and parameter counts are documented", "Changed in the last ten days" ], "weaknesses": [ "No released runtime runs it. vLLM and SGLang need unmerged pull requests", "Loading it runs Python from the repository", "The files are 38.1 GB, mostly F32, though the card says BF16", "No quantised build", "No tool-calling documentation beyond the template", "No limitations, safety results or data sources, and the technical report isn't out", "None of 5 questions answered" ], "agentNotes": [ "Read modeling_sensenova_loop.py before you load it. trust_remote_code runs it", "Pin transformers 5.18.0, the version the card checked", "Load with dtype=torch.bfloat16 as the card does, or the 38 GB of F32 weights stay at full size", "Pass enable_thinking=False to apply_chat_template for a direct answer", "Tool calls come back as tool_call blocks with function and parameter tags. Parse them yourself, since no runtime ships a parser", "Wait for the vLLM or SGLang pull request to land in a release before putting it behind an API" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 0, "avgRating": 0, "history": [ { "basis": "public evidence", "confidence": "low", "grade": "D", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 46.8 } ], "editorialScores": { "interface": 60, "reach": 40, "risk": 50, "upkeep": 44 }, "provenanceScore": 51, "checked": { "categories": { "interface": "2026-11-10", "reach": "2026-11-10", "risk": "2026-11-10", "upkeep": "2026-11-10" }, "last": "2026-11-10", "oldest": "2026-11-10", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "pip install \"transformers==5.18.0\" accelerate safetensors\n# then AutoModelForCausalLM.from_pretrained(\"sensenova/SenseNova-Loop-9B-Preview\", trust_remote_code=True, dtype=torch.bfloat16)\n# trust_remote_code runs modeling_sensenova_loop.py from the repository. Read it first", "http": "# No released server runs it. The card's vLLM and SGLang commands build from open pull requests,\n# and its LightLLM example serves an OpenAI-compatible API at http://localhost:8000/v1 on four GPUs" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/sensenova-loop-9b-preview" }, "weights": { "trainedBy": "SenseTime (SenseNova)", "params": "9B", "context": 262144, "goodFor": "Trying a looped transformer for long-context, tool-using work, if you can run the repository's own code on a large GPU.", "runtimes": [], "builds": [ { "name": "SenseNova-Loop-9B-Preview (safetensors, mostly F32)", "url": "https://huggingface.co/sensenova/SenseNova-Loop-9B-Preview/tree/main", "who": "publisher", "precision": "f32", "sizeGB": 38.14, "memoryGB": 48, "format": "safetensors", "note": "Eight shards and the MTP head. Stored mostly as 32-bit floats; the card's example loads it as BF16, about half the size in memory" } ], "run": "python -c \"from transformers import AutoModelForCausalLM; AutoModelForCausalLM.from_pretrained('sensenova/SenseNova-Loop-9B-Preview', trust_remote_code=True)\"", "runBuild": "SenseNova-Loop-9B-Preview (safetensors, mostly F32)", "checked": "2026-11-10", "minMemoryGB": 48 }, "family": { "lab": "SenseTime (SenseNova)", "family": "SenseNova-Loop", "url": "https://www.anchorterminal.com/open-weights/#sensenova" }, "area": "models", "provenance": { "legalEntity": "SenseTime, listed on the Stock Exchange of Hong Kong (00020) per its investor page. SenseNova is its foundation model series, per its site", "domain": "sensetime.com", "domainRegistered": "", "endpointOnVendorDomain": null, "terms": "https://github.com/OpenSenseNova/SenseNova-Loop/blob/main/LICENSE", "privacy": "", "statusPage": "", "changelog": "https://huggingface.co/sensenova/SenseNova-Loop-9B-Preview/commits/main", "securityTxt": "none", "checked": "2026-11-10", "notes": [ "The card names SenseNova only. SenseTime's site presents SenseNova as its foundation model series, and that is the tie between the two; the site wasn't read for a link to this repository.", "The domain's registration date wasn't looked up.", "Nothing is hosted under this listing, so Apache 2.0 stands in for terms of service and no privacy policy is scored.", "www.sensetime.com has no security.txt (404 on 10 November 2026). Its about page gives a security contact.", "The changelog is the commit history, 39 commits from 28 October to 2 November 2026." ], "score": 51 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/sensenova-loop-9b-preview.json" }, "edges": [ { "by": 20, "category": "reach", "leads": "lbs-27b", "name": "Reach", "reasons": { "a": "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 25 from released vLLM, SGLang, Transformers and llama.cpp, which all carry Qwen3.8-27B's architecture, and Ollama through the llama.cpp release it pins, so 25. Builds earn 0, as inclusionAI publishes no quantised build and the GGUF copies are bartowski's and mradermacher's. 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.", "b": "Memory 15, the only build is the safetensors set, 38.14 GB with its MTP head, since the Hub counts 9.41B of its parameters in F32. Runtimes 0, none of the six runs it in a released version. Builds 0, SenseNova publishes one set of weights and no quantised build. Terms 15, Apache 2.0 in the card's metadata and on the card, which links the LICENSE in the GitHub repository, and nothing added. Download 10, the Hub API reports gated false on 10 November 2026, so the files download with no account and nothing to accept. Hosted 0, Hugging Face maps no inference provider to it, and the card names no API that serves it. 15 + 0 + 0 + 15 + 10 + 0 = 40." }, "scores": { "a": 60, "b": 40 } }, { "by": 10, "category": "interface", "leads": "lbs-27b", "name": "Agent interface", "reasons": { "a": "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.", "b": "Chat template 20, chat_template.jinja in the repository (7,756 characters). Tool-calling format 10, only the template says it: a tools block, and calls written as tool_call blocks with function and parameter tags. Trained tool calling 10, the card says the training recipes include tool trajectories and that the model is for agentic workflows. Structured output 0, Nothing on the card about JSON or schema-constrained output. Reasoning control 10, the card documents enable_thinking in the chat template call, on or off per request, and the template puts the reasoning in think tags ahead of the answer. Context 10, 262,144 tokens in training, per the card's specification table, and config.json gives the language model 262,144 positions. 20 + 10 + 10 + 0 + 10 + 10 = 60." }, "scores": { "a": 70, "b": 60 } } ], "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "inclusionAI (Ant Group)", "b": "SenseTime (SenseNova)", "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": "Apache-2.0", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "no", "b": "no", "name": "llms.txt" }, { "a": "2026-10-26", "b": "2026-11-02", "name": "Last release" }, { "a": "no document linked", "b": "not read yet", "name": "Terms last updated" }, { "a": "no document linked", "b": "no document linked", "name": "Privacy policy last updated" } ], "faq": [ { "answer": "LBS-27B scores 55.8 (C) on agent readiness against SenseNova-Loop-9B-Preview's 46.8 (D), and leads in 2 of 4 scored categories.", "question": "Which is better for AI agents, LBS-27B or SenseNova-Loop-9B-Preview?" } ], "goodFor": [ { "aheadOn": [ "Reach, 60 against 40", "Agent interface, 70 against 60" ], "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": null, "also": null, "goodFor": "Researchers who want to study or fine-tune a looped transformer, more than agents that need a model to call today", "slug": "sensenova-loop-9b-preview", "watchFor": "No released runtime runs it. vLLM and SGLang need unmerged pull requests" } ], "history": { "lbs-27b": [ { "causes": [ "correction" ], "date": "2026-11-04", "delta": 1.9, "from": 53.9, "fromGrade": "D", "to": 55.8, "toGrade": "C" } ], "sensenova-loop-9b-preview": null }, "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-sensenova-loop-9b-preview.json", "title": "Agents-A1-4B vs SenseNova-Loop-9B-Preview", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-sensenova-loop-9b-preview" }, { "json": 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"json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-sensenova-loop-9b-preview.json", "title": "Laguna M.1 vs SenseNova-Loop-9B-Preview", "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-sensenova-loop-9b-preview" }, { "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-sensenova-loop-9b-preview.json", "title": "Laguna S 2.1 vs SenseNova-Loop-9B-Preview", "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-sensenova-loop-9b-preview" }, { "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-sensenova-loop-9b-preview.json", "title": "Laguna XS 2.1 vs 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"url": "https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-step-3-7-flash" }, { "json": "https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-swift-1-5-qwen3-8-27b.json", "title": "SenseNova-Loop-9B-Preview vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-swift-1-5-qwen3-8-flash-next.json", "title": "SenseNova-Loop-9B-Preview vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-thinkingcap-qwen3-8-27b.json", "title": "SenseNova-Loop-9B-Preview vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-unlimited-ocr-2.json", "title": "SenseNova-Loop-9B-Preview vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-wevisone-vl-2b.json", "title": "SenseNova-Loop-9B-Preview vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-wevisone-vl-4b.json", "title": "SenseNova-Loop-9B-Preview vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-zaya1-8b.json", "title": "SenseNova-Loop-9B-Preview vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-zaya1-8b" } ], "panel": null, "scores": [ { "key": "capability", "name": "Capability", "pending": true, "weight": 35 }, { "by": 20, "edge": "lbs-27b", "key": "reach", "lbs-27b": 60, "name": "Reach", "sensenova-loop-9b-preview": 40, "weight": 25 }, { "by": 0, "edge": "", "key": "risk", "lbs-27b": 50, "name": "Risk", "sensenova-loop-9b-preview": 50, "weight": 20 }, { "by": 10, "edge": "lbs-27b", "key": "interface", "lbs-27b": 70, "name": "Agent interface", "sensenova-loop-9b-preview": 60, "weight": 10 }, { "by": 1, "edge": "sensenova-loop-9b-preview", "key": "upkeep", "lbs-27b": 43, "name": "Documentation and upkeep", "sensenova-loop-9b-preview": 44, "weight": 10 } ], "summary": "LBS-27B scores 55.8 (C) on agent readiness against SenseNova-Loop-9B-Preview's 46.8 (D), and leads in 2 of 4 scored categories. Both do open-weight models you run yourself.", "tasks": null, "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.", "sensenova-loop-9b-preview": "An unusual architecture under Apache 2.0, with a 256K training context, a thinking switch and tool calling in the template. It's a preview in practice too: no released runtime runs it, loading it runs the repository's code, the files are twice the size the card implies, and nobody from SenseNova has answered a question yet." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/lbs-27b-vs-sensenova-loop-9b-preview", "json": "https://www.anchorterminal.com/compare/lbs-27b-vs-sensenova-loop-9b-preview.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/lbs-27b-vs-sensenova-loop-9b-preview.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/lbs-27b-vs-sensenova-loop-9b-preview.min.md" }, "markdown": "LBS-27B scores 55.8 (C) on agent readiness against SenseNova-Loop-9B-Preview's 46.8 (D), and leads in 2 of 4 scored categories. Both do open-weight models you run yourself.\n\n- LBS-27B: grade C, 55.8/100, rank #118 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/lbs-27b.md · JSON https://www.anchorterminal.com/api/v1/tools/lbs-27b.json\n- SenseNova-Loop-9B-Preview: grade D, 46.8/100, rank #155 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/sensenova-loop-9b-preview.md · JSON https://www.anchorterminal.com/api/v1/tools/sensenova-loop-9b-preview.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 6359 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md\n\n## Which one, for what\n\n### LBS-27B (C)\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- Reach, 60 against 40\n- Agent interface, 70 against 60\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### SenseNova-Loop-9B-Preview (D)\n\nGood for: Researchers who want to study or fine-tune a looped transformer, more than agents that need a model to call today\n\nWatch for: No released runtime runs it. vLLM and SGLang need unmerged pull requests\n\n\n## Why the edge\n\n### Reach: LBS-27B +20\n\n- LBS-27B (60): 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 25 from released vLLM, SGLang, Transformers and llama.cpp, which all carry Qwen3.8-27B's architecture, and Ollama through the llama.cpp release it pins, so 25. Builds earn 0, as inclusionAI publishes no quantised build and the GGUF copies are bartowski's and mradermacher's. 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- SenseNova-Loop-9B-Preview (40): Memory 15, the only build is the safetensors set, 38.14 GB with its MTP head, since the Hub counts 9.41B of its parameters in F32. Runtimes 0, none of the six runs it in a released version. Builds 0, SenseNova publishes one set of weights and no quantised build. Terms 15, Apache 2.0 in the card's metadata and on the card, which links the LICENSE in the GitHub repository, and nothing added. Download 10, the Hub API reports gated false on 10 November 2026, so the files download with no account and nothing to accept. Hosted 0, Hugging Face maps no inference provider to it, and the card names no API that serves it. 15 + 0 + 0 + 15 + 10 + 0 = 40.\n\n### Agent interface: LBS-27B +10\n\n- LBS-27B (70): 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- SenseNova-Loop-9B-Preview (60): Chat template 20, chat_template.jinja in the repository (7,756 characters). Tool-calling format 10, only the template says it: a tools block, and calls written as tool_call blocks with function and parameter tags. Trained tool calling 10, the card says the training recipes include tool trajectories and that the model is for agentic workflows. Structured output 0, Nothing on the card about JSON or schema-constrained output. Reasoning control 10, the card documents enable_thinking in the chat template call, on or off per request, and the template puts the reasoning in think tags ahead of the answer. Context 10, 262,144 tokens in training, per the card's specification table, and config.json gives the language model 262,144 positions. 20 + 10 + 10 + 0 + 10 + 10 = 60.\n\n## Score by category\n\n| Category | Weight | LBS-27B | SenseNova-Loop-9B-Preview | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 60 | 40 | LBS-27B +20 |\n| Risk | 20% (20 this run) | 50 | 50 | even |\n| Agent interface | 10% (10 this run) | 70 | 60 | LBS-27B +10 |\n| Documentation and upkeep | 10% (10 this run) | 43 | 44 | SenseNova-Loop-9B-Preview +1 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **55.8 · C** | **46.8 · D** | |\n\n## Facts side by side\n\n| Fact | LBS-27B | SenseNova-Loop-9B-Preview |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | inclusionAI (Ant Group) | SenseTime (SenseNova) |\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) | Apache-2.0 |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-10-26 | 2026-11-02 |\n| Terms last updated | no document linked | not read yet |\n| Privacy policy last updated | no document linked | no document linked |\n\n## How the scores have moved\n\n### LBS-27B\n\n- +1.9 on 4 November 2026 (D to C): correction\n\n### SenseNova-Loop-9B-Preview\n\nNo score change since it was listed.\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**SenseNova-Loop-9B-Preview.** An unusual architecture under Apache 2.0, with a 256K training context, a thinking switch and tool calling in the template. It's a preview in practice too: no released runtime runs it, loading it runs the repository's code, the files are twice the size the card implies, and nobody from SenseNova has answered a question yet.\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### SenseNova-Loop-9B-Preview\n\n1. Read modeling_sensenova_loop.py before you load it. trust_remote_code runs it\n2. Pin transformers 5.18.0, the version the card checked\n3. Load with dtype=torch.bfloat16 as the card does, or the 38 GB of F32 weights stay at full size\n4. Pass enable_thinking=False to apply_chat_template for a direct answer\n5. Tool calls come back as tool_call blocks with function and parameter tags. Parse them yourself, since no runtime ships a parser\n6. Wait for the vLLM or SGLang pull request to land in a release before putting it behind an API\n\n## Questions\n\n### Which is better for AI agents, LBS-27B or SenseNova-Loop-9B-Preview?\n\nLBS-27B scores 55.8 (C) on agent readiness against SenseNova-Loop-9B-Preview's 46.8 (D), and leads in 2 of 4 scored categories.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/lbs-27b-vs-sensenova-loop-9b-preview.json, and with the fewest tokens: https://www.anchorterminal.com/compare/lbs-27b-vs-sensenova-loop-9b-preview.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"lbs-27b\", \"b\": \"sensenova-loop-9b-preview\"}`. From a terminal: `anchor compare lbs-27b sensenova-loop-9b-preview`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/lbs-27b.json and https://www.anchorterminal.com/api/v1/tools/sensenova-loop-9b-preview.json\n\n## Other comparisons with LBS-27B or SenseNova-Loop-9B-Preview\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 SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/agents-a1-4b-vs-sensenova-loop-9b-preview.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 SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-sensenova-loop-9b-preview.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 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[SenseNova-Loop-9B-Preview vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-unlimited-ocr-2.md)\n- [SenseNova-Loop-9B-Preview vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-wevisone-vl-2b.md)\n- [SenseNova-Loop-9B-Preview vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-wevisone-vl-4b.md)\n- [SenseNova-Loop-9B-Preview vs ZAYA1-8B](https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-zaya1-8b.md)\n", "meta": { "attribution": "Anchor Terminal (https://www.anchorterminal.com)", "attributionRequired": true, "credit": "CC BY 4.0, credit Anchor Terminal and link the page or API address you took it from (https://www.anchorterminal.com/terms/#data)", "docs": "https://www.anchorterminal.com/docs/", "generatedAt": "2026-11-11", "license": "CC-BY-4.0", "method": "https://www.anchorterminal.com/benchmark/", "methodology": "0.7", "openapi": 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