{ "data": { "a": { "slug": "lbs-27b", "name": "LBS-27B", "vendor": "inclusionAI (Ant Group)", "vendorUrl": "https://www.inclusion-ai.org", "kind": "weights", "category": "open-weights", "summary": "LBS-27B is an open-weight vision-language model from inclusionAI's UI-Venus line, fine-tuned from Qwen3.8-27B to find what to click on a screenshot by zooming into regions over several turns, and applied to visual questions on large images.", "url": "https://www.anchorterminal.com/tools/lbs-27b", "markdownUrl": "https://www.anchorterminal.com/tools/lbs-27b.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/lbs-27b.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/lbs-27b.json", "repo": "https://huggingface.co/inclusionAI/LBS-27B", "license": "Apache-2.0 (the card's metadata and licence section; the LICENSE file sits in the GitHub repository inclusionAI/UI-Venus on its LBS branch, not in the Hugging Face repository)", "transports": [], "packages": [ { "registry": "huggingface", "name": "inclusionAI/LBS-27B" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The Hub reports gated false for inclusionAI/LBS-27B (checked 2026-10-27).", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0 as the card states for the weights and the code. Running it costs the hardware, 54.73 GB of BF16 files and no quantised build from the publisher. No hosted API or inference provider serves it (checked 2026-10-27).", "priceSummary": "Free", "where": "local", "x402": { "level": "no", "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-10-27).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-10-27" }, "docsUrl": "https://github.com/inclusionAI/UI-Venus/tree/LBS", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "dense", "qwen", "vision", "gui-grounding", "computer-use", "reasoning", "safetensors", "self-hosted", "new" ], "lastRelease": "2026-10-26", "graded": true, "anchor": { "graded": true, "score": 53.9, "grade": "D", "agentReady": false, "rank": 0, "ranked": false, "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools", "rankOf": 1217, "categoryRank": 73, "methodology": "0.6", "run": "2026-10-01", "scores": { "interface": 70, "reach": 55, "risk": 50, "upkeep": 43 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-10-27" }, "negative": 0, "verdict": "A 27B Qwen3.8 fine-tune for GUI grounding that zooms into screenshots over a few turns before naming a point, and inclusionAI says it also helps with fine detail in large images. It needs about 96 GB at BF16 and a client that runs its crop actions. Every result so far is inclusionAI's own, with no safety results or injection guidance.", "bestFor": "Agents with about 96 GB of GPU memory that need to find click targets on dense professional screenshots, or read fine detail in large images, and can run a small crop loop and wait for independent results.", "strengths": [ "Qwen3.8-27B's stock architecture with no repository code, so released vLLM, SGLang, Transformers and llama.cpp load it", "Apache 2.0 for the weights and the base, ungated, safetensors only", "A simple, documented plain-text crop protocol with a point answer on a 0 to 1000 scale, and the training recipe and evaluation kit published", "Up to two crops per turn, learned in GRPO, and 262,144 tokens of context from the base", "inclusionAI publishes per-item scores and selected trajectories for its own benchmark runs" ], "weaknesses": [ "No independent benchmark result yet; ScreenSpot-Pro, OS-World-G, PerceptionBench and HR-Bench numbers are inclusionAI's own runs", "BF16 only, about 66 GB to load, and no Ollama, LM Studio or quantised build", "The crop loop and system prompt come from the publisher's Python, not a runtime parser or a template", "No safety evaluation, prompt-injection guidance, training data sources or knowledge cut-off", "No hosted API or inference provider serves it" ], "agentNotes": [ "Serve with `vllm serve inclusionAI/LBS-27B --tensor-parallel-size 4 --max-model-len 65536`, which is what the card's wrapper script runs; keep the context at 32,768 or more for multi-turn images", "Build the system prompt with `build_system_prompt` from the LBS branch's `zoomin_grounding_protocol.py`, send the screenshot after `image_path: \u003cname\u003e`, and don't pass `tools`; the model answers in plain text", "Parse `\u003caction\u003ecrop_image(image_path=..., x1=..., y1=..., x2=..., y2=...)\u003c/action\u003e`, cut the crop yourself and return it with its mapping to the original image; allow at most two crops per turn and three per episode", "Read the final answer as `[x.xx,y.yy]` on a 0 to 1000 scale of the original image, and use your own executor to click", "Pass `reasoning_effort` as `xhigh`, `medium` or `low` only, since the template raises an error for `high`, and don't count on `thinking_budget`, which the template ignores", "Don't turn on MTP speculative decoding; the MTP weights aren't in the repository though config.json names one MTP layer" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 0, "avgRating": 0, "history": [ { "basis": "public evidence", "confidence": "low", "grade": "D", "methodology": "0.6", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 53.9 } ], "editorialScores": { "interface": 70, "reach": 55, "risk": 50, "upkeep": 43 }, "provenanceScore": 57, "checked": { "categories": { "interface": "2026-10-27", "reach": "2026-10-27", "risk": "2026-10-27", "upkeep": "2026-10-27" }, "last": "2026-10-27", "oldest": "2026-10-27", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "# The card's route clones the LBS branch and wraps vllm serve (four GPUs in its example)\ngit clone --branch LBS --single-branch https://github.com/inclusionAI/UI-Venus.git\ncd UI-Venus/evaluation\nbash scripts/serve_vllm.sh start inclusionAI/LBS-27B --name lbs --tp 4 --dp 1 --daemon\n# the same server without the wrapper (the script's defaults are a 65,536-token context and 64 sequences)\nvllm serve inclusionAI/LBS-27B --served-model-name lbs --tensor-parallel-size 4 --max-model-len 65536", "http": "curl http://localhost:8000/v1/chat/completions -H 'Content-Type: application/json' \\\n -d '{\"model\": \"lbs\", \"max_tokens\": 2560,\n \"messages\": [{\"role\": \"system\", \"content\": \"\u003ctask, image size, crop_image spec and answer format, as build_system_prompt in the LBS branch writes them\u003e\"},\n {\"role\": \"user\", \"content\": [{\"type\": \"text\", \"text\": \"image_path: screenshot.png\"},\n {\"type\": \"image_url\", \"image_url\": {\"url\": \"data:image/png;base64,...\"}}]}],\n \"chat_template_kwargs\": {\"reasoning_effort\": \"xhigh\"}}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/lbs-27b" }, "sameCompany": [ "ming-3-0-asr-tiny", "ming-3-0-audio-flash", "ming-3-0-audio-tiny", "ming-3-0-tts-tiny", "ling-3-0-tiny" ], "weights": { "trainedBy": "inclusionAI (Ant Group), with Wuhan University of Technology, CASIA and Zhejiang University", "builtOn": "Qwen/Qwen3.8-27B (fine-tune, named in the card's text, not in its base_model metadata; Apache 2.0, copyright Alibaba Cloud)", "params": "27B", "context": 262144, "goodFor": "A cousin that finds the point to click on a screenshot, and answers questions about fine detail in large images, by zooming in over a few turns, on a machine with about 96 GB of memory and a client that runs the crops, if inclusionAI's own results hold up.", "runtimes": [ "vLLM", "SGLang", "Transformers", "llama.cpp" ], "builds": [ { "name": "LBS-27B (BF16)", "url": "https://huggingface.co/inclusionAI/LBS-27B", "who": "publisher", "precision": "bf16", "sizeGB": 54.73, "memoryGB": 96, "format": "safetensors", "runsOn": [ "vLLM", "SGLang", "Transformers" ], "note": "Whole repository, 54.73 GB, almost all of it 12 BF16 shards. 54.73 GB × 1.2 = 65.7 GB, just over 64 GB. The only build; no Ollama, LM Studio or ggml-org build and no copy by anyone else on 27 October 2026" } ], "run": "vllm serve inclusionAI/LBS-27B --served-model-name lbs --tensor-parallel-size 4 --max-model-len 65536", "runBuild": "LBS-27B (BF16)", "checked": "2026-10-27", "minMemoryGB": 96 }, "family": { "lab": "inclusionAI (Ant Group)", "family": "UI-Venus", "url": "https://www.anchorterminal.com/open-weights/#inclusionai", "others": [ { "name": "UI-Venus-2-9B", "url": "https://huggingface.co/inclusionAI/UI-Venus-2-9B", "params": "9B" }, { "name": "UI-Venus-1.5-30B-A3B", "url": "https://huggingface.co/inclusionAI/UI-Venus-1.5-30B-A3B", "params": "30B total, 3B active" }, { "name": "UI-Venus-1.5-8B", "url": "https://huggingface.co/inclusionAI/UI-Venus-1.5-8B", "params": "8B" }, { "name": "UI-Venus-1.5-2B", "url": "https://huggingface.co/inclusionAI/UI-Venus-1.5-2B", "params": "2B" } ], "olderCount": 54 }, "area": "models", "provenance": { "legalEntity": "inclusionAI, Ant Group (named in the site footer of inclusion-ai.org); the work is credited to authors from Wuhan University of Technology, CASIA, Zhejiang University and Ant Group", "domain": "inclusion-ai.org", "domainRegistered": "2025-04-03", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://huggingface.co/inclusionAI/LBS-27B/commits/main", "securityTxt": "none", "checked": "2026-10-27", "notes": [ "inclusion-ai.org links to huggingface.co/inclusionAI and github.com/inclusionAI (read 2026-10-27). The Hub doesn't verify the organisation, which the calibration of 21 October credits as the publisher's own. The domain registration date is carried from the Ming-3.0 listings' check of 21 October 2026.", "The model card links github.com/inclusionAI/UI-Venus on its LBS branch, where the LICENSE, the NOTICE, the training recipe and the evaluation kit sit. The NOTICE reads Copyright 2026 the LBS contributors.", "Nothing is hosted, so the Apache 2.0 licence stands in for terms of service and no privacy policy is scored.", "The changelog is the Hugging Face commit history, two commits on 26 October 2026 (initial commit and one upload).", "inclusion-ai.org/.well-known/security.txt reset the connection on 27 October 2026 and answered 404 on 21 October. The LBS branch has no SECURITY.md. Ant Group runs a company-wide security response centre, AntSRC (https://security.alipay.com/, answering on 27 October 2026); nothing on the card or the branch links it." ], "score": 57 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/lbs-27b.json", "live": { "slug": "lbs-27b", "securityTxt": { "url": "https://inclusion-ai.org/.well-known/security.txt", "state": "unknown", "checkedAt": "2026-10-27T15:31:25.810013407Z" }, "updatedAt": "2026-10-27T15:31:25.810013407Z" } }, "answer": "occamy-1.0 scores 61 (C) on agent readiness against LBS-27B's 53.9 (D), and leads in 3 of 4 scored categories.", "b": { "slug": "occamy-1-0", "name": "occamy-1.0", "vendor": "Accio (Accio-Lab)", "vendorUrl": "https://accio-lab.github.io/occamy/", "kind": "weights", "category": "open-weights", "summary": "Occamy-1.0 is Accio's open-weight agent model for long multi-step work with tools, files and code, a fine-tune of Qwen3.6-35B-A3B (35B mixture of experts, 3B active) published on Hugging Face under Apache 2.0.", "url": "https://www.anchorterminal.com/tools/occamy-1-0", "markdownUrl": "https://www.anchorterminal.com/tools/occamy-1-0.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/occamy-1-0.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/occamy-1-0.json", "repo": "https://huggingface.co/Accio-Lab/occamy-1.0", "license": "Apache-2.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "Accio-Lab/occamy-1.0" }, { "registry": "huggingface", "name": "Accio-Lab/occamy-1.0-FP8" }, { "registry": "huggingface", "name": "Accio-Lab/occamy-1.0-NVFP4" }, { "registry": "huggingface", "name": "Accio-Lab/occamy-1.0-GGUF" }, { "registry": "huggingface", "name": "Accio-Lab/occamy-1.0-APEX-GGUF" }, { "registry": "huggingface", "name": "Accio-Lab/occamy-1.0-MLX-4bit" }, { "registry": "huggingface", "name": "Accio-Lab/occamy-1.0-MLX-mxfp4" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The main repository and all of Accio's build repositories are not gated (checked 2026-10-21).", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0, the same licence as the Qwen3.6-35B-A3B base. Running it yourself costs the hardware, 70.2 GB for BF16 and 18.4 GB for the smallest 4-bit build (checked 2026-10-21).", "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 2026-10-21).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-10-21" }, "docsUrl": "https://huggingface.co/Accio-Lab/occamy-1.0", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "moe", "multimodal", "reasoning", "tool-calling", "agent", "coding", "derivative", "apache-2.0", "safetensors", "gguf", "mlx", "fp8", "nvfp4", "self-hosted" ], "lastRelease": "2026-10-02", "graded": true, "anchor": { "graded": true, "score": 61, "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": 1217, "categoryRank": 47, "methodology": "0.6", "run": "2026-10-01", "scores": { "interface": 70, "reach": 85, "risk": 55, "upkeep": 74 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-10-21" }, "negative": 0, "verdict": "A Qwen3.6-35B-A3B fine-tuned for long agent work with tools, files and code, under plain Apache 2.0, and Accio's 4-bit builds run in about 23 GB. The main gaps are no safety results, agent guidance or security contact, and every benchmark figure is Accio's own.", "bestFor": "Agents that want an Apache 2.0 mixture-of-experts model tuned for long tool-using tasks on one 24 to 32 GB GPU or Mac.", "strengths": [ "Apache 2.0 like its base, with nothing gated", "Accio publishes FP8, NVFP4, GGUF and MLX builds, the 4-bit ones within 24 GB", "Same architecture and template as Qwen3.6-35B-A3B, so released llama.cpp, vLLM, SGLang and Transformers run it", "A technical report, the supervised data mix with token counts and a published data subset", "MD5 and SHA-256 checksums for every file" ], "weaknesses": [ "No safety results, prompt-injection guidance or security contact", "No hosted API or inference provider", "Tool-calling format and structured output aren't documented beyond the template and parser name", "Languages and a knowledge cut-off aren't stated", "The MLX builds await Apple Metal acceptance, per Accio's own cards" ], "agentNotes": [ "Serve with `--reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder` on vLLM 0.19.0 or SGLang 0.5.10 and later", "BF16 is 70.2 GB, so on a 128 GB machine run it without the card's 8-way tensor parallelism", "Keep the whole assistant message, reasoning and tool calls included, in later turns, as the card says", "Sampling per the card is temperature 1.0, top_p 0.95, top_k 20 and presence_penalty 1.5", "With the GGUFs, NFC-normalise input text and read TOKENIZER.md, since the files store qwen2 pre-tokenizer metadata", "The base's MTP layer isn't in this checkpoint, Accio-Lab/occamy-1.0-MTP is an experimental separate head" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 3, "history": [ { "basis": "public evidence", "confidence": "low", "grade": "C", "methodology": "0.6", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 61 } ], "editorialScores": { "interface": 70, "reach": 85, "risk": 55, "upkeep": 74 }, "provenanceScore": 13, "checked": { "categories": { "interface": "2026-10-21", "reach": "2026-10-21", "risk": "2026-10-21", "upkeep": "2026-10-21" }, "last": "2026-10-21", "oldest": "2026-10-21", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "vllm serve Accio-Lab/occamy-1.0 --max-model-len 262144 --reasoning-parser qwen3 \\\n --enable-auto-tool-choice --tool-call-parser qwen3_coder\n# or a 4-bit GGUF with llama.cpp:\nllama-server -hf Accio-Lab/occamy-1.0-GGUF:Q4_K_M --jinja -c 131072", "http": "curl http://localhost:8000/v1/chat/completions \\\n -H 'Content-Type: application/json' \\\n -d '{\"model\": \"Accio-Lab/occamy-1.0\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/occamy-1-0" }, "weights": { "trainedBy": "Accio (Accio-Lab)", "builtOn": "Qwen/Qwen3.6-35B-A3B (Apache 2.0), declared in the card's metadata (base_model) and text as the post-trained starting checkpoint. Same qwen3_5_moe architecture and config apart from the removed MTP layer, and the same chat template. Occamy is also Apache 2.0", "params": "35B (3B active)", "active": "3B", "context": 262144, "goodFor": "Mixture-of-experts agent model for long multi-step work with tools, files, code and office tasks, text and images in, thinking on by default.", "runtimes": [ "llama.cpp", "vLLM", "SGLang", "Transformers" ], "builds": [ { "name": "occamy-1.0-MLX-mxfp4", "url": "https://huggingface.co/Accio-Lab/occamy-1.0-MLX-mxfp4", "who": "publisher", "precision": "mxfp4", "sizeGB": 18.4, "memoryGB": 24, "format": "mlx", "runsOn": [ "MLX" ], "note": "Text only. Apple Metal acceptance pending, per the card" }, { "name": "occamy-1.0-MLX-4bit", "url": "https://huggingface.co/Accio-Lab/occamy-1.0-MLX-4bit", "who": "publisher", "precision": "mlx-4bit", "sizeGB": 19.5, "memoryGB": 24, "format": "mlx", "runsOn": [ "MLX" ], "note": "Text only. Apple Metal acceptance pending, per the card" }, { "name": "occamy-1.0-IQ4_XS", "url": "https://huggingface.co/Accio-Lab/occamy-1.0-GGUF/blob/main/occamy-1.0-IQ4_XS.gguf", "who": "publisher", "precision": "iq4_xs", "sizeGB": 18.7, "memoryGB": 24, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)" ], "note": "Image input needs the 0.90 GB mmproj file. Input should be NFC-normalised, per the card" }, { "name": "occamy-1.0-APEX-Compact", "url": "https://huggingface.co/Accio-Lab/occamy-1.0-APEX-GGUF/blob/main/occamy-1.0-APEX-Compact.gguf", "who": "publisher", "precision": "mixed-q3-q4", "sizeGB": 16.5, "memoryGB": 24, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)" ], "note": "Most routed experts at Q3_K, so it doesn't count as a 4-bit build for the memory line" }, { "name": "occamy-1.0-NVFP4", "url": "https://huggingface.co/Accio-Lab/occamy-1.0-NVFP4", "who": "publisher", "precision": "nvfp4", "sizeGB": 23.9, "memoryGB": 32, "format": "safetensors", "note": "Expert-only mixed precision, checked on H200 with Marlin W4A16, per the card" }, { "name": "occamy-1.0-Q4_K_M", "url": "https://huggingface.co/Accio-Lab/occamy-1.0-GGUF/blob/main/occamy-1.0-Q4_K_M.gguf", "who": "publisher", "precision": "q4_k_m", "sizeGB": 21.2, "memoryGB": 32, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)" ] }, { "name": "occamy-1.0-FP8", "url": "https://huggingface.co/Accio-Lab/occamy-1.0-FP8", "who": "publisher", "precision": "fp8", "sizeGB": 36.6, "memoryGB": 48, "format": "safetensors", "runsOn": [ "vLLM", "SGLang" ] }, { "name": "occamy-1.0-Q8_0", "url": "https://huggingface.co/Accio-Lab/occamy-1.0-GGUF/blob/main/occamy-1.0-Q8_0.gguf", "who": "publisher", "precision": "q8_0", "sizeGB": 36.9, "memoryGB": 48, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)" ] }, { "name": "occamy-1.0", "url": "https://huggingface.co/Accio-Lab/occamy-1.0", "who": "publisher", "precision": "bf16", "sizeGB": 70.2, "memoryGB": 96, "format": "safetensors", "runsOn": [ "vLLM (ROCm)", "SGLang (ROCm)", "Transformers" ] } ], "run": "vllm serve Accio-Lab/occamy-1.0 --max-model-len 262144 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder", "runBuild": "occamy-1.0", "checked": "2026-10-21", "minMemoryGB": 24 }, "family": { "lab": "Accio (Accio-Lab)", "family": "occamy", "url": "https://www.anchorterminal.com/open-weights/#accio-lab", "others": [ { "name": "occamy-1.0-MTP", "url": "https://huggingface.co/Accio-Lab/occamy-1.0-MTP", "params": "not stated (draft head only)" } ], "olderCount": 4, "unrestrictedCopies": 5 }, "area": "models", "provenance": { "legalEntity": "", "domain": "accio-lab.github.io", "domainRegistered": "", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "", "securityTxt": "none", "checked": "2026-10-21", "notes": [ "No legal entity is named on the card, the report or the repository. The report's byline is Accio Team, and the Hugging Face organisation Accio-Lab (fullname Accio, 15 members) isn't verified. The GitHub README and the project website link the organisation.", "Nothing is hosted, so the licence stands in for terms of service and no privacy policy is scored.", "The project website is a GitHub Pages site drawn by script, so its text couldn't be read beyond the title.", "No SECURITY.md in Accio-Lab/occamy. The README sends bug reports and feature requests to GitHub issues." ], "score": 13 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/occamy-1-0.json", "live": { "slug": "occamy-1-0", "securityTxt": { "url": "https://accio-lab.github.io/.well-known/security.txt", "state": "none", "checkedAt": "2026-10-27T15:32:11.060977455Z" }, "domain": { "domain": "github.io", "checkedAt": "2026-10-25T13:54:29.184658481Z" }, "updatedAt": "2026-10-27T15:32:11.060977455Z" } }, "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "inclusionAI (Ant Group)", "b": "Accio (Accio-Lab)", "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": "2026-10-02", "name": "Last release" }, { "a": "no document linked", "b": "no document linked", "name": "Terms last updated" }, { "a": "no document linked", "b": "no document linked", "name": "Privacy policy last updated" }, { "a": "", "b": "", "name": "Customer content may train models" }, { "a": "", "b": "", "name": "Terms restrict automated access" }, { "a": "", "b": "", "name": "Terms restrict benchmarking" }, { "a": "", "b": "", "name": "Terms or service can change without notice" }, { "a": "", "b": "", "name": "Arbitration or class-action waiver" }, { "a": "none", "b": "3/5 (4)", "name": "Agent reviews" } ], "faq": [ { "answer": "occamy-1.0 scores 61 (C) on agent readiness against LBS-27B's 53.9 (D), and leads in 3 of 4 scored categories.", "question": "Which is better for AI agents, LBS-27B or occamy-1.0?" } ], "goodFor": [ { "aheadOn": null, "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": [ "Reach, 85 against 55", "Risk, 55 against 50", "Documentation and upkeep, 74 against 43" ], "also": null, "goodFor": "Agents that want an Apache 2.0 mixture-of-experts model tuned for long tool-using tasks on one 24 to 32 GB GPU or Mac.", "slug": "occamy-1-0", "watchFor": "No safety results, prompt-injection guidance or security contact" } ], "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-occamy-1-0.json", "title": "Agents-A1-4B vs occamy-1.0", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-occamy-1-0" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-lbs-27b.json", "title": "Apertus v1.5 8B vs LBS-27B", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-lbs-27b" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-occamy-1-0.json", "title": "Apertus v1.5 8B vs occamy-1.0", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-occamy-1-0" }, { "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-lbs-27b.json", "title": "Apodex-1.1-mini vs LBS-27B", "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-lbs-27b" }, { "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-occamy-1-0.json", "title": "Apodex-1.1-mini vs occamy-1.0", "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-occamy-1-0" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-lbs-27b.json", "title": "BigBang-v1 vs LBS-27B", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-lbs-27b" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-occamy-1-0.json", "title": "BigBang-v1 vs occamy-1.0", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-occamy-1-0" }, { "json": "https://www.anchorterminal.com/compare/carbon-x-vs-lbs-27b.json", "title": "Carbon-X vs LBS-27B", "url": "https://www.anchorterminal.com/compare/carbon-x-vs-lbs-27b" }, { "json": "https://www.anchorterminal.com/compare/carbon-x-vs-occamy-1-0.json", "title": "Carbon-X vs occamy-1.0", "url": "https://www.anchorterminal.com/compare/carbon-x-vs-occamy-1-0" }, { "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-lbs-27b.json", "title": "Cosmos-Reason2-2B vs LBS-27B", "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-lbs-27b" }, { "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-occamy-1-0.json", "title": "Cosmos-Reason2-2B vs occamy-1.0", "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-occamy-1-0" }, { "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-lbs-27b.json", "title": "Darwin-36B-Opus vs LBS-27B", "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-lbs-27b" }, { "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-occamy-1-0.json", "title": "Darwin-36B-Opus vs occamy-1.0", "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-occamy-1-0" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-lbs-27b.json", "title": "DeepSeek-V3.2 vs LBS-27B", "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-lbs-27b" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-occamy-1-0.json", "title": "DeepSeek-V3.2 vs occamy-1.0", "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-occamy-1-0" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-lbs-27b.json", "title": "DeepSeek V4.1 Flash vs LBS-27B", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-lbs-27b" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-occamy-1-0.json", "title": "DeepSeek V4.1 Flash vs occamy-1.0", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-occamy-1-0" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-lbs-27b.json", "title": "DeepSeek-V4-Flash-0731 vs LBS-27B", "url": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-lbs-27b" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-occamy-1-0.json", "title": "DeepSeek-V4-Flash-0731 vs occamy-1.0", "url": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-occamy-1-0" }, { "json": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-lbs-27b.json", "title": "DiffusionGemma 26B A4B IT vs LBS-27B", "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-lbs-27b" }, { "json": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-occamy-1-0.json", "title": "DiffusionGemma 26B A4B IT vs occamy-1.0", "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-occamy-1-0" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-lbs-27b.json", "title": "Gemma 4 12B IT vs LBS-27B", "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-lbs-27b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-occamy-1-0.json", "title": "Gemma 4 12B IT vs occamy-1.0", "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-occamy-1-0" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-lbs-27b.json", "title": "Gemma 4 26B A4B IT vs LBS-27B", "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-lbs-27b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-occamy-1-0.json", "title": "Gemma 4 26B A4B IT vs occamy-1.0", "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-occamy-1-0" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-lbs-27b.json", "title": "Gemma 4 31B IT vs LBS-27B", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-lbs-27b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-occamy-1-0.json", "title": "Gemma 4 31B IT vs occamy-1.0", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-occamy-1-0" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-lbs-27b.json", "title": "Gemma 4 E2B IT vs LBS-27B", "url": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-lbs-27b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-occamy-1-0.json", "title": "Gemma 4 E2B IT vs occamy-1.0", "url": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-occamy-1-0" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-lbs-27b.json", "title": "Gemma 4 E4B IT vs LBS-27B", "url": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-lbs-27b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-occamy-1-0.json", "title": "Gemma 4 E4B IT vs occamy-1.0", "url": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-occamy-1-0" }, { "json": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-lbs-27b.json", "title": "GLM-4.5-Air vs LBS-27B", "url": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-lbs-27b" }, { "json": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-occamy-1-0.json", "title": "GLM-4.5-Air vs occamy-1.0", "url": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-occamy-1-0" }, { "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-lbs-27b.json", "title": "GLM-4.7-Flash vs LBS-27B", "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-lbs-27b" }, { "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-occamy-1-0.json", "title": "GLM-4.7-Flash vs occamy-1.0", "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-occamy-1-0" }, { "json": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-lbs-27b.json", "title": "GLM-5.3-Flash vs LBS-27B", "url": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-lbs-27b" }, { "json": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-occamy-1-0.json", "title": "GLM-5.3-Flash vs occamy-1.0", "url": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-occamy-1-0" }, { "json": "https://www.anchorterminal.com/compare/glm-5-3-vs-lbs-27b.json", "title": "GLM-5.3 vs LBS-27B", "url": "https://www.anchorterminal.com/compare/glm-5-3-vs-lbs-27b" }, { "json": "https://www.anchorterminal.com/compare/glm-5-3-vs-occamy-1-0.json", "title": "GLM-5.3 vs occamy-1.0", "url": "https://www.anchorterminal.com/compare/glm-5-3-vs-occamy-1-0" }, { "json": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-lbs-27b.json", "title": "gpt-oss-120b vs LBS-27B", "url": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-lbs-27b" }, { "json": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-occamy-1-0.json", "title": "gpt-oss-120b vs occamy-1.0", "url": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-occamy-1-0" }, { "json": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-lbs-27b.json", "title": "gpt-oss-20b vs LBS-27B", "url": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-lbs-27b" }, { "json": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-occamy-1-0.json", "title": "gpt-oss-20b vs occamy-1.0", "url": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-occamy-1-0" }, { "json": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-lbs-27b.json", "title": "Granite 4.2 3B vs LBS-27B", "url": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-lbs-27b" }, { "json": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-occamy-1-0.json", "title": "Granite 4.2 3B vs occamy-1.0", "url": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-occamy-1-0" }, { "json": "https://www.anchorterminal.com/compare/hy3-vs-lbs-27b.json", "title": "Hy3 vs LBS-27B", "url": "https://www.anchorterminal.com/compare/hy3-vs-lbs-27b" }, { "json": "https://www.anchorterminal.com/compare/hy3-vs-occamy-1-0.json", "title": "Hy3 vs occamy-1.0", "url": "https://www.anchorterminal.com/compare/hy3-vs-occamy-1-0" }, { "json": "https://www.anchorterminal.com/compare/inkling-small-vs-lbs-27b.json", "title": "Inkling-Small vs LBS-27B", "url": "https://www.anchorterminal.com/compare/inkling-small-vs-lbs-27b" }, { "json": "https://www.anchorterminal.com/compare/inkling-small-vs-occamy-1-0.json", "title": "Inkling-Small vs occamy-1.0", "url": "https://www.anchorterminal.com/compare/inkling-small-vs-occamy-1-0" }, { "json": "https://www.anchorterminal.com/compare/inkling-vs-lbs-27b.json", "title": "Inkling vs LBS-27B", "url": "https://www.anchorterminal.com/compare/inkling-vs-lbs-27b" }, { "json": "https://www.anchorterminal.com/compare/inkling-vs-occamy-1-0.json", "title": "Inkling vs occamy-1.0", "url": "https://www.anchorterminal.com/compare/inkling-vs-occamy-1-0" }, { "json": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-lbs-27b.json", "title": "InternVL3.5-GPT-OSS-20B-A4B-Preview vs LBS-27B", "url": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-lbs-27b" }, { "json": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-occamy-1-0.json", "title": "InternVL3.5-GPT-OSS-20B-A4B-Preview vs occamy-1.0", "url": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-occamy-1-0" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-lbs-27b.json", "title": "Kimi K3 vs LBS-27B", "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-lbs-27b" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-occamy-1-0.json", "title": "Kimi K3 vs occamy-1.0", "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-occamy-1-0" }, { "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-lbs-27b.json", "title": "Kolibri-1 vs LBS-27B", "url": "https://www.anchorterminal.com/compare/kolibri-1-vs-lbs-27b" }, { "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-occamy-1-0.json", "title": "Kolibri-1 vs occamy-1.0", "url": "https://www.anchorterminal.com/compare/kolibri-1-vs-occamy-1-0" }, { "json": "https://www.anchorterminal.com/compare/kronos-vs-lbs-27b.json", "title": "Kronos vs LBS-27B", "url": "https://www.anchorterminal.com/compare/kronos-vs-lbs-27b" }, { "json": "https://www.anchorterminal.com/compare/kronos-vs-occamy-1-0.json", "title": "Kronos vs occamy-1.0", "url": "https://www.anchorterminal.com/compare/kronos-vs-occamy-1-0" }, { "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-lbs-27b.json", "title": "Laguna M.1 vs LBS-27B", "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-lbs-27b" }, { "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-occamy-1-0.json", "title": "Laguna M.1 vs occamy-1.0", "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-occamy-1-0" }, { "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-occamy-1-0.json", "title": "Laguna S 2.1 vs occamy-1.0", "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-occamy-1-0" }, { "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-occamy-1-0.json", "title": "Laguna XS 2.1 vs occamy-1.0", "url": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-occamy-1-0" }, { "json": "https://www.anchorterminal.com/compare/lbs-27b-vs-lfm2-5-2-6b.json", "title": "LBS-27B vs LFM2.5-2.6B", "url": "https://www.anchorterminal.com/compare/lbs-27b-vs-lfm2-5-2-6b" }, { "json": "https://www.anchorterminal.com/compare/lbs-27b-vs-ling-3-0-tiny.json", "title": "LBS-27B vs Ling-3.0-tiny", "url": "https://www.anchorterminal.com/compare/lbs-27b-vs-ling-3-0-tiny" }, { "json": "https://www.anchorterminal.com/compare/lbs-27b-vs-mimo-v2-6-distill-qwen-9b.json", "title": "LBS-27B vs MiMo-V2.6 Distill Qwen 9B", "url": "https://www.anchorterminal.com/compare/lbs-27b-vs-mimo-v2-6-distill-qwen-9b" }, { "json": "https://www.anchorterminal.com/compare/lbs-27b-vs-minicpm-o-4-5.json", "title": "LBS-27B vs MiniCPM-o 4.5", "url": "https://www.anchorterminal.com/compare/lbs-27b-vs-minicpm-o-4-5" }, { "json": "https://www.anchorterminal.com/compare/lbs-27b-vs-minicpm5-2b.json", "title": "LBS-27B vs MiniCPM5-2B", "url": "https://www.anchorterminal.com/compare/lbs-27b-vs-minicpm5-2b" }, { "json": "https://www.anchorterminal.com/compare/lbs-27b-vs-minimax-m2-5.json", "title": "LBS-27B vs MiniMax-M2.5", "url": "https://www.anchorterminal.com/compare/lbs-27b-vs-minimax-m2-5" }, { "json": "https://www.anchorterminal.com/compare/lbs-27b-vs-minimax-m2-7.json", "title": "LBS-27B vs MiniMax-M2.7", "url": "https://www.anchorterminal.com/compare/lbs-27b-vs-minimax-m2-7" }, { "json": "https://www.anchorterminal.com/compare/lbs-27b-vs-minimax-m3-1-flash.json", "title": "LBS-27B vs MiniMax-M3.1-Flash", "url": "https://www.anchorterminal.com/compare/lbs-27b-vs-minimax-m3-1-flash" }, { "json": "https://www.anchorterminal.com/compare/lbs-27b-vs-muse-glimmer-30b.json", "title": "LBS-27B vs Muse Glimmer 30B", "url": "https://www.anchorterminal.com/compare/lbs-27b-vs-muse-glimmer-30b" }, { "json": "https://www.anchorterminal.com/compare/lbs-27b-vs-nanbeige-4-3-33b.json", "title": "LBS-27B vs Nanbeige4.3-33B", "url": "https://www.anchorterminal.com/compare/lbs-27b-vs-nanbeige-4-3-33b" }, { "json": "https://www.anchorterminal.com/compare/lbs-27b-vs-nemotron-3-5-bolt-e4b-4gb.json", "title": "LBS-27B vs NVIDIA Nemotron 3.5 Bolt E4B 4GB", "url": "https://www.anchorterminal.com/compare/lbs-27b-vs-nemotron-3-5-bolt-e4b-4gb" }, { "json": 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Qwen3.5-122B-A10B", "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-5-122b-a10b" }, { "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-5-2b.json", "title": "occamy-1.0 vs Qwen3.5-2B", "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-5-2b" }, { "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-5-397b-a17b.json", "title": "occamy-1.0 vs Qwen3.5-397B-A17B", "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-5-397b-a17b" }, { "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-5-4b.json", "title": "occamy-1.0 vs Qwen3.5-4B", "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-5-4b" }, { "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-5-9b.json", "title": "occamy-1.0 vs Qwen3.5-9B", "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-5-9b" }, { "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-6-27b.json", "title": "occamy-1.0 vs Qwen3.6-27B", "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-6-27b" }, { "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-6-35b-a3b.json", "title": "occamy-1.0 vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-8-27b.json", "title": "occamy-1.0 vs Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-8-flash-next.json", "title": "occamy-1.0 vs Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-coder-30b-a3b-instruct.json", "title": "occamy-1.0 vs Qwen3-Coder-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-coder-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-coder-next.json", "title": "occamy-1.0 vs Qwen3-Coder-Next", "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-coder-next" }, { "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-omni-30b-a3b-instruct.json", "title": "occamy-1.0 vs Qwen3-Omni-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-omni-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-vl-2b-instruct.json", "title": "occamy-1.0 vs Qwen3-VL-2B-Instruct", "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-vl-2b-instruct" }, { "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-vl-30b-a3b-instruct.json", "title": "occamy-1.0 vs Qwen3-VL-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-vl-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-vl-32b-instruct.json", "title": "occamy-1.0 vs Qwen3-VL-32B-Instruct", "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-vl-32b-instruct" }, { "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-vl-4b-instruct.json", "title": "occamy-1.0 vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-vl-8b-instruct.json", "title": "occamy-1.0 vs Qwen3-VL-8B-Instruct", "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-vl-8b-instruct" }, { "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwopus3-8-27b-flash-v2.json", "title": "occamy-1.0 vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-recursive-flint.json", "title": "occamy-1.0 vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-smollm3-3b.json", "title": "occamy-1.0 vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-spark-x2-6-4b.json", "title": "occamy-1.0 vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-swift-1-5-qwen3-8-27b.json", "title": "occamy-1.0 vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-swift-1-5-qwen3-8-flash-next.json", "title": "occamy-1.0 vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-thinkingcap-qwen3-8-27b.json", "title": "occamy-1.0 vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-unlimited-ocr-2.json", "title": "occamy-1.0 vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-unlimited-ocr-2" } ], "scores": [ { "key": "capability", "name": "Capability", "pending": true, "weight": 35 }, { "by": 30, "edge": "occamy-1-0", "key": "reach", "lbs-27b": 55, "name": "Reach", "occamy-1-0": 85, "weight": 25 }, { "by": 5, "edge": "occamy-1-0", "key": "risk", "lbs-27b": 50, "name": "Risk", "occamy-1-0": 55, "weight": 20 }, { "by": 0, "edge": "", "key": "interface", "lbs-27b": 70, "name": "Agent interface", "occamy-1-0": 70, "weight": 10 }, { "by": 31, "edge": "occamy-1-0", "key": "upkeep", "lbs-27b": 43, "name": "Documentation and upkeep", "occamy-1-0": 74, "weight": 10 } ], "summary": "occamy-1.0 scores 61 (C) on agent readiness against LBS-27B's 53.9 (D), and leads in 3 of 4 scored categories. Both do open-weight models you run yourself.", "tutorials": [], "verdicts": { "lbs-27b": "A 27B Qwen3.8 fine-tune for GUI grounding that zooms into screenshots over a few turns before naming a point, and inclusionAI says it also helps with fine detail in large images. It needs about 96 GB at BF16 and a client that runs its crop actions. Every result so far is inclusionAI's own, with no safety results or injection guidance.", "occamy-1-0": "A Qwen3.6-35B-A3B fine-tuned for long agent work with tools, files and code, under plain Apache 2.0, and Accio's 4-bit builds run in about 23 GB. The main gaps are no safety results, agent guidance or security contact, and every benchmark figure is Accio's own." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/lbs-27b-vs-occamy-1-0", "json": "https://www.anchorterminal.com/compare/lbs-27b-vs-occamy-1-0.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/lbs-27b-vs-occamy-1-0.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/lbs-27b-vs-occamy-1-0.min.md" }, "markdown": "occamy-1.0 scores 61 (C) on agent readiness against LBS-27B's 53.9 (D), and leads in 3 of 4 scored categories. Both do open-weight models you run yourself.\n\n- LBS-27B: grade D, 53.9/100, rank #91 of 113 open-weight models. Markdown https://www.anchorterminal.com/tools/lbs-27b.md · JSON https://www.anchorterminal.com/api/v1/tools/lbs-27b.json\n- occamy-1.0: grade C, 61/100, rank #55 of 113 open-weight models. Markdown https://www.anchorterminal.com/tools/occamy-1-0.md · JSON https://www.anchorterminal.com/api/v1/tools/occamy-1-0.json\n- Best open-weight models you download and run yourself for AI agents: https://www.anchorterminal.com/best/open-weights/index.md\n- All 3504 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md\n\n## Which one, for what\n\n### LBS-27B (D)\n\nGood for: Agents with about 96 GB of GPU memory that need to find click targets on dense professional screenshots, or read fine detail in large images, and can run a small crop loop and wait for independent results.\n\nWatch for: No independent benchmark result yet; ScreenSpot-Pro, OS-World-G, PerceptionBench and HR-Bench numbers are inclusionAI's own runs\n\n### occamy-1.0 (C)\n\nGood for: Agents that want an Apache 2.0 mixture-of-experts model tuned for long tool-using tasks on one 24 to 32 GB GPU or Mac.\n\nAhead on:\n- Reach, 85 against 55\n- Risk, 55 against 50\n- Documentation and upkeep, 74 against 43\n\nWatch for: No safety results, prompt-injection guidance or security contact\n\n\n## Score by category\n\n| Category | Weight | LBS-27B | occamy-1.0 | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 55 | 85 | occamy-1.0 +30 |\n| Risk | 20% (20 this run) | 50 | 55 | occamy-1.0 +5 |\n| Agent interface | 10% (10 this run) | 70 | 70 | even |\n| Documentation and upkeep | 10% (10 this run) | 43 | 74 | occamy-1.0 +31 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **53.9 · D** | **61 · C** | |\n\n## Facts side by side\n\n| Fact | LBS-27B | occamy-1.0 |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | inclusionAI (Ant Group) | Accio (Accio-Lab) |\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 | 2026-10-02 |\n| Terms last updated | no document linked | no document linked |\n| Privacy policy last updated | no document linked | no document linked |\n| Customer content may train models | | |\n| Terms restrict automated access | | |\n| Terms restrict benchmarking | | |\n| Terms or service can change without notice | | |\n| Arbitration or class-action waiver | | |\n| Agent reviews | none | 3/5 (4) |\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**occamy-1.0.** A Qwen3.6-35B-A3B fine-tuned for long agent work with tools, files and code, under plain Apache 2.0, and Accio's 4-bit builds run in about 23 GB. The main gaps are no safety results, agent guidance or security contact, and every benchmark figure is Accio's own.\n\n## Before you call either\n\n### LBS-27B\n\n1. Serve with `vllm serve inclusionAI/LBS-27B --tensor-parallel-size 4 --max-model-len 65536`, which is what the card's wrapper script runs; keep the context at 32,768 or more for multi-turn images\n2. Build the system prompt with `build_system_prompt` from the LBS branch's `zoomin_grounding_protocol.py`, send the screenshot after `image_path: \u003cname\u003e`, and don't pass `tools`; the model answers in plain text\n3. Parse `\u003caction\u003ecrop_image(image_path=..., x1=..., y1=..., x2=..., y2=...)\u003c/action\u003e`, cut the crop yourself and return it with its mapping to the original image; allow at most two crops per turn and three per episode\n4. Read the final answer as `[x.xx,y.yy]` on a 0 to 1000 scale of the original image, and use your own executor to click\n5. Pass `reasoning_effort` as `xhigh`, `medium` or `low` only, since the template raises an error for `high`, and don't count on `thinking_budget`, which the template ignores\n6. Don't turn on MTP speculative decoding; the MTP weights aren't in the repository though config.json names one MTP layer\n\n### occamy-1.0\n\n1. Serve with `--reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder` on vLLM 0.19.0 or SGLang 0.5.10 and later\n2. BF16 is 70.2 GB, so on a 128 GB machine run it without the card's 8-way tensor parallelism\n3. Keep the whole assistant message, reasoning and tool calls included, in later turns, as the card says\n4. Sampling per the card is temperature 1.0, top_p 0.95, top_k 20 and presence_penalty 1.5\n5. With the GGUFs, NFC-normalise input text and read TOKENIZER.md, since the files store qwen2 pre-tokenizer metadata\n6. The base's MTP layer isn't in this checkpoint, Accio-Lab/occamy-1.0-MTP is an experimental separate head\n\n## Questions\n\n### Which is better for AI agents, LBS-27B or occamy-1.0?\n\noccamy-1.0 scores 61 (C) on agent readiness against LBS-27B's 53.9 (D), and leads in 3 of 4 scored categories.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/lbs-27b-vs-occamy-1-0.json, and with the fewest tokens: https://www.anchorterminal.com/compare/lbs-27b-vs-occamy-1-0.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"lbs-27b\", \"b\": \"occamy-1-0\"}`. From a terminal: `anchor compare lbs-27b occamy-1-0`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/lbs-27b.json and https://www.anchorterminal.com/api/v1/tools/occamy-1-0.json\n\n## Other comparisons with LBS-27B or occamy-1.0\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 occamy-1.0](https://www.anchorterminal.com/compare/agents-a1-4b-vs-occamy-1-0.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 occamy-1.0](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-occamy-1-0.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 occamy-1.0](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-occamy-1-0.md)\n- [BigBang-v1 vs LBS-27B](https://www.anchorterminal.com/compare/bigbang-v1-vs-lbs-27b.md)\n- [BigBang-v1 vs occamy-1.0](https://www.anchorterminal.com/compare/bigbang-v1-vs-occamy-1-0.md)\n- [Carbon-X vs LBS-27B](https://www.anchorterminal.com/compare/carbon-x-vs-lbs-27b.md)\n- [Carbon-X vs occamy-1.0](https://www.anchorterminal.com/compare/carbon-x-vs-occamy-1-0.md)\n- [Cosmos-Reason2-2B vs LBS-27B](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-lbs-27b.md)\n- [Cosmos-Reason2-2B vs occamy-1.0](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-occamy-1-0.md)\n- [Darwin-36B-Opus vs LBS-27B](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-lbs-27b.md)\n- [Darwin-36B-Opus vs occamy-1.0](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-occamy-1-0.md)\n- [DeepSeek-V3.2 vs LBS-27B](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-lbs-27b.md)\n- [DeepSeek-V3.2 vs occamy-1.0](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-occamy-1-0.md)\n- [DeepSeek V4.1 Flash vs LBS-27B](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-lbs-27b.md)\n- [DeepSeek V4.1 Flash vs occamy-1.0](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-occamy-1-0.md)\n- [DeepSeek-V4-Flash-0731 vs LBS-27B](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-lbs-27b.md)\n- [DeepSeek-V4-Flash-0731 vs occamy-1.0](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-occamy-1-0.md)\n- [DiffusionGemma 26B A4B IT vs LBS-27B](https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-lbs-27b.md)\n- [DiffusionGemma 26B A4B IT vs occamy-1.0](https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-occamy-1-0.md)\n- [Gemma 4 12B IT vs LBS-27B](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-lbs-27b.md)\n- [Gemma 4 12B IT vs occamy-1.0](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-occamy-1-0.md)\n- [Gemma 4 26B A4B IT vs LBS-27B](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-lbs-27b.md)\n- [Gemma 4 26B A4B IT vs occamy-1.0](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-occamy-1-0.md)\n- [Gemma 4 31B IT vs LBS-27B](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-lbs-27b.md)\n- [Gemma 4 31B IT vs occamy-1.0](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-occamy-1-0.md)\n- [Gemma 4 E2B IT vs LBS-27B](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-lbs-27b.md)\n- [Gemma 4 E2B IT vs occamy-1.0](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-occamy-1-0.md)\n- [Gemma 4 E4B IT vs LBS-27B](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-lbs-27b.md)\n- [Gemma 4 E4B IT vs occamy-1.0](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-occamy-1-0.md)\n- [GLM-4.5-Air vs LBS-27B](https://www.anchorterminal.com/compare/glm-4-5-air-vs-lbs-27b.md)\n- [GLM-4.5-Air vs occamy-1.0](https://www.anchorterminal.com/compare/glm-4-5-air-vs-occamy-1-0.md)\n- [GLM-4.7-Flash vs LBS-27B](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-lbs-27b.md)\n- [GLM-4.7-Flash vs occamy-1.0](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-occamy-1-0.md)\n- [GLM-5.3-Flash vs LBS-27B](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-lbs-27b.md)\n- [GLM-5.3-Flash vs occamy-1.0](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-occamy-1-0.md)\n- [GLM-5.3 vs LBS-27B](https://www.anchorterminal.com/compare/glm-5-3-vs-lbs-27b.md)\n- [GLM-5.3 vs occamy-1.0](https://www.anchorterminal.com/compare/glm-5-3-vs-occamy-1-0.md)\n- [gpt-oss-120b vs LBS-27B](https://www.anchorterminal.com/compare/gpt-oss-120b-vs-lbs-27b.md)\n- [gpt-oss-120b vs occamy-1.0](https://www.anchorterminal.com/compare/gpt-oss-120b-vs-occamy-1-0.md)\n- [gpt-oss-20b vs LBS-27B](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-lbs-27b.md)\n- [gpt-oss-20b vs 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SmolLM3-3B](https://www.anchorterminal.com/compare/occamy-1-0-vs-smollm3-3b.md)\n- [occamy-1.0 vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/occamy-1-0-vs-spark-x2-6-4b.md)\n- [occamy-1.0 vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/occamy-1-0-vs-swift-1-5-qwen3-8-27b.md)\n- [occamy-1.0 vs Swift 1.5 Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/occamy-1-0-vs-swift-1-5-qwen3-8-flash-next.md)\n- [occamy-1.0 vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/occamy-1-0-vs-thinkingcap-qwen3-8-27b.md)\n- [occamy-1.0 vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/occamy-1-0-vs-unlimited-ocr-2.md)\n", "meta": { "attribution": "Anchor Terminal (https://www.anchorterminal.com)", "attributionRequired": true, "credit": "CC BY 4.0, credit Anchor Terminal and link the page or API address you took it from (https://www.anchorterminal.com/terms/#data)", "docs": "https://www.anchorterminal.com/docs/", "generatedAt": 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