{ "data": { "a": { "slug": "arete-27b", "name": "Arete-27B", "vendor": "Nous Research", "vendorUrl": "https://nousresearch.com", "kind": "weights", "category": "open-weights", "summary": "Arete-27B is Nous Research's fine-tune of Qwen3.8-27B, trained on 28,886 published examples so that its answers on politically sensitive topics stop following the Chinese state's account, with the base's tools, images and 262K context, under Apache 2.0.", "url": "https://www.anchorterminal.com/tools/arete-27b", "markdownUrl": "https://www.anchorterminal.com/tools/arete-27b.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/arete-27b.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/arete-27b.json", "dns": "arete-27b.grade.anchorterminal.com", "repo": "https://huggingface.co/NousResearch/Arete-27B", "license": "Apache-2.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "NousResearch/Arete-27B" } ], "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, commercial use included, under Apache 2.0, in the card's metadata and as a LICENSE file in the repository. Running it yourself costs the hardware, 54.7 GB for the BF16 files, or 18.1 GB for bartowski's Q4_K_M GGUF with its projector (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://huggingface.co/NousResearch/Arete-27B", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "fine-tune", "qwen", "multimodal", "vision", "reasoning", "tool-calling", "apache-2.0", "safetensors", "self-hosted" ], "lastRelease": "2026-11-06", "graded": true, "anchor": { "graded": true, "score": 55.1, "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": 92, "methodology": "0.7", "run": "2026-10-01", "scores": { "interface": 60, "reach": 50, "risk": 55, "upkeep": 63 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-11-10" }, "negative": 0, "verdict": "Qwen3.8-27B with its answers on politically sensitive topics retrained, four days old when read. The training data is published, which is rare. Nous Research ships BF16 only, so a small machine runs it from bartowski's GGUF, and the card documents none of the tool or thinking controls it inherits.", "bestFor": "Agents that want Qwen3.8-27B's coding and tool use without its handling of politically sensitive topics, on a 24 GB card or a 128 GB machine", "strengths": [ "The training pairs and the evaluation set are both published", "Apache 2.0, ungated, safetensors only, no remote code", "The base's architecture, so vLLM, SGLang and Transformers run it from their releases", "Fits a 128 GB machine in BF16, and 24 GB from bartowski's 4-bit GGUF", "Nous Research reports no meaningful loss against the base on its agent benchmarks" ], "weaknesses": [ "No quantised build from Nous Research, so the GGUF route depends on a third party", "The card doesn't document tool calling, structured output or how to switch thinking", "No limitations section, safety results or security contact", "None of the 13 discussions has a reply from Nous Research", "The results are Nous Research's own evaluation and nobody else has rerun it yet" ], "agentNotes": [ "Start it with `llama-server -hf bartowski/NousResearch_Arete-27B-GGUF:Q4_K_M --jinja`, a third party's conversion, or serve the BF16 files with vLLM", "Use the base's parsers in vLLM, `--reasoning-parser qwen3 --tool-call-parser qwen3_coder`, and check tool calls first, since the template differs slightly from Qwen3.8-27B's", "Thinking switches with `enable_thinking` in the template. The card's own evaluation ran with it off", "Expect answers on politically sensitive topics to differ from Qwen3.8-27B's. That is what the fine-tune is for", "Validate JSON output yourself, since structured output isn't documented" ], "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": 55.1 } ], "editorialScores": { "interface": 60, "reach": 50, "risk": 55, "upkeep": 63 }, "provenanceScore": 61, "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": "vllm serve NousResearch/Arete-27B --max-model-len 262144 --reasoning-parser qwen3 \\\n --enable-auto-tool-choice --tool-call-parser qwen3_coder\n# or, with llama.cpp and bartowski's GGUF, a third party's conversion:\nllama-server -hf bartowski/NousResearch_Arete-27B-GGUF:Q4_K_M --jinja", "http": "curl http://localhost:8000/v1/chat/completions \\\n -H \"Content-Type: application/json\" \\\n -d '{\"model\": \"NousResearch/Arete-27B\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/arete-27b" }, "sameCompany": [ "hermes-agent" ], "weights": { "trainedBy": "Nous Research", "builtOn": "Qwen3.8-27B, declared as base_model in the card's metadata. Supervised fine-tuning with Inkling as the teacher", "params": "27.4B", "context": 262144, "goodFor": "Qwen3.8-27B for coding and agent work, text, images and video in, with its answers on politically sensitive topics retrained away from the Chinese state's account.", "runtimes": [ "vLLM", "SGLang", "Transformers", "llama.cpp" ], "builds": [ { "name": "bartowski NousResearch_Arete-27B-GGUF (Q4_K_M)", "url": "https://huggingface.co/bartowski/NousResearch_Arete-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 Nous Research'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. Reach doesn't count it" }, { "name": "bartowski NousResearch_Arete-27B-GGUF (Q8_0)", "url": "https://huggingface.co/bartowski/NousResearch_Arete-27B-GGUF", "who": "bartowski", "precision": "q8_0", "sizeGB": 29.6, "memoryGB": 48, "format": "gguf", "runsOn": [ "llama.cpp" ], "note": "From bartowski, a third party's conversion and not Nous Research'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. Reach doesn't count it" }, { "name": "Arete-27B", "url": "https://huggingface.co/NousResearch/Arete-27B", "who": "publisher", "precision": "bf16", "sizeGB": 54.7, "memoryGB": 96, "format": "safetensors", "runsOn": [ "vLLM (ROCm)", "SGLang (ROCm)", "Transformers" ], "note": "The only build Nous Research publishes" } ], "run": "llama-server -hf bartowski/NousResearch_Arete-27B-GGUF:Q4_K_M --jinja", "runBuild": "bartowski NousResearch_Arete-27B-GGUF (Q4_K_M)", "checked": "2026-11-10", "minMemoryGB": 24 }, "family": { "lab": "Nous Research (NousResearch)", "family": "Arete", "url": "https://www.anchorterminal.com/open-weights/#nous-research" }, "area": "models", "provenance": { "legalEntity": "Nous Research, Inc. (named in the footer of nousresearch.com)", "domain": "nousresearch.com", "domainRegistered": "", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://huggingface.co/NousResearch/Arete-27B/commits/main", "securityTxt": "none", "checked": "2026-11-10", "notes": [ "Nous Research, Inc. is the name in the site's footer, as the Hermes Agent listing records.", "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 repository's commit history, eight commits on 6 November 2026, the upload and updates to the card.", "The Hermes Agent listing found no security.txt on nousresearch.com. unchecked on 10 November.", "unchecked: the domain's registration date." ], "score": 61 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/arete-27b.json" }, "answer": "LBS-27B and Arete-27B score within a point of each other on agent readiness, 55.8 (C) and 55.1 (C). Arete-27B leads on risk and documentation and upkeep.", "b": { "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" } }, "edges": [ { "by": 20, "category": "upkeep", "leads": "arete-27b", "name": "Documentation and upkeep", "reasons": { "a": "Model card 8, no limitations, context length, languages or cut-off. Training data 20, the training pairs and the evaluation set are published with their counts. Last change 20, a commit on 6 November 2026. Responsiveness 0, none of 13 discussions has a reply from Nous Research. Version policy 5, a first release. File health 10, the files agree. 8 + 20 + 20 + 0 + 5 + 10 = 63.", "b": "The model card earns 8, with intended use, one limitation and parameters, and no architecture, context, languages or cut-off. Training data earns 5 for a general description of synthetic GUI data with no cut-off. Last change earns 20 for 26 October 2026. Responsiveness earns 0, the one discussion unanswered. Version policy earns 5 for a first release. File health earns 5, as the config still sets an MTP layer the weights lack and the card names a reasoning setting the template refuses." }, "scores": { "a": 63, "b": 43 } }, { "by": 10, "category": "reach", "leads": "lbs-27b", "name": "Reach", "reasons": { "a": "Memory 10, the only build from Nous Research is the 54.7 GB BF16 release, which needs about 66 GB and fits the reference machine. Runtimes 15, vLLM, SGLang and Transformers run the base's architecture from those files, and llama.cpp isn't credited because Nous Research publishes no GGUF. Builds 0, no quantised build of its own. Terms 15, Apache 2.0. Download 10, not gated. Hosted 0, no provider. 10 + 15 + 0 + 15 + 10 + 0 = 50.", "b": "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." }, "scores": { "a": 50, "b": 60 } }, { "by": 10, "category": "interface", "leads": "lbs-27b", "name": "Agent interface", "reasons": { "a": "Chat template 20, a Jinja file in the repository. Tool-calling format 10, only the template says it. Trained tool calling 20, several calls a turn and agentic work as an intended use. Structured output 0 and reasoning control 0, neither is documented on the card. Context 10, 262,144 tokens native. 20 + 10 + 20 + 0 + 0 + 10 = 60.", "b": "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." }, "scores": { "a": 60, "b": 70 } }, { "by": 5, "category": "risk", "leads": "arete-27b", "name": "Risk", "reasons": { "a": "File safety 25, safetensors only and no remote code. Provenance 15, Nous Research's own organisation, linked from its blog, with a hash on every file. Safety evaluation 0 and agent guidance 0, nothing published on either. Licence risk 15, plain Apache 2.0. Security programme 0, no way to report a flaw found. 25 + 15 + 0 + 0 + 15 + 0 = 55.", "b": "File safety earns 15, safetensors only but with the documented route running the publisher's scripts to serve it and to write its system prompt. Provenance earns 15, inclusionAI's own organisation with a hash for every file. Safety evaluation and agent guidance earn 0, as nothing is published on either, prompt injection through screenshots included. Licence risk keeps all 15 under Apache 2.0. The security programme earns 5 for Ant Group's AntSRC, which isn't linked to the model." }, "scores": { "a": 55, "b": 50 } } ], "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "Nous Research", "b": "inclusionAI (Ant Group)", "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", "b": "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)", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "no", "b": "no", "name": "llms.txt" }, { "a": "2026-11-06", "b": "2026-10-26", "name": "Last release" }, { "a": "no document linked", "b": "no document linked", "name": "Terms last updated" }, { "a": "no document linked", "b": "no document linked", "name": "Privacy policy last updated" } ], "faq": [ { "answer": "LBS-27B and Arete-27B score within a point of each other on agent readiness, 55.8 (C) and 55.1 (C). Arete-27B leads on risk and documentation and upkeep.", "question": "Which is better for AI agents, Arete-27B or LBS-27B?" } ], "goodFor": [ { "aheadOn": [ "Risk, 55 against 50", "Documentation and upkeep, 63 against 43" ], "also": null, "goodFor": "Agents that want Qwen3.8-27B's coding and tool use without its handling of politically sensitive topics, on a 24 GB card or a 128 GB machine", "slug": "arete-27b", "watchFor": "No quantised build from Nous Research, so the GGUF route depends on a third party" }, { "aheadOn": [ "Reach, 60 against 50", "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" } ], "history": { "arete-27b": null, "lbs-27b": [ { "causes": [ "correction" ], "date": "2026-11-04", "delta": 1.9, "from": 53.9, "fromGrade": "D", "to": 55.8, "toGrade": "C" } ] }, "job": { "capability": "model.open-weights", "name": "Open-weight models you run yourself" }, "others": [ { "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-arete-27b.json", "title": "Agents-A1-4B vs Arete-27B", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-arete-27b" }, { "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/apertus-v1-5-8b-vs-arete-27b.json", "title": "Apertus v1.5 8B vs Arete-27B", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-arete-27b" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-lbs-27b.json", "title": "Apertus v1.5 8B vs LBS-27B", "url": 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"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/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-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/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/kimi-k3-1-pro-vs-lbs-27b.json", "title": "Kimi K3.1 Pro vs LBS-27B", "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-lbs-27b" }, { "json": 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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/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-lfm3-1-7b.json", "title": "LBS-27B vs LFM3-1.7B", "url": "https://www.anchorterminal.com/compare/lbs-27b-vs-lfm3-1-7b" }, { "json": "https://www.anchorterminal.com/compare/lbs-27b-vs-lfm3-audio-2b.json", "title": "LBS-27B vs LFM3-Audio-2B", "url": "https://www.anchorterminal.com/compare/lbs-27b-vs-lfm3-audio-2b" }, { "json": "https://www.anchorterminal.com/compare/lbs-27b-vs-lfm3-omni-2b.json", "title": "LBS-27B vs LFM3-Omni-2B", "url": "https://www.anchorterminal.com/compare/lbs-27b-vs-lfm3-omni-2b" }, { "json": "https://www.anchorterminal.com/compare/lbs-27b-vs-lfm3-vl-2b.json", "title": "LBS-27B vs LFM3-VL-2B", "url": "https://www.anchorterminal.com/compare/lbs-27b-vs-lfm3-vl-2b" }, { "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-ling-3-1-flash-1019.json", "title": "LBS-27B vs Ling-3.1-flash-1019", "url": "https://www.anchorterminal.com/compare/lbs-27b-vs-ling-3-1-flash-1019" }, { "json": "https://www.anchorterminal.com/compare/lbs-27b-vs-longcat-2-5.json", "title": "LBS-27B vs LongCat-2.5", "url": "https://www.anchorterminal.com/compare/lbs-27b-vs-longcat-2-5" }, { "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-mimo-v2-6-flash-rl.json", "title": "LBS-27B vs MiMo-V2.6-Flash-RL", "url": "https://www.anchorterminal.com/compare/lbs-27b-vs-mimo-v2-6-flash-rl" }, { "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-minicpm-v-4-7-1b.json", "title": "LBS-27B vs MiniCPM-V 4.7 1B", "url": "https://www.anchorterminal.com/compare/lbs-27b-vs-minicpm-v-4-7-1b" }, { "json": "https://www.anchorterminal.com/compare/lbs-27b-vs-minicpm-v-4-7-35b-a3b.json", "title": "LBS-27B vs MiniCPM-V 4.7 35B-A3B", "url": "https://www.anchorterminal.com/compare/lbs-27b-vs-minicpm-v-4-7-35b-a3b" }, { "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-mistral-large-4.json", "title": "LBS-27B vs Mistral Large 4", "url": "https://www.anchorterminal.com/compare/lbs-27b-vs-mistral-large-4" }, { "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-4-2-3b.json", "title": "LBS-27B vs Nanbeige4.2-3B", "url": "https://www.anchorterminal.com/compare/lbs-27b-vs-nanbeige-4-2-3b" }, { "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": "https://www.anchorterminal.com/compare/lbs-27b-vs-nemotron-3-5-lightning-30b-a3b.json", "title": "LBS-27B vs Nemotron 3.5 Lightning 30B A3B", "url": "https://www.anchorterminal.com/compare/lbs-27b-vs-nemotron-3-5-lightning-30b-a3b" }, { "json": "https://www.anchorterminal.com/compare/lbs-27b-vs-nemotron-3-nano-30b-a3b.json", "title": "LBS-27B vs Nemotron 3 Nano 30B A3B", "url": "https://www.anchorterminal.com/compare/lbs-27b-vs-nemotron-3-nano-30b-a3b" }, { "json": "https://www.anchorterminal.com/compare/lbs-27b-vs-nemotron-3-nano-4b.json", "title": "LBS-27B vs Nemotron 3 Nano 4B", "url": "https://www.anchorterminal.com/compare/lbs-27b-vs-nemotron-3-nano-4b" }, { "json": "https://www.anchorterminal.com/compare/lbs-27b-vs-nemotron-3-nano-omni-30b-a3b-reasoning.json", "title": "LBS-27B vs Nemotron 3 Nano Omni 30B A3B Reasoning", "url": "https://www.anchorterminal.com/compare/lbs-27b-vs-nemotron-3-nano-omni-30b-a3b-reasoning" }, { "json": "https://www.anchorterminal.com/compare/lbs-27b-vs-nemotron-3-super-120b-a12b.json", "title": "LBS-27B vs Nemotron 3 Super 120B A12B", "url": "https://www.anchorterminal.com/compare/lbs-27b-vs-nemotron-3-super-120b-a12b" }, { "json": "https://www.anchorterminal.com/compare/lbs-27b-vs-nemotron-3-ultra-550b-a55b.json", "title": "LBS-27B vs Nemotron 3 Ultra 550B A55B", "url": "https://www.anchorterminal.com/compare/lbs-27b-vs-nemotron-3-ultra-550b-a55b" }, { "json": "https://www.anchorterminal.com/compare/lbs-27b-vs-north-mini-code-1-0.json", "title": "LBS-27B vs North Mini Code 1.0", "url": "https://www.anchorterminal.com/compare/lbs-27b-vs-north-mini-code-1-0" }, { "json": "https://www.anchorterminal.com/compare/lbs-27b-vs-occamy-1-0.json", "title": "LBS-27B vs occamy-1.0", "url": "https://www.anchorterminal.com/compare/lbs-27b-vs-occamy-1-0" }, { "json": "https://www.anchorterminal.com/compare/lbs-27b-vs-olmo-3-7b-think.json", "title": "LBS-27B vs Olmo 3 7B Think", "url": "https://www.anchorterminal.com/compare/lbs-27b-vs-olmo-3-7b-think" }, { "json": "https://www.anchorterminal.com/compare/lbs-27b-vs-ornith-1-5-122b.json", "title": "LBS-27B vs Ornith-1.5-122B", "url": "https://www.anchorterminal.com/compare/lbs-27b-vs-ornith-1-5-122b" }, { "json": "https://www.anchorterminal.com/compare/lbs-27b-vs-ornith-1-5-35b-a3b.json", "title": "LBS-27B vs Ornith-1.5-35B-A3B", "url": "https://www.anchorterminal.com/compare/lbs-27b-vs-ornith-1-5-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/lbs-27b-vs-ornith-1-5-397b.json", "title": "LBS-27B vs Ornith-1.5-397B", "url": "https://www.anchorterminal.com/compare/lbs-27b-vs-ornith-1-5-397b" }, { "json": "https://www.anchorterminal.com/compare/lbs-27b-vs-ornith-1-5-9b.json", "title": "LBS-27B vs Ornith-1.5-9B", "url": "https://www.anchorterminal.com/compare/lbs-27b-vs-ornith-1-5-9b" }, { "json": "https://www.anchorterminal.com/compare/lbs-27b-vs-otel-2-0-llm-31b-it.json", "title": "LBS-27B vs OTel-2.0-LLM-31B-IT", "url": "https://www.anchorterminal.com/compare/lbs-27b-vs-otel-2-0-llm-31b-it" }, { "json": "https://www.anchorterminal.com/compare/lbs-27b-vs-qwen-agentworld-35b-a3b.json", "title": "LBS-27B vs Qwen-AgentWorld-35B-A3B", "url": "https://www.anchorterminal.com/compare/lbs-27b-vs-qwen-agentworld-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/lbs-27b-vs-qwen3-30b-a3b-instruct-2507.json", "title": "LBS-27B vs Qwen3-30B-A3B-Instruct-2507", "url": "https://www.anchorterminal.com/compare/lbs-27b-vs-qwen3-30b-a3b-instruct-2507" }, { "json": "https://www.anchorterminal.com/compare/lbs-27b-vs-qwen3-4b-instruct-2507.json", "title": "LBS-27B vs Qwen3-4B-Instruct-2507", "url": "https://www.anchorterminal.com/compare/lbs-27b-vs-qwen3-4b-instruct-2507" }, { "json": "https://www.anchorterminal.com/compare/lbs-27b-vs-qwen3-5-0-8b.json", "title": "LBS-27B vs Qwen3.5-0.8B", "url": "https://www.anchorterminal.com/compare/lbs-27b-vs-qwen3-5-0-8b" }, { "json": "https://www.anchorterminal.com/compare/lbs-27b-vs-qwen3-5-122b-a10b.json", "title": "LBS-27B vs Qwen3.5-122B-A10B", "url": "https://www.anchorterminal.com/compare/lbs-27b-vs-qwen3-5-122b-a10b" }, { "json": "https://www.anchorterminal.com/compare/lbs-27b-vs-qwen3-5-2b.json", "title": "LBS-27B vs Qwen3.5-2B", "url": "https://www.anchorterminal.com/compare/lbs-27b-vs-qwen3-5-2b" }, { "json": "https://www.anchorterminal.com/compare/lbs-27b-vs-qwen3-5-397b-a17b.json", "title": "LBS-27B vs Qwen3.5-397B-A17B", "url": "https://www.anchorterminal.com/compare/lbs-27b-vs-qwen3-5-397b-a17b" }, { "json": "https://www.anchorterminal.com/compare/lbs-27b-vs-qwen3-5-4b.json", "title": "LBS-27B vs Qwen3.5-4B", "url": "https://www.anchorterminal.com/compare/lbs-27b-vs-qwen3-5-4b" }, { "json": "https://www.anchorterminal.com/compare/lbs-27b-vs-qwen3-5-9b.json", "title": "LBS-27B vs Qwen3.5-9B", "url": "https://www.anchorterminal.com/compare/lbs-27b-vs-qwen3-5-9b" }, { "json": "https://www.anchorterminal.com/compare/lbs-27b-vs-qwen3-6-27b.json", "title": "LBS-27B vs Qwen3.6-27B", "url": "https://www.anchorterminal.com/compare/lbs-27b-vs-qwen3-6-27b" }, { "json": "https://www.anchorterminal.com/compare/lbs-27b-vs-qwen3-6-35b-a3b.json", "title": "LBS-27B vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/lbs-27b-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/lbs-27b-vs-qwen3-8-27b.json", "title": "LBS-27B vs Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/lbs-27b-vs-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/lbs-27b-vs-qwen3-8-flash-next.json", "title": "LBS-27B vs Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/lbs-27b-vs-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/lbs-27b-vs-qwen3-coder-30b-a3b-instruct.json", "title": "LBS-27B vs Qwen3-Coder-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/lbs-27b-vs-qwen3-coder-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/lbs-27b-vs-qwen3-coder-next.json", "title": "LBS-27B vs Qwen3-Coder-Next", "url": "https://www.anchorterminal.com/compare/lbs-27b-vs-qwen3-coder-next" }, { "json": "https://www.anchorterminal.com/compare/lbs-27b-vs-qwen3-omni-30b-a3b-instruct.json", "title": "LBS-27B vs Qwen3-Omni-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/lbs-27b-vs-qwen3-omni-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/lbs-27b-vs-qwen3-vl-2b-instruct.json", "title": "LBS-27B vs Qwen3-VL-2B-Instruct", "url": "https://www.anchorterminal.com/compare/lbs-27b-vs-qwen3-vl-2b-instruct" }, { "json": "https://www.anchorterminal.com/compare/lbs-27b-vs-qwen3-vl-30b-a3b-instruct.json", "title": "LBS-27B vs Qwen3-VL-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/lbs-27b-vs-qwen3-vl-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/lbs-27b-vs-qwen3-vl-32b-instruct.json", "title": "LBS-27B vs Qwen3-VL-32B-Instruct", "url": "https://www.anchorterminal.com/compare/lbs-27b-vs-qwen3-vl-32b-instruct" }, { "json": "https://www.anchorterminal.com/compare/lbs-27b-vs-qwen3-vl-4b-instruct.json", "title": "LBS-27B vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/lbs-27b-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/lbs-27b-vs-qwen3-vl-8b-instruct.json", "title": "LBS-27B vs Qwen3-VL-8B-Instruct", "url": "https://www.anchorterminal.com/compare/lbs-27b-vs-qwen3-vl-8b-instruct" }, { "json": "https://www.anchorterminal.com/compare/lbs-27b-vs-qwopus3-8-27b-flash-v2.json", "title": "LBS-27B vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/lbs-27b-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/lbs-27b-vs-recursive-flint.json", "title": "LBS-27B vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/lbs-27b-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/lbs-27b-vs-ripple-65b-preview.json", "title": "LBS-27B vs Ripple-65B Preview", "url": "https://www.anchorterminal.com/compare/lbs-27b-vs-ripple-65b-preview" }, { "json": "https://www.anchorterminal.com/compare/lbs-27b-vs-sensenova-loop-9b-preview.json", "title": "LBS-27B vs SenseNova-Loop-9B-Preview", "url": "https://www.anchorterminal.com/compare/lbs-27b-vs-sensenova-loop-9b-preview" }, { "json": "https://www.anchorterminal.com/compare/lbs-27b-vs-smollm3-3b.json", "title": "LBS-27B vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/lbs-27b-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/lbs-27b-vs-spark-x2-6-4b.json", "title": "LBS-27B vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/lbs-27b-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/lbs-27b-vs-step-3-7-flash.json", "title": "LBS-27B vs Step 3.7 Flash", "url": "https://www.anchorterminal.com/compare/lbs-27b-vs-step-3-7-flash" }, { "json": "https://www.anchorterminal.com/compare/lbs-27b-vs-swift-1-5-qwen3-8-27b.json", "title": "LBS-27B vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/lbs-27b-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/lbs-27b-vs-swift-1-5-qwen3-8-flash-next.json", "title": "LBS-27B vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/lbs-27b-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/lbs-27b-vs-thinkingcap-qwen3-8-27b.json", "title": "LBS-27B vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/lbs-27b-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/lbs-27b-vs-unlimited-ocr-2.json", "title": "LBS-27B vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/lbs-27b-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/lbs-27b-vs-wevisone-vl-2b.json", "title": "LBS-27B vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/lbs-27b-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/lbs-27b-vs-wevisone-vl-4b.json", "title": "LBS-27B vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/lbs-27b-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/lbs-27b-vs-zaya1-8b.json", "title": "LBS-27B vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/lbs-27b-vs-zaya1-8b" } ], "panel": null, "scores": [ { "key": "capability", "name": "Capability", "pending": true, "weight": 35 }, { "arete-27b": 50, "by": 10, "edge": "lbs-27b", "key": "reach", "lbs-27b": 60, "name": "Reach", "weight": 25 }, { "arete-27b": 55, "by": 5, "edge": "arete-27b", "key": "risk", "lbs-27b": 50, "name": "Risk", "weight": 20 }, { "arete-27b": 60, "by": 10, "edge": "lbs-27b", "key": "interface", "lbs-27b": 70, "name": "Agent interface", "weight": 10 }, { "arete-27b": 63, "by": 20, "edge": "arete-27b", "key": "upkeep", "lbs-27b": 43, "name": "Documentation and upkeep", "weight": 10 } ], "summary": "LBS-27B and Arete-27B score within a point of each other on agent readiness, 55.8 (C) and 55.1 (C). Arete-27B leads on risk and documentation and upkeep. Both do open-weight models you run yourself.", "tasks": null, "tutorials": [], "verdicts": { "arete-27b": "Qwen3.8-27B with its answers on politically sensitive topics retrained, four days old when read. The training data is published, which is rare. Nous Research ships BF16 only, so a small machine runs it from bartowski's GGUF, and the card documents none of the tool or thinking controls it inherits.", "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." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/arete-27b-vs-lbs-27b", "json": "https://www.anchorterminal.com/compare/arete-27b-vs-lbs-27b.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/arete-27b-vs-lbs-27b.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/arete-27b-vs-lbs-27b.min.md" }, "markdown": "LBS-27B and Arete-27B score within a point of each other on agent readiness, 55.8 (C) and 55.1 (C). Arete-27B leads on risk and documentation and upkeep. Both do open-weight models you run yourself.\n\n- Arete-27B: grade C, 55.1/100, rank #123 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/arete-27b.md · JSON https://www.anchorterminal.com/api/v1/tools/arete-27b.json\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- 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### Arete-27B (C)\n\nGood for: Agents that want Qwen3.8-27B's coding and tool use without its handling of politically sensitive topics, on a 24 GB card or a 128 GB machine\n\nAhead on:\n- Risk, 55 against 50\n- Documentation and upkeep, 63 against 43\n\nWatch for: No quantised build from Nous Research, so the GGUF route depends on a third party\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 50\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\n## Why the edge\n\n### Documentation and upkeep: Arete-27B +20\n\n- Arete-27B (63): Model card 8, no limitations, context length, languages or cut-off. Training data 20, the training pairs and the evaluation set are published with their counts. Last change 20, a commit on 6 November 2026. Responsiveness 0, none of 13 discussions has a reply from Nous Research. Version policy 5, a first release. File health 10, the files agree. 8 + 20 + 20 + 0 + 5 + 10 = 63.\n- LBS-27B (43): The model card earns 8, with intended use, one limitation and parameters, and no architecture, context, languages or cut-off. Training data earns 5 for a general description of synthetic GUI data with no cut-off. Last change earns 20 for 26 October 2026. Responsiveness earns 0, the one discussion unanswered. Version policy earns 5 for a first release. File health earns 5, as the config still sets an MTP layer the weights lack and the card names a reasoning setting the template refuses.\n\n### Reach: LBS-27B +10\n\n- Arete-27B (50): Memory 10, the only build from Nous Research is the 54.7 GB BF16 release, which needs about 66 GB and fits the reference machine. Runtimes 15, vLLM, SGLang and Transformers run the base's architecture from those files, and llama.cpp isn't credited because Nous Research publishes no GGUF. Builds 0, no quantised build of its own. Terms 15, Apache 2.0. Download 10, not gated. Hosted 0, no provider. 10 + 15 + 0 + 15 + 10 + 0 = 50.\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\n### Agent interface: LBS-27B +10\n\n- Arete-27B (60): Chat template 20, a Jinja file in the repository. Tool-calling format 10, only the template says it. Trained tool calling 20, several calls a turn and agentic work as an intended use. Structured output 0 and reasoning control 0, neither is documented on the card. Context 10, 262,144 tokens native. 20 + 10 + 20 + 0 + 0 + 10 = 60.\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\n### Risk: Arete-27B +5\n\n- Arete-27B (55): File safety 25, safetensors only and no remote code. Provenance 15, Nous Research's own organisation, linked from its blog, with a hash on every file. Safety evaluation 0 and agent guidance 0, nothing published on either. Licence risk 15, plain Apache 2.0. Security programme 0, no way to report a flaw found. 25 + 15 + 0 + 0 + 15 + 0 = 55.\n- LBS-27B (50): File safety earns 15, safetensors only but with the documented route running the publisher's scripts to serve it and to write its system prompt. Provenance earns 15, inclusionAI's own organisation with a hash for every file. Safety evaluation and agent guidance earn 0, as nothing is published on either, prompt injection through screenshots included. Licence risk keeps all 15 under Apache 2.0. The security programme earns 5 for Ant Group's AntSRC, which isn't linked to the model.\n\n## Score by category\n\n| Category | Weight | Arete-27B | LBS-27B | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 50 | 60 | LBS-27B +10 |\n| Risk | 20% (20 this run) | 55 | 50 | Arete-27B +5 |\n| Agent interface | 10% (10 this run) | 60 | 70 | LBS-27B +10 |\n| Documentation and upkeep | 10% (10 this run) | 63 | 43 | Arete-27B +20 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **55.1 · C** | **55.8 · C** | |\n\n## Facts side by side\n\n| Fact | Arete-27B | LBS-27B |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Nous Research | inclusionAI (Ant Group) |\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 | Apache-2.0 (the card's metadata and licence section; the LICENSE file sits in the GitHub repository inclusionAI/UI-Venus on its LBS branch, not in the Hugging Face repository) |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-11-06 | 2026-10-26 |\n| Terms last updated | no document linked | no document linked |\n| Privacy policy last updated | no document linked | no document linked |\n\n## How the scores have moved\n\n### Arete-27B\n\nNo score change since it was listed.\n\n### LBS-27B\n\n- +1.9 on 4 November 2026 (D to C): correction\n\n## Verdicts\n\n**Arete-27B.** Qwen3.8-27B with its answers on politically sensitive topics retrained, four days old when read. The training data is published, which is rare. Nous Research ships BF16 only, so a small machine runs it from bartowski's GGUF, and the card documents none of the tool or thinking controls it inherits.\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## Before you call either\n\n### Arete-27B\n\n1. Start it with `llama-server -hf bartowski/NousResearch_Arete-27B-GGUF:Q4_K_M --jinja`, a third party's conversion, or serve the BF16 files with vLLM\n2. Use the base's parsers in vLLM, `--reasoning-parser qwen3 --tool-call-parser qwen3_coder`, and check tool calls first, since the template differs slightly from Qwen3.8-27B's\n3. Thinking switches with `enable_thinking` in the template. The card's own evaluation ran with it off\n4. Expect answers on politically sensitive topics to differ from Qwen3.8-27B's. That is what the fine-tune is for\n5. Validate JSON output yourself, since structured output isn't documented\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## Questions\n\n### Which is better for AI agents, Arete-27B or LBS-27B?\n\nLBS-27B and Arete-27B score within a point of each other on agent readiness, 55.8 (C) and 55.1 (C). Arete-27B leads on risk and documentation and upkeep.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/arete-27b-vs-lbs-27b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/arete-27b-vs-lbs-27b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"arete-27b\", \"b\": \"lbs-27b\"}`. From a terminal: `anchor compare arete-27b lbs-27b`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/arete-27b.json and https://www.anchorterminal.com/api/v1/tools/lbs-27b.json\n\n## Other comparisons with Arete-27B or LBS-27B\n\n- [Agents-A1-4B vs Arete-27B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-arete-27b.md)\n- [Agents-A1-4B vs LBS-27B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-lbs-27b.md)\n- [Apertus v1.5 8B vs Arete-27B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-arete-27b.md)\n- [Apertus v1.5 8B vs LBS-27B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-lbs-27b.md)\n- [Apodex-1.1-mini vs Arete-27B](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-arete-27b.md)\n- [Apodex-1.1-mini vs LBS-27B](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-lbs-27b.md)\n- [Arete-27B vs BigBang-v1](https://www.anchorterminal.com/compare/arete-27b-vs-bigbang-v1.md)\n- [Arete-27B vs 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Qwen3-VL-8B-Instruct](https://www.anchorterminal.com/compare/lbs-27b-vs-qwen3-vl-8b-instruct.md)\n- [LBS-27B vs Qwopus3.8-27B-Flash-V2](https://www.anchorterminal.com/compare/lbs-27b-vs-qwopus3-8-27b-flash-v2.md)\n- [LBS-27B vs Recursive Flint](https://www.anchorterminal.com/compare/lbs-27b-vs-recursive-flint.md)\n- [LBS-27B vs Ripple-65B Preview](https://www.anchorterminal.com/compare/lbs-27b-vs-ripple-65b-preview.md)\n- [LBS-27B vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/lbs-27b-vs-sensenova-loop-9b-preview.md)\n- [LBS-27B vs SmolLM3-3B](https://www.anchorterminal.com/compare/lbs-27b-vs-smollm3-3b.md)\n- [LBS-27B vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/lbs-27b-vs-spark-x2-6-4b.md)\n- [LBS-27B vs Step 3.7 Flash](https://www.anchorterminal.com/compare/lbs-27b-vs-step-3-7-flash.md)\n- [LBS-27B vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/lbs-27b-vs-swift-1-5-qwen3-8-27b.md)\n- [LBS-27B vs Swift 1.5 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"https://www.anchorterminal.com/benchmark/", "methodology": "0.7", "openapi": "https://www.anchorterminal.com/openapi.json", "preview": false, "run": "2026-10-01", "runLabel": "October 2026 research run", "terms": "https://www.anchorterminal.com/terms/#data" }, "page": { "breadcrumbs": [ { "name": "Home", "url": "https://www.anchorterminal.com/" }, { "name": "Compare", "url": "https://www.anchorterminal.com/compare/" }, { "name": "Arete-27B vs LBS-27B", "url": "" } ], "description": "LBS-27B and Arete-27B score within a point of each other for open-weight models you run yourself, 55.8 and 55.1 out of 100. 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