{
  "data": {
    "a": {
      "slug": "granite-4-2-3b",
      "name": "Granite 4.2 3B",
      "vendor": "IBM",
      "vendorUrl": "https://www.ibm.com/granite",
      "kind": "weights",
      "category": "open-weights",
      "summary": "Granite 4.2 3B is IBM's open-weight dense reasoning model of 3.7B parameters, post-trained from Granite 4.1 3B Base, with switchable thinking, tool calling and a 131,072-token context, published in BF16, FP8, FP4, GGUF and MLX under Apache 2.0.",
      "url": "https://www.anchorterminal.com/tools/granite-4-2-3b",
      "markdownUrl": "https://www.anchorterminal.com/tools/granite-4-2-3b.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/granite-4-2-3b.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/granite-4-2-3b.json",
      "repo": "https://huggingface.co/ibm-granite/granite-4.2-3b",
      "license": "Apache-2.0",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "ibm-granite/granite-4.2-3b"
        },
        {
          "registry": "huggingface",
          "name": "ibm-granite/granite-4.2-3b-GGUF"
        },
        {
          "registry": "huggingface",
          "name": "ibm-granite/granite-4.2-3b-fp8"
        },
        {
          "registry": "huggingface",
          "name": "ibm-granite/granite-4.2-3b-mxfp4"
        },
        {
          "registry": "huggingface",
          "name": "ibm-granite/granite-4.2-3b-nvfp4"
        },
        {
          "registry": "huggingface",
          "name": "ibm-granite/granite-4.2-3b-q4-mlx"
        },
        {
          "registry": "ollama",
          "name": "granite4.2:3b"
        }
      ],
      "auth": "none",
      "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. None of IBM's 4.2 3B repositories is gated (checked 2026-10-21). The files carry a sigstore signature (model.sig) that IBM documents how to verify.",
      "pricing": "free",
      "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0. watsonx.ai lists Granite 4.1 models but not 4.2, and DeepInfra is the only inference provider Hugging Face maps for it. Running it yourself costs the hardware, 7.3 GB for the BF16 files and 2.2 GB for Ollama's Q4_K_M 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/ibm-granite/granite-4.2-3b",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "dense",
        "small",
        "reasoning",
        "tool-calling",
        "multilingual",
        "apache-2.0",
        "safetensors",
        "fp8",
        "fp4",
        "gguf",
        "mlx",
        "signed",
        "self-hosted"
      ],
      "lastRelease": "2026-10-20",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 59.8,
        "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": 1217,
        "categoryRank": 53,
        "methodology": "0.6",
        "run": "2026-10-01",
        "scores": {
          "capability": 21,
          "interface": 85,
          "reach": 95,
          "risk": 55,
          "upkeep": 92
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "high",
          "date": "2026-10-21"
        },
        "negative": 0,
        "verdict": "A small reasoning cousin for tool calls, coding and multilingual chat that runs almost anywhere, from a 2.2 GB Ollama build to vLLM and SGLang, under plain Apache 2.0 with signed files and a dataset disclosure. Thinking turns off or shortens per request. It has no published safety results and no documented JSON mode.",
        "bestFor": "Agents on small hardware that want a permissive reasoning model for tool calls and coding, with thinking they can switch off",
        "strengths": [
          "Apache 2.0 with nothing added, ungated, signed with sigstore in IBM's own organisation",
          "2.2 GB as Ollama's Q4_K_M build, with IBM's own FP8, FP4, GGUF and MLX builds",
          "Runs in llama.cpp, Ollama, vLLM, SGLang and Transformers",
          "Tool format and parser documented for vLLM and SGLang, with several calls a turn",
          "Thinking that turns off or runs at low effort per request",
          "A machine-readable disclosure of the training datasets with sources and sizes"
        ],
        "weaknesses": [
          "No safety results published for 4.2, and only general safety cautions",
          "No documented JSON or schema output",
          "The card's vLLM route loads a parser plugin from the repository",
          "Only one inference provider, and watsonx.ai doesn't list 4.2",
          "No knowledge cut-off, and Granite 4.1 3B carries no note that 4.2 replaced it"
        ],
        "agentNotes": [
          "Start it with `ollama run granite4.2:3b`; set options.num_ctx in the request, since Ollama's default context is short and the model takes up to 131,072",
          "With vLLM 0.32.0 or later, serve `ibm-granite/granite-4.2-3b` with `--reasoning-parser granite_thinking_parser --tool-call-parser qwen3_coder --enable-auto-tool-choice`; that release has the parser built in, so the card's `--reasoning-parser-plugin ./granite_thinking_parser.py` isn't needed",
          "With SGLang 0.5.18 or later, `--reasoning-parser auto --tool-call-parser auto` resolve to IBM's recommended parsers, and the reasoning comes back in reasoning_content",
          "Use temperature 1.0 and top_p 0.95 for every mode, as IBM requires",
          "Pass `enable_thinking` false for direct answers or `low_effort` true for short reasoning, through chat_template_kwargs on an OpenAI-compatible server",
          "Earlier turns' thinking is stripped by default (`truncate_history_thinking`), so pass it false to keep reasoning in multi-turn history",
          "For JSON, use the runtime's guided decoding, since IBM documents no JSON mode for 4.2",
          "Verify the files with `python -m model_signing verify sigstore` and IBM's identity, per IBM's signature guide"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 4,
        "avgRating": 3.8,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "high",
            "grade": "C",
            "methodology": "0.6",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 59.8
          }
        ],
        "editorialScores": {
          "capability": 21,
          "interface": 85,
          "reach": 95,
          "risk": 55,
          "upkeep": 92
        },
        "provenanceScore": 87,
        "checked": {
          "categories": {
            "capability": "2026-10-21",
            "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": "ollama run granite4.2:3b\n# or, with vLLM 0.32.0 or later:\nvllm serve ibm-granite/granite-4.2-3b --max-model-len 131072 \\\n  --reasoning-parser granite_thinking_parser \\\n  --tool-call-parser qwen3_coder --enable-auto-tool-choice",
        "http": "curl http://localhost:11434/api/chat \\\n  -d '{\"model\": \"granite4.2:3b\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/granite-4-2-3b"
      },
      "sameCompany": [
        "granite-embedding-small-english-r2",
        "granite-guardian",
        "granite-switch"
      ],
      "weights": {
        "trainedBy": "IBM",
        "builtOn": "ibm-granite/granite-4.1-3b-base (IBM's own base checkpoint, post-trained; named on the card and in its metadata)",
        "params": "3.66B",
        "context": 131072,
        "goodFor": "Small dense reasoning model for tool calling, coding and multilingual chat, with thinking that is on, off or short per request.",
        "runtimes": [
          "llama.cpp",
          "Ollama",
          "vLLM",
          "SGLang",
          "Transformers"
        ],
        "builds": [
          {
            "name": "granite-4.2-3b-fp8",
            "url": "https://huggingface.co/ibm-granite/granite-4.2-3b-fp8",
            "who": "publisher",
            "precision": "fp8",
            "sizeGB": 4.19,
            "memoryGB": 8,
            "format": "safetensors",
            "runsOn": [
              "vLLM"
            ]
          },
          {
            "name": "granite-4.2-3b-nvfp4",
            "url": "https://huggingface.co/ibm-granite/granite-4.2-3b-nvfp4",
            "who": "publisher",
            "precision": "nvfp4",
            "sizeGB": 2.81,
            "memoryGB": 8,
            "format": "safetensors",
            "runsOn": [
              "vLLM"
            ],
            "note": "GPTQ calibrated with LLM Compressor, per IBM's blog"
          },
          {
            "name": "granite-4.2-3b-mxfp4",
            "url": "https://huggingface.co/ibm-granite/granite-4.2-3b-mxfp4",
            "who": "publisher",
            "precision": "mxfp4",
            "sizeGB": 2.71,
            "memoryGB": 8,
            "format": "safetensors",
            "runsOn": [
              "vLLM"
            ],
            "note": "GPTQ calibrated with LLM Compressor, per IBM's blog"
          },
          {
            "name": "granite-4.2-3b-Q8_0",
            "url": "https://huggingface.co/ibm-granite/granite-4.2-3b-GGUF/blob/main/granite-4.2-3b-Q8_0.gguf",
            "who": "publisher",
            "precision": "q8_0",
            "sizeGB": 3.89,
            "memoryGB": 8,
            "format": "gguf",
            "runsOn": [
              "llama.cpp"
            ]
          },
          {
            "name": "granite-4.2-3b-Q4_K_M",
            "url": "https://huggingface.co/ibm-granite/granite-4.2-3b-GGUF/blob/main/granite-4.2-3b-Q4_K_M.gguf",
            "who": "publisher",
            "precision": "q4_k_m",
            "sizeGB": 2.24,
            "memoryGB": 8,
            "format": "gguf",
            "runsOn": [
              "llama.cpp"
            ],
            "note": "IBM's GGUF repository also holds Q2_K to Q6_K, Q4_0, Q4_1, Q5_0, Q5_1 and BF16 files; Q4_0 is the smallest 4-bit GGUF at 2.13 GB"
          },
          {
            "name": "granite-4.2-3b-q4-mlx",
            "url": "https://huggingface.co/ibm-granite/granite-4.2-3b-q4-mlx",
            "who": "publisher",
            "precision": "q4",
            "sizeGB": 2.07,
            "memoryGB": 8,
            "format": "mlx",
            "runsOn": [
              "mlx-lm"
            ],
            "note": "The smallest build at 4-bit or better. IBM also publishes q8-mlx (3.90 GB) and bf16-mlx (7.33 GB)"
          },
          {
            "name": "ollama granite4.2:3b",
            "url": "https://ollama.com/library/granite4.2:3b",
            "who": "ollama",
            "precision": "q4_k_m",
            "sizeGB": 2.2,
            "memoryGB": 8,
            "format": "ollama",
            "runsOn": [
              "Ollama"
            ],
            "note": "Q4_K_M, 3.66B parameters, granite architecture, per Ollama's model page (digest 40577dc168a3). Size as Ollama shows it"
          },
          {
            "name": "granite-4.2-3b",
            "url": "https://huggingface.co/ibm-granite/granite-4.2-3b",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 7.33,
            "memoryGB": 12,
            "format": "safetensors",
            "runsOn": [
              "vLLM",
              "SGLang",
              "Transformers"
            ]
          }
        ],
        "run": "ollama run granite4.2:3b",
        "runBuild": "ollama granite4.2:3b",
        "checked": "2026-10-21",
        "minMemoryGB": 8
      },
      "family": {
        "lab": "IBM (Granite)",
        "family": "Granite 4 (4.0, 4.1, 4.2)",
        "url": "https://www.anchorterminal.com/open-weights/#ibm",
        "others": [
          {
            "name": "granite-4.2-8b",
            "url": "https://huggingface.co/ibm-granite/granite-4.2-8b",
            "params": "8B (Hub safetensors count 8.79B)"
          },
          {
            "name": "granite-4.2-30b",
            "url": "https://huggingface.co/ibm-granite/granite-4.2-30b",
            "params": "30B (Hub safetensors count 29.28B)"
          },
          {
            "name": "granite-4.1-8b-base",
            "url": "https://huggingface.co/ibm-granite/granite-4.1-8b-base",
            "params": "8B (Hub safetensors count 8.38B)"
          },
          {
            "name": "granite-4.1-8b",
            "url": "https://huggingface.co/ibm-granite/granite-4.1-8b",
            "params": "8B (Hub safetensors count 8.79B)"
          },
          {
            "name": "granite-4.1-3b-base",
            "url": "https://huggingface.co/ibm-granite/granite-4.1-3b-base",
            "params": "3B (Hub safetensors count 3.40B)"
          },
          {
            "name": "granite-4.1-3b",
            "url": "https://huggingface.co/ibm-granite/granite-4.1-3b",
            "params": "3B (Hub safetensors count 3.40B)"
          },
          {
            "name": "granite-4.1-30b-base",
            "url": "https://huggingface.co/ibm-granite/granite-4.1-30b-base",
            "params": "30B (Hub safetensors count 28.87B)"
          },
          {
            "name": "granite-4.1-30b",
            "url": "https://huggingface.co/ibm-granite/granite-4.1-30b",
            "params": "30B (Hub safetensors count 28.87B)"
          },
          {
            "name": "granite-4.0-h-350m-base",
            "url": "https://huggingface.co/ibm-granite/granite-4.0-h-350m-base",
            "params": "350M (Hub safetensors count 340M)"
          },
          {
            "name": "granite-4.0-h-350m",
            "url": "https://huggingface.co/ibm-granite/granite-4.0-h-350m",
            "params": "350M (Hub safetensors count 340M)"
          },
          {
            "name": "granite-4.0-h-1b-base",
            "url": "https://huggingface.co/ibm-granite/granite-4.0-h-1b-base",
            "params": "1B (Hub safetensors count 1.46B)"
          },
          {
            "name": "granite-4.0-h-1b",
            "url": "https://huggingface.co/ibm-granite/granite-4.0-h-1b",
            "params": "1B (Hub safetensors count 1.46B)"
          },
          {
            "name": "granite-4.0-350m-base",
            "url": "https://huggingface.co/ibm-granite/granite-4.0-350m-base",
            "params": "350M (Hub safetensors count 352M)"
          },
          {
            "name": "granite-4.0-350m",
            "url": "https://huggingface.co/ibm-granite/granite-4.0-350m",
            "params": "350M (Hub safetensors count 352M)"
          },
          {
            "name": "granite-4.0-1b-base",
            "url": "https://huggingface.co/ibm-granite/granite-4.0-1b-base",
            "params": "1B (Hub safetensors count 1.63B)"
          },
          {
            "name": "granite-4.0-1b",
            "url": "https://huggingface.co/ibm-granite/granite-4.0-1b",
            "params": "1B (Hub safetensors count 1.63B)"
          },
          {
            "name": "granite-4.0-micro-base",
            "url": "https://huggingface.co/ibm-granite/granite-4.0-micro-base",
            "params": "3.40B (Hub safetensors count; the name states none)"
          },
          {
            "name": "granite-4.0-micro",
            "url": "https://huggingface.co/ibm-granite/granite-4.0-micro",
            "params": "3.40B (Hub safetensors count; the name states none)"
          },
          {
            "name": "granite-4.0-h-tiny-base",
            "url": "https://huggingface.co/ibm-granite/granite-4.0-h-tiny-base",
            "params": "6.94B (Hub safetensors count; the name states none)"
          },
          {
            "name": "granite-4.0-h-tiny",
            "url": "https://huggingface.co/ibm-granite/granite-4.0-h-tiny",
            "params": "6.94B (Hub safetensors count; the name states none)"
          },
          {
            "name": "granite-4.0-h-small-base",
            "url": "https://huggingface.co/ibm-granite/granite-4.0-h-small-base",
            "params": "32.21B (Hub safetensors count; the name states none)"
          },
          {
            "name": "granite-4.0-h-small",
            "url": "https://huggingface.co/ibm-granite/granite-4.0-h-small",
            "params": "32.21B (Hub safetensors count; the name states none)"
          },
          {
            "name": "granite-4.0-h-micro-base",
            "url": "https://huggingface.co/ibm-granite/granite-4.0-h-micro-base",
            "params": "3.19B (Hub safetensors count; the name states none)"
          },
          {
            "name": "granite-4.0-h-micro",
            "url": "https://huggingface.co/ibm-granite/granite-4.0-h-micro",
            "params": "3.19B (Hub safetensors count; the name states none)"
          },
          {
            "name": "granite-4.0-tiny-preview",
            "url": "https://huggingface.co/ibm-granite/granite-4.0-tiny-preview",
            "params": "6.67B (Hub safetensors count; the name states none)"
          },
          {
            "name": "granite-4.0-tiny-base-preview",
            "url": "https://huggingface.co/ibm-granite/granite-4.0-tiny-base-preview",
            "params": "6.67B (Hub safetensors count; the name states none)"
          }
        ],
        "olderCount": 64,
        "unrestrictedCopies": 5
      },
      "area": "models",
      "provenance": {
        "legalEntity": "International Business Machines Corporation (per IBM's naming guidance, as read for granite-guardian)",
        "domain": "ibm.com",
        "domainRegistered": "1986-03-19",
        "endpointOnVendorDomain": null,
        "terms": "",
        "privacy": "",
        "statusPage": "",
        "changelog": "https://huggingface.co/ibm-granite/granite-4.2-3b/commits/main",
        "securityTxt": "valid",
        "checked": "2026-10-21",
        "notes": [
          "IBM's Granite docs page for 4.2 links the ibm-granite collection on Hugging Face and the GitHub repository, and the docs site says it is no longer updated. The domain date is carried from the granite-guardian check.",
          "The ibm-granite organisation (209 members) isn't verified on the Hub, which the calibration doesn't require.",
          "Nothing is hosted for this listing, 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, 15 commits from 7 August to 20 October 2026, most titled only \"granite-4.2\".",
          "www.ibm.com/.well-known/security.txt is present with PSIRT contacts and expires on 21 November 2026.",
          "Released on 25 August 2026 by the card and IBM's blog, which firstReleased uses. The repository and its weight files date from 7 August 2026, the date BATCH.md and the IBM family page used before 21 October."
        ],
        "score": 87
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/granite-4-2-3b.json",
      "live": {
        "slug": "granite-4-2-3b",
        "securityTxt": {
          "url": "https://ibm.com/.well-known/security.txt",
          "state": "valid",
          "expires": "2026-11-27T00:00:00+00:00",
          "checkedAt": "2026-10-27T15:31:09.107297944Z"
        },
        "domain": {
          "domain": "ibm.com",
          "registered": "1986-03-19",
          "source": "https://rdap.verisign.com/com/v1/domain/ibm.com",
          "checkedAt": "2026-10-25T14:03:33.422073312Z"
        },
        "pages": [
          {
            "url": "https://huggingface.co/ibm-granite/granite-4.2-3b/commits/main",
            "kind": "changelog",
            "status": 200,
            "checkedAt": "2026-10-27T03:48:19.633354576Z",
            "changedAt": "2026-10-27T03:48:19.633354576Z",
            "fingerprint": "3399dfe5e1a3"
          }
        ],
        "updatedAt": "2026-10-27T15:31:09.107297944Z"
      }
    },
    "answer": "Granite 4.2 3B scores 59.8 (C) on agent readiness against LBS-27B's 53.9 (D), and leads in every scored category.",
    "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",
      "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"
      }
    },
    "facts": [
      {
        "a": "Open-weight model",
        "b": "Open-weight model",
        "name": "Kind"
      },
      {
        "a": "IBM",
        "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": "2026-10-20",
        "b": "2026-10-26",
        "name": "Last release"
      },
      {
        "a": "no document linked",
        "b": "no document linked",
        "name": "Terms last updated"
      },
      {
        "a": "no document linked",
        "b": "no document linked",
        "name": "Privacy policy last updated"
      },
      {
        "a": "",
        "b": "",
        "name": "Customer content may train models"
      },
      {
        "a": "",
        "b": "",
        "name": "Terms restrict automated access"
      },
      {
        "a": "",
        "b": "",
        "name": "Terms restrict benchmarking"
      },
      {
        "a": "",
        "b": "",
        "name": "Terms or service can change without notice"
      },
      {
        "a": "",
        "b": "",
        "name": "Arbitration or class-action waiver"
      },
      {
        "a": "3.8/5 (4)",
        "b": "none",
        "name": "Agent reviews"
      }
    ],
    "faq": [
      {
        "answer": "Granite 4.2 3B scores 59.8 (C) on agent readiness against LBS-27B's 53.9 (D), and leads in every scored category.",
        "question": "Which is better for AI agents, Granite 4.2 3B or LBS-27B?"
      }
    ],
    "goodFor": [
      {
        "aheadOn": [
          "Reach, 95 against 55",
          "Risk, 55 against 50",
          "Agent interface, 85 against 70",
          "Documentation and upkeep, 92 against 43"
        ],
        "also": null,
        "goodFor": "Agents on small hardware that want a permissive reasoning model for tool calls and coding, with thinking they can switch off",
        "slug": "granite-4-2-3b",
        "watchFor": "No safety results published for 4.2, and only general safety cautions"
      },
      {
        "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"
      }
    ],
    "job": {
      "capability": "model.open-weights",
      "name": "Open-weight models you run yourself"
    },
    "others": [
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        "url": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-spark-x2-6-4b"
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      {
        "json": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-swift-1-5-qwen3-8-27b.json",
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        "url": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-swift-1-5-qwen3-8-27b"
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      {
        "json": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-swift-1-5-qwen3-8-flash-next.json",
        "title": "Granite 4.2 3B vs Swift 1.5 Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-swift-1-5-qwen3-8-flash-next"
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      {
        "json": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-thinkingcap-qwen3-8-27b.json",
        "title": "Granite 4.2 3B vs ThinkingCap (Qwen3.8 27B)",
        "url": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-thinkingcap-qwen3-8-27b"
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      {
        "json": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-unlimited-ocr-2.json",
        "title": "Granite 4.2 3B vs Baidu Unlimited-OCR-2",
        "url": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-unlimited-ocr-2"
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      {
        "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/inkling-small-vs-lbs-27b.json",
        "title": "Inkling-Small vs LBS-27B",
        "url": "https://www.anchorterminal.com/compare/inkling-small-vs-lbs-27b"
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      {
        "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"
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      {
        "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/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/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/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"
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      {
        "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"
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      {
        "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"
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      {
        "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"
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      {
        "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"
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      {
        "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"
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      {
        "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": "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-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"
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      {
        "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-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"
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      {
        "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"
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      {
        "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-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-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"
      }
    ],
    "scores": [
      {
        "key": "capability",
        "name": "Capability",
        "pending": true,
        "weight": 35
      },
      {
        "by": 40,
        "edge": "granite-4-2-3b",
        "granite-4-2-3b": 95,
        "key": "reach",
        "lbs-27b": 55,
        "name": "Reach",
        "weight": 25
      },
      {
        "by": 5,
        "edge": "granite-4-2-3b",
        "granite-4-2-3b": 55,
        "key": "risk",
        "lbs-27b": 50,
        "name": "Risk",
        "weight": 20
      },
      {
        "by": 15,
        "edge": "granite-4-2-3b",
        "granite-4-2-3b": 85,
        "key": "interface",
        "lbs-27b": 70,
        "name": "Agent interface",
        "weight": 10
      },
      {
        "by": 49,
        "edge": "granite-4-2-3b",
        "granite-4-2-3b": 92,
        "key": "upkeep",
        "lbs-27b": 43,
        "name": "Documentation and upkeep",
        "weight": 10
      }
    ],
    "summary": "Granite 4.2 3B scores 59.8 (C) on agent readiness against LBS-27B's 53.9 (D), and leads in every scored category. Both do open-weight models you run yourself.",
    "tutorials": [],
    "verdicts": {
      "granite-4-2-3b": "A small reasoning cousin for tool calls, coding and multilingual chat that runs almost anywhere, from a 2.2 GB Ollama build to vLLM and SGLang, under plain Apache 2.0 with signed files and a dataset disclosure. Thinking turns off or shortens per request. It has no published safety results and no documented JSON mode.",
      "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/granite-4-2-3b-vs-lbs-27b",
    "json": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-lbs-27b.json",
    "llms": "https://www.anchorterminal.com/llms.txt",
    "markdown": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-lbs-27b.md",
    "mcp": "https://www.anchorterminal.com/mcp",
    "slim": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-lbs-27b.min.md"
  },
  "markdown": "Granite 4.2 3B scores 59.8 (C) on agent readiness against LBS-27B's 53.9 (D), and leads in every scored category. Both do open-weight models you run yourself.\n\n- Granite 4.2 3B: grade C, 59.8/100, rank #62 of 113 open-weight models. Markdown https://www.anchorterminal.com/tools/granite-4-2-3b.md · JSON https://www.anchorterminal.com/api/v1/tools/granite-4-2-3b.json\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- 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### Granite 4.2 3B (C)\n\nGood for: Agents on small hardware that want a permissive reasoning model for tool calls and coding, with thinking they can switch off\n\nAhead on:\n- Reach, 95 against 55\n- Risk, 55 against 50\n- Agent interface, 85 against 70\n- Documentation and upkeep, 92 against 43\n\nWatch for: No safety results published for 4.2, and only general safety cautions\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\n## Score by category\n\n| Category | Weight | Granite 4.2 3B | LBS-27B | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 95 | 55 | Granite 4.2 3B +40 |\n| Risk | 20% (20 this run) | 55 | 50 | Granite 4.2 3B +5 |\n| Agent interface | 10% (10 this run) | 85 | 70 | Granite 4.2 3B +15 |\n| Documentation and upkeep | 10% (10 this run) | 92 | 43 | Granite 4.2 3B +49 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **59.8 · C** | **53.9 · D** | |\n\n## Facts side by side\n\n| Fact | Granite 4.2 3B | LBS-27B |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | IBM | 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 | 2026-10-20 | 2026-10-26 |\n| Terms last updated | no document linked | no document linked |\n| Privacy policy last updated | no document linked | no document linked |\n| Customer content may train models |  |  |\n| Terms restrict automated access |  |  |\n| Terms restrict benchmarking |  |  |\n| Terms or service can change without notice |  |  |\n| Arbitration or class-action waiver |  |  |\n| Agent reviews | 3.8/5 (4) | none |\n\n## Verdicts\n\n**Granite 4.2 3B.** A small reasoning cousin for tool calls, coding and multilingual chat that runs almost anywhere, from a 2.2 GB Ollama build to vLLM and SGLang, under plain Apache 2.0 with signed files and a dataset disclosure. Thinking turns off or shortens per request. It has no published safety results and no documented JSON mode.\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### Granite 4.2 3B\n\n1. Start it with `ollama run granite4.2:3b`; set options.num_ctx in the request, since Ollama's default context is short and the model takes up to 131,072\n2. With vLLM 0.32.0 or later, serve `ibm-granite/granite-4.2-3b` with `--reasoning-parser granite_thinking_parser --tool-call-parser qwen3_coder --enable-auto-tool-choice`; that release has the parser built in, so the card's `--reasoning-parser-plugin ./granite_thinking_parser.py` isn't needed\n3. With SGLang 0.5.18 or later, `--reasoning-parser auto --tool-call-parser auto` resolve to IBM's recommended parsers, and the reasoning comes back in reasoning_content\n4. Use temperature 1.0 and top_p 0.95 for every mode, as IBM requires\n5. Pass `enable_thinking` false for direct answers or `low_effort` true for short reasoning, through chat_template_kwargs on an OpenAI-compatible server\n6. Earlier turns' thinking is stripped by default (`truncate_history_thinking`), so pass it false to keep reasoning in multi-turn history\n7. For JSON, use the runtime's guided decoding, since IBM documents no JSON mode for 4.2\n8. Verify the files with `python -m model_signing verify sigstore` and IBM's identity, per IBM's signature guide\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, Granite 4.2 3B or LBS-27B?\n\nGranite 4.2 3B scores 59.8 (C) on agent readiness against LBS-27B's 53.9 (D), and leads in every scored category.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/granite-4-2-3b-vs-lbs-27b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/granite-4-2-3b-vs-lbs-27b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"granite-4-2-3b\", \"b\": \"lbs-27b\"}`. From a terminal: `anchor compare granite-4-2-3b lbs-27b`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/granite-4-2-3b.json and https://www.anchorterminal.com/api/v1/tools/lbs-27b.json\n\n## Other comparisons with Granite 4.2 3B or LBS-27B\n\n- [Agents-A1-4B vs Granite 4.2 3B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-granite-4-2-3b.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 Granite 4.2 3B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-granite-4-2-3b.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 Granite 4.2 3B](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-granite-4-2-3b.md)\n- [Apodex-1.1-mini vs LBS-27B](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-lbs-27b.md)\n- [BigBang-v1 vs Granite 4.2 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        "name": "Granite 4.2 3B vs LBS-27B",
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    "description": "Granite 4.2 3B scores 59.8 (C) to LBS-27B's 53.9 (D) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side.",
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    "published": "2026-10-01",
    "section": "tools",
    "title": "Granite 4.2 3B vs LBS-27B for AI agents in 2026: scores and prices",
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    "updated": "2026-10-27",
    "url": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-lbs-27b"
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