{
  "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",
      "dns": "granite-4-2-3b.grade.anchorterminal.com",
      "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": 1518,
        "categoryRank": 72,
        "methodology": "0.7",
        "run": "2026-10-01",
        "scores": {
          "capability": 21,
          "interface": 85,
          "reach": 95,
          "risk": 55,
          "upkeep": 92
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "high",
          "date": "2026-11-04"
        },
        "negative": 0,
        "verdict": "A small reasoning model for tool calls, coding and multilingual chat, published by IBM under plain Apache 2.0 with signed files and a dataset disclosure, and run by llama.cpp, Ollama, vLLM, SGLang and Transformers from a 2.2 GB Ollama build. 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": "0.7",
            "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": 88,
        "checked": {
          "categories": {
            "capability": "2026-11-04",
            "interface": "2026-11-04",
            "reach": "2026-11-04",
            "risk": "2026-11-04",
            "upkeep": "2026-11-04"
          },
          "last": "2026-11-04",
          "oldest": "2026-11-04",
          "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-speech-5-0-2b",
        "ibm-watson-stt",
        "granite-switch",
        "granite-4-2-30b",
        "granite-4-2-8b"
      ],
      "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)",
            "listing": "https://www.anchorterminal.com/tools/granite-4-2-8b"
          },
          {
            "name": "granite-4.2-30b",
            "url": "https://huggingface.co/ibm-granite/granite-4.2-30b",
            "params": "30B (Hub safetensors count 29.28B)",
            "listing": "https://www.anchorterminal.com/tools/granite-4-2-30b"
          },
          {
            "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": 88
      },
      "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": "2026-12-10T00:00:00+00:00",
          "checkedAt": "2026-11-10T16:09:13.66976132Z"
        },
        "domain": {
          "domain": "ibm.com",
          "registered": "1986-03-19",
          "source": "https://rdap.verisign.com/com/v1/domain/ibm.com",
          "checkedAt": "2026-11-08T15:04:54.376665564Z"
        },
        "pages": [
          {
            "url": "https://huggingface.co/ibm-granite/granite-4.2-3b/commits/main",
            "kind": "changelog",
            "status": 200,
            "checkedAt": "2026-11-10T16:22:46.723800083Z",
            "changedAt": "2026-11-10T16:22:46.723800083Z",
            "fingerprint": "9d41684a25f6"
          }
        ],
        "updatedAt": "2026-11-10T16:22:46.723800083Z"
      }
    },
    "answer": "WeVisOne-VL-4B scores 61 (C) on agent readiness against Granite 4.2 3B's 59.8 (C), and leads in 0 of 4 scored categories. Granite 4.2 3B leads on reach, agent interface and documentation and upkeep.",
    "b": {
      "slug": "wevisone-vl-4b",
      "name": "WeVisOne-VL-4B",
      "vendor": "Tencent (WeChat Vision)",
      "vendorUrl": "https://www.tencent.com",
      "kind": "weights",
      "category": "open-weights",
      "summary": "WeVisOne-VL-4B is an open-weight perception model of 4.8B parameters from Tencent's WeChat Vision team, built on Qwen3-VL. Name something in an image and it returns boxes, points, counts, text, masks or alpha mattes. Apache 2.0.",
      "url": "https://www.anchorterminal.com/tools/wevisone-vl-4b",
      "markdownUrl": "https://www.anchorterminal.com/tools/wevisone-vl-4b.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/wevisone-vl-4b.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/wevisone-vl-4b.json",
      "dns": "wevisone-vl-4b.grade.anchorterminal.com",
      "repo": "https://huggingface.co/tencent/WeVisOne-VL-4B",
      "license": "Apache-2.0",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "tencent/WeVisOne-VL-4B"
        }
      ],
      "auth": "none",
      "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The repository is not gated (checked 2026-11-10).",
      "pricing": "free",
      "pricingNotes": "Free to download and use under Apache 2.0. Running it yourself costs the hardware, 9.66 GB in BF16 (checked 2026-11-10).",
      "priceSummary": "Free",
      "where": "local",
      "x402": {
        "level": "no",
        "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-11-10).",
        "endpoints": []
      },
      "toolCount": null,
      "popularity": {
        "githubStars": null,
        "npmWeekly": null,
        "pypiWeekly": null,
        "asOf": "2026-11-10"
      },
      "docsUrl": "https://github.com/Tencent/WeVisOne-VL",
      "capabilities": [
        "model.open-weights",
        "docs.ocr"
      ],
      "tags": [
        "open-weights",
        "model",
        "vision",
        "grounding",
        "object-detection",
        "segmentation",
        "matting",
        "ocr",
        "counting",
        "apache-2.0",
        "safetensors",
        "self-hosted"
      ],
      "lastRelease": "2026-10-27",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 61,
        "grade": "C",
        "agentReady": false,
        "rank": 0,
        "ranked": false,
        "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools",
        "rankOf": 1518,
        "categoryRank": 66,
        "methodology": "0.7",
        "run": "2026-10-01",
        "scores": {
          "interface": 70,
          "reach": 70,
          "risk": 55,
          "upkeep": 48
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "low",
          "date": "2026-11-10"
        },
        "negative": 0,
        "verdict": "A small Apache 2.0 perception model that finds, counts, reads and cuts out what you name in an image, and says None when it isn't there. Boxes and points run in vLLM, SGLang or Transformers. Masks and mattes need Tencent's package and a pickle file. Two weeks old, with no questions answered yet and no safety results.",
        "bestFor": "Agents that need to locate, count or cut out objects in images on a small GPU, under a permissive licence",
        "strengths": [
          "Seven perception tasks in one model, from boxes to alpha mattes",
          "Returns None for a target that isn't in the image",
          "Apache 2.0, ungated",
          "Each task's input and output is documented",
          "9.66 GB in BF16, so it fits a 12 GB machine"
        ],
        "weaknesses": [
          "The pixel decoder ships as a pickle file",
          "Masks and mattes need Tencent's package from GitHub",
          "No quantised build from Tencent",
          "One image and one turn at a time, no video",
          "No reply from Tencent on the repository's discussions",
          "No context length, languages, safety results or data sources on the card"
        ],
        "agentNotes": [
          "Use Tencent's package: `WeVisOneVL(\"tencent/WeVisOne-VL-4B\", backend=\"vllm\")` then `model.predict(image, task=..., query=...)`",
          "Tasks are detection, referring, point, count_box, count_point, ocr, ocr_grounding, segmentation and matting",
          "A None answer means the target isn't in the image. Treat it as a result, not an error",
          "mask_decoder.pt is a pickle. Load it only from the tencent repository, and check its SHA-256 against the Hub's",
          "Send one image a turn. Video grids are rejected by the decoder",
          "The card says it isn't for identifying individuals or for high-impact decisions on its own"
        ],
        "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": 61
          }
        ],
        "editorialScores": {
          "interface": 70,
          "reach": 70,
          "risk": 55,
          "upkeep": 48
        },
        "provenanceScore": 68,
        "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": "git clone https://github.com/Tencent/WeVisOne-VL.git \u0026\u0026 cd WeVisOne-VL\npip install -r requirements.txt \u0026\u0026 pip install -e .\n# then, in Python: WeVisOneVL(\"tencent/WeVisOne-VL-4B\", backend=\"transformers\") or backend=\"vllm\"",
        "http": "# No HTTP API of its own. Tencent's package calls model.predict(image, task=\"detection\", query=[...]).\n# For boxes and points only, vLLM serves the backbone at http://localhost:8000/v1 with: vllm serve tencent/WeVisOne-VL-4B"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/wevisone-vl-4b"
      },
      "sameCompany": [
        "hy-memory",
        "tencentdb-agent-memory",
        "hunyuanocr",
        "hy-memory-30b-a3b",
        "hy3",
        "wevisone-vl-2b"
      ],
      "weights": {
        "trainedBy": "Tencent WeChat Vision",
        "params": "4.8B",
        "goodFor": "Finding, counting, reading and cutting out what you name in an image, on a 12 GB machine.",
        "runtimes": [
          "vLLM",
          "SGLang",
          "Transformers"
        ],
        "builds": [
          {
            "name": "WeVisOne-VL-4B (BF16 safetensors)",
            "url": "https://huggingface.co/tencent/WeVisOne-VL-4B/blob/main/model.safetensors",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 9.66,
            "memoryGB": 12,
            "format": "safetensors",
            "runsOn": [
              "vLLM",
              "SGLang",
              "Transformers"
            ],
            "note": "The only build. Masks and mattes also need mask_decoder.pt and Tencent's package"
          }
        ],
        "run": "vllm serve tencent/WeVisOne-VL-4B",
        "runBuild": "WeVisOne-VL-4B (BF16 safetensors)",
        "checked": "2026-11-10",
        "minMemoryGB": 12
      },
      "family": {
        "lab": "Tencent (Hunyuan / Hy)",
        "family": "WeVisOne-VL",
        "url": "https://www.anchorterminal.com/open-weights/#tencent",
        "others": [
          {
            "name": "WeVisOne-VL-2B",
            "url": "https://huggingface.co/tencent/WeVisOne-VL-2B",
            "params": "2.4B (2,441,744,384 by the Hub's safetensors count)",
            "listing": "https://www.anchorterminal.com/tools/wevisone-vl-2b"
          }
        ],
        "olderCount": 22
      },
      "area": "models",
      "provenance": {
        "legalEntity": "Tencent (named in the LICENSE file's copyright line)",
        "domain": "tencent.com",
        "domainRegistered": "1998-09-14",
        "endpointOnVendorDomain": null,
        "terms": "https://huggingface.co/tencent/WeVisOne-VL-4B/blob/main/LICENSE",
        "privacy": "",
        "statusPage": "",
        "changelog": "https://huggingface.co/tencent/WeVisOne-VL-4B/commits/main",
        "securityTxt": "none",
        "checked": "2026-11-10",
        "notes": [
          "The card names WeChat Vision, Tencent Inc. The domain's registration date is carried from our other Tencent listings.",
          "Nothing is hosted under this listing, so the model licence stands in for terms of service and no privacy policy is scored.",
          "The changelog is the commit history.",
          "Tencent's Security Response Center takes reports company-wide, per the Hy3 listing; nothing on the card links it."
        ],
        "score": 68
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/wevisone-vl-4b.json"
    },
    "edges": [
      {
        "by": 44,
        "category": "upkeep",
        "leads": "granite-4-2-3b",
        "name": "Documentation and upkeep",
        "reasons": {
          "a": "The card covers everything but a knowledge cut-off (17). IBM's disclosure lists the datasets with sources and sizes (20). The index file changed on 20 October 2026, 15 days before this check, and nothing in IBM's 4.2 repositories or the GitHub repository changed since (20). The two questions asked on IBM's 3B repositories have an IBM reply, and no new thread was opened after 21 October (20). Granite 4.1 3B stays in its own repository with no note (5), and the files agree with each other (10).",
          "b": "Model card 8, Intended use, limitations and the architecture are on the card. Training data 5, A general description in the README of a data engine and over 100 million annotated images, with no sources named and no cut-off. Last change 20, the last commit, on 27 October 2026, added links to the paper and project page, 14 days before this check. Responsiveness 0, Neither of the 2 discussions that aren't pull requests (23 and 26 October 2026) has a reply from a tencent member, counted on 10 November by author flags and dates with a script, text not read. Version policy 5, A first release, 14 days old. File health 10, Config, template and card agree on the Qwen3-VL classes, and the card names mask_decoder.pt as required and it's there. 8 + 5 + 20 + 0 + 5 + 10 = 48."
        },
        "scores": {
          "a": 92,
          "b": 48
        }
      },
      {
        "by": 25,
        "category": "reach",
        "leads": "granite-4-2-3b",
        "name": "Reach",
        "reasons": {
          "a": "Memory earns the full 30, since IBM's 4-bit MLX build is 2.07 GB and Ollama's Q4_K_M build 2.2 GB, about 2.5 to 2.6 GB with the fifth added. Five named runtimes run the granite architecture in released versions or their own libraries, so runtimes take the cap of 25, though LM Studio has no 4.2 entry. IBM's FP8, FP4, GGUF and MLX builds earn 15. Apache 2.0 with nothing added gives terms 15 and the ungated repositories download 10. Hosted too is 0, since only DeepInfra serves it and watsonx.ai stops at 4.1.",
          "b": "Memory 30, the only build from Tencent is the BF16 safetensors file with its decoder, 9.7 GB, and 9.7 GB × 1.2 = 11.6 GB, within 16 GB. No runtime library has a build. Runtimes 15, the architecture is Qwen3VLForConditionalGeneration, which vLLM 0.33.0, SGLang 0.5.23 and Transformers 5.22.2 run from the safetensors file, three at 5 each. Builds 0, Tencent publishes the BF16 weights only. Terms 15, Apache 2.0 in the card's metadata with the LICENSE file in the repository, and nothing added. Download 10, the Hub API reports gated false on 10 November 2026, so the files download with no account and nothing to accept. Hosted 0, Hugging Face maps no inference provider to it. 30 + 15 + 0 + 15 + 10 + 0 = 70."
        },
        "scores": {
          "a": 95,
          "b": 70
        }
      },
      {
        "by": 15,
        "category": "interface",
        "leads": "granite-4-2-3b",
        "name": "Agent interface",
        "reasons": {
          "a": "The template ships in the repository (20). The card shows how tools are declared and how calls come back, and names qwen3_coder for vLLM and SGLang (25). The template renders several calls in a turn (20). JSON output isn't documented for 4.2 (0). Thinking turns off or goes to low effort per request, with the reasoning returned apart in SGLang (10), and 131,072 tokens are native (10).",
          "b": "Chat template 20, chat_template.jinja in the repository (5,292 characters), Qwen3-VL's template. Tool-calling format 25, Read for a perception model as what it takes and returns. Trained tool calling 10, Read for a perception model as the inputs it was trained to use. Structured output 15, Read for a perception model as typed outputs. Reasoning control 0, the card says it writes structured output directly with no chain-of-thought reasoning. Context 0, Tencent states no context length for it. 20 + 25 + 10 + 15 + 0 + 0 = 70."
        },
        "scores": {
          "a": 85,
          "b": 70
        }
      }
    ],
    "facts": [
      {
        "a": "Open-weight model",
        "b": "Open-weight model",
        "name": "Kind"
      },
      {
        "a": "IBM",
        "b": "Tencent (WeChat Vision)",
        "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",
        "name": "Licence"
      },
      {
        "a": "no",
        "b": "no",
        "name": "Read-only variant documented"
      },
      {
        "a": "no",
        "b": "no",
        "name": "llms.txt"
      },
      {
        "a": "2026-10-20",
        "b": "2026-10-27",
        "name": "Last release"
      },
      {
        "a": "no document linked",
        "b": "not read yet",
        "name": "Terms last updated"
      },
      {
        "a": "no document linked",
        "b": "no document linked",
        "name": "Privacy policy last updated"
      },
      {
        "a": "3.8/5 (4)",
        "b": "none",
        "name": "Agent reviews"
      }
    ],
    "faq": [
      {
        "answer": "WeVisOne-VL-4B scores 61 (C) on agent readiness against Granite 4.2 3B's 59.8 (C), and leads in 0 of 4 scored categories. Granite 4.2 3B leads on reach, agent interface and documentation and upkeep.",
        "question": "Which is better for AI agents, Granite 4.2 3B or WeVisOne-VL-4B?"
      }
    ],
    "goodFor": [
      {
        "aheadOn": [
          "Reach, 95 against 70",
          "Agent interface, 85 against 70",
          "Documentation and upkeep, 92 against 48"
        ],
        "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 that need to locate, count or cut out objects in images on a small GPU, under a permissive licence",
        "slug": "wevisone-vl-4b",
        "watchFor": "The pixel decoder ships as a pickle file"
      }
    ],
    "history": null,
    "job": {
      "capability": "model.open-weights",
      "name": "Open-weight models you run yourself"
    },
    "others": [
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        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-granite-4-2-3b"
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      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-wevisone-vl-4b.json",
        "title": "Agents-A1-4B vs WeVisOne-VL-4B",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-wevisone-vl-4b"
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      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-granite-4-2-3b.json",
        "title": "Apertus v1.5 8B vs Granite 4.2 3B",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-granite-4-2-3b"
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        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-wevisone-vl-4b.json",
        "title": "Apertus v1.5 8B vs WeVisOne-VL-4B",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-wevisone-vl-4b"
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        "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-granite-4-2-3b.json",
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        "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-wevisone-vl-4b.json",
        "title": "Apodex-1.1-mini vs WeVisOne-VL-4B",
        "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-wevisone-vl-4b"
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        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-wevisone-vl-4b"
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        "url": "https://www.anchorterminal.com/compare/carbon-x-vs-wevisone-vl-4b"
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        "json": "https://www.anchorterminal.com/compare/cola2-vs-granite-4-2-3b.json",
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        "url": "https://www.anchorterminal.com/compare/cola2-vs-wevisone-vl-4b"
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        "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-granite-4-2-3b.json",
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        "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-wevisone-vl-4b"
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      {
        "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-granite-4-2-3b.json",
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      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-granite-4-2-3b.json",
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        "url": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-qwen3-5-0-8b"
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        "url": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-qwen3-8-flash-next"
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        "json": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-qwen3-vl-8b-instruct.json",
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        "json": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-ripple-65b-preview.json",
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        "json": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-wevisone-vl-4b.json",
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        "json": "https://www.anchorterminal.com/compare/kronos-vs-wevisone-vl-4b.json",
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        "json": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-wevisone-vl-4b.json",
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        "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-wevisone-vl-4b.json",
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        "json": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-wevisone-vl-4b.json",
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        "json": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-wevisone-vl-4b.json",
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        "json": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-wevisone-vl-4b.json",
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        "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-wevisone-vl-4b.json",
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        "json": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-wevisone-vl-4b.json",
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        "json": "https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-wevisone-vl-4b.json",
        "title": "Ling-3.1-flash-1019 vs WeVisOne-VL-4B",
        "url": "https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-wevisone-vl-4b"
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      {
        "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-wevisone-vl-4b.json",
        "title": "LongCat-2.5 vs WeVisOne-VL-4B",
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        "json": "https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-wevisone-vl-4b.json",
        "title": "MiMo-V2.6 Distill Qwen 9B vs WeVisOne-VL-4B",
        "url": "https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-wevisone-vl-4b"
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        "json": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-wevisone-vl-4b.json",
        "title": "MiMo-V2.6-Flash-RL vs WeVisOne-VL-4B",
        "url": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-wevisone-vl-4b"
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        "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-wevisone-vl-4b.json",
        "title": "MiniCPM-o 4.5 vs WeVisOne-VL-4B",
        "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-wevisone-vl-4b"
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      {
        "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-wevisone-vl-4b.json",
        "title": "MiniCPM-V 4.7 1B vs WeVisOne-VL-4B",
        "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-wevisone-vl-4b"
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        "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-wevisone-vl-4b.json",
        "title": "MiniCPM-V 4.7 35B-A3B vs WeVisOne-VL-4B",
        "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-wevisone-vl-4b"
      },
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        "json": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-wevisone-vl-4b.json",
        "title": "MiniCPM5-2B vs WeVisOne-VL-4B",
        "url": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-wevisone-vl-4b"
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      {
        "json": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-wevisone-vl-4b.json",
        "title": "MiniMax-M2.5 vs WeVisOne-VL-4B",
        "url": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-wevisone-vl-4b"
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      {
        "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-wevisone-vl-4b.json",
        "title": "MiniMax-M2.7 vs WeVisOne-VL-4B",
        "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-wevisone-vl-4b"
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      {
        "json": "https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-wevisone-vl-4b.json",
        "title": "MiniMax-M3.1-Flash vs WeVisOne-VL-4B",
        "url": "https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-wevisone-vl-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/mistral-large-4-vs-wevisone-vl-4b.json",
        "title": "Mistral Large 4 vs WeVisOne-VL-4B",
        "url": "https://www.anchorterminal.com/compare/mistral-large-4-vs-wevisone-vl-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-wevisone-vl-4b.json",
        "title": "Muse Glimmer 30B vs WeVisOne-VL-4B",
        "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-wevisone-vl-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-wevisone-vl-4b.json",
        "title": "Nanbeige4.2-3B vs WeVisOne-VL-4B",
        "url": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-wevisone-vl-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-wevisone-vl-4b.json",
        "title": "Nanbeige4.3-33B vs WeVisOne-VL-4B",
        "url": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-wevisone-vl-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-wevisone-vl-4b.json",
        "title": "NVIDIA Nemotron 3.5 Bolt E4B 4GB vs WeVisOne-VL-4B",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-wevisone-vl-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-wevisone-vl-4b.json",
        "title": "Nemotron 3.5 Lightning 30B A3B vs WeVisOne-VL-4B",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-wevisone-vl-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-wevisone-vl-4b.json",
        "title": "Nemotron 3 Nano 30B A3B vs WeVisOne-VL-4B",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-wevisone-vl-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-wevisone-vl-4b.json",
        "title": "Nemotron 3 Nano 4B vs WeVisOne-VL-4B",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-wevisone-vl-4b"
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      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-wevisone-vl-4b.json",
        "title": "Nemotron 3 Nano Omni 30B A3B Reasoning vs WeVisOne-VL-4B",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-wevisone-vl-4b"
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        "json": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-wevisone-vl-4b.json",
        "title": "Nemotron 3 Super 120B A12B vs WeVisOne-VL-4B",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-wevisone-vl-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-wevisone-vl-4b.json",
        "title": "Nemotron 3 Ultra 550B A55B vs WeVisOne-VL-4B",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-wevisone-vl-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-wevisone-vl-4b.json",
        "title": "North Mini Code 1.0 vs WeVisOne-VL-4B",
        "url": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-wevisone-vl-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-wevisone-vl-4b.json",
        "title": "occamy-1.0 vs WeVisOne-VL-4B",
        "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-wevisone-vl-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-wevisone-vl-4b.json",
        "title": "Olmo 3 7B Think vs WeVisOne-VL-4B",
        "url": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-wevisone-vl-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-wevisone-vl-4b.json",
        "title": "Ornith-1.5-122B vs WeVisOne-VL-4B",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-wevisone-vl-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-wevisone-vl-4b.json",
        "title": "Ornith-1.5-35B-A3B vs WeVisOne-VL-4B",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-wevisone-vl-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-wevisone-vl-4b.json",
        "title": "Ornith-1.5-397B vs WeVisOne-VL-4B",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-wevisone-vl-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-wevisone-vl-4b.json",
        "title": "Ornith-1.5-9B vs WeVisOne-VL-4B",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-wevisone-vl-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-wevisone-vl-4b.json",
        "title": "OTel-2.0-LLM-31B-IT vs WeVisOne-VL-4B",
        "url": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-wevisone-vl-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-wevisone-vl-4b.json",
        "title": "Qwen-AgentWorld-35B-A3B vs WeVisOne-VL-4B",
        "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-wevisone-vl-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-30b-a3b-instruct-2507-vs-wevisone-vl-4b.json",
        "title": "Qwen3-30B-A3B-Instruct-2507 vs WeVisOne-VL-4B",
        "url": "https://www.anchorterminal.com/compare/qwen3-30b-a3b-instruct-2507-vs-wevisone-vl-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-4b-instruct-2507-vs-wevisone-vl-4b.json",
        "title": "Qwen3-4B-Instruct-2507 vs WeVisOne-VL-4B",
        "url": "https://www.anchorterminal.com/compare/qwen3-4b-instruct-2507-vs-wevisone-vl-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-wevisone-vl-4b.json",
        "title": "Qwen3.5-0.8B vs WeVisOne-VL-4B",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-wevisone-vl-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-wevisone-vl-4b.json",
        "title": "Qwen3.5-122B-A10B vs WeVisOne-VL-4B",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-wevisone-vl-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-5-2b-vs-wevisone-vl-4b.json",
        "title": "Qwen3.5-2B vs WeVisOne-VL-4B",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-2b-vs-wevisone-vl-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-wevisone-vl-4b.json",
        "title": "Qwen3.5-397B-A17B vs WeVisOne-VL-4B",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-wevisone-vl-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-5-4b-vs-wevisone-vl-4b.json",
        "title": "Qwen3.5-4B vs WeVisOne-VL-4B",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-4b-vs-wevisone-vl-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-wevisone-vl-4b.json",
        "title": "Qwen3.5-9B vs WeVisOne-VL-4B",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-wevisone-vl-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-6-27b-vs-wevisone-vl-4b.json",
        "title": "Qwen3.6-27B vs WeVisOne-VL-4B",
        "url": "https://www.anchorterminal.com/compare/qwen3-6-27b-vs-wevisone-vl-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-wevisone-vl-4b.json",
        "title": "Qwen3.6-35B-A3B vs WeVisOne-VL-4B",
        "url": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-wevisone-vl-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-8-27b-vs-wevisone-vl-4b.json",
        "title": "Qwen3.8-27B vs WeVisOne-VL-4B",
        "url": "https://www.anchorterminal.com/compare/qwen3-8-27b-vs-wevisone-vl-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-wevisone-vl-4b.json",
        "title": "Qwen3.8-Flash-Next vs WeVisOne-VL-4B",
        "url": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-wevisone-vl-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-coder-30b-a3b-instruct-vs-wevisone-vl-4b.json",
        "title": "Qwen3-Coder-30B-A3B-Instruct vs WeVisOne-VL-4B",
        "url": "https://www.anchorterminal.com/compare/qwen3-coder-30b-a3b-instruct-vs-wevisone-vl-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-coder-next-vs-wevisone-vl-4b.json",
        "title": "Qwen3-Coder-Next vs WeVisOne-VL-4B",
        "url": "https://www.anchorterminal.com/compare/qwen3-coder-next-vs-wevisone-vl-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-omni-30b-a3b-instruct-vs-wevisone-vl-4b.json",
        "title": "Qwen3-Omni-30B-A3B-Instruct vs WeVisOne-VL-4B",
        "url": "https://www.anchorterminal.com/compare/qwen3-omni-30b-a3b-instruct-vs-wevisone-vl-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-vl-2b-instruct-vs-wevisone-vl-4b.json",
        "title": "Qwen3-VL-2B-Instruct vs WeVisOne-VL-4B",
        "url": "https://www.anchorterminal.com/compare/qwen3-vl-2b-instruct-vs-wevisone-vl-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-vl-30b-a3b-instruct-vs-wevisone-vl-4b.json",
        "title": "Qwen3-VL-30B-A3B-Instruct vs WeVisOne-VL-4B",
        "url": "https://www.anchorterminal.com/compare/qwen3-vl-30b-a3b-instruct-vs-wevisone-vl-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-vl-32b-instruct-vs-wevisone-vl-4b.json",
        "title": "Qwen3-VL-32B-Instruct vs WeVisOne-VL-4B",
        "url": "https://www.anchorterminal.com/compare/qwen3-vl-32b-instruct-vs-wevisone-vl-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-wevisone-vl-4b.json",
        "title": "Qwen3-VL-4B-Instruct vs WeVisOne-VL-4B",
        "url": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-wevisone-vl-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-vl-8b-instruct-vs-wevisone-vl-4b.json",
        "title": "Qwen3-VL-8B-Instruct vs WeVisOne-VL-4B",
        "url": "https://www.anchorterminal.com/compare/qwen3-vl-8b-instruct-vs-wevisone-vl-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-wevisone-vl-4b.json",
        "title": "Qwopus3.8-27B-Flash-V2 vs WeVisOne-VL-4B",
        "url": "https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-wevisone-vl-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/recursive-flint-vs-wevisone-vl-4b.json",
        "title": "Recursive Flint vs WeVisOne-VL-4B",
        "url": "https://www.anchorterminal.com/compare/recursive-flint-vs-wevisone-vl-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ripple-65b-preview-vs-wevisone-vl-4b.json",
        "title": "Ripple-65B Preview vs WeVisOne-VL-4B",
        "url": "https://www.anchorterminal.com/compare/ripple-65b-preview-vs-wevisone-vl-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-wevisone-vl-4b.json",
        "title": "SenseNova-Loop-9B-Preview vs WeVisOne-VL-4B",
        "url": "https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-wevisone-vl-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/smollm3-3b-vs-wevisone-vl-4b.json",
        "title": "SmolLM3-3B vs WeVisOne-VL-4B",
        "url": "https://www.anchorterminal.com/compare/smollm3-3b-vs-wevisone-vl-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/spark-x2-6-4b-vs-wevisone-vl-4b.json",
        "title": "Spark-X2.6-4B vs WeVisOne-VL-4B",
        "url": "https://www.anchorterminal.com/compare/spark-x2-6-4b-vs-wevisone-vl-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/step-3-7-flash-vs-wevisone-vl-4b.json",
        "title": "Step 3.7 Flash vs WeVisOne-VL-4B",
        "url": "https://www.anchorterminal.com/compare/step-3-7-flash-vs-wevisone-vl-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/swift-1-5-qwen3-8-27b-vs-wevisone-vl-4b.json",
        "title": "Swift 1.5 Qwen3.8-27B vs WeVisOne-VL-4B",
        "url": "https://www.anchorterminal.com/compare/swift-1-5-qwen3-8-27b-vs-wevisone-vl-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/swift-1-5-qwen3-8-flash-next-vs-wevisone-vl-4b.json",
        "title": "Swift 1.5 Qwen3.8-Flash-Next vs WeVisOne-VL-4B",
        "url": "https://www.anchorterminal.com/compare/swift-1-5-qwen3-8-flash-next-vs-wevisone-vl-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/thinkingcap-qwen3-8-27b-vs-wevisone-vl-4b.json",
        "title": "ThinkingCap (Qwen3.8 27B) vs WeVisOne-VL-4B",
        "url": "https://www.anchorterminal.com/compare/thinkingcap-qwen3-8-27b-vs-wevisone-vl-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/wevisone-vl-2b-vs-wevisone-vl-4b.json",
        "title": "WeVisOne-VL-2B vs WeVisOne-VL-4B",
        "url": "https://www.anchorterminal.com/compare/wevisone-vl-2b-vs-wevisone-vl-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/wevisone-vl-4b-vs-zaya1-8b.json",
        "title": "WeVisOne-VL-4B vs ZAYA1-8B",
        "url": "https://www.anchorterminal.com/compare/wevisone-vl-4b-vs-zaya1-8b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/unlimited-ocr-2-vs-wevisone-vl-4b.json",
        "title": "Baidu Unlimited-OCR-2 vs WeVisOne-VL-4B",
        "url": "https://www.anchorterminal.com/compare/unlimited-ocr-2-vs-wevisone-vl-4b"
      }
    ],
    "panel": null,
    "scores": [
      {
        "key": "capability",
        "name": "Capability",
        "pending": true,
        "weight": 35
      },
      {
        "by": 25,
        "edge": "granite-4-2-3b",
        "granite-4-2-3b": 95,
        "key": "reach",
        "name": "Reach",
        "weight": 25,
        "wevisone-vl-4b": 70
      },
      {
        "by": 0,
        "edge": "",
        "granite-4-2-3b": 55,
        "key": "risk",
        "name": "Risk",
        "weight": 20,
        "wevisone-vl-4b": 55
      },
      {
        "by": 15,
        "edge": "granite-4-2-3b",
        "granite-4-2-3b": 85,
        "key": "interface",
        "name": "Agent interface",
        "weight": 10,
        "wevisone-vl-4b": 70
      },
      {
        "by": 44,
        "edge": "granite-4-2-3b",
        "granite-4-2-3b": 92,
        "key": "upkeep",
        "name": "Documentation and upkeep",
        "weight": 10,
        "wevisone-vl-4b": 48
      }
    ],
    "summary": "WeVisOne-VL-4B scores 61 (C) on agent readiness against Granite 4.2 3B's 59.8 (C), and leads in 0 of 4 scored categories. Granite 4.2 3B leads on reach, agent interface and documentation and upkeep. Both do open-weight models you run yourself.",
    "tasks": null,
    "tutorials": [],
    "verdicts": {
      "granite-4-2-3b": "A small reasoning model for tool calls, coding and multilingual chat, published by IBM under plain Apache 2.0 with signed files and a dataset disclosure, and run by llama.cpp, Ollama, vLLM, SGLang and Transformers from a 2.2 GB Ollama build. Thinking turns off or shortens per request. It has no published safety results and no documented JSON mode.",
      "wevisone-vl-4b": "A small Apache 2.0 perception model that finds, counts, reads and cuts out what you name in an image, and says None when it isn't there. Boxes and points run in vLLM, SGLang or Transformers. Masks and mattes need Tencent's package and a pickle file. Two weeks old, with no questions answered yet and no safety results."
    }
  },
  "kind": "anchor.page",
  "links": {
    "api": "https://www.anchorterminal.com/api/v1/index.json",
    "html": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-wevisone-vl-4b",
    "json": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-wevisone-vl-4b.json",
    "llms": "https://www.anchorterminal.com/llms.txt",
    "markdown": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-wevisone-vl-4b.md",
    "mcp": "https://www.anchorterminal.com/mcp",
    "slim": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-wevisone-vl-4b.min.md"
  },
  "markdown": "WeVisOne-VL-4B scores 61 (C) on agent readiness against Granite 4.2 3B's 59.8 (C), and leads in 0 of 4 scored categories. Granite 4.2 3B leads on reach, agent interface and documentation and upkeep. Both do open-weight models you run yourself.\n\n- Granite 4.2 3B: grade C, 59.8/100, rank #92 of 160 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- WeVisOne-VL-4B: grade C, 61/100, rank #84 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/wevisone-vl-4b.md · JSON https://www.anchorterminal.com/api/v1/tools/wevisone-vl-4b.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### 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 70\n- Agent interface, 85 against 70\n- Documentation and upkeep, 92 against 48\n\nWatch for: No safety results published for 4.2, and only general safety cautions\n\n### WeVisOne-VL-4B (C)\n\nGood for: Agents that need to locate, count or cut out objects in images on a small GPU, under a permissive licence\n\nWatch for: The pixel decoder ships as a pickle file\n\n\n## Why the edge\n\n### Documentation and upkeep: Granite 4.2 3B +44\n\n- Granite 4.2 3B (92): The card covers everything but a knowledge cut-off (17). IBM's disclosure lists the datasets with sources and sizes (20). The index file changed on 20 October 2026, 15 days before this check, and nothing in IBM's 4.2 repositories or the GitHub repository changed since (20). The two questions asked on IBM's 3B repositories have an IBM reply, and no new thread was opened after 21 October (20). Granite 4.1 3B stays in its own repository with no note (5), and the files agree with each other (10).\n- WeVisOne-VL-4B (48): Model card 8, Intended use, limitations and the architecture are on the card. Training data 5, A general description in the README of a data engine and over 100 million annotated images, with no sources named and no cut-off. Last change 20, the last commit, on 27 October 2026, added links to the paper and project page, 14 days before this check. Responsiveness 0, Neither of the 2 discussions that aren't pull requests (23 and 26 October 2026) has a reply from a tencent member, counted on 10 November by author flags and dates with a script, text not read. Version policy 5, A first release, 14 days old. File health 10, Config, template and card agree on the Qwen3-VL classes, and the card names mask_decoder.pt as required and it's there. 8 + 5 + 20 + 0 + 5 + 10 = 48.\n\n### Reach: Granite 4.2 3B +25\n\n- Granite 4.2 3B (95): Memory earns the full 30, since IBM's 4-bit MLX build is 2.07 GB and Ollama's Q4_K_M build 2.2 GB, about 2.5 to 2.6 GB with the fifth added. Five named runtimes run the granite architecture in released versions or their own libraries, so runtimes take the cap of 25, though LM Studio has no 4.2 entry. IBM's FP8, FP4, GGUF and MLX builds earn 15. Apache 2.0 with nothing added gives terms 15 and the ungated repositories download 10. Hosted too is 0, since only DeepInfra serves it and watsonx.ai stops at 4.1.\n- WeVisOne-VL-4B (70): Memory 30, the only build from Tencent is the BF16 safetensors file with its decoder, 9.7 GB, and 9.7 GB × 1.2 = 11.6 GB, within 16 GB. No runtime library has a build. Runtimes 15, the architecture is Qwen3VLForConditionalGeneration, which vLLM 0.33.0, SGLang 0.5.23 and Transformers 5.22.2 run from the safetensors file, three at 5 each. Builds 0, Tencent publishes the BF16 weights only. Terms 15, Apache 2.0 in the card's metadata with the LICENSE file in the repository, and nothing added. Download 10, the Hub API reports gated false on 10 November 2026, so the files download with no account and nothing to accept. Hosted 0, Hugging Face maps no inference provider to it. 30 + 15 + 0 + 15 + 10 + 0 = 70.\n\n### Agent interface: Granite 4.2 3B +15\n\n- Granite 4.2 3B (85): The template ships in the repository (20). The card shows how tools are declared and how calls come back, and names qwen3_coder for vLLM and SGLang (25). The template renders several calls in a turn (20). JSON output isn't documented for 4.2 (0). Thinking turns off or goes to low effort per request, with the reasoning returned apart in SGLang (10), and 131,072 tokens are native (10).\n- WeVisOne-VL-4B (70): Chat template 20, chat_template.jinja in the repository (5,292 characters), Qwen3-VL's template. Tool-calling format 25, Read for a perception model as what it takes and returns. Trained tool calling 10, Read for a perception model as the inputs it was trained to use. Structured output 15, Read for a perception model as typed outputs. Reasoning control 0, the card says it writes structured output directly with no chain-of-thought reasoning. Context 0, Tencent states no context length for it. 20 + 25 + 10 + 15 + 0 + 0 = 70.\n\n## Score by category\n\n| Category | Weight | Granite 4.2 3B | WeVisOne-VL-4B | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 95 | 70 | Granite 4.2 3B +25 |\n| Risk | 20% (20 this run) | 55 | 55 | even |\n| Agent interface | 10% (10 this run) | 85 | 70 | Granite 4.2 3B +15 |\n| Documentation and upkeep | 10% (10 this run) | 92 | 48 | Granite 4.2 3B +44 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **59.8 · C** | **61 · C** | |\n\n## Facts side by side\n\n| Fact | Granite 4.2 3B | WeVisOne-VL-4B |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | IBM | Tencent (WeChat Vision) |\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 |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-10-20 | 2026-10-27 |\n| Terms last updated | no document linked | not read yet |\n| Privacy policy last updated | no document linked | no document linked |\n| Agent reviews | 3.8/5 (4) | none |\n\n## Verdicts\n\n**Granite 4.2 3B.** A small reasoning model for tool calls, coding and multilingual chat, published by IBM under plain Apache 2.0 with signed files and a dataset disclosure, and run by llama.cpp, Ollama, vLLM, SGLang and Transformers from a 2.2 GB Ollama build. Thinking turns off or shortens per request. It has no published safety results and no documented JSON mode.\n\n**WeVisOne-VL-4B.** A small Apache 2.0 perception model that finds, counts, reads and cuts out what you name in an image, and says None when it isn't there. Boxes and points run in vLLM, SGLang or Transformers. Masks and mattes need Tencent's package and a pickle file. Two weeks old, with no questions answered yet and no safety results.\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### WeVisOne-VL-4B\n\n1. Use Tencent's package: `WeVisOneVL(\"tencent/WeVisOne-VL-4B\", backend=\"vllm\")` then `model.predict(image, task=..., query=...)`\n2. Tasks are detection, referring, point, count_box, count_point, ocr, ocr_grounding, segmentation and matting\n3. A None answer means the target isn't in the image. Treat it as a result, not an error\n4. mask_decoder.pt is a pickle. Load it only from the tencent repository, and check its SHA-256 against the Hub's\n5. Send one image a turn. Video grids are rejected by the decoder\n6. The card says it isn't for identifying individuals or for high-impact decisions on its own\n\n## Questions\n\n### Which is better for AI agents, Granite 4.2 3B or WeVisOne-VL-4B?\n\nWeVisOne-VL-4B scores 61 (C) on agent readiness against Granite 4.2 3B's 59.8 (C), and leads in 0 of 4 scored categories. Granite 4.2 3B leads on reach, agent interface and documentation and upkeep.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/granite-4-2-3b-vs-wevisone-vl-4b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/granite-4-2-3b-vs-wevisone-vl-4b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"granite-4-2-3b\", \"b\": \"wevisone-vl-4b\"}`. 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