{
  "data": {
    "a": {
      "slug": "laguna-s-2-1",
      "name": "Laguna S 2.1",
      "vendor": "Poolside",
      "vendorUrl": "https://poolside.ai",
      "kind": "weights",
      "category": "open-weights",
      "summary": "Laguna S 2.1 is Poolside's open-weight Mixture-of-Experts model for agentic coding, 118B total and 8B active parameters, with switchable reasoning and a 1,048,576-token context, published on Hugging Face under OpenMDW-1.1.",
      "url": "https://www.anchorterminal.com/tools/laguna-s-2-1",
      "markdownUrl": "https://www.anchorterminal.com/tools/laguna-s-2-1.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/laguna-s-2-1.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/laguna-s-2-1.json",
      "dns": "laguna-s-2-1.grade.anchorterminal.com",
      "repo": "https://huggingface.co/poolside/Laguna-S-2.1",
      "license": "OpenMDW-1.1",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "poolside/Laguna-S-2.1"
        },
        {
          "registry": "huggingface",
          "name": "poolside/Laguna-S-2.1-FP8"
        },
        {
          "registry": "huggingface",
          "name": "poolside/Laguna-S-2.1-INT4"
        },
        {
          "registry": "huggingface",
          "name": "poolside/Laguna-S-2.1-NVFP4"
        },
        {
          "registry": "huggingface",
          "name": "poolside/Laguna-S-2.1-NVFP4-mlx"
        },
        {
          "registry": "huggingface",
          "name": "poolside/Laguna-S-2.1-GGUF"
        },
        {
          "registry": "ollama",
          "name": "laguna-s-2.1"
        }
      ],
      "auth": "none",
      "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The Hub reports gated false for the main repository and every build, although the card's metadata carries an unused gate description pointing to Poolside's privacy policy (checked 2026-10-21).",
      "pricing": "free",
      "pricingNotes": "Free to download and use, commercial use included, under OpenMDW-1.1. Running it yourself costs the hardware, 235.1 GB for the BF16 files, 96.0 GB for Poolside's Q4_K_M GGUF and 66 GB for Ollama's MLX NVFP4 build. Poolside's API and Featherless AI host it; Poolside's OpenRouter endpoints expired on 31 October 2026 (checked 2026-11-03).",
      "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/poolside/Laguna-S-2.1",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "moe",
        "coding",
        "reasoning",
        "tool-calling",
        "long-context",
        "openmdw-1.1",
        "safetensors",
        "gguf",
        "fp8",
        "nvfp4",
        "self-hosted"
      ],
      "lastRelease": "2026-08-19",
      "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": 35,
        "methodology": "0.7",
        "run": "2026-10-01",
        "scores": {
          "interface": 85,
          "reach": 75,
          "risk": 55,
          "upkeep": 41
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "low",
          "date": "2026-11-03"
        },
        "negative": 0,
        "verdict": "A strong coding and agent cousin for long sessions, with 8B active parameters and a 1M-token context. At 4-bit it needs a 96 to 128 GB machine, tight on a 128 GB one. The main risks are that nobody at Poolside answers its many reports, there are no safety results, and a build card misdescribes its file.",
        "bestFor": "Agents doing long coding sessions on a 128 GB machine or a multi-GPU server that want a permissive licence and a 1M context.",
        "strengths": [
          "Released support in llama.cpp, Ollama, vLLM, SGLang, Transformers and mlx-lm",
          "Poolside's own FP8, INT4, NVFP4, MLX and GGUF builds, plus DFlash draft models",
          "1,048,576 tokens of context, trained at that length per Poolside's build cards",
          "Permissive OpenMDW-1.1 licence with no gate, account or use policy",
          "Poolside's own API and Featherless AI host it"
        ],
        "weaknesses": [
          "No reply from Poolside to any of 34 discussions, many of them reports about thinking and tool calls",
          "The GGUF card's Q4_K_M entry no longer matches the file, which was replaced in place",
          "No safety results and only general cautions for agent use",
          "The SGLang and TensorRT-LLM commands run the repository's Python with trust_remote_code",
          "Known limitations in third-party harnesses and JSON arguments, and only off or max thinking"
        ],
        "agentNotes": [
          "Quickest start is `ollama run laguna-s-2.1`, 96 GB for the default Q4_K_M, or the 66 GB nvfp4 tag on Apple Silicon",
          "On our reference machine try `llama-server -hf poolside/Laguna-S-2.1-GGUF:Q4_K_M --jinja -c 65536`, from a llama.cpp release with the laguna architecture (b11772 has it). The 96 GB file leaves little room for context in 107.4 GB",
          "With vLLM or SGLang use `--tool-call-parser poolside_v1 --reasoning-parser poolside_v1`, and with TensorRT-LLM `--tool_parser poolside_v1 --reasoning_parser laguna`",
          "Thinking is on by default in the template, pass `chat_template_kwargs` with `enable_thinking` false to turn it off, and keep `reasoning_content` from earlier turns",
          "The GGUFs ship set to 262,144 tokens. For 1M with llama.cpp add `--ctx-size 1048576 --rope-scaling yarn --rope-scale 128 --yarn-orig-ctx 8192`, which Poolside says can cost quality",
          "Sampling per the generation config is temperature 1.0 and top_k 20, and Poolside suggests temperature 0.7 and top_p 0.95 at 1M",
          "Expect occasional tool-schema slips in harnesses unlike Poolside's own, and validate JSON arguments, which Poolside lists as known limitations"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 4,
        "avgRating": 2.3,
        "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": 85,
          "reach": 75,
          "risk": 55,
          "upkeep": 41
        },
        "provenanceScore": 69,
        "checked": {
          "categories": {
            "interface": "2026-11-03",
            "reach": "2026-11-03",
            "risk": "2026-11-03",
            "upkeep": "2026-11-03"
          },
          "last": "2026-11-03",
          "oldest": "2026-11-03",
          "stale": false,
          "staleAfterDays": 90
        }
      },
      "connect": {
        "install": "ollama run laguna-s-2.1\n# or, with vLLM on four GPUs:\nvllm serve poolside/Laguna-S-2.1 --tensor-parallel-size 4 --tool-call-parser poolside_v1 \\\n  --reasoning-parser poolside_v1 --enable-auto-tool-choice --default-chat-template-kwargs '{\"enable_thinking\": true}'\n# or, with llama.cpp and Unsloth's GGUF, a third party's conversion:\nllama-server -hf unsloth/Laguna-S-2.1-GGUF:UD-Q4_K_M --jinja",
        "http": "curl http://localhost:11434/api/chat \\\n  -d '{\"model\": \"laguna-s-2.1\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/laguna-s-2-1"
      },
      "sameCompany": [
        "laguna-m-1",
        "laguna-xs-2-1"
      ],
      "weights": {
        "trainedBy": "Poolside",
        "params": "117.6B total, 8B active",
        "context": 1048576,
        "goodFor": "Long-horizon agentic coding and terminal work with up to 1M tokens of context, on a 128 GB machine at 4-bit or a multi-GPU server.",
        "runtimes": [
          "llama.cpp",
          "Ollama",
          "vLLM",
          "SGLang",
          "LM Studio",
          "Transformers"
        ],
        "builds": [
          {
            "name": "Laguna-S-2.1-NVFP4-mlx",
            "url": "https://huggingface.co/poolside/Laguna-S-2.1-NVFP4-mlx",
            "who": "publisher",
            "precision": "nvfp4",
            "sizeGB": 71.9,
            "memoryGB": 96,
            "format": "mlx",
            "runsOn": [
              "MLX"
            ]
          },
          {
            "name": "ollama laguna-s-2.1:nvfp4",
            "url": "https://ollama.com/library/laguna-s-2.1:nvfp4",
            "who": "ollama",
            "precision": "nvfp4",
            "sizeGB": 66,
            "memoryGB": 96,
            "format": "mlx",
            "runsOn": [
              "Ollama"
            ],
            "note": "Ollama's MLX build, for Apple Silicon. Size as Ollama shows it, rounded to whole GB, with a 1M context window"
          },
          {
            "name": "unsloth Laguna-S-2.1-GGUF (UD-Q4_K_M)",
            "url": "https://huggingface.co/unsloth/Laguna-S-2.1-GGUF",
            "who": "unsloth",
            "precision": "q4_k_m",
            "sizeGB": 73.12,
            "memoryGB": 96,
            "format": "gguf",
            "runsOn": [
              "llama.cpp"
            ],
            "note": "From Unsloth, a third party's conversion and not Poolside's. 73.12 GB of UD-Q4_K_M weights, from the Hub's model information on 10 November 2026. Recorded after the last grading, so Reach doesn't count it"
          },
          {
            "name": "Laguna-S-2.1-INT4",
            "url": "https://huggingface.co/poolside/Laguna-S-2.1-INT4",
            "who": "publisher",
            "precision": "int4 (w4a16)",
            "sizeGB": 99.7,
            "memoryGB": 128,
            "format": "safetensors"
          },
          {
            "name": "Laguna-S-2.1-NVFP4",
            "url": "https://huggingface.co/poolside/Laguna-S-2.1-NVFP4",
            "who": "publisher",
            "precision": "nvfp4",
            "sizeGB": 99.7,
            "memoryGB": 128,
            "format": "safetensors",
            "note": "For NVIDIA Blackwell GPUs and DGX Spark"
          },
          {
            "name": "Laguna-S-2.1-GGUF Q4_K_M",
            "url": "https://huggingface.co/poolside/Laguna-S-2.1-GGUF",
            "who": "publisher",
            "precision": "q4_k_m",
            "sizeGB": 96,
            "memoryGB": 128,
            "format": "gguf",
            "runsOn": [
              "llama.cpp"
            ],
            "note": "laguna-s-2.1-Q4_K_M.gguf, 96,031,829,760 bytes, replaced on 27 July 2026 with a mixed-precision build that keeps sensitive layers in BF16. The build card still lists it as 68 GB. The repository also holds Q8_0 (128.8 GB, 192 GB of memory), F16 (235.2 GB) and a 2.2 GB DFlash draft"
          },
          {
            "name": "ollama laguna-s-2.1:latest",
            "url": "https://ollama.com/library/laguna-s-2.1:latest",
            "who": "ollama",
            "precision": "q4_k_m",
            "sizeGB": 96,
            "memoryGB": 128,
            "format": "ollama",
            "runsOn": [
              "Ollama"
            ],
            "note": "Size as Ollama's tags page shows it, rounded to whole GB. Its model blob (948255a146c0) isn't Poolside's GGUF file. The library also has q8_0 (128 GB), f16 (235 GB) and MLX tags"
          },
          {
            "name": "Laguna-S-2.1-FP8",
            "url": "https://huggingface.co/poolside/Laguna-S-2.1-FP8",
            "who": "publisher",
            "precision": "fp8",
            "sizeGB": 131.3,
            "memoryGB": 192,
            "format": "safetensors"
          },
          {
            "name": "Laguna-S-2.1",
            "url": "https://huggingface.co/poolside/Laguna-S-2.1",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 235.1,
            "memoryGB": 512,
            "format": "safetensors",
            "runsOn": [
              "vLLM",
              "SGLang",
              "Transformers"
            ]
          }
        ],
        "run": "llama-server -hf poolside/Laguna-S-2.1-GGUF:Q4_K_M --jinja -c 65536 --port 8000",
        "runBuild": "Laguna-S-2.1-GGUF Q4_K_M",
        "checked": "2026-10-21",
        "minMemoryGB": 96
      },
      "family": {
        "lab": "Poolside",
        "family": "Laguna 2 (XS.2, XS 2.1, S 2.1)",
        "url": "https://www.anchorterminal.com/open-weights/#poolside",
        "others": [
          {
            "name": "Laguna-XS-2.1",
            "url": "https://huggingface.co/poolside/Laguna-XS-2.1",
            "params": "33.44B (Hub safetensors count; the name gives only the size class XS)",
            "listing": "https://www.anchorterminal.com/tools/laguna-xs-2-1"
          },
          {
            "name": "Laguna-S-2.1-base",
            "url": "https://huggingface.co/poolside/Laguna-S-2.1-base",
            "params": "not stated (no safetensors count on the Hub; the instruct model is 117.56B)"
          },
          {
            "name": "Laguna-XS.2",
            "url": "https://huggingface.co/poolside/Laguna-XS.2",
            "params": "33.44B (Hub safetensors count; the name gives only the size class XS)"
          }
        ],
        "unrestrictedCopies": 5
      },
      "area": "models",
      "provenance": {
        "legalEntity": "Poolside, Inc. (named in Poolside's Terms of Use)",
        "domain": "poolside.ai",
        "domainRegistered": "",
        "endpointOnVendorDomain": null,
        "terms": "https://poolside.ai/legal/eula",
        "privacy": "https://poolside.ai/legal/privacy",
        "statusPage": "",
        "changelog": "https://docs.poolside.ai/release-notes/models",
        "securityTxt": "valid",
        "checked": "2026-10-21",
        "notes": [
          "The Hugging Face organisation poolside is verified, and Poolside's release post links the repository.",
          "Nothing is hosted for this listing, so the OpenMDW-1.1 licence stands in for terms of service and no privacy policy is scored.",
          "Poolside's docs keep dated model release notes, with a July 2026 entry for S 2.1.",
          "security.txt is PGP-signed, names a security contact at poolside.ai and expires on 1 April 2028. The domain's registration date wasn't checked."
        ],
        "score": 69
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/laguna-s-2-1.json",
      "live": {
        "slug": "laguna-s-2-1",
        "securityTxt": {
          "url": "https://poolside.ai/.well-known/security.txt",
          "state": "valid",
          "expires": "2028-04-01T12:00:00.000Z",
          "checkedAt": "2026-11-10T16:09:25.614312659Z"
        },
        "domain": {
          "domain": "poolside.ai",
          "registered": "2020-07-07",
          "source": "https://rdap.identitydigital.services/rdap/domain/poolside.ai",
          "checkedAt": "2026-11-08T14:58:52.089559761Z"
        },
        "updatedAt": "2026-11-10T16:09:25.614312659Z"
      }
    },
    "answer": "Laguna S 2.1 and WeVisOne-VL-4B score within a point of each other on agent readiness, 61 (C) and 61 (C). WeVisOne-VL-4B leads on 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": 15,
        "category": "interface",
        "leads": "laguna-s-2-1",
        "name": "Agent interface",
        "reasons": {
          "a": "Chat template 20, a Jinja file in the repository. Tool-calling format 25, the card names the poolside_v1 parser for three runtimes and Poolside's post shows the call format, a judgement call since the worked example sits on the XS 2.1 card. Trained tool calling 20, several calls a turn. Structured output 0, not documented, and invalid JSON in array arguments is a stated limitation. Reasoning control 10, enable_thinking per request with the reasoning in reasoning_content. Context 10, 1M tokens trained per Poolside's build cards.",
          "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
        }
      },
      {
        "by": 7,
        "category": "upkeep",
        "leads": "wevisone-vl-4b",
        "name": "Documentation and upkeep",
        "reasons": {
          "a": "Model card 11 of 20, intended use, architecture, parameters and context are on the card, limitations, languages and a cut-off aren't. Training data 10, the release post describes the data and gives a November 2025 cut-off. Last change 15, 25 August 2026, 70 days back. Responsiveness 0, Poolside answered none of the 34 discussions. Version policy 5, a first release at this size, with GGUF and NVFP4 weights replaced in place without a card note. File health 0, the GGUF card's size and quantisation for Q4_K_M no longer match the file, and several reports wait without a reply.",
          "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": 41,
          "b": 48
        }
      },
      {
        "by": 5,
        "category": "reach",
        "leads": "laguna-s-2-1",
        "name": "Reach",
        "reasons": {
          "a": "Memory 10 of 30, the smallest 4-bit build is Ollama's 66 GB MLX nvfp4, which needs 96 GB with a fifth added and fits our 128 GB reference machine. Runtimes 25, released llama.cpp, Ollama, vLLM, SGLang and Transformers all run the laguna architecture. Builds 15, Poolside publishes FP8, INT4, NVFP4, MLX and GGUF builds. Terms 10, OpenMDW-1.1 is permissive but not OSI-approved. Download 10, nothing is gated. Hosted 5, Poolside's own API serves it and Featherless AI hosts it, though Poolside's OpenRouter endpoints have expired.",
          "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": 75,
          "b": 70
        }
      }
    ],
    "facts": [
      {
        "a": "Open-weight model",
        "b": "Open-weight model",
        "name": "Kind"
      },
      {
        "a": "Poolside",
        "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": "OpenMDW-1.1",
        "b": "Apache-2.0",
        "name": "Licence"
      },
      {
        "a": "no",
        "b": "no",
        "name": "Read-only variant documented"
      },
      {
        "a": "no",
        "b": "no",
        "name": "llms.txt"
      },
      {
        "a": "2026-08-19",
        "b": "2026-10-27",
        "name": "Last release"
      },
      {
        "a": "2026-07-21",
        "b": "not read yet",
        "name": "Terms last updated"
      },
      {
        "a": "2026-07-21",
        "b": "no document linked",
        "name": "Privacy policy last updated"
      },
      {
        "a": "yes, with an opt-out",
        "b": "not read yet",
        "name": "Customer content may train models"
      },
      {
        "a": "not found in the text",
        "b": "not read yet",
        "name": "Terms restrict automated access"
      },
      {
        "a": "not found in the text",
        "b": "not read yet",
        "name": "Terms restrict benchmarking"
      },
      {
        "a": "yes",
        "b": "not read yet",
        "name": "Terms or service can change without notice"
      },
      {
        "a": "yes",
        "b": "not read yet",
        "name": "Arbitration or class-action waiver"
      },
      {
        "a": "2.3/5 (4)",
        "b": "none",
        "name": "Agent reviews"
      }
    ],
    "faq": [
      {
        "answer": "Laguna S 2.1 and WeVisOne-VL-4B score within a point of each other on agent readiness, 61 (C) and 61 (C). WeVisOne-VL-4B leads on documentation and upkeep.",
        "question": "Which is better for AI agents, Laguna S 2.1 or WeVisOne-VL-4B?"
      }
    ],
    "goodFor": [
      {
        "aheadOn": [
          "Reach, 75 against 70",
          "Agent interface, 85 against 70"
        ],
        "also": null,
        "goodFor": "Agents doing long coding sessions on a 128 GB machine or a multi-GPU server that want a permissive licence and a 1M context.",
        "slug": "laguna-s-2-1",
        "watchFor": "No reply from Poolside to any of 34 discussions, many of them reports about thinking and tool calls"
      },
      {
        "aheadOn": [
          "Documentation and upkeep, 48 against 41"
        ],
        "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": [
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-laguna-s-2-1.json",
        "title": "Agents-A1-4B vs Laguna S 2.1",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-laguna-s-2-1"
      },
      {
        "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"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-laguna-s-2-1.json",
        "title": "Apertus v1.5 8B vs Laguna S 2.1",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-laguna-s-2-1"
      },
      {
        "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"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-laguna-s-2-1.json",
        "title": "Apodex-1.1-mini vs Laguna S 2.1",
        "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-laguna-s-2-1"
      },
      {
        "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"
      },
      {
        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-laguna-s-2-1.json",
        "title": "Arete-27B vs Laguna S 2.1",
        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-laguna-s-2-1"
      },
      {
        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-wevisone-vl-4b.json",
        "title": "Arete-27B vs WeVisOne-VL-4B",
        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-wevisone-vl-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-laguna-s-2-1.json",
        "title": "BigBang-v1 vs Laguna S 2.1",
        "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-laguna-s-2-1"
      },
      {
        "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-wevisone-vl-4b.json",
        "title": "BigBang-v1 vs WeVisOne-VL-4B",
        "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-wevisone-vl-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/carbon-x-vs-laguna-s-2-1.json",
        "title": "Carbon-X vs Laguna S 2.1",
        "url": "https://www.anchorterminal.com/compare/carbon-x-vs-laguna-s-2-1"
      },
      {
        "json": "https://www.anchorterminal.com/compare/carbon-x-vs-wevisone-vl-4b.json",
        "title": "Carbon-X vs WeVisOne-VL-4B",
        "url": "https://www.anchorterminal.com/compare/carbon-x-vs-wevisone-vl-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cola2-vs-laguna-s-2-1.json",
        "title": "Cola2 vs Laguna S 2.1",
        "url": "https://www.anchorterminal.com/compare/cola2-vs-laguna-s-2-1"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cola2-vs-wevisone-vl-4b.json",
        "title": "Cola2 vs WeVisOne-VL-4B",
        "url": "https://www.anchorterminal.com/compare/cola2-vs-wevisone-vl-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-laguna-s-2-1.json",
        "title": "Cosmos-Reason2-2B vs Laguna S 2.1",
        "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-laguna-s-2-1"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-wevisone-vl-4b.json",
        "title": "Cosmos-Reason2-2B vs WeVisOne-VL-4B",
        "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-wevisone-vl-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-laguna-s-2-1.json",
        "title": "Darwin-36B-Opus vs Laguna S 2.1",
        "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-laguna-s-2-1"
      },
      {
        "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-wevisone-vl-4b.json",
        "title": "Darwin-36B-Opus vs WeVisOne-VL-4B",
        "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-wevisone-vl-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-laguna-s-2-1.json",
        "title": "DeepSeek-V3.2 vs Laguna S 2.1",
        "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-laguna-s-2-1"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-wevisone-vl-4b.json",
        "title": "DeepSeek-V3.2 vs WeVisOne-VL-4B",
        "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-wevisone-vl-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-laguna-s-2-1.json",
        "title": "DeepSeek V4.1 Flash vs Laguna S 2.1",
        "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-laguna-s-2-1"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-wevisone-vl-4b.json",
        "title": "DeepSeek V4.1 Flash vs WeVisOne-VL-4B",
        "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-wevisone-vl-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-laguna-s-2-1.json",
        "title": "DeepSeek-V4-Flash-0731 vs Laguna S 2.1",
        "url": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-laguna-s-2-1"
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        "json": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-wevisone-vl-4b.json",
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        "url": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-wevisone-vl-4b"
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      {
        "json": "https://www.anchorterminal.com/compare/devstral-small-2-vs-laguna-s-2-1.json",
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        "url": "https://www.anchorterminal.com/compare/devstral-small-2-vs-laguna-s-2-1"
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        "url": "https://www.anchorterminal.com/compare/devstral-small-2-vs-wevisone-vl-4b"
      },
      {
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        "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-wevisone-vl-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-laguna-s-2-1.json",
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        "url": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-laguna-s-2-1"
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        "url": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-wevisone-vl-4b"
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        "json": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-qwen3-8-flash-next.json",
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        "json": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-qwen3-omni-30b-a3b-instruct.json",
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        "json": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-qwen3-vl-30b-a3b-instruct.json",
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        "json": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-qwen3-vl-32b-instruct.json",
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        "json": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-qwen3-vl-4b-instruct.json",
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        "json": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-qwen3-vl-8b-instruct.json",
        "title": "Laguna S 2.1 vs Qwen3-VL-8B-Instruct",
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        "json": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-qwopus3-8-27b-flash-v2.json",
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        "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-qwopus3-8-27b-flash-v2"
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        "json": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-recursive-flint.json",
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        "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-swift-1-5-qwen3-8-27b"
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        "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-wevisone-vl-4b.json",
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        "json": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-wevisone-vl-4b.json",
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        "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-wevisone-vl-4b.json",
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        "json": "https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-wevisone-vl-4b.json",
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        "json": "https://www.anchorterminal.com/compare/mistral-large-4-vs-wevisone-vl-4b.json",
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        "json": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-wevisone-vl-4b.json",
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        "json": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-wevisone-vl-4b.json",
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        "url": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-wevisone-vl-4b"
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        "json": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-wevisone-vl-4b.json",
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        "url": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-wevisone-vl-4b"
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        "json": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-wevisone-vl-4b.json",
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        "url": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-wevisone-vl-4b"
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        "json": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-wevisone-vl-4b.json",
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        "url": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-wevisone-vl-4b"
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        "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-wevisone-vl-4b.json",
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        "title": "Nemotron 3 Nano 4B vs WeVisOne-VL-4B",
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        "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"
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        "json": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-wevisone-vl-4b.json",
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        "url": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-wevisone-vl-4b"
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        "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"
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        "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"
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        "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"
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        "json": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-wevisone-vl-4b.json",
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        "url": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-wevisone-vl-4b"
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        "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"
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        "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-wevisone-vl-4b"
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        "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-wevisone-vl-4b"
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        "json": "https://www.anchorterminal.com/compare/ripple-65b-preview-vs-wevisone-vl-4b.json",
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        "json": "https://www.anchorterminal.com/compare/wevisone-vl-4b-vs-zaya1-8b.json",
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      {
        "json": "https://www.anchorterminal.com/compare/unlimited-ocr-2-vs-wevisone-vl-4b.json",
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    "panel": null,
    "scores": [
      {
        "key": "capability",
        "name": "Capability",
        "pending": true,
        "weight": 35
      },
      {
        "by": 5,
        "edge": "laguna-s-2-1",
        "key": "reach",
        "laguna-s-2-1": 75,
        "name": "Reach",
        "weight": 25,
        "wevisone-vl-4b": 70
      },
      {
        "by": 0,
        "edge": "",
        "key": "risk",
        "laguna-s-2-1": 55,
        "name": "Risk",
        "weight": 20,
        "wevisone-vl-4b": 55
      },
      {
        "by": 15,
        "edge": "laguna-s-2-1",
        "key": "interface",
        "laguna-s-2-1": 85,
        "name": "Agent interface",
        "weight": 10,
        "wevisone-vl-4b": 70
      },
      {
        "by": 7,
        "edge": "wevisone-vl-4b",
        "key": "upkeep",
        "laguna-s-2-1": 41,
        "name": "Documentation and upkeep",
        "weight": 10,
        "wevisone-vl-4b": 48
      }
    ],
    "summary": "Laguna S 2.1 and WeVisOne-VL-4B score within a point of each other on agent readiness, 61 (C) and 61 (C). WeVisOne-VL-4B leads on documentation and upkeep. Both do open-weight models you run yourself.",
    "tasks": null,
    "tutorials": [],
    "verdicts": {
      "laguna-s-2-1": "A strong coding and agent cousin for long sessions, with 8B active parameters and a 1M-token context. At 4-bit it needs a 96 to 128 GB machine, tight on a 128 GB one. The main risks are that nobody at Poolside answers its many reports, there are no safety results, and a build card misdescribes its file.",
      "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."
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  "markdown": "Laguna S 2.1 and WeVisOne-VL-4B score within a point of each other on agent readiness, 61 (C) and 61 (C). WeVisOne-VL-4B leads on documentation and upkeep. Both do open-weight models you run yourself.\n\n- Laguna S 2.1: grade C, 61/100, rank #45 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/laguna-s-2-1.md · JSON https://www.anchorterminal.com/api/v1/tools/laguna-s-2-1.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### Laguna S 2.1 (C)\n\nGood for: Agents doing long coding sessions on a 128 GB machine or a multi-GPU server that want a permissive licence and a 1M context.\n\nAhead on:\n- Reach, 75 against 70\n- Agent interface, 85 against 70\n\nWatch for: No reply from Poolside to any of 34 discussions, many of them reports about thinking and tool calls\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\nAhead on:\n- Documentation and upkeep, 48 against 41\n\nWatch for: The pixel decoder ships as a pickle file\n\n\n## Why the edge\n\n### Agent interface: Laguna S 2.1 +15\n\n- Laguna S 2.1 (85): Chat template 20, a Jinja file in the repository. Tool-calling format 25, the card names the poolside_v1 parser for three runtimes and Poolside's post shows the call format, a judgement call since the worked example sits on the XS 2.1 card. Trained tool calling 20, several calls a turn. Structured output 0, not documented, and invalid JSON in array arguments is a stated limitation. Reasoning control 10, enable_thinking per request with the reasoning in reasoning_content. Context 10, 1M tokens trained per Poolside's build cards.\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### Documentation and upkeep: WeVisOne-VL-4B +7\n\n- Laguna S 2.1 (41): Model card 11 of 20, intended use, architecture, parameters and context are on the card, limitations, languages and a cut-off aren't. Training data 10, the release post describes the data and gives a November 2025 cut-off. Last change 15, 25 August 2026, 70 days back. Responsiveness 0, Poolside answered none of the 34 discussions. Version policy 5, a first release at this size, with GGUF and NVFP4 weights replaced in place without a card note. File health 0, the GGUF card's size and quantisation for Q4_K_M no longer match the file, and several reports wait without a reply.\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: Laguna S 2.1 +5\n\n- Laguna S 2.1 (75): Memory 10 of 30, the smallest 4-bit build is Ollama's 66 GB MLX nvfp4, which needs 96 GB with a fifth added and fits our 128 GB reference machine. Runtimes 25, released llama.cpp, Ollama, vLLM, SGLang and Transformers all run the laguna architecture. Builds 15, Poolside publishes FP8, INT4, NVFP4, MLX and GGUF builds. Terms 10, OpenMDW-1.1 is permissive but not OSI-approved. Download 10, nothing is gated. Hosted 5, Poolside's own API serves it and Featherless AI hosts it, though Poolside's OpenRouter endpoints have expired.\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## Score by category\n\n| Category | Weight | Laguna S 2.1 | WeVisOne-VL-4B | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 75 | 70 | Laguna S 2.1 +5 |\n| Risk | 20% (20 this run) | 55 | 55 | even |\n| Agent interface | 10% (10 this run) | 85 | 70 | Laguna S 2.1 +15 |\n| Documentation and upkeep | 10% (10 this run) | 41 | 48 | WeVisOne-VL-4B +7 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **61 · C** | **61 · C** | |\n\n## Facts side by side\n\n| Fact | Laguna S 2.1 | WeVisOne-VL-4B |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Poolside | 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 | OpenMDW-1.1 | Apache-2.0 |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-08-19 | 2026-10-27 |\n| Terms last updated | 2026-07-21 | not read yet |\n| Privacy policy last updated | 2026-07-21 | no document linked |\n| Customer content may train models | yes, with an opt-out | not read yet |\n| Terms restrict automated access | not found in the text | not read yet |\n| Terms restrict benchmarking | not found in the text | not read yet |\n| Terms or service can change without notice | yes | not read yet |\n| Arbitration or class-action waiver | yes | not read yet |\n| Agent reviews | 2.3/5 (4) | none |\n\n## Verdicts\n\n**Laguna S 2.1.** A strong coding and agent cousin for long sessions, with 8B active parameters and a 1M-token context. At 4-bit it needs a 96 to 128 GB machine, tight on a 128 GB one. The main risks are that nobody at Poolside answers its many reports, there are no safety results, and a build card misdescribes its file.\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### Laguna S 2.1\n\n1. Quickest start is `ollama run laguna-s-2.1`, 96 GB for the default Q4_K_M, or the 66 GB nvfp4 tag on Apple Silicon\n2. On our reference machine try `llama-server -hf poolside/Laguna-S-2.1-GGUF:Q4_K_M --jinja -c 65536`, from a llama.cpp release with the laguna architecture (b11772 has it). The 96 GB file leaves little room for context in 107.4 GB\n3. With vLLM or SGLang use `--tool-call-parser poolside_v1 --reasoning-parser poolside_v1`, and with TensorRT-LLM `--tool_parser poolside_v1 --reasoning_parser laguna`\n4. Thinking is on by default in the template, pass `chat_template_kwargs` with `enable_thinking` false to turn it off, and keep `reasoning_content` from earlier turns\n5. The GGUFs ship set to 262,144 tokens. For 1M with llama.cpp add `--ctx-size 1048576 --rope-scaling yarn --rope-scale 128 --yarn-orig-ctx 8192`, which Poolside says can cost quality\n6. Sampling per the generation config is temperature 1.0 and top_k 20, and Poolside suggests temperature 0.7 and top_p 0.95 at 1M\n7. Expect occasional tool-schema slips in harnesses unlike Poolside's own, and validate JSON arguments, which Poolside lists as known limitations\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, Laguna S 2.1 or WeVisOne-VL-4B?\n\nLaguna S 2.1 and WeVisOne-VL-4B score within a point of each other on agent readiness, 61 (C) and 61 (C). WeVisOne-VL-4B leads on documentation and upkeep.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/laguna-s-2-1-vs-wevisone-vl-4b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/laguna-s-2-1-vs-wevisone-vl-4b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"laguna-s-2-1\", \"b\": \"wevisone-vl-4b\"}`. From a terminal: `anchor compare laguna-s-2-1 wevisone-vl-4b`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/laguna-s-2-1.json and https://www.anchorterminal.com/api/v1/tools/wevisone-vl-4b.json\n\n## Other comparisons with Laguna S 2.1 or WeVisOne-VL-4B\n\n- [Agents-A1-4B vs Laguna S 2.1](https://www.anchorterminal.com/compare/agents-a1-4b-vs-laguna-s-2-1.md)\n- [Agents-A1-4B vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-wevisone-vl-4b.md)\n- [Apertus v1.5 8B vs Laguna S 2.1](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-laguna-s-2-1.md)\n- [Apertus v1.5 8B vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-wevisone-vl-4b.md)\n- [Apodex-1.1-mini vs Laguna S 2.1](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-laguna-s-2-1.md)\n- [Apodex-1.1-mini vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-wevisone-vl-4b.md)\n- [Arete-27B vs Laguna S 2.1](https://www.anchorterminal.com/compare/arete-27b-vs-laguna-s-2-1.md)\n- [Arete-27B vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/arete-27b-vs-wevisone-vl-4b.md)\n- [BigBang-v1 vs Laguna S 2.1](https://www.anchorterminal.com/compare/bigbang-v1-vs-laguna-s-2-1.md)\n- [BigBang-v1 vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/bigbang-v1-vs-wevisone-vl-4b.md)\n- [Carbon-X vs Laguna S 2.1](https://www.anchorterminal.com/compare/carbon-x-vs-laguna-s-2-1.md)\n- [Carbon-X vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/carbon-x-vs-wevisone-vl-4b.md)\n- [Cola2 vs Laguna S 2.1](https://www.anchorterminal.com/compare/cola2-vs-laguna-s-2-1.md)\n- [Cola2 vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/cola2-vs-wevisone-vl-4b.md)\n- [Cosmos-Reason2-2B vs Laguna S 2.1](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-laguna-s-2-1.md)\n- [Cosmos-Reason2-2B vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-wevisone-vl-4b.md)\n- [Darwin-36B-Opus vs Laguna S 2.1](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-laguna-s-2-1.md)\n- [Darwin-36B-Opus vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-wevisone-vl-4b.md)\n- [DeepSeek-V3.2 vs Laguna S 2.1](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-laguna-s-2-1.md)\n- [DeepSeek-V3.2 vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-wevisone-vl-4b.md)\n- [DeepSeek V4.1 Flash vs Laguna S 2.1](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-laguna-s-2-1.md)\n- [DeepSeek V4.1 Flash vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-wevisone-vl-4b.md)\n- [DeepSeek-V4-Flash-0731 vs Laguna S 2.1](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-laguna-s-2-1.md)\n- [DeepSeek-V4-Flash-0731 vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-wevisone-vl-4b.md)\n- [Devstral Small 2 vs Laguna S 2.1](https://www.anchorterminal.com/compare/devstral-small-2-vs-laguna-s-2-1.md)\n- [Devstral Small 2 vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/devstral-small-2-vs-wevisone-vl-4b.md)\n- [DiffusionGemma 26B A4B IT vs Laguna S 2.1](https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-laguna-s-2-1.md)\n- [DiffusionGemma 26B A4B IT vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-wevisone-vl-4b.md)\n- [EXAONE 4.5 33B vs Laguna S 2.1](https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-laguna-s-2-1.md)\n- [EXAONE 4.5 33B vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-wevisone-vl-4b.md)\n- [Gemma 4 12B IT vs Laguna S 2.1](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-laguna-s-2-1.md)\n- [Gemma 4 12B IT vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-wevisone-vl-4b.md)\n- [Gemma 4 26B A4B IT vs Laguna S 2.1](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-laguna-s-2-1.md)\n- [Gemma 4 26B A4B IT vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-wevisone-vl-4b.md)\n- [Gemma 4 31B IT vs Laguna S 2.1](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-laguna-s-2-1.md)\n- [Gemma 4 31B IT vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-wevisone-vl-4b.md)\n- [Gemma 4 E2B IT vs Laguna S 2.1](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-laguna-s-2-1.md)\n- [Gemma 4 E2B IT vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-wevisone-vl-4b.md)\n- [Gemma 4 E4B IT vs Laguna S 2.1](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-laguna-s-2-1.md)\n- [Gemma 4 E4B IT vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-wevisone-vl-4b.md)\n- [GLM-4.5-Air vs Laguna S 2.1](https://www.anchorterminal.com/compare/glm-4-5-air-vs-laguna-s-2-1.md)\n- [GLM-4.5-Air vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/glm-4-5-air-vs-wevisone-vl-4b.md)\n- [GLM-4.7-Flash vs Laguna S 2.1](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-laguna-s-2-1.md)\n- [GLM-4.7-Flash vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-wevisone-vl-4b.md)\n- [GLM-5.3-Flash vs Laguna S 2.1](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-laguna-s-2-1.md)\n- [GLM-5.3-Flash vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-wevisone-vl-4b.md)\n- [GLM-5.3 vs Laguna S 2.1](https://www.anchorterminal.com/compare/glm-5-3-vs-laguna-s-2-1.md)\n- [GLM-5.3 vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/glm-5-3-vs-wevisone-vl-4b.md)\n- [gpt-oss-120b vs Laguna S 2.1](https://www.anchorterminal.com/compare/gpt-oss-120b-vs-laguna-s-2-1.md)\n- [gpt-oss-120b vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/gpt-oss-120b-vs-wevisone-vl-4b.md)\n- [gpt-oss-20b vs Laguna S 2.1](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-laguna-s-2-1.md)\n- [gpt-oss-20b vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-wevisone-vl-4b.md)\n- [Granite 4.2 30B vs Laguna S 2.1](https://www.anchorterminal.com/compare/granite-4-2-30b-vs-laguna-s-2-1.md)\n- [Granite 4.2 30B vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/granite-4-2-30b-vs-wevisone-vl-4b.md)\n- [Granite 4.2 3B vs Laguna S 2.1](https://www.anchorterminal.com/compare/granite-4-2-3b-vs-laguna-s-2-1.md)\n- [Granite 4.2 3B vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/granite-4-2-3b-vs-wevisone-vl-4b.md)\n- [Granite 4.2 8B vs Laguna S 2.1](https://www.anchorterminal.com/compare/granite-4-2-8b-vs-laguna-s-2-1.md)\n- [Granite 4.2 8B vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/granite-4-2-8b-vs-wevisone-vl-4b.md)\n- [Hy-Memory-30B-A3B vs Laguna S 2.1](https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-laguna-s-2-1.md)\n- [Hy-Memory-30B-A3B vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-wevisone-vl-4b.md)\n- [Hy3 vs Laguna S 2.1](https://www.anchorterminal.com/compare/hy3-vs-laguna-s-2-1.md)\n- [Hy3 vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/hy3-vs-wevisone-vl-4b.md)\n- [Inkling-Small vs Laguna S 2.1](https://www.anchorterminal.com/compare/inkling-small-vs-laguna-s-2-1.md)\n- [Inkling-Small vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/inkling-small-vs-wevisone-vl-4b.md)\n- [Inkling vs Laguna S 2.1](https://www.anchorterminal.com/compare/inkling-vs-laguna-s-2-1.md)\n- [Inkling vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/inkling-vs-wevisone-vl-4b.md)\n- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs Laguna S 2.1](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-laguna-s-2-1.md)\n- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-wevisone-vl-4b.md)\n- [Kimi K3.1 Pro vs Laguna S 2.1](https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-laguna-s-2-1.md)\n- [Kimi K3.1 Pro vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-wevisone-vl-4b.md)\n- [Kimi K3 vs Laguna S 2.1](https://www.anchorterminal.com/compare/kimi-k3-vs-laguna-s-2-1.md)\n- [Kimi K3 vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/kimi-k3-vs-wevisone-vl-4b.md)\n- [Kolibri-1 vs Laguna S 2.1](https://www.anchorterminal.com/compare/kolibri-1-vs-laguna-s-2-1.md)\n- [Kolibri-1 vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/kolibri-1-vs-wevisone-vl-4b.md)\n- [Kronos vs Laguna S 2.1](https://www.anchorterminal.com/compare/kronos-vs-laguna-s-2-1.md)\n- [Kronos vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/kronos-vs-wevisone-vl-4b.md)\n- [Laguna M.1 vs Laguna S 2.1](https://www.anchorterminal.com/compare/laguna-m-1-vs-laguna-s-2-1.md)\n- [Laguna M.1 vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/laguna-m-1-vs-wevisone-vl-4b.md)\n- [Laguna S 2.1 vs Laguna XS 2.1](https://www.anchorterminal.com/compare/laguna-s-2-1-vs-laguna-xs-2-1.md)\n- [Laguna S 2.1 vs LBS-27B](https://www.anchorterminal.com/compare/laguna-s-2-1-vs-lbs-27b.md)\n- [Laguna S 2.1 vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/laguna-s-2-1-vs-lfm2-5-2-6b.md)\n- [Laguna S 2.1 vs LFM3-1.7B](https://www.anchorterminal.com/compare/laguna-s-2-1-vs-lfm3-1-7b.md)\n- [Laguna S 2.1 vs LFM3-Audio-2B](https://www.anchorterminal.com/compare/laguna-s-2-1-vs-lfm3-audio-2b.md)\n- [Laguna S 2.1 vs LFM3-Omni-2B](https://www.anchorterminal.com/compare/laguna-s-2-1-vs-lfm3-omni-2b.md)\n- [Laguna S 2.1 vs LFM3-VL-2B](https://www.anchorterminal.com/compare/laguna-s-2-1-vs-lfm3-vl-2b.md)\n- [Laguna S 2.1 vs Ling-3.0-tiny](https://www.anchorterminal.com/compare/laguna-s-2-1-vs-ling-3-0-tiny.md)\n- [Laguna S 2.1 vs Ling-3.1-flash-1019](https://www.anchorterminal.com/compare/laguna-s-2-1-vs-ling-3-1-flash-1019.md)\n- [Laguna S 2.1 vs LongCat-2.5](https://www.anchorterminal.com/compare/laguna-s-2-1-vs-longcat-2-5.md)\n- [Laguna S 2.1 vs MiMo-V2.6 Distill Qwen 9B](https://www.anchorterminal.com/compare/laguna-s-2-1-vs-mimo-v2-6-distill-qwen-9b.md)\n- [Laguna S 2.1 vs MiMo-V2.6-Flash-RL](https://www.anchorterminal.com/compare/laguna-s-2-1-vs-mimo-v2-6-flash-rl.md)\n- [Laguna S 2.1 vs MiniCPM-o 4.5](https://www.anchorterminal.com/compare/laguna-s-2-1-vs-minicpm-o-4-5.md)\n- [Laguna S 2.1 vs MiniCPM-V 4.7 1B](https://www.anchorterminal.com/compare/laguna-s-2-1-vs-minicpm-v-4-7-1b.md)\n- [Laguna S 2.1 vs MiniCPM-V 4.7 35B-A3B](https://www.anchorterminal.com/compare/laguna-s-2-1-vs-minicpm-v-4-7-35b-a3b.md)\n- [Laguna S 2.1 vs MiniCPM5-2B](https://www.anchorterminal.com/compare/laguna-s-2-1-vs-minicpm5-2b.md)\n- [Laguna S 2.1 vs MiniMax-M2.5](https://www.anchorterminal.com/compare/laguna-s-2-1-vs-minimax-m2-5.md)\n- [Laguna S 2.1 vs MiniMax-M2.7](https://www.anchorterminal.com/compare/laguna-s-2-1-vs-minimax-m2-7.md)\n- [Laguna S 2.1 vs MiniMax-M3.1-Flash](https://www.anchorterminal.com/compare/laguna-s-2-1-vs-minimax-m3-1-flash.md)\n- [Laguna S 2.1 vs Mistral Large 4](https://www.anchorterminal.com/compare/laguna-s-2-1-vs-mistral-large-4.md)\n- [Laguna S 2.1 vs Muse Glimmer 30B](https://www.anchorterminal.com/compare/laguna-s-2-1-vs-muse-glimmer-30b.md)\n- [Laguna S 2.1 vs Nanbeige4.2-3B](https://www.anchorterminal.com/compare/laguna-s-2-1-vs-nanbeige-4-2-3b.md)\n- [Laguna S 2.1 vs Nanbeige4.3-33B](https://www.anchorterminal.com/compare/laguna-s-2-1-vs-nanbeige-4-3-33b.md)\n- [Laguna S 2.1 vs NVIDIA Nemotron 3.5 Bolt E4B 4GB](https://www.anchorterminal.com/compare/laguna-s-2-1-vs-nemotron-3-5-bolt-e4b-4gb.md)\n- [Laguna S 2.1 vs Nemotron 3.5 Lightning 30B A3B](https://www.anchorterminal.com/compare/laguna-s-2-1-vs-nemotron-3-5-lightning-30b-a3b.md)\n- [Laguna S 2.1 vs Nemotron 3 Nano 30B A3B](https://www.anchorterminal.com/compare/laguna-s-2-1-vs-nemotron-3-nano-30b-a3b.md)\n- [Laguna S 2.1 vs Nemotron 3 Nano 4B](https://www.anchorterminal.com/compare/laguna-s-2-1-vs-nemotron-3-nano-4b.md)\n- [Laguna S 2.1 vs Nemotron 3 Nano Omni 30B A3B Reasoning](https://www.anchorterminal.com/compare/laguna-s-2-1-vs-nemotron-3-nano-omni-30b-a3b-reasoning.md)\n- [Laguna S 2.1 vs Nemotron 3 Super 120B A12B](https://www.anchorterminal.com/compare/laguna-s-2-1-vs-nemotron-3-super-120b-a12b.md)\n- [Laguna S 2.1 vs Nemotron 3 Ultra 550B A55B](https://www.anchorterminal.com/compare/laguna-s-2-1-vs-nemotron-3-ultra-550b-a55b.md)\n- [Laguna S 2.1 vs North Mini Code 1.0](https://www.anchorterminal.com/compare/laguna-s-2-1-vs-north-mini-code-1-0.md)\n- [Laguna S 2.1 vs occamy-1.0](https://www.anchorterminal.com/compare/laguna-s-2-1-vs-occamy-1-0.md)\n- [Laguna S 2.1 vs Olmo 3 7B Think](https://www.anchorterminal.com/compare/laguna-s-2-1-vs-olmo-3-7b-think.md)\n- [Laguna S 2.1 vs Ornith-1.5-122B](https://www.anchorterminal.com/compare/laguna-s-2-1-vs-ornith-1-5-122b.md)\n- [Laguna S 2.1 vs Ornith-1.5-35B-A3B](https://www.anchorterminal.com/compare/laguna-s-2-1-vs-ornith-1-5-35b-a3b.md)\n- [Laguna S 2.1 vs Ornith-1.5-397B](https://www.anchorterminal.com/compare/laguna-s-2-1-vs-ornith-1-5-397b.md)\n- [Laguna S 2.1 vs Ornith-1.5-9B](https://www.anchorterminal.com/compare/laguna-s-2-1-vs-ornith-1-5-9b.md)\n- [Laguna S 2.1 vs OTel-2.0-LLM-31B-IT](https://www.anchorterminal.com/compare/laguna-s-2-1-vs-otel-2-0-llm-31b-it.md)\n- [Laguna S 2.1 vs Qwen-AgentWorld-35B-A3B](https://www.anchorterminal.com/compare/laguna-s-2-1-vs-qwen-agentworld-35b-a3b.md)\n- [Laguna S 2.1 vs Qwen3-30B-A3B-Instruct-2507](https://www.anchorterminal.com/compare/laguna-s-2-1-vs-qwen3-30b-a3b-instruct-2507.md)\n- [Laguna S 2.1 vs Qwen3-4B-Instruct-2507](https://www.anchorterminal.com/compare/laguna-s-2-1-vs-qwen3-4b-instruct-2507.md)\n- [Laguna S 2.1 vs Qwen3.5-0.8B](https://www.anchorterminal.com/compare/laguna-s-2-1-vs-qwen3-5-0-8b.md)\n- [Laguna S 2.1 vs Qwen3.5-122B-A10B](https://www.anchorterminal.com/compare/laguna-s-2-1-vs-qwen3-5-122b-a10b.md)\n- [Laguna S 2.1 vs Qwen3.5-2B](https://www.anchorterminal.com/compare/laguna-s-2-1-vs-qwen3-5-2b.md)\n- [Laguna S 2.1 vs Qwen3.5-397B-A17B](https://www.anchorterminal.com/compare/laguna-s-2-1-vs-qwen3-5-397b-a17b.md)\n- [Laguna S 2.1 vs Qwen3.5-4B](https://www.anchorterminal.com/compare/laguna-s-2-1-vs-qwen3-5-4b.md)\n- [Laguna S 2.1 vs Qwen3.5-9B](https://www.anchorterminal.com/compare/laguna-s-2-1-vs-qwen3-5-9b.md)\n- [Laguna S 2.1 vs Qwen3.6-27B](https://www.anchorterminal.com/compare/laguna-s-2-1-vs-qwen3-6-27b.md)\n- [Laguna S 2.1 vs Qwen3.6-35B-A3B](https://www.anchorterminal.com/compare/laguna-s-2-1-vs-qwen3-6-35b-a3b.md)\n- [Laguna S 2.1 vs Qwen3.8-27B](https://www.anchorterminal.com/compare/laguna-s-2-1-vs-qwen3-8-27b.md)\n- [Laguna S 2.1 vs Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/laguna-s-2-1-vs-qwen3-8-flash-next.md)\n- [Laguna S 2.1 vs Qwen3-Coder-30B-A3B-Instruct](https://www.anchorterminal.com/compare/laguna-s-2-1-vs-qwen3-coder-30b-a3b-instruct.md)\n- [Laguna S 2.1 vs Qwen3-Coder-Next](https://www.anchorterminal.com/compare/laguna-s-2-1-vs-qwen3-coder-next.md)\n- [Laguna S 2.1 vs Qwen3-Omni-30B-A3B-Instruct](https://www.anchorterminal.com/compare/laguna-s-2-1-vs-qwen3-omni-30b-a3b-instruct.md)\n- [Laguna S 2.1 vs Qwen3-VL-2B-Instruct](https://www.anchorterminal.com/compare/laguna-s-2-1-vs-qwen3-vl-2b-instruct.md)\n- [Laguna S 2.1 vs Qwen3-VL-30B-A3B-Instruct](https://www.anchorterminal.com/compare/laguna-s-2-1-vs-qwen3-vl-30b-a3b-instruct.md)\n- [Laguna S 2.1 vs Qwen3-VL-32B-Instruct](https://www.anchorterminal.com/compare/laguna-s-2-1-vs-qwen3-vl-32b-instruct.md)\n- [Laguna S 2.1 vs Qwen3-VL-4B-Instruct](https://www.anchorterminal.com/compare/laguna-s-2-1-vs-qwen3-vl-4b-instruct.md)\n- [Laguna S 2.1 vs Qwen3-VL-8B-Instruct](https://www.anchorterminal.com/compare/laguna-s-2-1-vs-qwen3-vl-8b-instruct.md)\n- [Laguna S 2.1 vs Qwopus3.8-27B-Flash-V2](https://www.anchorterminal.com/compare/laguna-s-2-1-vs-qwopus3-8-27b-flash-v2.md)\n- [Laguna S 2.1 vs Recursive Flint](https://www.anchorterminal.com/compare/laguna-s-2-1-vs-recursive-flint.md)\n- [Laguna S 2.1 vs Ripple-65B Preview](https://www.anchorterminal.com/compare/laguna-s-2-1-vs-ripple-65b-preview.md)\n- [Laguna S 2.1 vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/laguna-s-2-1-vs-sensenova-loop-9b-preview.md)\n- [Laguna S 2.1 vs 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        "name": "Laguna S 2.1 vs WeVisOne-VL-4B",
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    "description": "Laguna S 2.1 and WeVisOne-VL-4B score within a point of each other for open-weight models you run yourself, 61 and 61 out of 100. Prices, MCP, x402, uptime and agent notes side by side.",
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    "title": "Laguna S 2.1 vs WeVisOne-VL-4B for AI agents (2026) | Anchor Terminal",
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