{
  "data": {
    "a": {
      "slug": "occamy-1-0",
      "name": "occamy-1.0",
      "vendor": "Accio (Accio-Lab)",
      "vendorUrl": "https://accio-lab.github.io/occamy/",
      "kind": "weights",
      "category": "open-weights",
      "summary": "Occamy-1.0 is Accio's open-weight agent model for long multi-step work with tools, files and code, a fine-tune of Qwen3.6-35B-A3B (35B mixture of experts, 3B active) published on Hugging Face under Apache 2.0.",
      "url": "https://www.anchorterminal.com/tools/occamy-1-0",
      "markdownUrl": "https://www.anchorterminal.com/tools/occamy-1-0.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/occamy-1-0.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/occamy-1-0.json",
      "dns": "occamy-1-0.grade.anchorterminal.com",
      "repo": "https://huggingface.co/Accio-Lab/occamy-1.0",
      "license": "Apache-2.0",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "Accio-Lab/occamy-1.0"
        },
        {
          "registry": "huggingface",
          "name": "Accio-Lab/occamy-1.0-FP8"
        },
        {
          "registry": "huggingface",
          "name": "Accio-Lab/occamy-1.0-NVFP4"
        },
        {
          "registry": "huggingface",
          "name": "Accio-Lab/occamy-1.0-GGUF"
        },
        {
          "registry": "huggingface",
          "name": "Accio-Lab/occamy-1.0-APEX-GGUF"
        },
        {
          "registry": "huggingface",
          "name": "Accio-Lab/occamy-1.0-MLX-4bit"
        },
        {
          "registry": "huggingface",
          "name": "Accio-Lab/occamy-1.0-MLX-mxfp4"
        }
      ],
      "auth": "none",
      "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The main repository and all of Accio's build repositories are not gated (checked 2026-10-21).",
      "pricing": "free",
      "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0, the same licence as the Qwen3.6-35B-A3B base. Running it yourself costs the hardware, 70.2 GB for BF16 and 18.4 GB for the smallest 4-bit build (checked 2026-10-21).",
      "priceSummary": "Free",
      "where": "local",
      "x402": {
        "level": "no",
        "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-10-21).",
        "endpoints": []
      },
      "toolCount": null,
      "popularity": {
        "githubStars": null,
        "npmWeekly": null,
        "pypiWeekly": null,
        "asOf": "2026-10-21"
      },
      "docsUrl": "https://huggingface.co/Accio-Lab/occamy-1.0",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "moe",
        "multimodal",
        "reasoning",
        "tool-calling",
        "agent",
        "coding",
        "derivative",
        "apache-2.0",
        "safetensors",
        "gguf",
        "mlx",
        "fp8",
        "nvfp4",
        "self-hosted"
      ],
      "lastRelease": "2026-10-02",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 61,
        "grade": "C",
        "agentReady": false,
        "rank": 0,
        "ranked": false,
        "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools",
        "rankOf": 1518,
        "categoryRank": 49,
        "methodology": "0.7",
        "run": "2026-10-01",
        "scores": {
          "interface": 70,
          "reach": 90,
          "risk": 55,
          "upkeep": 74
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "low",
          "date": "2026-11-03"
        },
        "negative": 0,
        "verdict": "A Qwen3.6-35B-A3B fine-tuned for long agent work with tools, files and code, under plain Apache 2.0, and Accio's 4-bit builds run in about 23 GB. The main gaps are no safety results, agent guidance or security contact, and every benchmark figure is Accio's own.",
        "bestFor": "Agents that want an Apache 2.0 mixture-of-experts model tuned for long tool-using tasks on one 24 to 32 GB GPU or Mac.",
        "strengths": [
          "Apache 2.0 like its base, with nothing gated",
          "Accio publishes FP8, NVFP4, GGUF and MLX builds, the 4-bit ones within 24 GB",
          "Same architecture and template as Qwen3.6-35B-A3B, so released llama.cpp, Ollama, vLLM, SGLang and Transformers run it",
          "A technical report, the supervised data mix with token counts and a published data subset",
          "MD5 and SHA-256 checksums for every file"
        ],
        "weaknesses": [
          "No safety results, prompt-injection guidance or security contact",
          "No hosted API or inference provider",
          "Tool-calling format and structured output aren't documented beyond the template and parser name",
          "Languages and a knowledge cut-off aren't stated",
          "The MLX builds await Apple Metal acceptance, per Accio's own cards"
        ],
        "agentNotes": [
          "Serve with `--reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder` on vLLM 0.19.0 or SGLang 0.5.10 and later",
          "BF16 is 70.2 GB, so on a 128 GB machine run it without the card's 8-way tensor parallelism",
          "Keep the whole assistant message, reasoning and tool calls included, in later turns, as the card says",
          "Sampling per the card is temperature 1.0, top_p 0.95, top_k 20 and presence_penalty 1.5",
          "With the GGUFs, NFC-normalise input text and read TOKENIZER.md, since the files store qwen2 pre-tokenizer metadata",
          "The base's MTP layer isn't in this checkpoint, Accio-Lab/occamy-1.0-MTP is an experimental separate head",
          "Ollama has no Occamy library entry, so load Accio's GGUF in Ollama 0.40.14 or later, which builds a llama.cpp with the qwen35moe architecture"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 4,
        "avgRating": 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": 70,
          "reach": 90,
          "risk": 55,
          "upkeep": 74
        },
        "provenanceScore": 24,
        "checked": {
          "categories": {
            "interface": "2026-11-03",
            "reach": "2026-11-03",
            "risk": "2026-11-03",
            "upkeep": "2026-11-03"
          },
          "last": "2026-11-03",
          "lastMoved": "2026-11-03",
          "oldest": "2026-11-03",
          "stale": false,
          "staleAfterDays": 90
        }
      },
      "connect": {
        "install": "vllm serve Accio-Lab/occamy-1.0 --max-model-len 262144 --reasoning-parser qwen3 \\\n  --enable-auto-tool-choice --tool-call-parser qwen3_coder\n# or a 4-bit GGUF with llama.cpp:\nllama-server -hf Accio-Lab/occamy-1.0-GGUF:Q4_K_M --jinja -c 131072",
        "http": "curl http://localhost:8000/v1/chat/completions \\\n  -H 'Content-Type: application/json' \\\n  -d '{\"model\": \"Accio-Lab/occamy-1.0\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/occamy-1-0"
      },
      "weights": {
        "trainedBy": "Accio (Accio-Lab)",
        "builtOn": "Qwen/Qwen3.6-35B-A3B (Apache 2.0), declared in the card's metadata (base_model) and text as the post-trained starting checkpoint. Same qwen3_5_moe architecture and config apart from the removed MTP layer, and the same chat template. Occamy is also Apache 2.0",
        "params": "35B (3B active)",
        "active": "3B",
        "context": 262144,
        "goodFor": "Mixture-of-experts agent model for long multi-step work with tools, files, code and office tasks, text and images in, thinking on by default.",
        "runtimes": [
          "llama.cpp",
          "Ollama",
          "vLLM",
          "SGLang",
          "Transformers"
        ],
        "builds": [
          {
            "name": "occamy-1.0-MLX-mxfp4",
            "url": "https://huggingface.co/Accio-Lab/occamy-1.0-MLX-mxfp4",
            "who": "publisher",
            "precision": "mxfp4",
            "sizeGB": 18.4,
            "memoryGB": 24,
            "format": "mlx",
            "runsOn": [
              "MLX"
            ],
            "note": "Text only. Apple Metal acceptance pending, per the card"
          },
          {
            "name": "occamy-1.0-MLX-4bit",
            "url": "https://huggingface.co/Accio-Lab/occamy-1.0-MLX-4bit",
            "who": "publisher",
            "precision": "mlx-4bit",
            "sizeGB": 19.5,
            "memoryGB": 24,
            "format": "mlx",
            "runsOn": [
              "MLX"
            ],
            "note": "Text only. Apple Metal acceptance pending, per the card"
          },
          {
            "name": "occamy-1.0-IQ4_XS",
            "url": "https://huggingface.co/Accio-Lab/occamy-1.0-GGUF/blob/main/occamy-1.0-IQ4_XS.gguf",
            "who": "publisher",
            "precision": "iq4_xs",
            "sizeGB": 18.7,
            "memoryGB": 24,
            "format": "gguf",
            "runsOn": [
              "llama.cpp (Vulkan)",
              "llama.cpp (ROCm)"
            ],
            "note": "Image input needs the 0.90 GB mmproj file. Input should be NFC-normalised, per the card"
          },
          {
            "name": "occamy-1.0-APEX-Compact",
            "url": "https://huggingface.co/Accio-Lab/occamy-1.0-APEX-GGUF/blob/main/occamy-1.0-APEX-Compact.gguf",
            "who": "publisher",
            "precision": "mixed-q3-q4",
            "sizeGB": 16.5,
            "memoryGB": 24,
            "format": "gguf",
            "runsOn": [
              "llama.cpp (Vulkan)",
              "llama.cpp (ROCm)"
            ],
            "note": "Most routed experts at Q3_K, so it doesn't count as a 4-bit build for the memory line"
          },
          {
            "name": "occamy-1.0-NVFP4",
            "url": "https://huggingface.co/Accio-Lab/occamy-1.0-NVFP4",
            "who": "publisher",
            "precision": "nvfp4",
            "sizeGB": 23.9,
            "memoryGB": 32,
            "format": "safetensors",
            "note": "Expert-only mixed precision, checked on H200 with Marlin W4A16, per the card"
          },
          {
            "name": "occamy-1.0-Q4_K_M",
            "url": "https://huggingface.co/Accio-Lab/occamy-1.0-GGUF/blob/main/occamy-1.0-Q4_K_M.gguf",
            "who": "publisher",
            "precision": "q4_k_m",
            "sizeGB": 21.2,
            "memoryGB": 32,
            "format": "gguf",
            "runsOn": [
              "llama.cpp (Vulkan)",
              "llama.cpp (ROCm)"
            ]
          },
          {
            "name": "occamy-1.0-FP8",
            "url": "https://huggingface.co/Accio-Lab/occamy-1.0-FP8",
            "who": "publisher",
            "precision": "fp8",
            "sizeGB": 36.6,
            "memoryGB": 48,
            "format": "safetensors",
            "runsOn": [
              "vLLM",
              "SGLang"
            ]
          },
          {
            "name": "occamy-1.0-Q8_0",
            "url": "https://huggingface.co/Accio-Lab/occamy-1.0-GGUF/blob/main/occamy-1.0-Q8_0.gguf",
            "who": "publisher",
            "precision": "q8_0",
            "sizeGB": 36.9,
            "memoryGB": 48,
            "format": "gguf",
            "runsOn": [
              "llama.cpp (Vulkan)",
              "llama.cpp (ROCm)"
            ]
          },
          {
            "name": "occamy-1.0",
            "url": "https://huggingface.co/Accio-Lab/occamy-1.0",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 70.2,
            "memoryGB": 96,
            "format": "safetensors",
            "runsOn": [
              "vLLM (ROCm)",
              "SGLang (ROCm)",
              "Transformers"
            ]
          }
        ],
        "run": "vllm serve Accio-Lab/occamy-1.0 --max-model-len 262144 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder",
        "runBuild": "occamy-1.0",
        "checked": "2026-11-03",
        "minMemoryGB": 24
      },
      "family": {
        "lab": "Accio (Accio-Lab)",
        "family": "occamy",
        "url": "https://www.anchorterminal.com/open-weights/#accio-lab",
        "others": [
          {
            "name": "occamy-1.0-MTP",
            "url": "https://huggingface.co/Accio-Lab/occamy-1.0-MTP",
            "params": "not stated (draft head only)"
          }
        ],
        "olderCount": 4,
        "unrestrictedCopies": 5
      },
      "area": "models",
      "provenance": {
        "legalEntity": "",
        "domain": "accio-lab.github.io",
        "domainRegistered": "",
        "endpointOnVendorDomain": null,
        "terms": "",
        "privacy": "",
        "statusPage": "",
        "changelog": "",
        "securityTxt": "none",
        "checked": "2026-10-21",
        "notes": [
          "No legal entity is named on the card, the report or the repository. The report's byline is Accio Team, and the Hugging Face organisation Accio-Lab (fullname Accio, 15 members) isn't verified. The GitHub README and the project website link the organisation.",
          "Nothing is hosted, so the licence stands in for terms of service and no privacy policy is scored.",
          "The project website is a GitHub Pages site drawn by script, so its text couldn't be read beyond the title.",
          "No SECURITY.md in Accio-Lab/occamy. The README sends bug reports and feature requests to GitHub issues."
        ],
        "score": 24
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/occamy-1-0.json",
      "live": {
        "slug": "occamy-1-0",
        "securityTxt": {
          "url": "https://accio-lab.github.io/.well-known/security.txt",
          "state": "none",
          "checkedAt": "2026-11-10T16:08:29.220890734Z"
        },
        "domain": {
          "domain": "github.io",
          "checkedAt": "2026-11-08T15:08:12.013832199Z"
        },
        "updatedAt": "2026-11-10T16:08:29.220890734Z"
      }
    },
    "answer": "occamy-1.0 and WeVisOne-VL-2B score within a point of each other on agent readiness, 61 (C) and 61 (C).",
    "b": {
      "slug": "wevisone-vl-2b",
      "name": "WeVisOne-VL-2B",
      "vendor": "Tencent (WeChat Vision)",
      "vendorUrl": "https://www.tencent.com",
      "kind": "weights",
      "category": "open-weights",
      "summary": "WeVisOne-VL-2B is an open-weight perception model of 2.4B 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-2b",
      "markdownUrl": "https://www.anchorterminal.com/tools/wevisone-vl-2b.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/wevisone-vl-2b.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/wevisone-vl-2b.json",
      "dns": "wevisone-vl-2b.grade.anchorterminal.com",
      "repo": "https://huggingface.co/tencent/WeVisOne-VL-2B",
      "license": "Apache-2.0",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "tencent/WeVisOne-VL-2B"
        }
      ],
      "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, 4.88 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": "2026-10-28",
      "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": 65,
        "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",
          "4.88 GB in BF16, so it fits a 8 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-2B\", 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-2B\", 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-2B"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/wevisone-vl-2b"
      },
      "sameCompany": [
        "hy-memory",
        "tencentdb-agent-memory",
        "hunyuanocr",
        "hy-memory-30b-a3b",
        "hy3",
        "wevisone-vl-4b"
      ],
      "weights": {
        "trainedBy": "Tencent WeChat Vision",
        "params": "2.4B",
        "goodFor": "Finding, counting, reading and cutting out what you name in an image, on a 8 GB machine.",
        "runtimes": [
          "vLLM",
          "SGLang",
          "Transformers"
        ],
        "builds": [
          {
            "name": "WeVisOne-VL-2B (BF16 safetensors)",
            "url": "https://huggingface.co/tencent/WeVisOne-VL-2B/blob/main/model.safetensors",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 4.88,
            "memoryGB": 8,
            "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-2B",
        "runBuild": "WeVisOne-VL-2B (BF16 safetensors)",
        "checked": "2026-11-10",
        "minMemoryGB": 8
      },
      "family": {
        "lab": "Tencent (Hunyuan / Hy)",
        "family": "WeVisOne-VL",
        "url": "https://www.anchorterminal.com/open-weights/#tencent",
        "others": [
          {
            "name": "WeVisOne-VL-4B",
            "url": "https://huggingface.co/tencent/WeVisOne-VL-4B",
            "params": "4.8B (4,830,581,248 by the Hub's safetensors count)",
            "listing": "https://www.anchorterminal.com/tools/wevisone-vl-4b"
          }
        ],
        "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-2B/blob/main/LICENSE",
        "privacy": "",
        "statusPage": "",
        "changelog": "https://huggingface.co/tencent/WeVisOne-VL-2B/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-2b.json"
    },
    "edges": [
      {
        "by": 26,
        "category": "upkeep",
        "leads": "occamy-1-0",
        "name": "Documentation and upkeep",
        "reasons": {
          "a": "Model card 14 of 20, languages and a knowledge cut-off are missing. Training data 15, the mix by type with token counts, the method and a published subset, without a cut-off. Last change 20, Accio's GGUF build card changed on 19 October 2026 and the main card on 2 October. Responsiveness 10, Accio answered 4 of 9 questions. Version policy 5, a first release with no files replaced. File health 10, the files agree with the card. 14 + 15 + 20 + 10 + 5 + 10 = 74.",
          "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 28 October 2026, put the weights back in BF16, 13 days before this check. Responsiveness 0, the one discussion that isn't a pull request (23 October 2026) has no 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. 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": 74,
          "b": 48
        }
      },
      {
        "by": 20,
        "category": "reach",
        "leads": "occamy-1-0",
        "name": "Reach",
        "reasons": {
          "a": "Memory 25 of 30, Accio's MLX mxfp4 build is 18.4 GB, 22.1 GB with a fifth added, and its IQ4_XS GGUF needs 22.5 GB, both within 24 GB. Runtimes 25, the most the line gives, it keeps the base's qwen3_5_moe architecture, which llama.cpp, vLLM, SGLang and Transformers run in released versions, and Ollama v0.40.14 builds a llama.cpp (b11963) that has it. Builds 15, FP8, NVFP4, GGUF and MLX from Accio. Terms 15, Apache 2.0 like its base. Download 10, nothing is gated. Hosted 0, no API or inference provider serves it. 25 + 25 + 15 + 15 + 10 + 0 = 90.",
          "b": "Memory 30, the only build from Tencent is the BF16 safetensors file with its decoder, 4.92 GB, and 4.92 GB × 1.2 = 5.9 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": 90,
          "b": 70
        }
      }
    ],
    "facts": [
      {
        "a": "Open-weight model",
        "b": "Open-weight model",
        "name": "Kind"
      },
      {
        "a": "Accio (Accio-Lab)",
        "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": "2026-10-02",
        "b": "2026-10-28",
        "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": "3/5 (4)",
        "b": "none",
        "name": "Agent reviews"
      }
    ],
    "faq": [
      {
        "answer": "occamy-1.0 and WeVisOne-VL-2B score within a point of each other on agent readiness, 61 (C) and 61 (C).",
        "question": "Which is better for AI agents, occamy-1.0 or WeVisOne-VL-2B?"
      }
    ],
    "goodFor": [
      {
        "aheadOn": [
          "Reach, 90 against 70",
          "Documentation and upkeep, 74 against 48"
        ],
        "also": null,
        "goodFor": "Agents that want an Apache 2.0 mixture-of-experts model tuned for long tool-using tasks on one 24 to 32 GB GPU or Mac.",
        "slug": "occamy-1-0",
        "watchFor": "No safety results, prompt-injection guidance or security contact"
      },
      {
        "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-2b",
        "watchFor": "The pixel decoder ships as a pickle file"
      }
    ],
    "history": {
      "occamy-1-0": [
        {
          "causes": [
            "correction"
          ],
          "date": "2026-11-03",
          "delta": 0,
          "from": 61,
          "fromGrade": "C",
          "to": 61,
          "toGrade": "C"
        }
      ],
      "wevisone-vl-2b": 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-wevisone-vl-2b"
      },
      {
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        "title": "Apertus v1.5 8B vs occamy-1.0",
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      },
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        "title": "Apertus v1.5 8B vs WeVisOne-VL-2B",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-wevisone-vl-2b"
      },
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        "title": "Apodex-1.1-mini vs occamy-1.0",
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        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-occamy-1-0"
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        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-wevisone-vl-2b.json",
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        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-wevisone-vl-2b"
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        "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-occamy-1-0"
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      {
        "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-wevisone-vl-2b.json",
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        "url": "https://www.anchorterminal.com/compare/carbon-x-vs-occamy-1-0"
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        "url": "https://www.anchorterminal.com/compare/cola2-vs-wevisone-vl-2b"
      },
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        "title": "Cosmos-Reason2-2B vs occamy-1.0",
        "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-occamy-1-0"
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      {
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        "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-wevisone-vl-2b"
      },
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        "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-occamy-1-0.json",
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        "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-occamy-1-0"
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      {
        "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-wevisone-vl-2b.json",
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        "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-wevisone-vl-2b"
      },
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        "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-occamy-1-0.json",
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        "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-occamy-1-0"
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      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-wevisone-vl-2b.json",
        "title": "DeepSeek-V3.2 vs WeVisOne-VL-2B",
        "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-wevisone-vl-2b"
      },
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        "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-wevisone-vl-2b"
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        "title": "Granite 4.2 30B vs WeVisOne-VL-2B",
        "url": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-wevisone-vl-2b"
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        "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-occamy-1-0.json",
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        "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-wevisone-vl-2b"
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        "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-occamy-1-0.json",
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        "url": "https://www.anchorterminal.com/compare/kolibri-1-vs-occamy-1-0"
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        "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-wevisone-vl-2b.json",
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        "json": "https://www.anchorterminal.com/compare/kronos-vs-occamy-1-0.json",
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        "json": "https://www.anchorterminal.com/compare/kronos-vs-wevisone-vl-2b.json",
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        "url": "https://www.anchorterminal.com/compare/kronos-vs-wevisone-vl-2b"
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        "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-occamy-1-0.json",
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        "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-occamy-1-0"
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        "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-wevisone-vl-2b.json",
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        "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-wevisone-vl-2b"
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        "json": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-occamy-1-0.json",
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        "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-occamy-1-0"
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        "json": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-wevisone-vl-2b.json",
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        "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-wevisone-vl-2b"
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        "json": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-occamy-1-0.json",
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        "json": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-wevisone-vl-2b.json",
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        "json": "https://www.anchorterminal.com/compare/lbs-27b-vs-wevisone-vl-2b.json",
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        "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-occamy-1-0.json",
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        "json": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-occamy-1-0.json",
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        "json": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-wevisone-vl-2b.json",
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        "json": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-occamy-1-0.json",
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        "json": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-wevisone-vl-2b.json",
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        "json": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-occamy-1-0.json",
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        "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-occamy-1-0.json",
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        "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-wevisone-vl-2b.json",
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        "url": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-occamy-1-0"
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        "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-wevisone-vl-2b.json",
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        "url": "https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-occamy-1-0"
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        "json": "https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-wevisone-vl-2b.json",
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        "json": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-wevisone-vl-2b.json",
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        "json": "https://www.anchorterminal.com/compare/mistral-large-4-vs-occamy-1-0.json",
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        "json": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-occamy-1-0.json",
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        "url": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-occamy-1-0"
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        "url": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-wevisone-vl-2b"
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        "json": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-occamy-1-0.json",
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        "url": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-occamy-1-0"
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        "url": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-occamy-1-0"
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        "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-5-0-8b"
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        "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-5-122b-a10b"
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        "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-5-2b"
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        "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-5-397b-a17b.json",
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        "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-5-397b-a17b"
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        "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-5-4b"
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        "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-5-9b.json",
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        "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-5-9b"
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        "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-8-27b"
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        "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-coder-next"
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        "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-omni-30b-a3b-instruct.json",
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        "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-vl-2b-instruct"
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        "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-vl-30b-a3b-instruct"
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        "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-vl-32b-instruct"
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        "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-vl-8b-instruct"
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      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-wevisone-vl-2b.json",
        "title": "Qwen3.5-9B vs WeVisOne-VL-2B",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-wevisone-vl-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-6-27b-vs-wevisone-vl-2b.json",
        "title": "Qwen3.6-27B vs WeVisOne-VL-2B",
        "url": "https://www.anchorterminal.com/compare/qwen3-6-27b-vs-wevisone-vl-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-wevisone-vl-2b.json",
        "title": "Qwen3.6-35B-A3B vs WeVisOne-VL-2B",
        "url": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-wevisone-vl-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-8-27b-vs-wevisone-vl-2b.json",
        "title": "Qwen3.8-27B vs WeVisOne-VL-2B",
        "url": "https://www.anchorterminal.com/compare/qwen3-8-27b-vs-wevisone-vl-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-wevisone-vl-2b.json",
        "title": "Qwen3.8-Flash-Next vs WeVisOne-VL-2B",
        "url": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-wevisone-vl-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-coder-30b-a3b-instruct-vs-wevisone-vl-2b.json",
        "title": "Qwen3-Coder-30B-A3B-Instruct vs WeVisOne-VL-2B",
        "url": "https://www.anchorterminal.com/compare/qwen3-coder-30b-a3b-instruct-vs-wevisone-vl-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-coder-next-vs-wevisone-vl-2b.json",
        "title": "Qwen3-Coder-Next vs WeVisOne-VL-2B",
        "url": "https://www.anchorterminal.com/compare/qwen3-coder-next-vs-wevisone-vl-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-omni-30b-a3b-instruct-vs-wevisone-vl-2b.json",
        "title": "Qwen3-Omni-30B-A3B-Instruct vs WeVisOne-VL-2B",
        "url": "https://www.anchorterminal.com/compare/qwen3-omni-30b-a3b-instruct-vs-wevisone-vl-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-vl-2b-instruct-vs-wevisone-vl-2b.json",
        "title": "Qwen3-VL-2B-Instruct vs WeVisOne-VL-2B",
        "url": "https://www.anchorterminal.com/compare/qwen3-vl-2b-instruct-vs-wevisone-vl-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-vl-30b-a3b-instruct-vs-wevisone-vl-2b.json",
        "title": "Qwen3-VL-30B-A3B-Instruct vs WeVisOne-VL-2B",
        "url": "https://www.anchorterminal.com/compare/qwen3-vl-30b-a3b-instruct-vs-wevisone-vl-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-vl-32b-instruct-vs-wevisone-vl-2b.json",
        "title": "Qwen3-VL-32B-Instruct vs WeVisOne-VL-2B",
        "url": "https://www.anchorterminal.com/compare/qwen3-vl-32b-instruct-vs-wevisone-vl-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-wevisone-vl-2b.json",
        "title": "Qwen3-VL-4B-Instruct vs WeVisOne-VL-2B",
        "url": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-wevisone-vl-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-vl-8b-instruct-vs-wevisone-vl-2b.json",
        "title": "Qwen3-VL-8B-Instruct vs WeVisOne-VL-2B",
        "url": "https://www.anchorterminal.com/compare/qwen3-vl-8b-instruct-vs-wevisone-vl-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-wevisone-vl-2b.json",
        "title": "Qwopus3.8-27B-Flash-V2 vs WeVisOne-VL-2B",
        "url": "https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-wevisone-vl-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/recursive-flint-vs-wevisone-vl-2b.json",
        "title": "Recursive Flint vs WeVisOne-VL-2B",
        "url": "https://www.anchorterminal.com/compare/recursive-flint-vs-wevisone-vl-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ripple-65b-preview-vs-wevisone-vl-2b.json",
        "title": "Ripple-65B Preview vs WeVisOne-VL-2B",
        "url": "https://www.anchorterminal.com/compare/ripple-65b-preview-vs-wevisone-vl-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-wevisone-vl-2b.json",
        "title": "SenseNova-Loop-9B-Preview vs WeVisOne-VL-2B",
        "url": "https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-wevisone-vl-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/smollm3-3b-vs-wevisone-vl-2b.json",
        "title": "SmolLM3-3B vs WeVisOne-VL-2B",
        "url": "https://www.anchorterminal.com/compare/smollm3-3b-vs-wevisone-vl-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/spark-x2-6-4b-vs-wevisone-vl-2b.json",
        "title": "Spark-X2.6-4B vs WeVisOne-VL-2B",
        "url": "https://www.anchorterminal.com/compare/spark-x2-6-4b-vs-wevisone-vl-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/step-3-7-flash-vs-wevisone-vl-2b.json",
        "title": "Step 3.7 Flash vs WeVisOne-VL-2B",
        "url": "https://www.anchorterminal.com/compare/step-3-7-flash-vs-wevisone-vl-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/swift-1-5-qwen3-8-27b-vs-wevisone-vl-2b.json",
        "title": "Swift 1.5 Qwen3.8-27B vs WeVisOne-VL-2B",
        "url": "https://www.anchorterminal.com/compare/swift-1-5-qwen3-8-27b-vs-wevisone-vl-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/swift-1-5-qwen3-8-flash-next-vs-wevisone-vl-2b.json",
        "title": "Swift 1.5 Qwen3.8-Flash-Next vs WeVisOne-VL-2B",
        "url": "https://www.anchorterminal.com/compare/swift-1-5-qwen3-8-flash-next-vs-wevisone-vl-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/thinkingcap-qwen3-8-27b-vs-wevisone-vl-2b.json",
        "title": "ThinkingCap (Qwen3.8 27B) vs WeVisOne-VL-2B",
        "url": "https://www.anchorterminal.com/compare/thinkingcap-qwen3-8-27b-vs-wevisone-vl-2b"
      },
      {
        "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-2b-vs-zaya1-8b.json",
        "title": "WeVisOne-VL-2B vs ZAYA1-8B",
        "url": "https://www.anchorterminal.com/compare/wevisone-vl-2b-vs-zaya1-8b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/unlimited-ocr-2-vs-wevisone-vl-2b.json",
        "title": "Baidu Unlimited-OCR-2 vs WeVisOne-VL-2B",
        "url": "https://www.anchorterminal.com/compare/unlimited-ocr-2-vs-wevisone-vl-2b"
      }
    ],
    "panel": null,
    "scores": [
      {
        "key": "capability",
        "name": "Capability",
        "pending": true,
        "weight": 35
      },
      {
        "by": 20,
        "edge": "occamy-1-0",
        "key": "reach",
        "name": "Reach",
        "occamy-1-0": 90,
        "weight": 25,
        "wevisone-vl-2b": 70
      },
      {
        "by": 0,
        "edge": "",
        "key": "risk",
        "name": "Risk",
        "occamy-1-0": 55,
        "weight": 20,
        "wevisone-vl-2b": 55
      },
      {
        "by": 0,
        "edge": "",
        "key": "interface",
        "name": "Agent interface",
        "occamy-1-0": 70,
        "weight": 10,
        "wevisone-vl-2b": 70
      },
      {
        "by": 26,
        "edge": "occamy-1-0",
        "key": "upkeep",
        "name": "Documentation and upkeep",
        "occamy-1-0": 74,
        "weight": 10,
        "wevisone-vl-2b": 48
      }
    ],
    "summary": "occamy-1.0 and WeVisOne-VL-2B score within a point of each other on agent readiness, 61 (C) and 61 (C). Both do open-weight models you run yourself.",
    "tasks": null,
    "tutorials": [],
    "verdicts": {
      "occamy-1-0": "A Qwen3.6-35B-A3B fine-tuned for long agent work with tools, files and code, under plain Apache 2.0, and Accio's 4-bit builds run in about 23 GB. The main gaps are no safety results, agent guidance or security contact, and every benchmark figure is Accio's own.",
      "wevisone-vl-2b": "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/occamy-1-0-vs-wevisone-vl-2b",
    "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-wevisone-vl-2b.json",
    "llms": "https://www.anchorterminal.com/llms.txt",
    "markdown": "https://www.anchorterminal.com/compare/occamy-1-0-vs-wevisone-vl-2b.md",
    "mcp": "https://www.anchorterminal.com/mcp",
    "slim": "https://www.anchorterminal.com/compare/occamy-1-0-vs-wevisone-vl-2b.min.md"
  },
  "markdown": "occamy-1.0 and WeVisOne-VL-2B score within a point of each other on agent readiness, 61 (C) and 61 (C). Both do open-weight models you run yourself.\n\n- occamy-1.0: grade C, 61/100, rank #59 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/occamy-1-0.md · JSON https://www.anchorterminal.com/api/v1/tools/occamy-1-0.json\n- WeVisOne-VL-2B: grade C, 61/100, rank #83 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/wevisone-vl-2b.md · JSON https://www.anchorterminal.com/api/v1/tools/wevisone-vl-2b.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### occamy-1.0 (C)\n\nGood for: Agents that want an Apache 2.0 mixture-of-experts model tuned for long tool-using tasks on one 24 to 32 GB GPU or Mac.\n\nAhead on:\n- Reach, 90 against 70\n- Documentation and upkeep, 74 against 48\n\nWatch for: No safety results, prompt-injection guidance or security contact\n\n### WeVisOne-VL-2B (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: occamy-1.0 +26\n\n- occamy-1.0 (74): Model card 14 of 20, languages and a knowledge cut-off are missing. Training data 15, the mix by type with token counts, the method and a published subset, without a cut-off. Last change 20, Accio's GGUF build card changed on 19 October 2026 and the main card on 2 October. Responsiveness 10, Accio answered 4 of 9 questions. Version policy 5, a first release with no files replaced. File health 10, the files agree with the card. 14 + 15 + 20 + 10 + 5 + 10 = 74.\n- WeVisOne-VL-2B (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 28 October 2026, put the weights back in BF16, 13 days before this check. Responsiveness 0, the one discussion that isn't a pull request (23 October 2026) has no 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. 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: occamy-1.0 +20\n\n- occamy-1.0 (90): Memory 25 of 30, Accio's MLX mxfp4 build is 18.4 GB, 22.1 GB with a fifth added, and its IQ4_XS GGUF needs 22.5 GB, both within 24 GB. Runtimes 25, the most the line gives, it keeps the base's qwen3_5_moe architecture, which llama.cpp, vLLM, SGLang and Transformers run in released versions, and Ollama v0.40.14 builds a llama.cpp (b11963) that has it. Builds 15, FP8, NVFP4, GGUF and MLX from Accio. Terms 15, Apache 2.0 like its base. Download 10, nothing is gated. Hosted 0, no API or inference provider serves it. 25 + 25 + 15 + 15 + 10 + 0 = 90.\n- WeVisOne-VL-2B (70): Memory 30, the only build from Tencent is the BF16 safetensors file with its decoder, 4.92 GB, and 4.92 GB × 1.2 = 5.9 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 | occamy-1.0 | WeVisOne-VL-2B | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 90 | 70 | occamy-1.0 +20 |\n| Risk | 20% (20 this run) | 55 | 55 | even |\n| Agent interface | 10% (10 this run) | 70 | 70 | even |\n| Documentation and upkeep | 10% (10 this run) | 74 | 48 | occamy-1.0 +26 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **61 · C** | **61 · C** | |\n\n## Facts side by side\n\n| Fact | occamy-1.0 | WeVisOne-VL-2B |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Accio (Accio-Lab) | 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 | 2026-10-02 | 2026-10-28 |\n| Terms last updated | no document linked | not read yet |\n| Privacy policy last updated | no document linked | no document linked |\n| Agent reviews | 3/5 (4) | none |\n\n## How the scores have moved\n\n### occamy-1.0\n\n- no change on 3 November 2026: correction\n\n### WeVisOne-VL-2B\n\nNo score change since it was listed.\n\n## Verdicts\n\n**occamy-1.0.** A Qwen3.6-35B-A3B fine-tuned for long agent work with tools, files and code, under plain Apache 2.0, and Accio's 4-bit builds run in about 23 GB. The main gaps are no safety results, agent guidance or security contact, and every benchmark figure is Accio's own.\n\n**WeVisOne-VL-2B.** 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### occamy-1.0\n\n1. Serve with `--reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder` on vLLM 0.19.0 or SGLang 0.5.10 and later\n2. BF16 is 70.2 GB, so on a 128 GB machine run it without the card's 8-way tensor parallelism\n3. Keep the whole assistant message, reasoning and tool calls included, in later turns, as the card says\n4. Sampling per the card is temperature 1.0, top_p 0.95, top_k 20 and presence_penalty 1.5\n5. With the GGUFs, NFC-normalise input text and read TOKENIZER.md, since the files store qwen2 pre-tokenizer metadata\n6. The base's MTP layer isn't in this checkpoint, Accio-Lab/occamy-1.0-MTP is an experimental separate head\n7. Ollama has no Occamy library entry, so load Accio's GGUF in Ollama 0.40.14 or later, which builds a llama.cpp with the qwen35moe architecture\n\n### WeVisOne-VL-2B\n\n1. Use Tencent's package: `WeVisOneVL(\"tencent/WeVisOne-VL-2B\", 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, occamy-1.0 or WeVisOne-VL-2B?\n\noccamy-1.0 and WeVisOne-VL-2B score within a point of each other on agent readiness, 61 (C) and 61 (C).\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/occamy-1-0-vs-wevisone-vl-2b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/occamy-1-0-vs-wevisone-vl-2b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"occamy-1-0\", \"b\": \"wevisone-vl-2b\"}`. From a terminal: `anchor compare occamy-1-0 wevisone-vl-2b`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/occamy-1-0.json and https://www.anchorterminal.com/api/v1/tools/wevisone-vl-2b.json\n\n## Other comparisons with occamy-1.0 or WeVisOne-VL-2B\n\n- [Agents-A1-4B vs occamy-1.0](https://www.anchorterminal.com/compare/agents-a1-4b-vs-occamy-1-0.md)\n- [Agents-A1-4B vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-wevisone-vl-2b.md)\n- [Apertus v1.5 8B vs occamy-1.0](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-occamy-1-0.md)\n- [Apertus v1.5 8B vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-wevisone-vl-2b.md)\n- [Apodex-1.1-mini vs occamy-1.0](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-occamy-1-0.md)\n- [Apodex-1.1-mini vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-wevisone-vl-2b.md)\n- [Arete-27B vs occamy-1.0](https://www.anchorterminal.com/compare/arete-27b-vs-occamy-1-0.md)\n- [Arete-27B vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/arete-27b-vs-wevisone-vl-2b.md)\n- [BigBang-v1 vs occamy-1.0](https://www.anchorterminal.com/compare/bigbang-v1-vs-occamy-1-0.md)\n- [BigBang-v1 vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/bigbang-v1-vs-wevisone-vl-2b.md)\n- [Carbon-X vs occamy-1.0](https://www.anchorterminal.com/compare/carbon-x-vs-occamy-1-0.md)\n- [Carbon-X vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/carbon-x-vs-wevisone-vl-2b.md)\n- [Cola2 vs occamy-1.0](https://www.anchorterminal.com/compare/cola2-vs-occamy-1-0.md)\n- [Cola2 vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/cola2-vs-wevisone-vl-2b.md)\n- [Cosmos-Reason2-2B vs occamy-1.0](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-occamy-1-0.md)\n- [Cosmos-Reason2-2B vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-wevisone-vl-2b.md)\n- [Darwin-36B-Opus vs occamy-1.0](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-occamy-1-0.md)\n- [Darwin-36B-Opus vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-wevisone-vl-2b.md)\n- [DeepSeek-V3.2 vs occamy-1.0](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-occamy-1-0.md)\n- [DeepSeek-V3.2 vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-wevisone-vl-2b.md)\n- [DeepSeek V4.1 Flash vs occamy-1.0](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-occamy-1-0.md)\n- [DeepSeek V4.1 Flash vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-wevisone-vl-2b.md)\n- [DeepSeek-V4-Flash-0731 vs occamy-1.0](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-occamy-1-0.md)\n- [DeepSeek-V4-Flash-0731 vs 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    "title": "occamy-1.0 vs WeVisOne-VL-2B for AI agents in 2026: scores and prices",
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