{
  "data": {
    "a": {
      "slug": "ornith-1-5-9b",
      "name": "Ornith-1.5-9B",
      "vendor": "Ornith",
      "vendorUrl": "https://ornith.ai",
      "kind": "weights",
      "category": "open-weights",
      "summary": "Ornith-1.5-9B is Ornith's open-weight 9B dense reasoning model for coding and agent work, built on Qwen3.5's architecture with a 262,144-token context, published on Hugging Face in BF16, GGUF, MLX and NVFP4 and marked MIT.",
      "url": "https://www.anchorterminal.com/tools/ornith-1-5-9b",
      "markdownUrl": "https://www.anchorterminal.com/tools/ornith-1-5-9b.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/ornith-1-5-9b.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/ornith-1-5-9b.json",
      "dns": "ornith-1-5-9b.grade.anchorterminal.com",
      "repo": "https://huggingface.co/ornith-ai/Ornith-1.5-9B",
      "license": "MIT",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "ornith-ai/Ornith-1.5-9B"
        },
        {
          "registry": "huggingface",
          "name": "ornith-ai/Ornith-1.5-9B-GGUF"
        },
        {
          "registry": "huggingface",
          "name": "ornith-ai/Ornith-1.5-9B-MLX-4bit"
        },
        {
          "registry": "huggingface",
          "name": "ornith-ai/Ornith-1.5-9B-NVFP4"
        },
        {
          "registry": "ollama",
          "name": "ornith-1.5:9b"
        }
      ],
      "auth": "none",
      "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The repository is not gated (checked 2026-10-21).",
      "pricing": "free",
      "pricingNotes": "Free to download and use, commercial use included, under the MIT licence named in the card's metadata. The repository has no LICENSE file, and the card's licence link returns 404. Running it yourself costs the hardware, about 19 GB for the BF16 files or 5.8 GB for Ornith's Q4_K_M GGUF (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/ornith-ai/Ornith-1.5-9B",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "dense",
        "multimodal",
        "reasoning",
        "tool-calling",
        "coding",
        "mit",
        "safetensors",
        "gguf",
        "mlx",
        "self-hosted"
      ],
      "lastRelease": "2026-08-23",
      "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": 41,
        "methodology": "0.7",
        "run": "2026-10-01",
        "scores": {
          "interface": 85,
          "reach": 95,
          "risk": 50,
          "upkeep": 31
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "low",
          "date": "2026-11-03"
        },
        "negative": 0,
        "verdict": "A small coding and terminal-agent cousin with thinking on by default, XML tool calls that vLLM and SGLang parse, and 262,144 tokens of context. Ornith's own 4-bit builds run in under 8 GB in most local runtimes. The main risk is an absent publisher, with no licence file, no safety results, files replaced without a note and no replies.",
        "bestFor": "Local coding and terminal agents on a laptop or a single consumer GPU, where a clear licence file and an answering publisher aren't required",
        "strengths": [
          "Fits in about 6 to 8 GB from Ornith's own MLX 4-bit, Q4_K_M GGUF or Ollama's build",
          "Runs in vLLM, SGLang, Transformers, llama.cpp, Ollama and LM Studio from their released versions",
          "The card names the tool and reasoning parsers for vLLM and SGLang, with a tool-call example",
          "262,144 tokens of native context",
          "Safetensors and GGUF only, no gate and no code in the repository"
        ],
        "weaknesses": [
          "No LICENSE file though the card links one, and no notice for the Qwen3.5 and Gemma 4 work it builds on",
          "No safety evaluation, agent-use guidance or security contact",
          "The fourth weight shard and every GGUF were replaced in place on 23 and 24 August 2026 with no note",
          "Ornith answered none of the 15 discussions",
          "The two chat templates disagree on keeping earlier reasoning, and structured output isn't documented"
        ],
        "agentNotes": [
          "Start it with `ollama run ornith-1.5:9b`, or `llama-server -hf ornith-ai/Ornith-1.5-9B-GGUF` with the Q4_K_M file",
          "In vLLM pass `--enable-auto-tool-choice --tool-call-parser qwen3_xml --reasoning-parser qwen3`, and in SGLang `--tool-call-parser qwen3_coder --reasoning-parser qwen3`, so tool calls and reasoning come back parsed",
          "Thinking is on by default. Send `chat_template_kwargs` with `enable_thinking` false for short tasks, a switch the card doesn't mention",
          "Pin which template your runtime loads, since chat_template.jinja keeps earlier reasoning and the tokenizer_config.json template drops it",
          "Use temperature 0.6, top_p 0.95, top_k 20 for coding and temperature 1.0 with presence_penalty 1.5 for general tasks, per the card",
          "Pin a revision when you download, since Ornith has replaced files in place, and add mmproj-Ornith-1.5-9B-BF16.gguf (0.9 GB) for image input in llama.cpp",
          "Validate JSON output yourself, since structured output isn't documented"
        ],
        "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": 95,
          "risk": 50,
          "upkeep": 31
        },
        "provenanceScore": 19,
        "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 ornith-1.5:9b\n# or, with the card's vLLM command:\nvllm serve ornith-ai/Ornith-1.5-9B --served-model-name Ornith-1.5-9B --max-model-len 262144 \\\n  --enable-auto-tool-choice --tool-call-parser qwen3_xml --reasoning-parser qwen3",
        "http": "curl http://localhost:11434/api/chat \\\n  -d '{\"model\": \"ornith-1.5:9b\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/ornith-1-5-9b"
      },
      "sameCompany": [
        "ornith-1-5-122b",
        "ornith-1-5-35b-a3b",
        "ornith-1-5-397b"
      ],
      "weights": {
        "trainedBy": "Ornith",
        "builtOn": "Ornith-1.0 (which Ornith says it built on Qwen3.5 and Gemma 4); qwen3_5 architecture",
        "params": "9B",
        "context": 262144,
        "goodFor": "Coding and terminal-agent model with tool calling and thinking on by default, text and images in.",
        "runtimes": [
          "llama.cpp",
          "Ollama",
          "vLLM",
          "SGLang",
          "LM Studio",
          "Transformers"
        ],
        "builds": [
          {
            "name": "Ornith-1.5-9B-Q5_K_M",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-9B-GGUF/blob/main/Ornith-1.5-9B-Q5_K_M.gguf",
            "who": "publisher",
            "precision": "q5_k_m",
            "sizeGB": 6.6,
            "memoryGB": 8,
            "format": "gguf",
            "runsOn": [
              "llama.cpp (Vulkan)",
              "llama.cpp (ROCm)"
            ]
          },
          {
            "name": "Ornith-1.5-9B-Q4_K_M",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-9B-GGUF/blob/main/Ornith-1.5-9B-Q4_K_M.gguf",
            "who": "publisher",
            "precision": "q4_k_m",
            "sizeGB": 5.8,
            "memoryGB": 8,
            "format": "gguf",
            "runsOn": [
              "llama.cpp (Vulkan)",
              "llama.cpp (ROCm)"
            ],
            "note": "Add mmproj-Ornith-1.5-9B-BF16.gguf (0.9 GB) for image input"
          },
          {
            "name": "Ornith-1.5-9B-MLX-4bit",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-9B-MLX-4bit",
            "who": "publisher",
            "precision": "4bit",
            "sizeGB": 5,
            "memoryGB": 8,
            "format": "mlx",
            "note": "MLX runs on Apple silicon only"
          },
          {
            "name": "ollama ornith-1.5:9b",
            "url": "https://ollama.com/library/ornith-1.5:9b",
            "who": "ollama",
            "precision": "q4_k_m",
            "sizeGB": 6.6,
            "memoryGB": 8,
            "format": "ollama",
            "runsOn": [
              "Ollama"
            ],
            "note": "Q4_K_M with a BF16 vision projector, linked from Ornith's card; the library has no latest tag. Size as Ollama's tags page shows it, rounded to whole GB above 10 GB; the Linux build is the llama.cpp variant"
          },
          {
            "name": "Ornith-1.5-9B-Q8_0",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-9B-GGUF/blob/main/Ornith-1.5-9B-Q8_0.gguf",
            "who": "publisher",
            "precision": "q8_0",
            "sizeGB": 9.8,
            "memoryGB": 12,
            "format": "gguf",
            "runsOn": [
              "llama.cpp (Vulkan)",
              "llama.cpp (ROCm)"
            ]
          },
          {
            "name": "Ornith-1.5-9B-Q6_K",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-9B-GGUF/blob/main/Ornith-1.5-9B-Q6_K.gguf",
            "who": "publisher",
            "precision": "q6_k",
            "sizeGB": 7.6,
            "memoryGB": 12,
            "format": "gguf",
            "runsOn": [
              "llama.cpp (Vulkan)",
              "llama.cpp (ROCm)"
            ]
          },
          {
            "name": "Ornith-1.5-9B-MLX-8bit",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-9B-MLX-8bit",
            "who": "publisher",
            "precision": "8bit",
            "sizeGB": 9.5,
            "memoryGB": 12,
            "format": "mlx",
            "note": "MLX runs on Apple silicon only"
          },
          {
            "name": "Ornith-1.5-9B-MLX-6bit",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-9B-MLX-6bit",
            "who": "publisher",
            "precision": "6bit",
            "sizeGB": 7.3,
            "memoryGB": 12,
            "format": "mlx",
            "note": "MLX runs on Apple silicon only"
          },
          {
            "name": "Ornith-1.5-9B-NVFP4",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-9B-NVFP4",
            "who": "publisher",
            "precision": "nvfp4",
            "sizeGB": 8.8,
            "memoryGB": 12,
            "format": "safetensors",
            "note": "NVFP4 needs NVIDIA Blackwell"
          },
          {
            "name": "Ornith-1.5-9B",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-9B",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 19.3,
            "memoryGB": 24,
            "format": "safetensors",
            "runsOn": [
              "vLLM (ROCm)",
              "SGLang (ROCm)"
            ]
          },
          {
            "name": "Ornith-1.5-9B-BF16",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-9B-GGUF/blob/main/Ornith-1.5-9B-BF16.gguf",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 18.4,
            "memoryGB": 24,
            "format": "gguf",
            "runsOn": [
              "llama.cpp (Vulkan)",
              "llama.cpp (ROCm)"
            ]
          },
          {
            "name": "Ornith-1.5-9B-MLX",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-9B-MLX",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 17.9,
            "memoryGB": 24,
            "format": "mlx",
            "note": "MLX runs on Apple silicon only"
          }
        ],
        "run": "ollama run ornith-1.5:9b",
        "runBuild": "ollama ornith-1.5:9b",
        "checked": "2026-10-21",
        "minMemoryGB": 8
      },
      "family": {
        "lab": "Ornith",
        "family": "Ornith-1",
        "url": "https://www.anchorterminal.com/open-weights/#ornith",
        "others": [
          {
            "name": "Ornith-1.0-9B",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.0-9B",
            "params": "9B (dense)"
          },
          {
            "name": "Ornith-1.0-35B",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.0-35B",
            "params": "35B (MoE)"
          },
          {
            "name": "Ornith-1.0-397B",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.0-397B",
            "params": "397B (MoE)"
          },
          {
            "name": "Ornith-1.5-9B-DFlash",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-9B-DFlash",
            "params": "1.29B (Hub safetensors count)"
          },
          {
            "name": "Ornith-1.5-35B-A3B",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-35B-A3B",
            "params": "35B total, 3B active (MoE)",
            "listing": "https://www.anchorterminal.com/tools/ornith-1-5-35b-a3b"
          },
          {
            "name": "Ornith-1.5-35B-A3B-DFlash",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-35B-A3B-DFlash",
            "params": "0.39B (Hub safetensors count)"
          },
          {
            "name": "Ornith-1.5-122B",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-122B",
            "params": "122B (MoE)",
            "listing": "https://www.anchorterminal.com/tools/ornith-1-5-122b"
          },
          {
            "name": "Ornith-1.5-397B",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-397B",
            "params": "397B (MoE)",
            "listing": "https://www.anchorterminal.com/tools/ornith-1-5-397b"
          },
          {
            "name": "Ornith-1.5-397B-DFlash",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-397B-DFlash",
            "params": "1.29B (Hub safetensors count)"
          }
        ],
        "unrestrictedCopies": 5
      },
      "area": "models",
      "provenance": {
        "legalEntity": "",
        "domain": "ornith.ai",
        "domainRegistered": "",
        "endpointOnVendorDomain": null,
        "terms": "",
        "privacy": "",
        "statusPage": "",
        "changelog": "",
        "securityTxt": "none",
        "checked": "2026-10-21",
        "notes": [
          "No legal entity named on the card, the blog or the repository. The blog's footer reads 2026 Ornith Team, and the Hugging Face organisation ornith-ai isn't verified.",
          "Nothing is hosted, so the licence stands in for terms of service and no privacy policy is scored. The licence is MIT in the card's metadata only; the LICENSE file the card links returns 404.",
          "No changelog for the files. Ollama's library page carries Ornith's dated release lines (19 August and 20 October 2026), and the commit messages are uploads and README updates.",
          "ornith.ai/.well-known/security.txt redirects to the home page, so there is no security.txt.",
          "unchecked: the domain's registration date, since RDAP answered 429 on 21 October 2026."
        ],
        "score": 19
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/ornith-1-5-9b.json",
      "live": {
        "slug": "ornith-1-5-9b",
        "securityTxt": {
          "url": "https://ornith.ai/.well-known/security.txt",
          "state": "none",
          "checkedAt": "2026-11-10T16:07:27.802812649Z"
        },
        "domain": {
          "domain": "ornith.ai",
          "registered": "2026-06-16",
          "source": "https://rdap.identitydigital.services/rdap/domain/ornith.ai",
          "checkedAt": "2026-11-08T14:47:46.372430234Z"
        },
        "updatedAt": "2026-11-10T16:07:27.802812649Z"
      }
    },
    "answer": "Ornith-1.5-9B and WeVisOne-VL-2B score within a point of each other on agent readiness, 61 (C) and 61 (C). WeVisOne-VL-2B leads on risk and documentation and upkeep.",
    "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": 25,
        "category": "reach",
        "leads": "ornith-1-5-9b",
        "name": "Reach",
        "reasons": {
          "a": "Memory 30, since Ornith's own MLX 4-bit build is 5.0 GB and needs about 6.0 GB with the fifth added, and its Q4_K_M GGUF and Ollama's build fit 16 GB too. Runtimes 25, the most the line gives, from vLLM 0.19.1 (still in v0.33.0), SGLang 0.5.9, Transformers 5.8.1, llama.cpp's qwen35 architecture, Ollama's library and LM Studio's catalogue for the qwen35 architecture. Builds 15, Ornith publishes GGUF, MLX and NVFP4 at several precisions. Terms 15, MIT as named in the card's metadata on 21 October 2026, with nothing added, read by its terms though the repository has no licence file. Download 10, nothing is gated. Hosted 0, Ornith runs no API and only one inference provider (featherless-ai) serves it. 30 + 25 + 15 + 15 + 10 + 0 = 95.",
          "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": 95,
          "b": 70
        }
      },
      {
        "by": 17,
        "category": "upkeep",
        "leads": "wevisone-vl-2b",
        "name": "Documentation and upkeep",
        "reasons": {
          "a": "Model card 11, it covers intended use, architecture, parameters and context but not limitations, languages or a knowledge cut-off. Training data 5, a description of the method with no data or cut-off. Last change 15, the newest change to the model's files was Ornith's NVFP4 build on 26 August 2026, 69 days ago, and no card of the model or its builds has changed since. Responsiveness 0, Ornith answered none of the 15 questions and reports. Version policy 0, the fourth safetensors shard and every GGUF file were replaced in place on 23 and 24 August with no note. File health 0, the card links a missing LICENSE and an unpublished Mobile build, and the two chat templates differ. 11 + 5 + 15 + 0 + 0 + 0 = 31.",
          "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": 31,
          "b": 48
        }
      },
      {
        "by": 15,
        "category": "interface",
        "leads": "ornith-1-5-9b",
        "name": "Agent interface",
        "reasons": {
          "a": "Chat template 20, a Jinja file in the repository. Tool-calling format 25, the card shows how tools go in and how calls come back and names the parser for vLLM and SGLang. Trained tool calling 20, Ornith trains it on agent tasks and the template renders several calls in one turn. Structured output 0, not documented or claimed. Reasoning control 10, Ornith's template turns thinking off per request with enable_thinking set to false and the card says the reasoning comes back in reasoning_content, though the card doesn't name the switch. Context 10, 262,144 tokens native.",
          "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": 5,
        "category": "risk",
        "leads": "wevisone-vl-2b",
        "name": "Risk",
        "reasons": {
          "a": "File safety 25, safetensors and GGUF only and no Python in any of Ornith's repositories, so the card's --trust-remote-code flag loads nothing from the repository. Provenance 15, the files come from the ornith-ai organisation Ornith's blog links to, with a SHA-256 for every weight file. Safety evaluation 0 and agent guidance 0, nothing published on either. Licence risk 10, MIT carries no policy, threshold or changeable terms, but the card's licence link is broken. Security programme 0, no security.txt, disclosure policy or security contact.",
          "b": "File safety 15, the backbone is safetensors, but mask_decoder.pt, a PyTorch pickle the card calls a required component, sits beside it. Provenance 15, the files sit in the tencent organisation, which Tencent's GitHub repository for the model links to (10), and the Hub gives a SHA-256 for the large files (5). Safety evaluation 0, no safety results on the card or in the README. Agent guidance 5, General cautions: the card says the release is for research, isn't for autonomous high-impact decisions or identifying individuals, and lists where masks go wrong. Licence risk 15, Apache 2.0 with no use policy, no threshold and no right to change the terms. Security programme 5, Tencent's Security Response Center takes vulnerability reports company-wide, as the Hy3 listing records (5). 15 + 15 + 0 + 5 + 15 + 5 = 55."
        },
        "scores": {
          "a": 50,
          "b": 55
        }
      }
    ],
    "facts": [
      {
        "a": "Open-weight model",
        "b": "Open-weight model",
        "name": "Kind"
      },
      {
        "a": "Ornith",
        "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": "MIT",
        "b": "Apache-2.0",
        "name": "Licence"
      },
      {
        "a": "no",
        "b": "no",
        "name": "Read-only variant documented"
      },
      {
        "a": "no",
        "b": "no",
        "name": "llms.txt"
      },
      {
        "a": "2026-08-23",
        "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": "2.3/5 (4)",
        "b": "none",
        "name": "Agent reviews"
      }
    ],
    "faq": [
      {
        "answer": "Ornith-1.5-9B and WeVisOne-VL-2B score within a point of each other on agent readiness, 61 (C) and 61 (C). WeVisOne-VL-2B leads on risk and documentation and upkeep.",
        "question": "Which is better for AI agents, Ornith-1.5-9B or WeVisOne-VL-2B?"
      }
    ],
    "goodFor": [
      {
        "aheadOn": [
          "Reach, 95 against 70",
          "Agent interface, 85 against 70"
        ],
        "also": null,
        "goodFor": "Local coding and terminal agents on a laptop or a single consumer GPU, where a clear licence file and an answering publisher aren't required",
        "slug": "ornith-1-5-9b",
        "watchFor": "No LICENSE file though the card links one, and no notice for the Qwen3.5 and Gemma 4 work it builds on"
      },
      {
        "aheadOn": [
          "Risk, 55 against 50",
          "Documentation and upkeep, 48 against 31"
        ],
        "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": null,
    "job": {
      "capability": "model.open-weights",
      "name": "Open-weight models you run yourself"
    },
    "others": [
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-ornith-1-5-9b.json",
        "title": "Agents-A1-4B vs Ornith-1.5-9B",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-ornith-1-5-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-wevisone-vl-2b.json",
        "title": "Agents-A1-4B vs WeVisOne-VL-2B",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-wevisone-vl-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-ornith-1-5-9b.json",
        "title": "Apertus v1.5 8B vs Ornith-1.5-9B",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-ornith-1-5-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-wevisone-vl-2b.json",
        "title": "Apertus v1.5 8B vs WeVisOne-VL-2B",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-wevisone-vl-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-ornith-1-5-9b.json",
        "title": "Apodex-1.1-mini vs Ornith-1.5-9B",
        "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-ornith-1-5-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-wevisone-vl-2b.json",
        "title": "Apodex-1.1-mini vs WeVisOne-VL-2B",
        "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-wevisone-vl-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-ornith-1-5-9b.json",
        "title": "Arete-27B vs Ornith-1.5-9B",
        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-ornith-1-5-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-wevisone-vl-2b.json",
        "title": "Arete-27B vs WeVisOne-VL-2B",
        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-wevisone-vl-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-ornith-1-5-9b.json",
        "title": "BigBang-v1 vs Ornith-1.5-9B",
        "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-ornith-1-5-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-wevisone-vl-2b.json",
        "title": "BigBang-v1 vs WeVisOne-VL-2B",
        "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-wevisone-vl-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/carbon-x-vs-ornith-1-5-9b.json",
        "title": "Carbon-X vs Ornith-1.5-9B",
        "url": "https://www.anchorterminal.com/compare/carbon-x-vs-ornith-1-5-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/carbon-x-vs-wevisone-vl-2b.json",
        "title": "Carbon-X vs WeVisOne-VL-2B",
        "url": "https://www.anchorterminal.com/compare/carbon-x-vs-wevisone-vl-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cola2-vs-ornith-1-5-9b.json",
        "title": "Cola2 vs Ornith-1.5-9B",
        "url": "https://www.anchorterminal.com/compare/cola2-vs-ornith-1-5-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cola2-vs-wevisone-vl-2b.json",
        "title": "Cola2 vs WeVisOne-VL-2B",
        "url": "https://www.anchorterminal.com/compare/cola2-vs-wevisone-vl-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-ornith-1-5-9b.json",
        "title": "Cosmos-Reason2-2B vs Ornith-1.5-9B",
        "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-ornith-1-5-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-wevisone-vl-2b.json",
        "title": "Cosmos-Reason2-2B vs WeVisOne-VL-2B",
        "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-wevisone-vl-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-ornith-1-5-9b.json",
        "title": "Darwin-36B-Opus vs Ornith-1.5-9B",
        "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-ornith-1-5-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-wevisone-vl-2b.json",
        "title": "Darwin-36B-Opus vs WeVisOne-VL-2B",
        "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-wevisone-vl-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-ornith-1-5-9b.json",
        "title": "DeepSeek-V3.2 vs Ornith-1.5-9B",
        "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-ornith-1-5-9b"
      },
      {
        "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"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-ornith-1-5-9b.json",
        "title": "DeepSeek V4.1 Flash vs Ornith-1.5-9B",
        "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-ornith-1-5-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-wevisone-vl-2b.json",
        "title": "DeepSeek V4.1 Flash vs WeVisOne-VL-2B",
        "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-wevisone-vl-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-ornith-1-5-9b.json",
        "title": "DeepSeek-V4-Flash-0731 vs Ornith-1.5-9B",
        "url": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-ornith-1-5-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-wevisone-vl-2b.json",
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        "url": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-wevisone-vl-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/devstral-small-2-vs-ornith-1-5-9b.json",
        "title": "Devstral Small 2 vs Ornith-1.5-9B",
        "url": "https://www.anchorterminal.com/compare/devstral-small-2-vs-ornith-1-5-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/devstral-small-2-vs-wevisone-vl-2b.json",
        "title": "Devstral Small 2 vs WeVisOne-VL-2B",
        "url": "https://www.anchorterminal.com/compare/devstral-small-2-vs-wevisone-vl-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-ornith-1-5-9b.json",
        "title": "DiffusionGemma 26B A4B IT vs Ornith-1.5-9B",
        "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-ornith-1-5-9b"
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        "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-wevisone-vl-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-ornith-1-5-9b.json",
        "title": "EXAONE 4.5 33B vs Ornith-1.5-9B",
        "url": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-ornith-1-5-9b"
      },
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        "title": "EXAONE 4.5 33B vs WeVisOne-VL-2B",
        "url": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-wevisone-vl-2b"
      },
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        "title": "Gemma 4 12B IT vs Ornith-1.5-9B",
        "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-ornith-1-5-9b"
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        "title": "Gemma 4 12B IT vs WeVisOne-VL-2B",
        "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-wevisone-vl-2b"
      },
      {
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        "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-ornith-1-5-9b"
      },
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        "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-wevisone-vl-2b"
      },
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        "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-ornith-1-5-9b.json",
        "title": "Gemma 4 31B IT vs Ornith-1.5-9B",
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        "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-wevisone-vl-2b.json",
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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-ornith-1-5-9b.json",
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        "title": "MiMo-V2.6 Distill Qwen 9B vs Ornith-1.5-9B",
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        "json": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-wevisone-vl-2b.json",
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        "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-wevisone-vl-2b.json",
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        "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-otel-2-0-llm-31b-it"
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        "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen-agentworld-35b-a3b.json",
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        "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-30b-a3b-instruct-2507.json",
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        "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-4b-instruct-2507.json",
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        "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-5-0-8b.json",
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        "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-5-122b-a10b"
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        "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-5-2b"
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        "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-5-4b"
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        "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-5-9b"
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        "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-6-27b"
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        "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-6-35b-a3b.json",
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        "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-6-35b-a3b"
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        "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-8-27b.json",
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        "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-8-flash-next.json",
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        "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-coder-30b-a3b-instruct.json",
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        "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-omni-30b-a3b-instruct.json",
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        "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-vl-30b-a3b-instruct.json",
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        "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-vl-4b-instruct.json",
        "title": "Ornith-1.5-9B vs Qwen3-VL-4B-Instruct",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-vl-4b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-vl-8b-instruct.json",
        "title": "Ornith-1.5-9B vs Qwen3-VL-8B-Instruct",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-vl-8b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwopus3-8-27b-flash-v2.json",
        "title": "Ornith-1.5-9B vs Qwopus3.8-27B-Flash-V2",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwopus3-8-27b-flash-v2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-recursive-flint.json",
        "title": "Ornith-1.5-9B vs Recursive Flint",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-recursive-flint"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-ripple-65b-preview.json",
        "title": "Ornith-1.5-9B vs Ripple-65B Preview",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-ripple-65b-preview"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-sensenova-loop-9b-preview.json",
        "title": "Ornith-1.5-9B vs SenseNova-Loop-9B-Preview",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-sensenova-loop-9b-preview"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-smollm3-3b.json",
        "title": "Ornith-1.5-9B vs SmolLM3-3B",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-smollm3-3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-spark-x2-6-4b.json",
        "title": "Ornith-1.5-9B vs Spark-X2.6-4B",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-spark-x2-6-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-step-3-7-flash.json",
        "title": "Ornith-1.5-9B vs Step 3.7 Flash",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-step-3-7-flash"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-swift-1-5-qwen3-8-27b.json",
        "title": "Ornith-1.5-9B vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-swift-1-5-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-swift-1-5-qwen3-8-flash-next.json",
        "title": "Ornith-1.5-9B vs Swift 1.5 Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-swift-1-5-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-thinkingcap-qwen3-8-27b.json",
        "title": "Ornith-1.5-9B vs ThinkingCap (Qwen3.8 27B)",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-thinkingcap-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-unlimited-ocr-2.json",
        "title": "Ornith-1.5-9B vs Baidu Unlimited-OCR-2",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-unlimited-ocr-2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-wevisone-vl-4b.json",
        "title": "Ornith-1.5-9B vs WeVisOne-VL-4B",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-wevisone-vl-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-zaya1-8b.json",
        "title": "Ornith-1.5-9B vs ZAYA1-8B",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-zaya1-8b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-wevisone-vl-2b.json",
        "title": "OTel-2.0-LLM-31B-IT vs WeVisOne-VL-2B",
        "url": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-wevisone-vl-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-wevisone-vl-2b.json",
        "title": "Qwen-AgentWorld-35B-A3B vs WeVisOne-VL-2B",
        "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-wevisone-vl-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-30b-a3b-instruct-2507-vs-wevisone-vl-2b.json",
        "title": "Qwen3-30B-A3B-Instruct-2507 vs WeVisOne-VL-2B",
        "url": "https://www.anchorterminal.com/compare/qwen3-30b-a3b-instruct-2507-vs-wevisone-vl-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-4b-instruct-2507-vs-wevisone-vl-2b.json",
        "title": "Qwen3-4B-Instruct-2507 vs WeVisOne-VL-2B",
        "url": "https://www.anchorterminal.com/compare/qwen3-4b-instruct-2507-vs-wevisone-vl-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-wevisone-vl-2b.json",
        "title": "Qwen3.5-0.8B vs WeVisOne-VL-2B",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-wevisone-vl-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-wevisone-vl-2b.json",
        "title": "Qwen3.5-122B-A10B vs WeVisOne-VL-2B",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-wevisone-vl-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-5-2b-vs-wevisone-vl-2b.json",
        "title": "Qwen3.5-2B vs WeVisOne-VL-2B",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-2b-vs-wevisone-vl-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-wevisone-vl-2b.json",
        "title": "Qwen3.5-397B-A17B vs WeVisOne-VL-2B",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-wevisone-vl-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-5-4b-vs-wevisone-vl-2b.json",
        "title": "Qwen3.5-4B vs WeVisOne-VL-2B",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-4b-vs-wevisone-vl-2b"
      },
      {
        "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": 25,
        "edge": "ornith-1-5-9b",
        "key": "reach",
        "name": "Reach",
        "ornith-1-5-9b": 95,
        "weight": 25,
        "wevisone-vl-2b": 70
      },
      {
        "by": 5,
        "edge": "wevisone-vl-2b",
        "key": "risk",
        "name": "Risk",
        "ornith-1-5-9b": 50,
        "weight": 20,
        "wevisone-vl-2b": 55
      },
      {
        "by": 15,
        "edge": "ornith-1-5-9b",
        "key": "interface",
        "name": "Agent interface",
        "ornith-1-5-9b": 85,
        "weight": 10,
        "wevisone-vl-2b": 70
      },
      {
        "by": 17,
        "edge": "wevisone-vl-2b",
        "key": "upkeep",
        "name": "Documentation and upkeep",
        "ornith-1-5-9b": 31,
        "weight": 10,
        "wevisone-vl-2b": 48
      }
    ],
    "summary": "Ornith-1.5-9B and WeVisOne-VL-2B score within a point of each other on agent readiness, 61 (C) and 61 (C). WeVisOne-VL-2B leads on risk and documentation and upkeep. Both do open-weight models you run yourself.",
    "tasks": null,
    "tutorials": [],
    "verdicts": {
      "ornith-1-5-9b": "A small coding and terminal-agent cousin with thinking on by default, XML tool calls that vLLM and SGLang parse, and 262,144 tokens of context. Ornith's own 4-bit builds run in under 8 GB in most local runtimes. The main risk is an absent publisher, with no licence file, no safety results, files replaced without a note and no replies.",
      "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/ornith-1-5-9b-vs-wevisone-vl-2b",
    "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-wevisone-vl-2b.json",
    "llms": "https://www.anchorterminal.com/llms.txt",
    "markdown": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-wevisone-vl-2b.md",
    "mcp": "https://www.anchorterminal.com/mcp",
    "slim": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-wevisone-vl-2b.min.md"
  },
  "markdown": "Ornith-1.5-9B and WeVisOne-VL-2B score within a point of each other on agent readiness, 61 (C) and 61 (C). WeVisOne-VL-2B leads on risk and documentation and upkeep. Both do open-weight models you run yourself.\n\n- Ornith-1.5-9B: grade C, 61/100, rank #51 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/ornith-1-5-9b.md · JSON https://www.anchorterminal.com/api/v1/tools/ornith-1-5-9b.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### Ornith-1.5-9B (C)\n\nGood for: Local coding and terminal agents on a laptop or a single consumer GPU, where a clear licence file and an answering publisher aren't required\n\nAhead on:\n- Reach, 95 against 70\n- Agent interface, 85 against 70\n\nWatch for: No LICENSE file though the card links one, and no notice for the Qwen3.5 and Gemma 4 work it builds on\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\nAhead on:\n- Risk, 55 against 50\n- Documentation and upkeep, 48 against 31\n\nWatch for: The pixel decoder ships as a pickle file\n\n\n## Why the edge\n\n### Reach: Ornith-1.5-9B +25\n\n- Ornith-1.5-9B (95): Memory 30, since Ornith's own MLX 4-bit build is 5.0 GB and needs about 6.0 GB with the fifth added, and its Q4_K_M GGUF and Ollama's build fit 16 GB too. Runtimes 25, the most the line gives, from vLLM 0.19.1 (still in v0.33.0), SGLang 0.5.9, Transformers 5.8.1, llama.cpp's qwen35 architecture, Ollama's library and LM Studio's catalogue for the qwen35 architecture. Builds 15, Ornith publishes GGUF, MLX and NVFP4 at several precisions. Terms 15, MIT as named in the card's metadata on 21 October 2026, with nothing added, read by its terms though the repository has no licence file. Download 10, nothing is gated. Hosted 0, Ornith runs no API and only one inference provider (featherless-ai) serves it. 30 + 25 + 15 + 15 + 10 + 0 = 95.\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### Documentation and upkeep: WeVisOne-VL-2B +17\n\n- Ornith-1.5-9B (31): Model card 11, it covers intended use, architecture, parameters and context but not limitations, languages or a knowledge cut-off. Training data 5, a description of the method with no data or cut-off. Last change 15, the newest change to the model's files was Ornith's NVFP4 build on 26 August 2026, 69 days ago, and no card of the model or its builds has changed since. Responsiveness 0, Ornith answered none of the 15 questions and reports. Version policy 0, the fourth safetensors shard and every GGUF file were replaced in place on 23 and 24 August with no note. File health 0, the card links a missing LICENSE and an unpublished Mobile build, and the two chat templates differ. 11 + 5 + 15 + 0 + 0 + 0 = 31.\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### Agent interface: Ornith-1.5-9B +15\n\n- Ornith-1.5-9B (85): Chat template 20, a Jinja file in the repository. Tool-calling format 25, the card shows how tools go in and how calls come back and names the parser for vLLM and SGLang. Trained tool calling 20, Ornith trains it on agent tasks and the template renders several calls in one turn. Structured output 0, not documented or claimed. Reasoning control 10, Ornith's template turns thinking off per request with enable_thinking set to false and the card says the reasoning comes back in reasoning_content, though the card doesn't name the switch. Context 10, 262,144 tokens native.\n- WeVisOne-VL-2B (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### Risk: WeVisOne-VL-2B +5\n\n- Ornith-1.5-9B (50): File safety 25, safetensors and GGUF only and no Python in any of Ornith's repositories, so the card's --trust-remote-code flag loads nothing from the repository. Provenance 15, the files come from the ornith-ai organisation Ornith's blog links to, with a SHA-256 for every weight file. Safety evaluation 0 and agent guidance 0, nothing published on either. Licence risk 10, MIT carries no policy, threshold or changeable terms, but the card's licence link is broken. Security programme 0, no security.txt, disclosure policy or security contact.\n- WeVisOne-VL-2B (55): File safety 15, the backbone is safetensors, but mask_decoder.pt, a PyTorch pickle the card calls a required component, sits beside it. Provenance 15, the files sit in the tencent organisation, which Tencent's GitHub repository for the model links to (10), and the Hub gives a SHA-256 for the large files (5). Safety evaluation 0, no safety results on the card or in the README. Agent guidance 5, General cautions: the card says the release is for research, isn't for autonomous high-impact decisions or identifying individuals, and lists where masks go wrong. Licence risk 15, Apache 2.0 with no use policy, no threshold and no right to change the terms. Security programme 5, Tencent's Security Response Center takes vulnerability reports company-wide, as the Hy3 listing records (5). 15 + 15 + 0 + 5 + 15 + 5 = 55.\n\n## Score by category\n\n| Category | Weight | Ornith-1.5-9B | WeVisOne-VL-2B | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 95 | 70 | Ornith-1.5-9B +25 |\n| Risk | 20% (20 this run) | 50 | 55 | WeVisOne-VL-2B +5 |\n| Agent interface | 10% (10 this run) | 85 | 70 | Ornith-1.5-9B +15 |\n| Documentation and upkeep | 10% (10 this run) | 31 | 48 | WeVisOne-VL-2B +17 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **61 · C** | **61 · C** | |\n\n## Facts side by side\n\n| Fact | Ornith-1.5-9B | WeVisOne-VL-2B |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Ornith | 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 | MIT | Apache-2.0 |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-08-23 | 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 | 2.3/5 (4) | none |\n\n## Verdicts\n\n**Ornith-1.5-9B.** A small coding and terminal-agent cousin with thinking on by default, XML tool calls that vLLM and SGLang parse, and 262,144 tokens of context. Ornith's own 4-bit builds run in under 8 GB in most local runtimes. The main risk is an absent publisher, with no licence file, no safety results, files replaced without a note and no replies.\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### Ornith-1.5-9B\n\n1. Start it with `ollama run ornith-1.5:9b`, or `llama-server -hf ornith-ai/Ornith-1.5-9B-GGUF` with the Q4_K_M file\n2. In vLLM pass `--enable-auto-tool-choice --tool-call-parser qwen3_xml --reasoning-parser qwen3`, and in SGLang `--tool-call-parser qwen3_coder --reasoning-parser qwen3`, so tool calls and reasoning come back parsed\n3. Thinking is on by default. Send `chat_template_kwargs` with `enable_thinking` false for short tasks, a switch the card doesn't mention\n4. Pin which template your runtime loads, since chat_template.jinja keeps earlier reasoning and the tokenizer_config.json template drops it\n5. Use temperature 0.6, top_p 0.95, top_k 20 for coding and temperature 1.0 with presence_penalty 1.5 for general tasks, per the card\n6. Pin a revision when you download, since Ornith has replaced files in place, and add mmproj-Ornith-1.5-9B-BF16.gguf (0.9 GB) for image input in llama.cpp\n7. Validate JSON output yourself, since structured output isn't documented\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, Ornith-1.5-9B or WeVisOne-VL-2B?\n\nOrnith-1.5-9B and WeVisOne-VL-2B score within a point of each other on agent readiness, 61 (C) and 61 (C). WeVisOne-VL-2B leads on risk and documentation and upkeep.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-wevisone-vl-2b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-wevisone-vl-2b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"ornith-1-5-9b\", \"b\": \"wevisone-vl-2b\"}`. 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