{
  "data": {
    "a": {
      "slug": "qwen3-vl-8b-instruct",
      "name": "Qwen3-VL-8B-Instruct",
      "vendor": "Alibaba (Qwen team)",
      "vendorUrl": "https://qwen.ai",
      "kind": "weights",
      "category": "open-weights",
      "summary": "Qwen3-VL-8B-Instruct is Alibaba's open-weight dense vision-language model of about 8B parameters, taking text, images and video with a 262,144-token context, published on Hugging Face in BF16, FP8 and GGUF under Apache 2.0.",
      "url": "https://www.anchorterminal.com/tools/qwen3-vl-8b-instruct",
      "markdownUrl": "https://www.anchorterminal.com/tools/qwen3-vl-8b-instruct.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/qwen3-vl-8b-instruct.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/qwen3-vl-8b-instruct.json",
      "dns": "qwen3-vl-8b-instruct.grade.anchorterminal.com",
      "repo": "https://huggingface.co/Qwen/Qwen3-VL-8B-Instruct",
      "license": "Apache-2.0",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "Qwen/Qwen3-VL-8B-Instruct"
        },
        {
          "registry": "huggingface",
          "name": "Qwen/Qwen3-VL-8B-Instruct-FP8"
        },
        {
          "registry": "huggingface",
          "name": "Qwen/Qwen3-VL-8B-Instruct-GGUF"
        },
        {
          "registry": "ollama",
          "name": "qwen3-vl:8b-instruct"
        }
      ],
      "auth": "none",
      "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. None of the three repositories is gated (checked 2026-10-21).",
      "pricing": "free",
      "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0. Running it yourself costs the hardware, 17.5 GB for the BF16 files, 10.6 GB for Alibaba's FP8 build and 5.0 GB plus a 0.75 to 1.16 GB vision file for its 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/Qwen/Qwen3-VL-8B-Instruct",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "dense",
        "multimodal",
        "vision",
        "video",
        "tool-calling",
        "apache-2.0",
        "safetensors",
        "fp8",
        "gguf",
        "self-hosted"
      ],
      "lastRelease": "2025-11-01",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 54.9,
        "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": 93,
        "methodology": "0.7",
        "run": "2026-10-01",
        "scores": {
          "capability": 28,
          "interface": 60,
          "reach": 100,
          "risk": 55,
          "upkeep": 31
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "high",
          "date": "2026-11-04"
        },
        "negative": 0,
        "verdict": "Qwen3-VL-8B-Instruct reads screenshots, documents, charts and video, and Alibaba's Q4_K_M GGUF needs about 6 GB. Transformers, vLLM, SGLang, llama.cpp and Ollama run it under plain Apache 2.0. It has no thinking mode, documents tool calls only in the template, and has no published safety work, so add guardrails before it drives a GUI.",
        "bestFor": "An agent with 8 GB or more to spare that needs a local model to read images, documents and video",
        "strengths": [
          "About 6 GB of memory for Alibaba's own Q4_K_M GGUF, with FP8 and Q8_0 builds as well",
          "Runs in Transformers, vLLM, SGLang, llama.cpp and Ollama in released versions",
          "Apache 2.0 with no use policy, downloadable with no account",
          "262,144 tokens of native context over text, images and video",
          "Safetensors and GGUF only, with a chat template in the repository",
          "Also served by Featherless and Parasail for agents without the hardware"
        ],
        "weaknesses": [
          "No safety evaluation, agent-use guidance or security contact",
          "Tool calling isn't documented beyond the template, and Alibaba names no parser",
          "No thinking mode in this edition and no documented JSON output",
          "No reply from Qwen to any of the 19 Hugging Face discussions that aren't pull requests, the newest from 23 August 2026",
          "The card leaves out limitations, languages, knowledge cut-off and memory needs"
        ],
        "agentNotes": [
          "Start it with `llama-server -hf Qwen/Qwen3-VL-8B-Instruct-GGUF:Q4_K_M --jinja -c 32768`, or `ollama run qwen3-vl:8b-instruct`",
          "In Ollama the plain `qwen3-vl:8b` tag is the Thinking edition, so name `8b-instruct`",
          "With the GGUF, load the mmproj vision file too, or images won't be read",
          "Parse tool calls as JSON inside `\u003ctool_call\u003e` tags, or use SGLang's `--tool-call-parser qwen`",
          "Use the card's sampling, `temperature=0.7, top_p=0.8, top_k=20, presence_penalty=1.5` for vision and `temperature=1.0, top_p=1.0, top_k=40, presence_penalty=2.0` for text",
          "Validate JSON output yourself, since structured output isn't documented",
          "vLLM needs 0.11.0 or later and Transformers 4.57.0 or later"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 4,
        "avgRating": 2.8,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "high",
            "grade": "C",
            "methodology": "0.7",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 54.9
          }
        ],
        "editorialScores": {
          "capability": 28,
          "interface": 60,
          "reach": 100,
          "risk": 55,
          "upkeep": 31
        },
        "provenanceScore": 54,
        "checked": {
          "categories": {
            "capability": "2026-11-04",
            "interface": "2026-11-04",
            "reach": "2026-11-04",
            "risk": "2026-11-04",
            "upkeep": "2026-11-04"
          },
          "last": "2026-11-04",
          "oldest": "2026-11-04",
          "stale": false,
          "staleAfterDays": 90
        }
      },
      "connect": {
        "install": "ollama run qwen3-vl:8b-instruct\n# or with vLLM 0.11.0 or later, per Qwen's README:\nvllm serve Qwen/Qwen3-VL-8B-Instruct-FP8\n# or with SGLang, using the tool parser its cookbook names for Qwen3-VL:\npython -m sglang.launch_server --model-path Qwen/Qwen3-VL-8B-Instruct --tool-call-parser qwen",
        "http": "curl http://localhost:11434/api/chat \\\n  -d '{\"model\": \"qwen3-vl:8b-instruct\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/qwen3-vl-8b-instruct"
      },
      "sameCompany": [
        "qwen3-embedding-0-6b",
        "qwen3-embedding-4b",
        "qwen3-embedding-8b",
        "qwen3-vl-embedding-2b",
        "qwen3-vl-embedding-8b",
        "qwen3-asr-0-6b",
        "qwen3-asr-1-7b",
        "qwen3-tts-1-7b-customvoice",
        "qwen-agentworld-35b-a3b",
        "qwen3-30b-a3b-instruct-2507",
        "qwen3-4b-instruct-2507",
        "qwen3-coder-30b-a3b-instruct",
        "qwen3-coder-next",
        "qwen3-omni-30b-a3b-instruct",
        "qwen3-vl-2b-instruct",
        "qwen3-vl-30b-a3b-instruct",
        "qwen3-vl-32b-instruct",
        "qwen3-vl-4b-instruct",
        "qwen3-5-0-8b",
        "qwen3-5-122b-a10b",
        "qwen3-5-2b",
        "qwen3-5-397b-a17b",
        "qwen3-5-4b",
        "qwen3-5-9b",
        "qwen3-6-27b",
        "qwen3-6-35b-a3b",
        "qwen3-8-27b",
        "qwen3-8-flash-next"
      ],
      "weights": {
        "trainedBy": "Alibaba (Qwen team)",
        "params": "8.77B",
        "context": 262144,
        "goodFor": "Vision-language model for images, documents and video with text out, no thinking mode in this Instruct edition.",
        "runtimes": [
          "llama.cpp",
          "Ollama",
          "vLLM",
          "SGLang",
          "Transformers"
        ],
        "builds": [
          {
            "name": "Qwen3VL-8B-Instruct-Q4_K_M",
            "url": "https://huggingface.co/Qwen/Qwen3-VL-8B-Instruct-GGUF/blob/main/Qwen3VL-8B-Instruct-Q4_K_M.gguf",
            "who": "publisher",
            "precision": "q4_k_m",
            "sizeGB": 5,
            "memoryGB": 8,
            "format": "gguf",
            "runsOn": [
              "llama.cpp (Vulkan)",
              "llama.cpp (ROCm)"
            ],
            "note": "Add mmproj-Qwen3VL-8B-Instruct-Q8_0.gguf (0.8 GB) for image input"
          },
          {
            "name": "ollama qwen3-vl:8b-instruct",
            "url": "https://ollama.com/library/qwen3-vl:8b-instruct",
            "who": "ollama",
            "precision": "q4_k_m",
            "sizeGB": 6.2,
            "memoryGB": 8,
            "format": "ollama",
            "runsOn": [
              "Ollama"
            ],
            "note": "Q4_K_M with its vision projector. qwen3-vl:8b, the library default, is the Thinking edition. Size as Ollama's tags page shows it, rounded to whole GB above 10 GB; the Linux build is the llama.cpp variant"
          },
          {
            "name": "Qwen3VL-8B-Instruct-Q8_0",
            "url": "https://huggingface.co/Qwen/Qwen3-VL-8B-Instruct-GGUF/blob/main/Qwen3VL-8B-Instruct-Q8_0.gguf",
            "who": "publisher",
            "precision": "q8_0",
            "sizeGB": 8.7,
            "memoryGB": 12,
            "format": "gguf",
            "runsOn": [
              "llama.cpp (Vulkan)",
              "llama.cpp (ROCm)"
            ],
            "note": "Add an mmproj file for image input"
          },
          {
            "name": "Qwen3-VL-8B-Instruct-FP8",
            "url": "https://huggingface.co/Qwen/Qwen3-VL-8B-Instruct-FP8",
            "who": "publisher",
            "precision": "fp8",
            "sizeGB": 10.6,
            "memoryGB": 16,
            "format": "safetensors"
          },
          {
            "name": "Qwen3-VL-8B-Instruct",
            "url": "https://huggingface.co/Qwen/Qwen3-VL-8B-Instruct",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 17.5,
            "memoryGB": 24,
            "format": "safetensors",
            "runsOn": [
              "vLLM (ROCm)",
              "SGLang (ROCm)"
            ]
          },
          {
            "name": "Qwen3VL-8B-Instruct-F16",
            "url": "https://huggingface.co/Qwen/Qwen3-VL-8B-Instruct-GGUF/blob/main/Qwen3VL-8B-Instruct-F16.gguf",
            "who": "publisher",
            "precision": "f16",
            "sizeGB": 16.4,
            "memoryGB": 24,
            "format": "gguf",
            "runsOn": [
              "llama.cpp (Vulkan)",
              "llama.cpp (ROCm)"
            ],
            "note": "Add an mmproj file (0.8 or 1.2 GB) for image input"
          }
        ],
        "run": "llama-server -hf Qwen/Qwen3-VL-8B-Instruct-GGUF:Q4_K_M --jinja -c 32768",
        "runBuild": "Qwen3VL-8B-Instruct-Q4_K_M",
        "checked": "2026-11-04",
        "minMemoryGB": 8
      },
      "family": {
        "lab": "Alibaba (Qwen)",
        "family": "Qwen3",
        "url": "https://www.anchorterminal.com/open-weights/#qwen",
        "others": [
          {
            "name": "Qwen3-0.6B",
            "url": "https://huggingface.co/Qwen/Qwen3-0.6B",
            "params": "0.6B"
          },
          {
            "name": "Qwen3-1.7B",
            "url": "https://huggingface.co/Qwen/Qwen3-1.7B",
            "params": "1.7B"
          },
          {
            "name": "Qwen3-14B",
            "url": "https://huggingface.co/Qwen/Qwen3-14B",
            "params": "14B"
          },
          {
            "name": "Qwen3-235B-A22B",
            "url": "https://huggingface.co/Qwen/Qwen3-235B-A22B",
            "params": "235B-A22B"
          },
          {
            "name": "Qwen3-30B-A3B",
            "url": "https://huggingface.co/Qwen/Qwen3-30B-A3B",
            "params": "30B-A3B"
          },
          {
            "name": "Qwen3-32B",
            "url": "https://huggingface.co/Qwen/Qwen3-32B",
            "params": "32B"
          },
          {
            "name": "Qwen3-4B",
            "url": "https://huggingface.co/Qwen/Qwen3-4B",
            "params": "4B"
          },
          {
            "name": "Qwen3-8B",
            "url": "https://huggingface.co/Qwen/Qwen3-8B",
            "params": "8B"
          },
          {
            "name": "Qwen3-0.6B-Base",
            "url": "https://huggingface.co/Qwen/Qwen3-0.6B-Base",
            "params": "0.6B"
          },
          {
            "name": "Qwen3-1.7B-Base",
            "url": "https://huggingface.co/Qwen/Qwen3-1.7B-Base",
            "params": "1.7B"
          },
          {
            "name": "Qwen3-14B-Base",
            "url": "https://huggingface.co/Qwen/Qwen3-14B-Base",
            "params": "14B"
          },
          {
            "name": "Qwen3-30B-A3B-Base",
            "url": "https://huggingface.co/Qwen/Qwen3-30B-A3B-Base",
            "params": "30B-A3B"
          },
          {
            "name": "Qwen3-4B-Base",
            "url": "https://huggingface.co/Qwen/Qwen3-4B-Base",
            "params": "4B"
          },
          {
            "name": "Qwen3-8B-Base",
            "url": "https://huggingface.co/Qwen/Qwen3-8B-Base",
            "params": "8B"
          },
          {
            "name": "Qwen3-Reranker-0.6B",
            "url": "https://huggingface.co/Qwen/Qwen3-Reranker-0.6B",
            "params": "0.6B"
          },
          {
            "name": "Qwen3-Reranker-8B",
            "url": "https://huggingface.co/Qwen/Qwen3-Reranker-8B",
            "params": "8B"
          },
          {
            "name": "Qwen3-Embedding-0.6B",
            "url": "https://huggingface.co/Qwen/Qwen3-Embedding-0.6B",
            "params": "0.6B",
            "listing": "https://www.anchorterminal.com/tools/qwen3-embedding-0-6b"
          },
          {
            "name": "Qwen3-Embedding-4B",
            "url": "https://huggingface.co/Qwen/Qwen3-Embedding-4B",
            "params": "4B",
            "listing": "https://www.anchorterminal.com/tools/qwen3-embedding-4b"
          },
          {
            "name": "Qwen3-Embedding-8B",
            "url": "https://huggingface.co/Qwen/Qwen3-Embedding-8B",
            "params": "8B",
            "listing": "https://www.anchorterminal.com/tools/qwen3-embedding-8b"
          },
          {
            "name": "Qwen3-Reranker-4B",
            "url": "https://huggingface.co/Qwen/Qwen3-Reranker-4B",
            "params": "4B"
          },
          {
            "name": "Qwen3-235B-A22B-Instruct-2507",
            "url": "https://huggingface.co/Qwen/Qwen3-235B-A22B-Instruct-2507",
            "params": "235B-A22B"
          },
          {
            "name": "Qwen3-Coder-480B-A35B-Instruct",
            "url": "https://huggingface.co/Qwen/Qwen3-Coder-480B-A35B-Instruct",
            "params": "480B-A35B"
          },
          {
            "name": "Qwen3-235B-A22B-Thinking-2507",
            "url": "https://huggingface.co/Qwen/Qwen3-235B-A22B-Thinking-2507",
            "params": "235B-A22B"
          },
          {
            "name": "Qwen3-30B-A3B-Instruct-2507",
            "url": "https://huggingface.co/Qwen/Qwen3-30B-A3B-Instruct-2507",
            "params": "30B-A3B",
            "listing": "https://www.anchorterminal.com/tools/qwen3-30b-a3b-instruct-2507"
          },
          {
            "name": "Qwen3-30B-A3B-Thinking-2507",
            "url": "https://huggingface.co/Qwen/Qwen3-30B-A3B-Thinking-2507",
            "params": "30B-A3B"
          },
          {
            "name": "Qwen3-Coder-30B-A3B-Instruct",
            "url": "https://huggingface.co/Qwen/Qwen3-Coder-30B-A3B-Instruct",
            "params": "30B-A3B",
            "listing": "https://www.anchorterminal.com/tools/qwen3-coder-30b-a3b-instruct"
          },
          {
            "name": "Qwen3-4B-Instruct-2507",
            "url": "https://huggingface.co/Qwen/Qwen3-4B-Instruct-2507",
            "params": "4B",
            "listing": "https://www.anchorterminal.com/tools/qwen3-4b-instruct-2507"
          },
          {
            "name": "Qwen3-4B-Thinking-2507",
            "url": "https://huggingface.co/Qwen/Qwen3-4B-Thinking-2507",
            "params": "4B"
          },
          {
            "name": "Qwen3-Next-80B-A3B-Instruct",
            "url": "https://huggingface.co/Qwen/Qwen3-Next-80B-A3B-Instruct",
            "params": "80B-A3B"
          },
          {
            "name": "Qwen3-Next-80B-A3B-Thinking",
            "url": "https://huggingface.co/Qwen/Qwen3-Next-80B-A3B-Thinking",
            "params": "80B-A3B"
          },
          {
            "name": "Qwen3-Omni-30B-A3B-Captioner",
            "url": "https://huggingface.co/Qwen/Qwen3-Omni-30B-A3B-Captioner",
            "params": "30B-A3B"
          },
          {
            "name": "Qwen3-Omni-30B-A3B-Thinking",
            "url": "https://huggingface.co/Qwen/Qwen3-Omni-30B-A3B-Thinking",
            "params": "30B-A3B"
          },
          {
            "name": "Qwen3-Omni-30B-A3B-Instruct",
            "url": "https://huggingface.co/Qwen/Qwen3-Omni-30B-A3B-Instruct",
            "params": "30B-A3B",
            "listing": "https://www.anchorterminal.com/tools/qwen3-omni-30b-a3b-instruct"
          },
          {
            "name": "Qwen3-VL-235B-A22B-Instruct",
            "url": "https://huggingface.co/Qwen/Qwen3-VL-235B-A22B-Instruct",
            "params": "235B-A22B"
          },
          {
            "name": "Qwen3-VL-235B-A22B-Thinking",
            "url": "https://huggingface.co/Qwen/Qwen3-VL-235B-A22B-Thinking",
            "params": "235B-A22B"
          },
          {
            "name": "Qwen3Guard-Gen-0.6B",
            "url": "https://huggingface.co/Qwen/Qwen3Guard-Gen-0.6B",
            "params": "0.6B"
          },
          {
            "name": "Qwen3Guard-Gen-4B",
            "url": "https://huggingface.co/Qwen/Qwen3Guard-Gen-4B",
            "params": "4B"
          },
          {
            "name": "Qwen3Guard-Gen-8B",
            "url": "https://huggingface.co/Qwen/Qwen3Guard-Gen-8B",
            "params": "8B"
          },
          {
            "name": "Qwen3Guard-Stream-0.6B",
            "url": "https://huggingface.co/Qwen/Qwen3Guard-Stream-0.6B",
            "params": "0.6B"
          },
          {
            "name": "Qwen3Guard-Stream-4B",
            "url": "https://huggingface.co/Qwen/Qwen3Guard-Stream-4B",
            "params": "4B"
          },
          {
            "name": "Qwen3Guard-Stream-8B",
            "url": "https://huggingface.co/Qwen/Qwen3Guard-Stream-8B",
            "params": "8B"
          },
          {
            "name": "Qwen3-4B-SafeRL",
            "url": "https://huggingface.co/Qwen/Qwen3-4B-SafeRL",
            "params": "4B"
          },
          {
            "name": "Qwen3-VL-30B-A3B-Instruct",
            "url": "https://huggingface.co/Qwen/Qwen3-VL-30B-A3B-Instruct",
            "params": "30B-A3B",
            "listing": "https://www.anchorterminal.com/tools/qwen3-vl-30b-a3b-instruct"
          },
          {
            "name": "Qwen3-VL-30B-A3B-Thinking",
            "url": "https://huggingface.co/Qwen/Qwen3-VL-30B-A3B-Thinking",
            "params": "30B-A3B"
          },
          {
            "name": "Qwen3-VL-4B-Instruct",
            "url": "https://huggingface.co/Qwen/Qwen3-VL-4B-Instruct",
            "params": "4B",
            "listing": "https://www.anchorterminal.com/tools/qwen3-vl-4b-instruct"
          },
          {
            "name": "Qwen3-VL-4B-Thinking",
            "url": "https://huggingface.co/Qwen/Qwen3-VL-4B-Thinking",
            "params": "4B"
          },
          {
            "name": "Qwen3-VL-8B-Thinking",
            "url": "https://huggingface.co/Qwen/Qwen3-VL-8B-Thinking",
            "params": "8B"
          },
          {
            "name": "Qwen3-VL-2B-Instruct",
            "url": "https://huggingface.co/Qwen/Qwen3-VL-2B-Instruct",
            "params": "2B",
            "listing": "https://www.anchorterminal.com/tools/qwen3-vl-2b-instruct"
          },
          {
            "name": "Qwen3-VL-2B-Thinking",
            "url": "https://huggingface.co/Qwen/Qwen3-VL-2B-Thinking",
            "params": "2B"
          },
          {
            "name": "Qwen3-VL-32B-Instruct",
            "url": "https://huggingface.co/Qwen/Qwen3-VL-32B-Instruct",
            "params": "32B",
            "listing": "https://www.anchorterminal.com/tools/qwen3-vl-32b-instruct"
          },
          {
            "name": "Qwen3-VL-32B-Thinking",
            "url": "https://huggingface.co/Qwen/Qwen3-VL-32B-Thinking",
            "params": "32B"
          },
          {
            "name": "Qwen3-VL-Embedding-2B",
            "url": "https://huggingface.co/Qwen/Qwen3-VL-Embedding-2B",
            "params": "2B",
            "listing": "https://www.anchorterminal.com/tools/qwen3-vl-embedding-2b"
          },
          {
            "name": "Qwen3-VL-Embedding-8B",
            "url": "https://huggingface.co/Qwen/Qwen3-VL-Embedding-8B",
            "params": "8B",
            "listing": "https://www.anchorterminal.com/tools/qwen3-vl-embedding-8b"
          },
          {
            "name": "Qwen3-VL-Reranker-2B",
            "url": "https://huggingface.co/Qwen/Qwen3-VL-Reranker-2B",
            "params": "2B"
          },
          {
            "name": "Qwen3-VL-Reranker-8B",
            "url": "https://huggingface.co/Qwen/Qwen3-VL-Reranker-8B",
            "params": "8B"
          },
          {
            "name": "Qwen3-TTS-12Hz-0.6B-Base",
            "url": "https://huggingface.co/Qwen/Qwen3-TTS-12Hz-0.6B-Base",
            "params": "0.6B"
          },
          {
            "name": "Qwen3-TTS-12Hz-0.6B-CustomVoice",
            "url": "https://huggingface.co/Qwen/Qwen3-TTS-12Hz-0.6B-CustomVoice",
            "params": "0.6B"
          },
          {
            "name": "Qwen3-TTS-12Hz-1.7B-Base",
            "url": "https://huggingface.co/Qwen/Qwen3-TTS-12Hz-1.7B-Base",
            "params": "1.7B"
          },
          {
            "name": "Qwen3-TTS-12Hz-1.7B-CustomVoice",
            "url": "https://huggingface.co/Qwen/Qwen3-TTS-12Hz-1.7B-CustomVoice",
            "params": "1.7B",
            "listing": "https://www.anchorterminal.com/tools/qwen3-tts-1-7b-customvoice"
          },
          {
            "name": "Qwen3-TTS-12Hz-1.7B-VoiceDesign",
            "url": "https://huggingface.co/Qwen/Qwen3-TTS-12Hz-1.7B-VoiceDesign",
            "params": "1.7B"
          },
          {
            "name": "Qwen3-TTS-Tokenizer-12Hz",
            "url": "https://huggingface.co/Qwen/Qwen3-TTS-Tokenizer-12Hz",
            "params": "not in name"
          },
          {
            "name": "Qwen3-ASR-0.6B",
            "url": "https://huggingface.co/Qwen/Qwen3-ASR-0.6B",
            "params": "0.6B",
            "listing": "https://www.anchorterminal.com/tools/qwen3-asr-0-6b"
          },
          {
            "name": "Qwen3-ASR-1.7B",
            "url": "https://huggingface.co/Qwen/Qwen3-ASR-1.7B",
            "params": "1.7B",
            "listing": "https://www.anchorterminal.com/tools/qwen3-asr-1-7b"
          },
          {
            "name": "Qwen3-ForcedAligner-0.6B",
            "url": "https://huggingface.co/Qwen/Qwen3-ForcedAligner-0.6B",
            "params": "0.6B"
          },
          {
            "name": "Qwen3-Coder-Next",
            "url": "https://huggingface.co/Qwen/Qwen3-Coder-Next",
            "params": "not in name; Hub safetensors total 79.7B",
            "listing": "https://www.anchorterminal.com/tools/qwen3-coder-next"
          },
          {
            "name": "Qwen3-Coder-Next-Base",
            "url": "https://huggingface.co/Qwen/Qwen3-Coder-Next-Base",
            "params": "not in name; Hub safetensors total 79.7B"
          },
          {
            "name": "WebWorld-14B",
            "url": "https://huggingface.co/Qwen/WebWorld-14B",
            "params": "14B"
          },
          {
            "name": "WebWorld-32B",
            "url": "https://huggingface.co/Qwen/WebWorld-32B",
            "params": "32B"
          },
          {
            "name": "WebWorld-8B",
            "url": "https://huggingface.co/Qwen/WebWorld-8B",
            "params": "8B"
          },
          {
            "name": "Qwen3.5-397B-A17B",
            "url": "https://huggingface.co/Qwen/Qwen3.5-397B-A17B",
            "params": "397B-A17B",
            "listing": "https://www.anchorterminal.com/tools/qwen3-5-397b-a17b"
          },
          {
            "name": "Qwen3.5-122B-A10B",
            "url": "https://huggingface.co/Qwen/Qwen3.5-122B-A10B",
            "params": "122B-A10B",
            "listing": "https://www.anchorterminal.com/tools/qwen3-5-122b-a10b"
          },
          {
            "name": "Qwen3.5-27B",
            "url": "https://huggingface.co/Qwen/Qwen3.5-27B",
            "params": "27B"
          },
          {
            "name": "Qwen3.5-35B-A3B",
            "url": "https://huggingface.co/Qwen/Qwen3.5-35B-A3B",
            "params": "35B-A3B"
          },
          {
            "name": "Qwen3.5-35B-A3B-Base",
            "url": "https://huggingface.co/Qwen/Qwen3.5-35B-A3B-Base",
            "params": "35B-A3B"
          },
          {
            "name": "Qwen3.5-9B-Base",
            "url": "https://huggingface.co/Qwen/Qwen3.5-9B-Base",
            "params": "9B"
          },
          {
            "name": "Qwen3.5-4B",
            "url": "https://huggingface.co/Qwen/Qwen3.5-4B",
            "params": "4B",
            "listing": "https://www.anchorterminal.com/tools/qwen3-5-4b"
          },
          {
            "name": "Qwen3.5-4B-Base",
            "url": "https://huggingface.co/Qwen/Qwen3.5-4B-Base",
            "params": "4B"
          },
          {
            "name": "Qwen3.5-9B",
            "url": "https://huggingface.co/Qwen/Qwen3.5-9B",
            "params": "9B",
            "listing": "https://www.anchorterminal.com/tools/qwen3-5-9b"
          },
          {
            "name": "Qwen3.5-0.8B",
            "url": "https://huggingface.co/Qwen/Qwen3.5-0.8B",
            "params": "0.8B",
            "listing": "https://www.anchorterminal.com/tools/qwen3-5-0-8b"
          },
          {
            "name": "Qwen3.5-0.8B-Base",
            "url": "https://huggingface.co/Qwen/Qwen3.5-0.8B-Base",
            "params": "0.8B"
          },
          {
            "name": "Qwen3.5-2B",
            "url": "https://huggingface.co/Qwen/Qwen3.5-2B",
            "params": "2B",
            "listing": "https://www.anchorterminal.com/tools/qwen3-5-2b"
          },
          {
            "name": "Qwen3.5-2B-Base",
            "url": "https://huggingface.co/Qwen/Qwen3.5-2B-Base",
            "params": "2B"
          },
          {
            "name": "Qwen3.6-35B-A3B",
            "url": "https://huggingface.co/Qwen/Qwen3.6-35B-A3B",
            "params": "35B-A3B",
            "listing": "https://www.anchorterminal.com/tools/qwen3-6-35b-a3b"
          },
          {
            "name": "Qwen3.6-27B",
            "url": "https://huggingface.co/Qwen/Qwen3.6-27B",
            "params": "27B",
            "listing": "https://www.anchorterminal.com/tools/qwen3-6-27b"
          },
          {
            "name": "Qwen-AgentWorld-35B-A3B",
            "url": "https://huggingface.co/Qwen/Qwen-AgentWorld-35B-A3B",
            "params": "35B-A3B"
          },
          {
            "name": "Qwen3.8-27B",
            "url": "https://huggingface.co/Qwen/Qwen3.8-27B",
            "params": "27B",
            "listing": "https://www.anchorterminal.com/tools/qwen3-8-27b"
          },
          {
            "name": "Qwen3.8-2.4T-A95B",
            "url": "https://huggingface.co/Qwen/Qwen3.8-2.4T-A95B",
            "params": "2.4T-A95B"
          },
          {
            "name": "Qwen3.8-Flash-Next",
            "url": "https://huggingface.co/Qwen/Qwen3.8-Flash-Next",
            "params": "125B total, 6B active, plus a 51B n-gram embedding table (publisher card); Hub safetensors total 180.0B",
            "listing": "https://www.anchorterminal.com/tools/qwen3-8-flash-next"
          },
          {
            "name": "Qwen-Drive-1.0-4B",
            "url": "https://huggingface.co/Qwen/Qwen-Drive-1.0-4B",
            "params": "4B"
          }
        ],
        "olderCount": 109,
        "unrestrictedCopies": 5
      },
      "area": "models",
      "provenance": {
        "legalEntity": "Alibaba Cloud (per the pilot's RDAP check of qwen.ai; this model's files name no copyright holder)",
        "domain": "qwen.ai",
        "domainRegistered": "2023-08-27",
        "endpointOnVendorDomain": null,
        "terms": "",
        "privacy": "",
        "statusPage": "",
        "changelog": "",
        "securityTxt": "none",
        "checked": "2026-10-21",
        "notes": [
          "The weights repository has no LICENSE file and the GitHub repository's Apache 2.0 LICENSE leaves the copyright line as the template. RDAP named Alibaba Cloud (Singapore) Private Limited as the registrant of qwen.ai at the pilot's check on 20 October 2026; rdap.org didn't answer with JSON on 21 October.",
          "Nothing is hosted, so the Apache 2.0 licence stands in for terms of service and no privacy policy is scored.",
          "No changelog. The GitHub README has a dated news list of releases, and the Hugging Face commit messages are README updates and four merged pull requests from 15 October 2025.",
          "qwen.ai/.well-known/security.txt redirects to the home page, so there is no security.txt."
        ],
        "score": 54
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/qwen3-vl-8b-instruct.json",
      "live": {
        "slug": "qwen3-vl-8b-instruct",
        "securityTxt": {
          "url": "https://qwen.ai/.well-known/security.txt",
          "state": "none",
          "checkedAt": "2026-11-10T16:09:18.413964975Z"
        },
        "domain": {
          "domain": "qwen.ai",
          "registered": "2023-08-27",
          "source": "https://rdap.identitydigital.services/rdap/domain/qwen.ai",
          "checkedAt": "2026-11-08T15:07:12.468678256Z"
        },
        "updatedAt": "2026-11-10T16:09:18.413964975Z"
      }
    },
    "answer": "WeVisOne-VL-4B scores 61 (C) on agent readiness against Qwen3-VL-8B-Instruct's 54.9 (C), and leads in 2 of 4 scored categories. Qwen3-VL-8B-Instruct leads on reach.",
    "b": {
      "slug": "wevisone-vl-4b",
      "name": "WeVisOne-VL-4B",
      "vendor": "Tencent (WeChat Vision)",
      "vendorUrl": "https://www.tencent.com",
      "kind": "weights",
      "category": "open-weights",
      "summary": "WeVisOne-VL-4B is an open-weight perception model of 4.8B parameters from Tencent's WeChat Vision team, built on Qwen3-VL. Name something in an image and it returns boxes, points, counts, text, masks or alpha mattes. Apache 2.0.",
      "url": "https://www.anchorterminal.com/tools/wevisone-vl-4b",
      "markdownUrl": "https://www.anchorterminal.com/tools/wevisone-vl-4b.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/wevisone-vl-4b.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/wevisone-vl-4b.json",
      "dns": "wevisone-vl-4b.grade.anchorterminal.com",
      "repo": "https://huggingface.co/tencent/WeVisOne-VL-4B",
      "license": "Apache-2.0",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "tencent/WeVisOne-VL-4B"
        }
      ],
      "auth": "none",
      "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The repository is not gated (checked 2026-11-10).",
      "pricing": "free",
      "pricingNotes": "Free to download and use under Apache 2.0. Running it yourself costs the hardware, 9.66 GB in BF16 (checked 2026-11-10).",
      "priceSummary": "Free",
      "where": "local",
      "x402": {
        "level": "no",
        "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-11-10).",
        "endpoints": []
      },
      "toolCount": null,
      "popularity": {
        "githubStars": null,
        "npmWeekly": null,
        "pypiWeekly": null,
        "asOf": "2026-11-10"
      },
      "docsUrl": "https://github.com/Tencent/WeVisOne-VL",
      "capabilities": [
        "model.open-weights",
        "docs.ocr"
      ],
      "tags": [
        "open-weights",
        "model",
        "vision",
        "grounding",
        "object-detection",
        "segmentation",
        "matting",
        "ocr",
        "counting",
        "apache-2.0",
        "safetensors",
        "self-hosted"
      ],
      "lastRelease": "2026-10-27",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 61,
        "grade": "C",
        "agentReady": false,
        "rank": 0,
        "ranked": false,
        "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools",
        "rankOf": 1518,
        "categoryRank": 66,
        "methodology": "0.7",
        "run": "2026-10-01",
        "scores": {
          "interface": 70,
          "reach": 70,
          "risk": 55,
          "upkeep": 48
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "low",
          "date": "2026-11-10"
        },
        "negative": 0,
        "verdict": "A small Apache 2.0 perception model that finds, counts, reads and cuts out what you name in an image, and says None when it isn't there. Boxes and points run in vLLM, SGLang or Transformers. Masks and mattes need Tencent's package and a pickle file. Two weeks old, with no questions answered yet and no safety results.",
        "bestFor": "Agents that need to locate, count or cut out objects in images on a small GPU, under a permissive licence",
        "strengths": [
          "Seven perception tasks in one model, from boxes to alpha mattes",
          "Returns None for a target that isn't in the image",
          "Apache 2.0, ungated",
          "Each task's input and output is documented",
          "9.66 GB in BF16, so it fits a 12 GB machine"
        ],
        "weaknesses": [
          "The pixel decoder ships as a pickle file",
          "Masks and mattes need Tencent's package from GitHub",
          "No quantised build from Tencent",
          "One image and one turn at a time, no video",
          "No reply from Tencent on the repository's discussions",
          "No context length, languages, safety results or data sources on the card"
        ],
        "agentNotes": [
          "Use Tencent's package: `WeVisOneVL(\"tencent/WeVisOne-VL-4B\", backend=\"vllm\")` then `model.predict(image, task=..., query=...)`",
          "Tasks are detection, referring, point, count_box, count_point, ocr, ocr_grounding, segmentation and matting",
          "A None answer means the target isn't in the image. Treat it as a result, not an error",
          "mask_decoder.pt is a pickle. Load it only from the tencent repository, and check its SHA-256 against the Hub's",
          "Send one image a turn. Video grids are rejected by the decoder",
          "The card says it isn't for identifying individuals or for high-impact decisions on its own"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 0,
        "avgRating": 0,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "low",
            "grade": "C",
            "methodology": "0.7",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 61
          }
        ],
        "editorialScores": {
          "interface": 70,
          "reach": 70,
          "risk": 55,
          "upkeep": 48
        },
        "provenanceScore": 68,
        "checked": {
          "categories": {
            "interface": "2026-11-10",
            "reach": "2026-11-10",
            "risk": "2026-11-10",
            "upkeep": "2026-11-10"
          },
          "last": "2026-11-10",
          "oldest": "2026-11-10",
          "stale": false,
          "staleAfterDays": 90
        }
      },
      "connect": {
        "install": "git clone https://github.com/Tencent/WeVisOne-VL.git \u0026\u0026 cd WeVisOne-VL\npip install -r requirements.txt \u0026\u0026 pip install -e .\n# then, in Python: WeVisOneVL(\"tencent/WeVisOne-VL-4B\", backend=\"transformers\") or backend=\"vllm\"",
        "http": "# No HTTP API of its own. Tencent's package calls model.predict(image, task=\"detection\", query=[...]).\n# For boxes and points only, vLLM serves the backbone at http://localhost:8000/v1 with: vllm serve tencent/WeVisOne-VL-4B"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/wevisone-vl-4b"
      },
      "sameCompany": [
        "hy-memory",
        "tencentdb-agent-memory",
        "hunyuanocr",
        "hy-memory-30b-a3b",
        "hy3",
        "wevisone-vl-2b"
      ],
      "weights": {
        "trainedBy": "Tencent WeChat Vision",
        "params": "4.8B",
        "goodFor": "Finding, counting, reading and cutting out what you name in an image, on a 12 GB machine.",
        "runtimes": [
          "vLLM",
          "SGLang",
          "Transformers"
        ],
        "builds": [
          {
            "name": "WeVisOne-VL-4B (BF16 safetensors)",
            "url": "https://huggingface.co/tencent/WeVisOne-VL-4B/blob/main/model.safetensors",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 9.66,
            "memoryGB": 12,
            "format": "safetensors",
            "runsOn": [
              "vLLM",
              "SGLang",
              "Transformers"
            ],
            "note": "The only build. Masks and mattes also need mask_decoder.pt and Tencent's package"
          }
        ],
        "run": "vllm serve tencent/WeVisOne-VL-4B",
        "runBuild": "WeVisOne-VL-4B (BF16 safetensors)",
        "checked": "2026-11-10",
        "minMemoryGB": 12
      },
      "family": {
        "lab": "Tencent (Hunyuan / Hy)",
        "family": "WeVisOne-VL",
        "url": "https://www.anchorterminal.com/open-weights/#tencent",
        "others": [
          {
            "name": "WeVisOne-VL-2B",
            "url": "https://huggingface.co/tencent/WeVisOne-VL-2B",
            "params": "2.4B (2,441,744,384 by the Hub's safetensors count)",
            "listing": "https://www.anchorterminal.com/tools/wevisone-vl-2b"
          }
        ],
        "olderCount": 22
      },
      "area": "models",
      "provenance": {
        "legalEntity": "Tencent (named in the LICENSE file's copyright line)",
        "domain": "tencent.com",
        "domainRegistered": "1998-09-14",
        "endpointOnVendorDomain": null,
        "terms": "https://huggingface.co/tencent/WeVisOne-VL-4B/blob/main/LICENSE",
        "privacy": "",
        "statusPage": "",
        "changelog": "https://huggingface.co/tencent/WeVisOne-VL-4B/commits/main",
        "securityTxt": "none",
        "checked": "2026-11-10",
        "notes": [
          "The card names WeChat Vision, Tencent Inc. The domain's registration date is carried from our other Tencent listings.",
          "Nothing is hosted under this listing, so the model licence stands in for terms of service and no privacy policy is scored.",
          "The changelog is the commit history.",
          "Tencent's Security Response Center takes reports company-wide, per the Hy3 listing; nothing on the card links it."
        ],
        "score": 68
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/wevisone-vl-4b.json"
    },
    "edges": [
      {
        "by": 30,
        "category": "reach",
        "leads": "qwen3-vl-8b-instruct",
        "name": "Reach",
        "reasons": {
          "a": "Alibaba's own Q4_K_M GGUF is 5.03 GB, so with a fifth added it needs about 6 GB and fits in 16 GB (30). Transformers 4.57.0, vLLM, SGLang and llama.cpp run it in released versions, and Ollama v0.40.14 pins llama.cpp b11963, which has the qwen3vl architecture, so Ollama is credited through its pin; five runtimes reach the 25 cap. Alibaba publishes FP8 and GGUF builds at F16, Q8_0 and Q4_K_M (15). Apache 2.0 with nothing added (15). No gate and no account (10). Featherless is live in the Hub's provider mapping on 4 November, and Parasail served it per the 21 October check, not re-read, so it's hosted too (5). Novita's mapping still shows an error and isn't counted. 30+25+15+15+10+5 = 100.",
          "b": "Memory 30, the only build from Tencent is the BF16 safetensors file with its decoder, 9.7 GB, and 9.7 GB × 1.2 = 11.6 GB, within 16 GB. No runtime library has a build. Runtimes 15, the architecture is Qwen3VLForConditionalGeneration, which vLLM 0.33.0, SGLang 0.5.23 and Transformers 5.22.2 run from the safetensors file, three at 5 each. Builds 0, Tencent publishes the BF16 weights only. Terms 15, Apache 2.0 in the card's metadata with the LICENSE file in the repository, and nothing added. Download 10, the Hub API reports gated false on 10 November 2026, so the files download with no account and nothing to accept. Hosted 0, Hugging Face maps no inference provider to it. 30 + 15 + 0 + 15 + 10 + 0 = 70."
        },
        "scores": {
          "a": 100,
          "b": 70
        }
      },
      {
        "by": 17,
        "category": "upkeep",
        "leads": "wevisone-vl-4b",
        "name": "Documentation and upkeep",
        "reasons": {
          "a": "The card misses limitations, languages and a cut-off (11). Training data is described only in the technical report, with no cut-off (5). The last change to code that runs it was the qwen-vl-utils fix merged on 4 January 2026, and the weights haven't changed since November 2025; the Hub commits for all three repositories and the QwenLM/Qwen3-VL head (30 January 2026) were the same on 4 November (5). Of 34 Hugging Face discussions on 4 November, the 19 that aren't pull requests (newest 23 August 2026) have no reply from Qwen, and GitHub issues weren't counted (0). Earlier versions kept in their own repositories without notes (5). Two reports wait without a reply (5). 11+5+5+0+5+5 = 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 27 October 2026, added links to the paper and project page, 14 days before this check. Responsiveness 0, Neither of the 2 discussions that aren't pull requests (23 and 26 October 2026) has a reply from a tencent member, counted on 10 November by author flags and dates with a script, text not read. Version policy 5, A first release, 14 days old. File health 10, Config, template and card agree on the Qwen3-VL classes, and the card names mask_decoder.pt as required and it's there. 8 + 5 + 20 + 0 + 5 + 10 = 48."
        },
        "scores": {
          "a": 31,
          "b": 48
        }
      },
      {
        "by": 10,
        "category": "interface",
        "leads": "wevisone-vl-4b",
        "name": "Agent interface",
        "reasons": {
          "a": "A Jinja chat template in the repository (20). The tool-call format lives only in the template, Hermes-style JSON in tool_call tags, and Alibaba names no parser (10). Trained for function calling with several calls allowed in a turn (20). No documented structured output (0). No thinking mode in this edition, so reasoning control scores 0 by the rule. 262,144 tokens of native context (10). 20+10+20+0+0+10 = 60.",
          "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": 60,
          "b": 70
        }
      }
    ],
    "facts": [
      {
        "a": "Open-weight model",
        "b": "Open-weight model",
        "name": "Kind"
      },
      {
        "a": "Alibaba (Qwen team)",
        "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": "2025-11-01",
        "b": "2026-10-27",
        "name": "Last release"
      },
      {
        "a": "no document linked",
        "b": "not read yet",
        "name": "Terms last updated"
      },
      {
        "a": "no document linked",
        "b": "no document linked",
        "name": "Privacy policy last updated"
      },
      {
        "a": "2.8/5 (4)",
        "b": "none",
        "name": "Agent reviews"
      }
    ],
    "faq": [
      {
        "answer": "WeVisOne-VL-4B scores 61 (C) on agent readiness against Qwen3-VL-8B-Instruct's 54.9 (C), and leads in 2 of 4 scored categories. Qwen3-VL-8B-Instruct leads on reach.",
        "question": "Which is better for AI agents, Qwen3-VL-8B-Instruct or WeVisOne-VL-4B?"
      }
    ],
    "goodFor": [
      {
        "aheadOn": [
          "Reach, 100 against 70"
        ],
        "also": null,
        "goodFor": "An agent with 8 GB or more to spare that needs a local model to read images, documents and video",
        "slug": "qwen3-vl-8b-instruct",
        "watchFor": "No safety evaluation, agent-use guidance or security contact"
      },
      {
        "aheadOn": [
          "Agent interface, 70 against 60",
          "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-4b",
        "watchFor": "The pixel decoder ships as a pickle file"
      }
    ],
    "history": null,
    "job": {
      "capability": "model.open-weights",
      "name": "Open-weight models you run yourself"
    },
    "others": [
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        "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-qwen3-vl-8b-instruct"
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        "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-wevisone-vl-4b"
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        "title": "Arete-27B vs WeVisOne-VL-4B",
        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-wevisone-vl-4b"
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      {
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        "json": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-wevisone-vl-4b.json",
        "title": "Granite 4.2 3B vs WeVisOne-VL-4B",
        "url": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-wevisone-vl-4b"
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        "json": "https://www.anchorterminal.com/compare/granite-4-2-8b-vs-qwen3-vl-8b-instruct.json",
        "title": "Granite 4.2 8B vs Qwen3-VL-8B-Instruct",
        "url": "https://www.anchorterminal.com/compare/granite-4-2-8b-vs-qwen3-vl-8b-instruct"
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        "json": "https://www.anchorterminal.com/compare/granite-4-2-8b-vs-wevisone-vl-4b.json",
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        "url": "https://www.anchorterminal.com/compare/granite-4-2-8b-vs-wevisone-vl-4b"
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        "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-vl-8b-instruct.json",
        "title": "Hy-Memory-30B-A3B vs Qwen3-VL-8B-Instruct",
        "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-vl-8b-instruct"
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        "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-wevisone-vl-4b.json",
        "title": "Hy-Memory-30B-A3B vs WeVisOne-VL-4B",
        "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-wevisone-vl-4b"
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        "json": "https://www.anchorterminal.com/compare/hy3-vs-qwen3-vl-8b-instruct.json",
        "title": "Hy3 vs Qwen3-VL-8B-Instruct",
        "url": "https://www.anchorterminal.com/compare/hy3-vs-qwen3-vl-8b-instruct"
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      {
        "json": "https://www.anchorterminal.com/compare/hy3-vs-wevisone-vl-4b.json",
        "title": "Hy3 vs WeVisOne-VL-4B",
        "url": "https://www.anchorterminal.com/compare/hy3-vs-wevisone-vl-4b"
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        "json": "https://www.anchorterminal.com/compare/inkling-small-vs-qwen3-vl-8b-instruct.json",
        "title": "Inkling-Small vs Qwen3-VL-8B-Instruct",
        "url": "https://www.anchorterminal.com/compare/inkling-small-vs-qwen3-vl-8b-instruct"
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      {
        "json": "https://www.anchorterminal.com/compare/inkling-small-vs-wevisone-vl-4b.json",
        "title": "Inkling-Small vs WeVisOne-VL-4B",
        "url": "https://www.anchorterminal.com/compare/inkling-small-vs-wevisone-vl-4b"
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      {
        "json": "https://www.anchorterminal.com/compare/inkling-vs-qwen3-vl-8b-instruct.json",
        "title": "Inkling vs Qwen3-VL-8B-Instruct",
        "url": "https://www.anchorterminal.com/compare/inkling-vs-qwen3-vl-8b-instruct"
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      {
        "json": "https://www.anchorterminal.com/compare/inkling-vs-wevisone-vl-4b.json",
        "title": "Inkling vs WeVisOne-VL-4B",
        "url": "https://www.anchorterminal.com/compare/inkling-vs-wevisone-vl-4b"
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        "json": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-qwen3-vl-8b-instruct.json",
        "title": "InternVL3.5-GPT-OSS-20B-A4B-Preview vs Qwen3-VL-8B-Instruct",
        "url": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-qwen3-vl-8b-instruct"
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        "json": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-wevisone-vl-4b.json",
        "title": "InternVL3.5-GPT-OSS-20B-A4B-Preview vs WeVisOne-VL-4B",
        "url": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-wevisone-vl-4b"
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        "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwen3-vl-8b-instruct.json",
        "title": "Kimi K3.1 Pro vs Qwen3-VL-8B-Instruct",
        "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwen3-vl-8b-instruct"
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        "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-wevisone-vl-4b.json",
        "title": "Kimi K3.1 Pro vs WeVisOne-VL-4B",
        "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-wevisone-vl-4b"
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      {
        "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-qwen3-vl-8b-instruct.json",
        "title": "Kimi K3 vs Qwen3-VL-8B-Instruct",
        "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-qwen3-vl-8b-instruct"
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      {
        "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-wevisone-vl-4b.json",
        "title": "Kimi K3 vs WeVisOne-VL-4B",
        "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-wevisone-vl-4b"
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      {
        "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-qwen3-vl-8b-instruct.json",
        "title": "Kolibri-1 vs Qwen3-VL-8B-Instruct",
        "url": "https://www.anchorterminal.com/compare/kolibri-1-vs-qwen3-vl-8b-instruct"
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      {
        "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-wevisone-vl-4b.json",
        "title": "Kolibri-1 vs WeVisOne-VL-4B",
        "url": "https://www.anchorterminal.com/compare/kolibri-1-vs-wevisone-vl-4b"
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      {
        "json": "https://www.anchorterminal.com/compare/kronos-vs-qwen3-vl-8b-instruct.json",
        "title": "Kronos vs Qwen3-VL-8B-Instruct",
        "url": "https://www.anchorterminal.com/compare/kronos-vs-qwen3-vl-8b-instruct"
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      {
        "json": "https://www.anchorterminal.com/compare/kronos-vs-wevisone-vl-4b.json",
        "title": "Kronos vs WeVisOne-VL-4B",
        "url": "https://www.anchorterminal.com/compare/kronos-vs-wevisone-vl-4b"
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      {
        "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-vl-8b-instruct.json",
        "title": "Laguna M.1 vs Qwen3-VL-8B-Instruct",
        "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-vl-8b-instruct"
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      {
        "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-wevisone-vl-4b.json",
        "title": "Laguna M.1 vs WeVisOne-VL-4B",
        "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-wevisone-vl-4b"
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      {
        "json": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-qwen3-vl-8b-instruct.json",
        "title": "Laguna S 2.1 vs Qwen3-VL-8B-Instruct",
        "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-qwen3-vl-8b-instruct"
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      {
        "json": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-wevisone-vl-4b.json",
        "title": "Laguna S 2.1 vs WeVisOne-VL-4B",
        "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-wevisone-vl-4b"
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      {
        "json": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwen3-vl-8b-instruct.json",
        "title": "Laguna XS 2.1 vs Qwen3-VL-8B-Instruct",
        "url": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwen3-vl-8b-instruct"
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      {
        "json": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-wevisone-vl-4b.json",
        "title": "Laguna XS 2.1 vs WeVisOne-VL-4B",
        "url": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-wevisone-vl-4b"
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      {
        "json": "https://www.anchorterminal.com/compare/lbs-27b-vs-qwen3-vl-8b-instruct.json",
        "title": "LBS-27B vs Qwen3-VL-8B-Instruct",
        "url": "https://www.anchorterminal.com/compare/lbs-27b-vs-qwen3-vl-8b-instruct"
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        "json": "https://www.anchorterminal.com/compare/lbs-27b-vs-wevisone-vl-4b.json",
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        "url": "https://www.anchorterminal.com/compare/lbs-27b-vs-wevisone-vl-4b"
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      {
        "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-vl-8b-instruct.json",
        "title": "LFM2.5-2.6B vs Qwen3-VL-8B-Instruct",
        "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-vl-8b-instruct"
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        "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-wevisone-vl-4b.json",
        "title": "LFM2.5-2.6B vs WeVisOne-VL-4B",
        "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-wevisone-vl-4b"
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      {
        "json": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-qwen3-vl-8b-instruct.json",
        "title": "LFM3-1.7B vs Qwen3-VL-8B-Instruct",
        "url": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-qwen3-vl-8b-instruct"
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      {
        "json": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-wevisone-vl-4b.json",
        "title": "LFM3-1.7B vs WeVisOne-VL-4B",
        "url": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-wevisone-vl-4b"
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        "json": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-qwen3-vl-8b-instruct.json",
        "title": "LFM3-Audio-2B vs Qwen3-VL-8B-Instruct",
        "url": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-qwen3-vl-8b-instruct"
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        "json": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-wevisone-vl-4b.json",
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        "url": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-wevisone-vl-4b"
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        "json": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-qwen3-vl-8b-instruct.json",
        "title": "LFM3-Omni-2B vs Qwen3-VL-8B-Instruct",
        "url": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-qwen3-vl-8b-instruct"
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        "json": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-wevisone-vl-4b.json",
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        "url": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-wevisone-vl-4b"
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        "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-qwen3-vl-8b-instruct.json",
        "title": "LFM3-VL-2B vs Qwen3-VL-8B-Instruct",
        "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-qwen3-vl-8b-instruct"
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        "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-wevisone-vl-4b.json",
        "title": "LFM3-VL-2B vs WeVisOne-VL-4B",
        "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-wevisone-vl-4b"
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        "json": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwen3-vl-8b-instruct.json",
        "title": "Ling-3.0-tiny vs Qwen3-VL-8B-Instruct",
        "url": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwen3-vl-8b-instruct"
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        "json": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-wevisone-vl-4b.json",
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        "url": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-wevisone-vl-4b"
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        "json": "https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-qwen3-vl-8b-instruct.json",
        "title": "Ling-3.1-flash-1019 vs Qwen3-VL-8B-Instruct",
        "url": "https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-qwen3-vl-8b-instruct"
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        "json": "https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-wevisone-vl-4b.json",
        "title": "Ling-3.1-flash-1019 vs WeVisOne-VL-4B",
        "url": "https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-wevisone-vl-4b"
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        "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen3-vl-8b-instruct.json",
        "title": "LongCat-2.5 vs Qwen3-VL-8B-Instruct",
        "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen3-vl-8b-instruct"
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      {
        "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-wevisone-vl-4b.json",
        "title": "LongCat-2.5 vs WeVisOne-VL-4B",
        "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-wevisone-vl-4b"
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      {
        "json": "https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-qwen3-vl-8b-instruct.json",
        "title": "MiMo-V2.6 Distill Qwen 9B vs Qwen3-VL-8B-Instruct",
        "url": "https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-qwen3-vl-8b-instruct"
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        "json": "https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-wevisone-vl-4b.json",
        "title": "MiMo-V2.6 Distill Qwen 9B vs WeVisOne-VL-4B",
        "url": "https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-wevisone-vl-4b"
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        "json": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-qwen3-vl-8b-instruct.json",
        "title": "MiMo-V2.6-Flash-RL vs Qwen3-VL-8B-Instruct",
        "url": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-qwen3-vl-8b-instruct"
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      {
        "json": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-wevisone-vl-4b.json",
        "title": "MiMo-V2.6-Flash-RL vs WeVisOne-VL-4B",
        "url": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-wevisone-vl-4b"
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        "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-vl-8b-instruct.json",
        "title": "MiniCPM-o 4.5 vs Qwen3-VL-8B-Instruct",
        "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-vl-8b-instruct"
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        "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-wevisone-vl-4b.json",
        "title": "MiniCPM-o 4.5 vs WeVisOne-VL-4B",
        "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-wevisone-vl-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-qwen3-vl-8b-instruct.json",
        "title": "MiniCPM-V 4.7 1B vs Qwen3-VL-8B-Instruct",
        "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-qwen3-vl-8b-instruct"
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        "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-wevisone-vl-4b.json",
        "title": "MiniCPM-V 4.7 1B vs WeVisOne-VL-4B",
        "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-wevisone-vl-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-qwen3-vl-8b-instruct.json",
        "title": "MiniCPM-V 4.7 35B-A3B vs Qwen3-VL-8B-Instruct",
        "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-qwen3-vl-8b-instruct"
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        "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-wevisone-vl-4b.json",
        "title": "MiniCPM-V 4.7 35B-A3B vs WeVisOne-VL-4B",
        "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-wevisone-vl-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-qwen3-vl-8b-instruct.json",
        "title": "MiniCPM5-2B vs Qwen3-VL-8B-Instruct",
        "url": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-qwen3-vl-8b-instruct"
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      {
        "json": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-wevisone-vl-4b.json",
        "title": "MiniCPM5-2B vs WeVisOne-VL-4B",
        "url": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-wevisone-vl-4b"
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      {
        "json": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-qwen3-vl-8b-instruct.json",
        "title": "MiniMax-M2.5 vs Qwen3-VL-8B-Instruct",
        "url": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-qwen3-vl-8b-instruct"
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        "json": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-wevisone-vl-4b.json",
        "title": "MiniMax-M2.5 vs WeVisOne-VL-4B",
        "url": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-wevisone-vl-4b"
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      {
        "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-vl-8b-instruct.json",
        "title": "MiniMax-M2.7 vs Qwen3-VL-8B-Instruct",
        "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-vl-8b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-wevisone-vl-4b.json",
        "title": "MiniMax-M2.7 vs WeVisOne-VL-4B",
        "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-wevisone-vl-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-qwen3-vl-8b-instruct.json",
        "title": "MiniMax-M3.1-Flash vs Qwen3-VL-8B-Instruct",
        "url": "https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-qwen3-vl-8b-instruct"
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      {
        "json": "https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-wevisone-vl-4b.json",
        "title": "MiniMax-M3.1-Flash vs WeVisOne-VL-4B",
        "url": "https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-wevisone-vl-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/mistral-large-4-vs-qwen3-vl-8b-instruct.json",
        "title": "Mistral Large 4 vs Qwen3-VL-8B-Instruct",
        "url": "https://www.anchorterminal.com/compare/mistral-large-4-vs-qwen3-vl-8b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/mistral-large-4-vs-wevisone-vl-4b.json",
        "title": "Mistral Large 4 vs WeVisOne-VL-4B",
        "url": "https://www.anchorterminal.com/compare/mistral-large-4-vs-wevisone-vl-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-vl-8b-instruct.json",
        "title": "Muse Glimmer 30B vs Qwen3-VL-8B-Instruct",
        "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-vl-8b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-wevisone-vl-4b.json",
        "title": "Muse Glimmer 30B vs WeVisOne-VL-4B",
        "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-wevisone-vl-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-qwen3-vl-8b-instruct.json",
        "title": "Nanbeige4.2-3B vs Qwen3-VL-8B-Instruct",
        "url": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-qwen3-vl-8b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-wevisone-vl-4b.json",
        "title": "Nanbeige4.2-3B vs WeVisOne-VL-4B",
        "url": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-wevisone-vl-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-qwen3-vl-8b-instruct.json",
        "title": "Nanbeige4.3-33B vs Qwen3-VL-8B-Instruct",
        "url": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-qwen3-vl-8b-instruct"
      },
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        "json": "https://www.anchorterminal.com/compare/qwen3-vl-30b-a3b-instruct-vs-qwen3-vl-8b-instruct.json",
        "title": "Qwen3-VL-30B-A3B-Instruct vs Qwen3-VL-8B-Instruct",
        "url": "https://www.anchorterminal.com/compare/qwen3-vl-30b-a3b-instruct-vs-qwen3-vl-8b-instruct"
      },
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        "json": "https://www.anchorterminal.com/compare/qwen3-vl-30b-a3b-instruct-vs-wevisone-vl-4b.json",
        "title": "Qwen3-VL-30B-A3B-Instruct vs WeVisOne-VL-4B",
        "url": "https://www.anchorterminal.com/compare/qwen3-vl-30b-a3b-instruct-vs-wevisone-vl-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-vl-32b-instruct-vs-qwen3-vl-8b-instruct.json",
        "title": "Qwen3-VL-32B-Instruct vs Qwen3-VL-8B-Instruct",
        "url": "https://www.anchorterminal.com/compare/qwen3-vl-32b-instruct-vs-qwen3-vl-8b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-vl-32b-instruct-vs-wevisone-vl-4b.json",
        "title": "Qwen3-VL-32B-Instruct vs WeVisOne-VL-4B",
        "url": "https://www.anchorterminal.com/compare/qwen3-vl-32b-instruct-vs-wevisone-vl-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-qwen3-vl-8b-instruct.json",
        "title": "Qwen3-VL-4B-Instruct vs Qwen3-VL-8B-Instruct",
        "url": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-qwen3-vl-8b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-wevisone-vl-4b.json",
        "title": "Qwen3-VL-4B-Instruct vs WeVisOne-VL-4B",
        "url": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-wevisone-vl-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-vl-8b-instruct-vs-qwopus3-8-27b-flash-v2.json",
        "title": "Qwen3-VL-8B-Instruct vs Qwopus3.8-27B-Flash-V2",
        "url": "https://www.anchorterminal.com/compare/qwen3-vl-8b-instruct-vs-qwopus3-8-27b-flash-v2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-vl-8b-instruct-vs-recursive-flint.json",
        "title": "Qwen3-VL-8B-Instruct vs Recursive Flint",
        "url": "https://www.anchorterminal.com/compare/qwen3-vl-8b-instruct-vs-recursive-flint"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-vl-8b-instruct-vs-ripple-65b-preview.json",
        "title": "Qwen3-VL-8B-Instruct vs Ripple-65B Preview",
        "url": "https://www.anchorterminal.com/compare/qwen3-vl-8b-instruct-vs-ripple-65b-preview"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-vl-8b-instruct-vs-sensenova-loop-9b-preview.json",
        "title": "Qwen3-VL-8B-Instruct vs SenseNova-Loop-9B-Preview",
        "url": "https://www.anchorterminal.com/compare/qwen3-vl-8b-instruct-vs-sensenova-loop-9b-preview"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-vl-8b-instruct-vs-smollm3-3b.json",
        "title": "Qwen3-VL-8B-Instruct vs SmolLM3-3B",
        "url": "https://www.anchorterminal.com/compare/qwen3-vl-8b-instruct-vs-smollm3-3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-vl-8b-instruct-vs-spark-x2-6-4b.json",
        "title": "Qwen3-VL-8B-Instruct vs Spark-X2.6-4B",
        "url": "https://www.anchorterminal.com/compare/qwen3-vl-8b-instruct-vs-spark-x2-6-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-vl-8b-instruct-vs-step-3-7-flash.json",
        "title": "Qwen3-VL-8B-Instruct vs Step 3.7 Flash",
        "url": "https://www.anchorterminal.com/compare/qwen3-vl-8b-instruct-vs-step-3-7-flash"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-vl-8b-instruct-vs-swift-1-5-qwen3-8-27b.json",
        "title": "Qwen3-VL-8B-Instruct vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/qwen3-vl-8b-instruct-vs-swift-1-5-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-vl-8b-instruct-vs-swift-1-5-qwen3-8-flash-next.json",
        "title": "Qwen3-VL-8B-Instruct vs Swift 1.5 Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/qwen3-vl-8b-instruct-vs-swift-1-5-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-vl-8b-instruct-vs-thinkingcap-qwen3-8-27b.json",
        "title": "Qwen3-VL-8B-Instruct vs ThinkingCap (Qwen3.8 27B)",
        "url": "https://www.anchorterminal.com/compare/qwen3-vl-8b-instruct-vs-thinkingcap-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-vl-8b-instruct-vs-unlimited-ocr-2.json",
        "title": "Qwen3-VL-8B-Instruct vs Baidu Unlimited-OCR-2",
        "url": "https://www.anchorterminal.com/compare/qwen3-vl-8b-instruct-vs-unlimited-ocr-2"
      },
      {
        "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/qwen3-vl-8b-instruct-vs-zaya1-8b.json",
        "title": "Qwen3-VL-8B-Instruct vs ZAYA1-8B",
        "url": "https://www.anchorterminal.com/compare/qwen3-vl-8b-instruct-vs-zaya1-8b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-wevisone-vl-4b.json",
        "title": "Qwopus3.8-27B-Flash-V2 vs WeVisOne-VL-4B",
        "url": "https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-wevisone-vl-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/recursive-flint-vs-wevisone-vl-4b.json",
        "title": "Recursive Flint vs WeVisOne-VL-4B",
        "url": "https://www.anchorterminal.com/compare/recursive-flint-vs-wevisone-vl-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ripple-65b-preview-vs-wevisone-vl-4b.json",
        "title": "Ripple-65B Preview vs WeVisOne-VL-4B",
        "url": "https://www.anchorterminal.com/compare/ripple-65b-preview-vs-wevisone-vl-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-wevisone-vl-4b.json",
        "title": "SenseNova-Loop-9B-Preview vs WeVisOne-VL-4B",
        "url": "https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-wevisone-vl-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/smollm3-3b-vs-wevisone-vl-4b.json",
        "title": "SmolLM3-3B vs WeVisOne-VL-4B",
        "url": "https://www.anchorterminal.com/compare/smollm3-3b-vs-wevisone-vl-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/spark-x2-6-4b-vs-wevisone-vl-4b.json",
        "title": "Spark-X2.6-4B vs WeVisOne-VL-4B",
        "url": "https://www.anchorterminal.com/compare/spark-x2-6-4b-vs-wevisone-vl-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/step-3-7-flash-vs-wevisone-vl-4b.json",
        "title": "Step 3.7 Flash vs WeVisOne-VL-4B",
        "url": "https://www.anchorterminal.com/compare/step-3-7-flash-vs-wevisone-vl-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/swift-1-5-qwen3-8-27b-vs-wevisone-vl-4b.json",
        "title": "Swift 1.5 Qwen3.8-27B vs WeVisOne-VL-4B",
        "url": "https://www.anchorterminal.com/compare/swift-1-5-qwen3-8-27b-vs-wevisone-vl-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/swift-1-5-qwen3-8-flash-next-vs-wevisone-vl-4b.json",
        "title": "Swift 1.5 Qwen3.8-Flash-Next vs WeVisOne-VL-4B",
        "url": "https://www.anchorterminal.com/compare/swift-1-5-qwen3-8-flash-next-vs-wevisone-vl-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/thinkingcap-qwen3-8-27b-vs-wevisone-vl-4b.json",
        "title": "ThinkingCap (Qwen3.8 27B) vs WeVisOne-VL-4B",
        "url": "https://www.anchorterminal.com/compare/thinkingcap-qwen3-8-27b-vs-wevisone-vl-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/wevisone-vl-2b-vs-wevisone-vl-4b.json",
        "title": "WeVisOne-VL-2B vs WeVisOne-VL-4B",
        "url": "https://www.anchorterminal.com/compare/wevisone-vl-2b-vs-wevisone-vl-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/wevisone-vl-4b-vs-zaya1-8b.json",
        "title": "WeVisOne-VL-4B vs ZAYA1-8B",
        "url": "https://www.anchorterminal.com/compare/wevisone-vl-4b-vs-zaya1-8b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/unlimited-ocr-2-vs-wevisone-vl-4b.json",
        "title": "Baidu Unlimited-OCR-2 vs WeVisOne-VL-4B",
        "url": "https://www.anchorterminal.com/compare/unlimited-ocr-2-vs-wevisone-vl-4b"
      }
    ],
    "panel": null,
    "scores": [
      {
        "key": "capability",
        "name": "Capability",
        "pending": true,
        "weight": 35
      },
      {
        "by": 30,
        "edge": "qwen3-vl-8b-instruct",
        "key": "reach",
        "name": "Reach",
        "qwen3-vl-8b-instruct": 100,
        "weight": 25,
        "wevisone-vl-4b": 70
      },
      {
        "by": 0,
        "edge": "",
        "key": "risk",
        "name": "Risk",
        "qwen3-vl-8b-instruct": 55,
        "weight": 20,
        "wevisone-vl-4b": 55
      },
      {
        "by": 10,
        "edge": "wevisone-vl-4b",
        "key": "interface",
        "name": "Agent interface",
        "qwen3-vl-8b-instruct": 60,
        "weight": 10,
        "wevisone-vl-4b": 70
      },
      {
        "by": 17,
        "edge": "wevisone-vl-4b",
        "key": "upkeep",
        "name": "Documentation and upkeep",
        "qwen3-vl-8b-instruct": 31,
        "weight": 10,
        "wevisone-vl-4b": 48
      }
    ],
    "summary": "WeVisOne-VL-4B scores 61 (C) on agent readiness against Qwen3-VL-8B-Instruct's 54.9 (C), and leads in 2 of 4 scored categories. Qwen3-VL-8B-Instruct leads on reach. Both do open-weight models you run yourself.",
    "tasks": null,
    "tutorials": [],
    "verdicts": {
      "qwen3-vl-8b-instruct": "Qwen3-VL-8B-Instruct reads screenshots, documents, charts and video, and Alibaba's Q4_K_M GGUF needs about 6 GB. Transformers, vLLM, SGLang, llama.cpp and Ollama run it under plain Apache 2.0. It has no thinking mode, documents tool calls only in the template, and has no published safety work, so add guardrails before it drives a GUI.",
      "wevisone-vl-4b": "A small Apache 2.0 perception model that finds, counts, reads and cuts out what you name in an image, and says None when it isn't there. Boxes and points run in vLLM, SGLang or Transformers. Masks and mattes need Tencent's package and a pickle file. Two weeks old, with no questions answered yet and no safety results."
    }
  },
  "kind": "anchor.page",
  "links": {
    "api": "https://www.anchorterminal.com/api/v1/index.json",
    "html": "https://www.anchorterminal.com/compare/qwen3-vl-8b-instruct-vs-wevisone-vl-4b",
    "json": "https://www.anchorterminal.com/compare/qwen3-vl-8b-instruct-vs-wevisone-vl-4b.json",
    "llms": "https://www.anchorterminal.com/llms.txt",
    "markdown": "https://www.anchorterminal.com/compare/qwen3-vl-8b-instruct-vs-wevisone-vl-4b.md",
    "mcp": "https://www.anchorterminal.com/mcp",
    "slim": "https://www.anchorterminal.com/compare/qwen3-vl-8b-instruct-vs-wevisone-vl-4b.min.md"
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  "markdown": "WeVisOne-VL-4B scores 61 (C) on agent readiness against Qwen3-VL-8B-Instruct's 54.9 (C), and leads in 2 of 4 scored categories. Qwen3-VL-8B-Instruct leads on reach. Both do open-weight models you run yourself.\n\n- Qwen3-VL-8B-Instruct: grade C, 54.9/100, rank #124 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/qwen3-vl-8b-instruct.md · JSON https://www.anchorterminal.com/api/v1/tools/qwen3-vl-8b-instruct.json\n- WeVisOne-VL-4B: grade C, 61/100, rank #84 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/wevisone-vl-4b.md · JSON https://www.anchorterminal.com/api/v1/tools/wevisone-vl-4b.json\n- Best open-weight models you download and run yourself for AI agents: https://www.anchorterminal.com/best/open-weights/index.md\n- All 6359 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md\n\n## Which one, for what\n\n### Qwen3-VL-8B-Instruct (C)\n\nGood for: An agent with 8 GB or more to spare that needs a local model to read images, documents and video\n\nAhead on:\n- Reach, 100 against 70\n\nWatch for: No safety evaluation, agent-use guidance or security contact\n\n### WeVisOne-VL-4B (C)\n\nGood for: Agents that need to locate, count or cut out objects in images on a small GPU, under a permissive licence\n\nAhead on:\n- Agent interface, 70 against 60\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: Qwen3-VL-8B-Instruct +30\n\n- Qwen3-VL-8B-Instruct (100): Alibaba's own Q4_K_M GGUF is 5.03 GB, so with a fifth added it needs about 6 GB and fits in 16 GB (30). Transformers 4.57.0, vLLM, SGLang and llama.cpp run it in released versions, and Ollama v0.40.14 pins llama.cpp b11963, which has the qwen3vl architecture, so Ollama is credited through its pin; five runtimes reach the 25 cap. Alibaba publishes FP8 and GGUF builds at F16, Q8_0 and Q4_K_M (15). Apache 2.0 with nothing added (15). No gate and no account (10). Featherless is live in the Hub's provider mapping on 4 November, and Parasail served it per the 21 October check, not re-read, so it's hosted too (5). Novita's mapping still shows an error and isn't counted. 30+25+15+15+10+5 = 100.\n- WeVisOne-VL-4B (70): Memory 30, the only build from Tencent is the BF16 safetensors file with its decoder, 9.7 GB, and 9.7 GB × 1.2 = 11.6 GB, within 16 GB. No runtime library has a build. Runtimes 15, the architecture is Qwen3VLForConditionalGeneration, which vLLM 0.33.0, SGLang 0.5.23 and Transformers 5.22.2 run from the safetensors file, three at 5 each. Builds 0, Tencent publishes the BF16 weights only. Terms 15, Apache 2.0 in the card's metadata with the LICENSE file in the repository, and nothing added. Download 10, the Hub API reports gated false on 10 November 2026, so the files download with no account and nothing to accept. Hosted 0, Hugging Face maps no inference provider to it. 30 + 15 + 0 + 15 + 10 + 0 = 70.\n\n### Documentation and upkeep: WeVisOne-VL-4B +17\n\n- Qwen3-VL-8B-Instruct (31): The card misses limitations, languages and a cut-off (11). Training data is described only in the technical report, with no cut-off (5). The last change to code that runs it was the qwen-vl-utils fix merged on 4 January 2026, and the weights haven't changed since November 2025; the Hub commits for all three repositories and the QwenLM/Qwen3-VL head (30 January 2026) were the same on 4 November (5). Of 34 Hugging Face discussions on 4 November, the 19 that aren't pull requests (newest 23 August 2026) have no reply from Qwen, and GitHub issues weren't counted (0). Earlier versions kept in their own repositories without notes (5). Two reports wait without a reply (5). 11+5+5+0+5+5 = 31.\n- WeVisOne-VL-4B (48): Model card 8, Intended use, limitations and the architecture are on the card. Training data 5, A general description in the README of a data engine and over 100 million annotated images, with no sources named and no cut-off. Last change 20, the last commit, on 27 October 2026, added links to the paper and project page, 14 days before this check. Responsiveness 0, Neither of the 2 discussions that aren't pull requests (23 and 26 October 2026) has a reply from a tencent member, counted on 10 November by author flags and dates with a script, text not read. Version policy 5, A first release, 14 days old. File health 10, Config, template and card agree on the Qwen3-VL classes, and the card names mask_decoder.pt as required and it's there. 8 + 5 + 20 + 0 + 5 + 10 = 48.\n\n### Agent interface: WeVisOne-VL-4B +10\n\n- Qwen3-VL-8B-Instruct (60): A Jinja chat template in the repository (20). The tool-call format lives only in the template, Hermes-style JSON in tool_call tags, and Alibaba names no parser (10). Trained for function calling with several calls allowed in a turn (20). No documented structured output (0). No thinking mode in this edition, so reasoning control scores 0 by the rule. 262,144 tokens of native context (10). 20+10+20+0+0+10 = 60.\n- WeVisOne-VL-4B (70): Chat template 20, chat_template.jinja in the repository (5,292 characters), Qwen3-VL's template. Tool-calling format 25, Read for a perception model as what it takes and returns. Trained tool calling 10, Read for a perception model as the inputs it was trained to use. Structured output 15, Read for a perception model as typed outputs. Reasoning control 0, the card says it writes structured output directly with no chain-of-thought reasoning. Context 0, Tencent states no context length for it. 20 + 25 + 10 + 15 + 0 + 0 = 70.\n\n## Score by category\n\n| Category | Weight | Qwen3-VL-8B-Instruct | WeVisOne-VL-4B | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 100 | 70 | Qwen3-VL-8B-Instruct +30 |\n| Risk | 20% (20 this run) | 55 | 55 | even |\n| Agent interface | 10% (10 this run) | 60 | 70 | WeVisOne-VL-4B +10 |\n| Documentation and upkeep | 10% (10 this run) | 31 | 48 | WeVisOne-VL-4B +17 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **54.9 · C** | **61 · C** | |\n\n## Facts side by side\n\n| Fact | Qwen3-VL-8B-Instruct | WeVisOne-VL-4B |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Alibaba (Qwen team) | 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 | 2025-11-01 | 2026-10-27 |\n| Terms last updated | no document linked | not read yet |\n| Privacy policy last updated | no document linked | no document linked |\n| Agent reviews | 2.8/5 (4) | none |\n\n## Verdicts\n\n**Qwen3-VL-8B-Instruct.** Qwen3-VL-8B-Instruct reads screenshots, documents, charts and video, and Alibaba's Q4_K_M GGUF needs about 6 GB. Transformers, vLLM, SGLang, llama.cpp and Ollama run it under plain Apache 2.0. It has no thinking mode, documents tool calls only in the template, and has no published safety work, so add guardrails before it drives a GUI.\n\n**WeVisOne-VL-4B.** A small Apache 2.0 perception model that finds, counts, reads and cuts out what you name in an image, and says None when it isn't there. Boxes and points run in vLLM, SGLang or Transformers. Masks and mattes need Tencent's package and a pickle file. Two weeks old, with no questions answered yet and no safety results.\n\n## Before you call either\n\n### Qwen3-VL-8B-Instruct\n\n1. Start it with `llama-server -hf Qwen/Qwen3-VL-8B-Instruct-GGUF:Q4_K_M --jinja -c 32768`, or `ollama run qwen3-vl:8b-instruct`\n2. In Ollama the plain `qwen3-vl:8b` tag is the Thinking edition, so name `8b-instruct`\n3. With the GGUF, load the mmproj vision file too, or images won't be read\n4. Parse tool calls as JSON inside `\u003ctool_call\u003e` tags, or use SGLang's `--tool-call-parser qwen`\n5. Use the card's sampling, `temperature=0.7, top_p=0.8, top_k=20, presence_penalty=1.5` for vision and `temperature=1.0, top_p=1.0, top_k=40, presence_penalty=2.0` for text\n6. Validate JSON output yourself, since structured output isn't documented\n7. vLLM needs 0.11.0 or later and Transformers 4.57.0 or later\n\n### WeVisOne-VL-4B\n\n1. Use Tencent's package: `WeVisOneVL(\"tencent/WeVisOne-VL-4B\", backend=\"vllm\")` then `model.predict(image, task=..., query=...)`\n2. Tasks are detection, referring, point, count_box, count_point, ocr, ocr_grounding, segmentation and matting\n3. A None answer means the target isn't in the image. Treat it as a result, not an error\n4. mask_decoder.pt is a pickle. Load it only from the tencent repository, and check its SHA-256 against the Hub's\n5. Send one image a turn. Video grids are rejected by the decoder\n6. The card says it isn't for identifying individuals or for high-impact decisions on its own\n\n## Questions\n\n### Which is better for AI agents, Qwen3-VL-8B-Instruct or WeVisOne-VL-4B?\n\nWeVisOne-VL-4B scores 61 (C) on agent readiness against Qwen3-VL-8B-Instruct's 54.9 (C), and leads in 2 of 4 scored categories. Qwen3-VL-8B-Instruct leads on reach.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/qwen3-vl-8b-instruct-vs-wevisone-vl-4b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/qwen3-vl-8b-instruct-vs-wevisone-vl-4b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"qwen3-vl-8b-instruct\", \"b\": \"wevisone-vl-4b\"}`. From a terminal: `anchor compare qwen3-vl-8b-instruct wevisone-vl-4b`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/qwen3-vl-8b-instruct.json and https://www.anchorterminal.com/api/v1/tools/wevisone-vl-4b.json\n\n## Other comparisons with Qwen3-VL-8B-Instruct or WeVisOne-VL-4B\n\n- [Agents-A1-4B vs Qwen3-VL-8B-Instruct](https://www.anchorterminal.com/compare/agents-a1-4b-vs-qwen3-vl-8b-instruct.md)\n- [Agents-A1-4B vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-wevisone-vl-4b.md)\n- [Apertus v1.5 8B vs Qwen3-VL-8B-Instruct](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-qwen3-vl-8b-instruct.md)\n- [Apertus v1.5 8B vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-wevisone-vl-4b.md)\n- [Apodex-1.1-mini vs Qwen3-VL-8B-Instruct](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-qwen3-vl-8b-instruct.md)\n- [Apodex-1.1-mini vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-wevisone-vl-4b.md)\n- [Arete-27B vs Qwen3-VL-8B-Instruct](https://www.anchorterminal.com/compare/arete-27b-vs-qwen3-vl-8b-instruct.md)\n- [Arete-27B vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/arete-27b-vs-wevisone-vl-4b.md)\n- [BigBang-v1 vs Qwen3-VL-8B-Instruct](https://www.anchorterminal.com/compare/bigbang-v1-vs-qwen3-vl-8b-instruct.md)\n- [BigBang-v1 vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/bigbang-v1-vs-wevisone-vl-4b.md)\n- [Carbon-X vs Qwen3-VL-8B-Instruct](https://www.anchorterminal.com/compare/carbon-x-vs-qwen3-vl-8b-instruct.md)\n- [Carbon-X vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/carbon-x-vs-wevisone-vl-4b.md)\n- [Cola2 vs Qwen3-VL-8B-Instruct](https://www.anchorterminal.com/compare/cola2-vs-qwen3-vl-8b-instruct.md)\n- [Cola2 vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/cola2-vs-wevisone-vl-4b.md)\n- [Cosmos-Reason2-2B vs Qwen3-VL-8B-Instruct](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-qwen3-vl-8b-instruct.md)\n- [Cosmos-Reason2-2B vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-wevisone-vl-4b.md)\n- [Darwin-36B-Opus vs Qwen3-VL-8B-Instruct](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-qwen3-vl-8b-instruct.md)\n- [Darwin-36B-Opus vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-wevisone-vl-4b.md)\n- [DeepSeek-V3.2 vs Qwen3-VL-8B-Instruct](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-qwen3-vl-8b-instruct.md)\n- [DeepSeek-V3.2 vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-wevisone-vl-4b.md)\n- [DeepSeek V4.1 Flash vs Qwen3-VL-8B-Instruct](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-vl-8b-instruct.md)\n- [DeepSeek V4.1 Flash vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-wevisone-vl-4b.md)\n- [DeepSeek-V4-Flash-0731 vs Qwen3-VL-8B-Instruct](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-qwen3-vl-8b-instruct.md)\n- [DeepSeek-V4-Flash-0731 vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-wevisone-vl-4b.md)\n- [Devstral Small 2 vs Qwen3-VL-8B-Instruct](https://www.anchorterminal.com/compare/devstral-small-2-vs-qwen3-vl-8b-instruct.md)\n- [Devstral Small 2 vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/devstral-small-2-vs-wevisone-vl-4b.md)\n- [DiffusionGemma 26B A4B IT vs Qwen3-VL-8B-Instruct](https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-qwen3-vl-8b-instruct.md)\n- [DiffusionGemma 26B A4B IT vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-wevisone-vl-4b.md)\n- [EXAONE 4.5 33B vs Qwen3-VL-8B-Instruct](https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-qwen3-vl-8b-instruct.md)\n- [EXAONE 4.5 33B vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-wevisone-vl-4b.md)\n- [Gemma 4 12B IT vs Qwen3-VL-8B-Instruct](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-qwen3-vl-8b-instruct.md)\n- [Gemma 4 12B IT vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-wevisone-vl-4b.md)\n- [Gemma 4 26B A4B IT vs Qwen3-VL-8B-Instruct](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-qwen3-vl-8b-instruct.md)\n- [Gemma 4 26B A4B IT vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-wevisone-vl-4b.md)\n- [Gemma 4 31B IT vs Qwen3-VL-8B-Instruct](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-vl-8b-instruct.md)\n- [Gemma 4 31B IT vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-wevisone-vl-4b.md)\n- [Gemma 4 E2B IT vs Qwen3-VL-8B-Instruct](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-qwen3-vl-8b-instruct.md)\n- [Gemma 4 E2B IT vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-wevisone-vl-4b.md)\n- [Gemma 4 E4B IT vs Qwen3-VL-8B-Instruct](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-qwen3-vl-8b-instruct.md)\n- [Gemma 4 E4B IT vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-wevisone-vl-4b.md)\n- [GLM-4.5-Air vs Qwen3-VL-8B-Instruct](https://www.anchorterminal.com/compare/glm-4-5-air-vs-qwen3-vl-8b-instruct.md)\n- [GLM-4.5-Air vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/glm-4-5-air-vs-wevisone-vl-4b.md)\n- [GLM-4.7-Flash vs Qwen3-VL-8B-Instruct](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwen3-vl-8b-instruct.md)\n- [GLM-4.7-Flash vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-wevisone-vl-4b.md)\n- [GLM-5.3-Flash vs Qwen3-VL-8B-Instruct](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-qwen3-vl-8b-instruct.md)\n- [GLM-5.3-Flash vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-wevisone-vl-4b.md)\n- [GLM-5.3 vs Qwen3-VL-8B-Instruct](https://www.anchorterminal.com/compare/glm-5-3-vs-qwen3-vl-8b-instruct.md)\n- [GLM-5.3 vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/glm-5-3-vs-wevisone-vl-4b.md)\n- [gpt-oss-120b vs Qwen3-VL-8B-Instruct](https://www.anchorterminal.com/compare/gpt-oss-120b-vs-qwen3-vl-8b-instruct.md)\n- [gpt-oss-120b vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/gpt-oss-120b-vs-wevisone-vl-4b.md)\n- [gpt-oss-20b vs Qwen3-VL-8B-Instruct](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-qwen3-vl-8b-instruct.md)\n- [gpt-oss-20b vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-wevisone-vl-4b.md)\n- [Granite 4.2 30B vs Qwen3-VL-8B-Instruct](https://www.anchorterminal.com/compare/granite-4-2-30b-vs-qwen3-vl-8b-instruct.md)\n- [Granite 4.2 30B vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/granite-4-2-30b-vs-wevisone-vl-4b.md)\n- [Granite 4.2 3B vs Qwen3-VL-8B-Instruct](https://www.anchorterminal.com/compare/granite-4-2-3b-vs-qwen3-vl-8b-instruct.md)\n- [Granite 4.2 3B vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/granite-4-2-3b-vs-wevisone-vl-4b.md)\n- [Granite 4.2 8B vs Qwen3-VL-8B-Instruct](https://www.anchorterminal.com/compare/granite-4-2-8b-vs-qwen3-vl-8b-instruct.md)\n- [Granite 4.2 8B vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/granite-4-2-8b-vs-wevisone-vl-4b.md)\n- [Hy-Memory-30B-A3B vs Qwen3-VL-8B-Instruct](https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-vl-8b-instruct.md)\n- [Hy-Memory-30B-A3B vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-wevisone-vl-4b.md)\n- [Hy3 vs Qwen3-VL-8B-Instruct](https://www.anchorterminal.com/compare/hy3-vs-qwen3-vl-8b-instruct.md)\n- [Hy3 vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/hy3-vs-wevisone-vl-4b.md)\n- [Inkling-Small vs Qwen3-VL-8B-Instruct](https://www.anchorterminal.com/compare/inkling-small-vs-qwen3-vl-8b-instruct.md)\n- [Inkling-Small vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/inkling-small-vs-wevisone-vl-4b.md)\n- [Inkling vs Qwen3-VL-8B-Instruct](https://www.anchorterminal.com/compare/inkling-vs-qwen3-vl-8b-instruct.md)\n- [Inkling vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/inkling-vs-wevisone-vl-4b.md)\n- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs Qwen3-VL-8B-Instruct](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-qwen3-vl-8b-instruct.md)\n- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-wevisone-vl-4b.md)\n- [Kimi K3.1 Pro vs Qwen3-VL-8B-Instruct](https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwen3-vl-8b-instruct.md)\n- [Kimi K3.1 Pro vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-wevisone-vl-4b.md)\n- [Kimi K3 vs Qwen3-VL-8B-Instruct](https://www.anchorterminal.com/compare/kimi-k3-vs-qwen3-vl-8b-instruct.md)\n- [Kimi K3 vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/kimi-k3-vs-wevisone-vl-4b.md)\n- [Kolibri-1 vs Qwen3-VL-8B-Instruct](https://www.anchorterminal.com/compare/kolibri-1-vs-qwen3-vl-8b-instruct.md)\n- [Kolibri-1 vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/kolibri-1-vs-wevisone-vl-4b.md)\n- [Kronos vs Qwen3-VL-8B-Instruct](https://www.anchorterminal.com/compare/kronos-vs-qwen3-vl-8b-instruct.md)\n- [Kronos vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/kronos-vs-wevisone-vl-4b.md)\n- [Laguna M.1 vs Qwen3-VL-8B-Instruct](https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-vl-8b-instruct.md)\n- [Laguna M.1 vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/laguna-m-1-vs-wevisone-vl-4b.md)\n- [Laguna S 2.1 vs Qwen3-VL-8B-Instruct](https://www.anchorterminal.com/compare/laguna-s-2-1-vs-qwen3-vl-8b-instruct.md)\n- [Laguna S 2.1 vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/laguna-s-2-1-vs-wevisone-vl-4b.md)\n- [Laguna XS 2.1 vs Qwen3-VL-8B-Instruct](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwen3-vl-8b-instruct.md)\n- [Laguna XS 2.1 vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-wevisone-vl-4b.md)\n- [LBS-27B vs Qwen3-VL-8B-Instruct](https://www.anchorterminal.com/compare/lbs-27b-vs-qwen3-vl-8b-instruct.md)\n- [LBS-27B vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/lbs-27b-vs-wevisone-vl-4b.md)\n- [LFM2.5-2.6B vs Qwen3-VL-8B-Instruct](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-vl-8b-instruct.md)\n- [LFM2.5-2.6B vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-wevisone-vl-4b.md)\n- [LFM3-1.7B vs Qwen3-VL-8B-Instruct](https://www.anchorterminal.com/compare/lfm3-1-7b-vs-qwen3-vl-8b-instruct.md)\n- [LFM3-1.7B vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/lfm3-1-7b-vs-wevisone-vl-4b.md)\n- [LFM3-Audio-2B vs Qwen3-VL-8B-Instruct](https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-qwen3-vl-8b-instruct.md)\n- [LFM3-Audio-2B vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-wevisone-vl-4b.md)\n- [LFM3-Omni-2B vs Qwen3-VL-8B-Instruct](https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-qwen3-vl-8b-instruct.md)\n- [LFM3-Omni-2B vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-wevisone-vl-4b.md)\n- [LFM3-VL-2B vs Qwen3-VL-8B-Instruct](https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-qwen3-vl-8b-instruct.md)\n- [LFM3-VL-2B vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-wevisone-vl-4b.md)\n- [Ling-3.0-tiny vs Qwen3-VL-8B-Instruct](https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwen3-vl-8b-instruct.md)\n- [Ling-3.0-tiny vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-wevisone-vl-4b.md)\n- [Ling-3.1-flash-1019 vs Qwen3-VL-8B-Instruct](https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-qwen3-vl-8b-instruct.md)\n- [Ling-3.1-flash-1019 vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-wevisone-vl-4b.md)\n- [LongCat-2.5 vs Qwen3-VL-8B-Instruct](https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen3-vl-8b-instruct.md)\n- [LongCat-2.5 vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/longcat-2-5-vs-wevisone-vl-4b.md)\n- [MiMo-V2.6 Distill Qwen 9B vs Qwen3-VL-8B-Instruct](https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-qwen3-vl-8b-instruct.md)\n- [MiMo-V2.6 Distill Qwen 9B vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-wevisone-vl-4b.md)\n- [MiMo-V2.6-Flash-RL vs Qwen3-VL-8B-Instruct](https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-qwen3-vl-8b-instruct.md)\n- [MiMo-V2.6-Flash-RL vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-wevisone-vl-4b.md)\n- [MiniCPM-o 4.5 vs Qwen3-VL-8B-Instruct](https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-vl-8b-instruct.md)\n- [MiniCPM-o 4.5 vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-wevisone-vl-4b.md)\n- [MiniCPM-V 4.7 1B vs Qwen3-VL-8B-Instruct](https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-qwen3-vl-8b-instruct.md)\n- [MiniCPM-V 4.7 1B vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-wevisone-vl-4b.md)\n- [MiniCPM-V 4.7 35B-A3B vs Qwen3-VL-8B-Instruct](https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-qwen3-vl-8b-instruct.md)\n- [MiniCPM-V 4.7 35B-A3B vs 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