{
  "data": {
    "a": {
      "slug": "qwen3-vl-30b-a3b-instruct",
      "name": "Qwen3-VL-30B-A3B-Instruct",
      "vendor": "Alibaba (Qwen team)",
      "vendorUrl": "https://qwen.ai",
      "kind": "weights",
      "category": "open-weights",
      "summary": "Qwen3-VL-30B-A3B-Instruct is Alibaba's open-weight vision-language mixture of experts with about 31B parameters and 3B active, 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-30b-a3b-instruct",
      "markdownUrl": "https://www.anchorterminal.com/tools/qwen3-vl-30b-a3b-instruct.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/qwen3-vl-30b-a3b-instruct.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/qwen3-vl-30b-a3b-instruct.json",
      "dns": "qwen3-vl-30b-a3b-instruct.grade.anchorterminal.com",
      "repo": "https://huggingface.co/Qwen/Qwen3-VL-30B-A3B-Instruct",
      "license": "Apache-2.0",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "Qwen/Qwen3-VL-30B-A3B-Instruct"
        },
        {
          "registry": "huggingface",
          "name": "Qwen/Qwen3-VL-30B-A3B-Instruct-FP8"
        },
        {
          "registry": "huggingface",
          "name": "Qwen/Qwen3-VL-30B-A3B-Instruct-GGUF"
        },
        {
          "registry": "ollama",
          "name": "qwen3-vl:30b-a3b-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, 62.2 GB for the BF16 files, 32.3 GB for Alibaba's FP8 build and 18.6 GB plus a 0.7 to 1.1 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-30B-A3B-Instruct",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "moe",
        "multimodal",
        "vision",
        "video",
        "tool-calling",
        "apache-2.0",
        "safetensors",
        "fp8",
        "gguf",
        "self-hosted"
      ],
      "lastRelease": "2025-11-01",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 58,
        "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": 79,
        "methodology": "0.7",
        "run": "2026-10-01",
        "scores": {
          "capability": 39,
          "interface": 60,
          "reach": 95,
          "risk": 55,
          "upkeep": 36
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "high",
          "date": "2026-11-04"
        },
        "negative": 0,
        "verdict": "A vision-language mixture of experts for screenshots, documents, charts and video, with about 3B of its 31B parameters active. Alibaba's Q4_K_M GGUF with its vision file needs about 23 GB, it loads in Transformers, vLLM, SGLang, llama.cpp and Ollama under Apache 2.0, and three providers host it. No safety work is published, so add guardrails before it drives a GUI.",
        "bestFor": "An agent with 24 GB or more to spare that wants a local reader of images, documents and video with 262,144 tokens of context",
        "strengths": [
          "About 23 GB of memory for Alibaba's own Q4_K_M GGUF and vision file, 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 and a checksum on every file",
          "Served by Novita, Featherless AI and DeepInfra through Hugging Face, and listed with SiliconFlow on OpenRouter, 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 eight Hugging Face discussions that aren't pull requests",
          "No change to the weights since September 2025, and the card leaves out limitations, languages, knowledge cut-off and memory needs"
        ],
        "agentNotes": [
          "Start it with `llama-server -hf Qwen/Qwen3-VL-30B-A3B-Instruct-GGUF:Q4_K_M --jinja -c 32768`, or `ollama run qwen3-vl:30b-a3b-instruct`",
          "In Ollama the plain `qwen3-vl:30b` tag is the Thinking edition, so name `30b-a3b-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 shipped sampling, temperature 0.7, top_p 0.8 and top_k 20, from generation_config.json",
          "Validate JSON output yourself, since structured output isn't documented",
          "vLLM needs 0.11.0 or later, Transformers 4.57.0 or later and llama.cpp b6907 or later"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 4,
        "avgRating": 3,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "high",
            "grade": "C",
            "methodology": "0.7",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 58
          }
        ],
        "editorialScores": {
          "capability": 39,
          "interface": 60,
          "reach": 95,
          "risk": 55,
          "upkeep": 36
        },
        "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:30b-a3b-instruct\n# or with llama.cpp and Alibaba's own GGUF (llama.cpp b6907 or later, per the GGUF card):\nllama-server -hf Qwen/Qwen3-VL-30B-A3B-Instruct-GGUF:Q4_K_M --jinja -c 32768\n# or with vLLM 0.11.0 or later:\nvllm serve Qwen/Qwen3-VL-30B-A3B-Instruct-FP8 --max-model-len 131072",
        "http": "curl http://localhost:11434/api/chat \\\n  -d '{\"model\": \"qwen3-vl:30b-a3b-instruct\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/qwen3-vl-30b-a3b-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-32b-instruct",
        "qwen3-vl-4b-instruct",
        "qwen3-vl-8b-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": "31.1B (3B active)",
        "context": 262144,
        "goodFor": "Vision-language mixture of experts for images, documents, screens and video with text out, about 3B of 31B parameters active per token, no thinking mode in this Instruct edition.",
        "runtimes": [
          "llama.cpp",
          "Ollama",
          "vLLM",
          "SGLang",
          "LM Studio",
          "Transformers"
        ],
        "builds": [
          {
            "name": "Qwen3VL-30B-A3B-Instruct-Q4_K_M",
            "url": "https://huggingface.co/Qwen/Qwen3-VL-30B-A3B-Instruct-GGUF/blob/main/Qwen3VL-30B-A3B-Instruct-Q4_K_M.gguf",
            "who": "publisher",
            "precision": "q4_k_m",
            "sizeGB": 18.6,
            "memoryGB": 24,
            "format": "gguf",
            "runsOn": [
              "llama.cpp (Vulkan)",
              "llama.cpp (ROCm)"
            ],
            "note": "Add mmproj-Qwen3VL-30B-A3B-Instruct-Q8_0.gguf (0.7 GB) for image input, 19.3 GB together, still within 24 GB with a fifth added"
          },
          {
            "name": "ollama qwen3-vl:30b-a3b-instruct",
            "url": "https://ollama.com/library/qwen3-vl:30b-a3b-instruct",
            "who": "ollama",
            "precision": "q4_k_m",
            "sizeGB": 20,
            "memoryGB": 24,
            "format": "ollama",
            "runsOn": [
              "Ollama"
            ],
            "note": "Q4_K_M with its vision projector (digest 9b053ed92b6a). qwen3-vl:30b, the library default for this size, is the Thinking edition. Size as Ollama's tags page shows it, rounded to whole GB above 10 GB"
          },
          {
            "name": "Qwen3-VL-30B-A3B-Instruct-FP8",
            "url": "https://huggingface.co/Qwen/Qwen3-VL-30B-A3B-Instruct-FP8",
            "who": "publisher",
            "precision": "fp8",
            "sizeGB": 32.3,
            "memoryGB": 48,
            "format": "safetensors"
          },
          {
            "name": "Qwen3VL-30B-A3B-Instruct-Q8_0",
            "url": "https://huggingface.co/Qwen/Qwen3-VL-30B-A3B-Instruct-GGUF/blob/main/Qwen3VL-30B-A3B-Instruct-Q8_0.gguf",
            "who": "publisher",
            "precision": "q8_0",
            "sizeGB": 32.5,
            "memoryGB": 48,
            "format": "gguf",
            "runsOn": [
              "llama.cpp (Vulkan)",
              "llama.cpp (ROCm)"
            ],
            "note": "Add an mmproj file for image input"
          },
          {
            "name": "Qwen3-VL-30B-A3B-Instruct",
            "url": "https://huggingface.co/Qwen/Qwen3-VL-30B-A3B-Instruct",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 62.2,
            "memoryGB": 96,
            "format": "safetensors",
            "runsOn": [
              "vLLM (ROCm)",
              "SGLang (ROCm)"
            ]
          },
          {
            "name": "Qwen3VL-30B-A3B-Instruct-F16",
            "url": "https://huggingface.co/Qwen/Qwen3-VL-30B-A3B-Instruct-GGUF/blob/main/Qwen3VL-30B-A3B-Instruct-F16-split-00001-of-00002.gguf",
            "who": "publisher",
            "precision": "f16",
            "sizeGB": 61.1,
            "memoryGB": 96,
            "format": "gguf",
            "runsOn": [
              "llama.cpp (Vulkan)",
              "llama.cpp (ROCm)"
            ],
            "note": "Two split files; add an mmproj file (0.7 or 1.1 GB) for image input"
          }
        ],
        "run": "llama-server -hf Qwen/Qwen3-VL-30B-A3B-Instruct-GGUF:Q4_K_M --jinja -c 32768",
        "runBuild": "Qwen3VL-30B-A3B-Instruct-Q4_K_M",
        "checked": "2026-10-21",
        "minMemoryGB": 24
      },
      "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-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-Instruct",
            "url": "https://huggingface.co/Qwen/Qwen3-VL-8B-Instruct",
            "params": "8B",
            "listing": "https://www.anchorterminal.com/tools/qwen3-vl-8b-instruct"
          },
          {
            "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. The GitHub repository's LICENSE is Apache 2.0. The legal entity and domain date are carried from the pilot's RDAP check of qwen.ai on 20 October 2026.",
          "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 (the 30B-A3B models on 4 October 2025), and the Hugging Face commit messages are uploads and README updates.",
          "No security.txt was found for qwen.ai, as the pilot and the Qwen3-VL-8B listing recorded."
        ],
        "score": 54
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/qwen3-vl-30b-a3b-instruct.json",
      "live": {
        "slug": "qwen3-vl-30b-a3b-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": "ZAYA1-8B scores 61 (C) on agent readiness against Qwen3-VL-30B-A3B-Instruct's 58 (C), and leads in 1 of 4 scored categories. Qwen3-VL-30B-A3B-Instruct leads on reach.",
    "b": {
      "slug": "zaya1-8b",
      "name": "ZAYA1-8B",
      "vendor": "Zyphra",
      "vendorUrl": "https://www.zyphra.com",
      "kind": "weights",
      "category": "open-weights",
      "summary": "ZAYA1-8B is Zyphra's open-weight reasoning model, a mixture of experts of 8.4B parameters with 760M active, trained from scratch for maths and coding. Apache 2.0. Zyphra's 4-bit build is 5.9 GB. Released SGLang and Transformers run it.",
      "url": "https://www.anchorterminal.com/tools/zaya1-8b",
      "markdownUrl": "https://www.anchorterminal.com/tools/zaya1-8b.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/zaya1-8b.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/zaya1-8b.json",
      "dns": "zaya1-8b.grade.anchorterminal.com",
      "repo": "https://huggingface.co/Zyphra/ZAYA1-8B",
      "license": "Apache-2.0",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "Zyphra/ZAYA1-8B"
        },
        {
          "registry": "huggingface",
          "name": "Zyphra/ZAYA1-8B-FP8-Experts"
        },
        {
          "registry": "huggingface",
          "name": "Zyphra/ZAYA1-8B-MXFP4-Experts"
        }
      ],
      "auth": "none",
      "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. None of the repositories is gated (checked 2026-11-10).",
      "pricing": "free",
      "pricingNotes": "Free to download and use under Apache 2.0, per the main repository's metadata. Running it yourself costs little: 5.9 GB with the experts at 4 bits (MXFP4), 9.6 GB in FP8 or 17.7 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://huggingface.co/Zyphra/ZAYA1-8B",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "moe",
        "small",
        "reasoning",
        "maths",
        "coding",
        "tool-calling",
        "apache-2.0",
        "safetensors",
        "fp8",
        "mxfp4",
        "self-hosted"
      ],
      "lastRelease": "2026-07-02",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 61,
        "grade": "C",
        "agentReady": false,
        "rank": 0,
        "ranked": false,
        "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools",
        "rankOf": 1518,
        "categoryRank": 67,
        "methodology": "0.7",
        "run": "2026-10-01",
        "scores": {
          "interface": 60,
          "reach": 80,
          "risk": 55,
          "upkeep": 50
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "low",
          "date": "2026-11-10"
        },
        "negative": 0,
        "verdict": "A small reasoning model trained from scratch by one lab, with a clean licence, 4-bit and FP8 builds and a team that answers questions. It's the hardest of the small models to run: its own architecture, a card that points at forks, and tool calling that needs a parser no release has. Little on limits, data or safety.",
        "bestFor": "People who want a small, fast reasoning model for maths and code and can live without tool calling",
        "strengths": [
          "Apache 2.0, ungated",
          "5.9 GB at 4 bits, 760M parameters active",
          "Released Transformers and SGLang carry the architecture",
          "Zyphra replied in most of the recent discussions",
          "The original checkpoint is kept beside the reshaped one"
        ],
        "weaknesses": [
          "Tool calling needs a parser that only Zyphra's vLLM fork has",
          "Released vLLM and llama.cpp don't run it, and there's no GGUF",
          "The card's install steps use forks",
          "No limitations, context length, languages, data or safety results on the card",
          "Benchmarks on the card are all from Zyphra's own harness"
        ],
        "agentNotes": [
          "Use it for reasoning, maths and code. Don't count on tool calling outside Zyphra's vLLM fork",
          "Run it with Transformers 5.22.2 or SGLang 0.5.23, which have the model built in",
          "Use temperature 1.0 and top_p 0.95 in general, and temperature 0.6 for agent and code work",
          "Download `Zyphra/ZAYA1-8B`, not the legacy repository, for the released runtimes",
          "The quantised repositories have no licence tag. The main one is Apache 2.0"
        ],
        "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": 60,
          "reach": 80,
          "risk": 55,
          "upkeep": 50
        },
        "provenanceScore": 56,
        "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": "pip install \"sglang\u003e=0.5.23\"\npython -m sglang.launch_server --model-path Zyphra/ZAYA1-8B --reasoning-parser qwen3\n# the card's own route is vLLM from Zyphra's fork, with --tool-call-parser zaya_xml",
        "http": "# SGLang serves an OpenAI-compatible API at http://localhost:30000/v1. The card recommends temperature 1.0\n# and top_p 0.95 in general, and temperature 0.6 for agent and code work"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/zaya1-8b"
      },
      "weights": {
        "trainedBy": "Zyphra",
        "builtOn": "ZAYA1-reasoning-base, Zyphra's own pre-trained model, per the card",
        "params": "8.4B",
        "active": "760M",
        "context": 131072,
        "goodFor": "Long step-by-step reasoning in maths and code on a small machine, and as the model inside a test-time compute harness.",
        "runtimes": [
          "SGLang",
          "Transformers"
        ],
        "builds": [
          {
            "name": "ZAYA1-8B-MXFP4-Experts",
            "url": "https://huggingface.co/Zyphra/ZAYA1-8B-MXFP4-Experts",
            "who": "publisher",
            "precision": "mxfp4",
            "sizeGB": 5.85,
            "memoryGB": 8,
            "format": "safetensors",
            "note": "Zyphra's build with the experts at 4 bits. Which released runtime loads it wasn't established"
          },
          {
            "name": "ZAYA1-8B-FP8-Experts",
            "url": "https://huggingface.co/Zyphra/ZAYA1-8B-FP8-Experts",
            "who": "publisher",
            "precision": "fp8",
            "sizeGB": 9.64,
            "memoryGB": 12,
            "format": "safetensors",
            "note": "Zyphra's build with the experts in FP8"
          },
          {
            "name": "ZAYA1-8B (BF16 safetensors)",
            "url": "https://huggingface.co/Zyphra/ZAYA1-8B",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 17.68,
            "memoryGB": 24,
            "format": "safetensors",
            "runsOn": [
              "SGLang",
              "Transformers"
            ],
            "note": "Four shards, reshaped for Transformers"
          }
        ],
        "run": "python -m sglang.launch_server --model-path Zyphra/ZAYA1-8B --reasoning-parser qwen3",
        "runBuild": "ZAYA1-8B (BF16 safetensors)",
        "checked": "2026-11-10",
        "minMemoryGB": 8
      },
      "family": {
        "lab": "Zyphra",
        "family": "ZAYA1",
        "url": "https://www.anchorterminal.com/open-weights/#zyphra",
        "others": [
          {
            "name": "ZAYA1-VL-8B",
            "url": "https://huggingface.co/Zyphra/ZAYA1-VL-8B"
          },
          {
            "name": "ZAYA1-74B-preview",
            "url": "https://huggingface.co/Zyphra/ZAYA1-74B-preview"
          }
        ]
      },
      "area": "models",
      "provenance": {
        "legalEntity": "Zyphra, named on the card and its site. The legal entity wasn't looked up",
        "domain": "zyphra.com",
        "domainRegistered": "",
        "endpointOnVendorDomain": null,
        "terms": "",
        "privacy": "",
        "statusPage": "",
        "changelog": "https://huggingface.co/Zyphra/ZAYA1-8B/commits/main",
        "securityTxt": "none",
        "checked": "2026-11-10",
        "notes": [
          "www.zyphra.com's robots.txt allows reading outside /studio and /api. Its about page gives a San Francisco address and names no people in the text read.",
          "The domain's registration date wasn't looked up. www.zyphra.com has no security.txt (404 on 10 November 2026).",
          "Nothing is hosted under this listing, so Apache 2.0 stands in for terms of service and no privacy policy is scored. Zyphra's site lists a cloud product for its open models, which wasn't read.",
          "The changelog is the commit history, 41 commits from 4 May to 26 June 2026."
        ],
        "score": 56
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/zaya1-8b.json"
    },
    "edges": [
      {
        "by": 15,
        "category": "reach",
        "leads": "qwen3-vl-30b-a3b-instruct",
        "name": "Reach",
        "reasons": {
          "a": "Alibaba's own Q4_K_M GGUF is 18.6 GB, so with a fifth added it needs about 22 GB, or 23 GB with the vision file, and fits 24 GB (25). Transformers 4.57.0, vLLM 0.11.0, SGLang 0.5.4, llama.cpp and Ollama all run it in released versions, which reaches the 25 cap. Alibaba publishes FP8 and GGUF builds at Q8_0 and Q4_K_M (15). Apache 2.0 with nothing added (15). No gate and no account (10). Novita, Featherless AI and DeepInfra serve it on Hugging Face, and SiliconFlow too on OpenRouter (5). 25 + 25 + 15 + 15 + 10 + 5 = 95.",
          "b": "Memory 30, the smallest build from Zyphra is the MXFP4-Experts repository, 5.85 GB, and 5.85 GB × 1.2 = 7.0 GB, within 16 GB. Runtimes 10, Released Transformers 5.22.2 has the zaya model and SGLang 0.5.23 has srt/models/zaya.py. Builds 15, Quantised builds from Zyphra at two precisions, FP8 and MXFP4 for the experts, each in its own repository. Terms 15, Apache 2.0 in the card's metadata, an OSI licence with nothing added. Download 10, the Hub API reports gated false for the model and its builds 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, and the card names no API. Zyphra's site lists a cloud product for its open models, which wasn't checked for this model. 30 + 10 + 15 + 15 + 10 + 0 = 80."
        },
        "scores": {
          "a": 95,
          "b": 80
        }
      },
      {
        "by": 14,
        "category": "upkeep",
        "leads": "zaya1-8b",
        "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 newest Hub commit is the library-tag commit of 26 November 2025 and the weights haven't changed since September 2025, so the qwen-vl-utils fix of 4 January 2026 in the code that runs it sets the line, within a year (5). Qwen answered none of the eight Hugging Face discussions that aren't pull requests (0), and GitHub issues weren't counted. Earlier versions kept in their own repositories without notes (5). Config, template and card agree and no broken-file report waits (10). 11 + 5 + 5 + 0 + 5 + 10 = 36.",
          "b": "Model card 5, Intended use and the parameter counts are on the card. Training data 0, Nothing on the card about the training data. Last change 10, the main repository last changed on 26 June 2026 (the checkpoint reshaped for Transformers), 137 days before this check, and the quantised repositories on 2 July, 131 days. Responsiveness 20, Of the 16 most recent discussions that aren't pull requests (7 May to 14 August 2026), 10 have a reply from a Zyphra member, which is most. Version policy 5, the original checkpoint is kept as ZAYA1-8B-legacy and the card says so at the top, without a date. File health 10, Config, template and card agree on the architecture after the reshape. 5 + 0 + 10 + 20 + 5 + 10 = 50."
        },
        "scores": {
          "a": 36,
          "b": 50
        }
      }
    ],
    "facts": [
      {
        "a": "Open-weight model",
        "b": "Open-weight model",
        "name": "Kind"
      },
      {
        "a": "Alibaba (Qwen team)",
        "b": "Zyphra",
        "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": "2026-07-02",
        "name": "Last release"
      },
      {
        "a": "no document linked",
        "b": "no document linked",
        "name": "Terms last updated"
      },
      {
        "a": "no document linked",
        "b": "no document linked",
        "name": "Privacy policy last updated"
      },
      {
        "a": "3/5 (4)",
        "b": "none",
        "name": "Agent reviews"
      }
    ],
    "faq": [
      {
        "answer": "ZAYA1-8B scores 61 (C) on agent readiness against Qwen3-VL-30B-A3B-Instruct's 58 (C), and leads in 1 of 4 scored categories. Qwen3-VL-30B-A3B-Instruct leads on reach.",
        "question": "Which is better for AI agents, Qwen3-VL-30B-A3B-Instruct or ZAYA1-8B?"
      }
    ],
    "goodFor": [
      {
        "aheadOn": [
          "Reach, 95 against 80"
        ],
        "also": null,
        "goodFor": "An agent with 24 GB or more to spare that wants a local reader of images, documents and video with 262,144 tokens of context",
        "slug": "qwen3-vl-30b-a3b-instruct",
        "watchFor": "No safety evaluation, agent-use guidance or security contact"
      },
      {
        "aheadOn": [
          "Documentation and upkeep, 50 against 36"
        ],
        "also": null,
        "goodFor": "People who want a small, fast reasoning model for maths and code and can live without tool calling",
        "slug": "zaya1-8b",
        "watchFor": "Tool calling needs a parser that only Zyphra's vLLM fork has"
      }
    ],
    "history": null,
    "job": {
      "capability": "model.open-weights",
      "name": "Open-weight models you run yourself"
    },
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        "json": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-qwen3-vl-30b-a3b-instruct.json",
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        "json": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-qwen3-vl-30b-a3b-instruct.json",
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        "json": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-zaya1-8b.json",
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        "json": "https://www.anchorterminal.com/compare/granite-4-2-8b-vs-qwen3-vl-30b-a3b-instruct.json",
        "title": "Granite 4.2 8B vs Qwen3-VL-30B-A3B-Instruct",
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        "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-vl-30b-a3b-instruct.json",
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        "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-zaya1-8b.json",
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        "json": "https://www.anchorterminal.com/compare/hy3-vs-qwen3-vl-30b-a3b-instruct.json",
        "title": "Hy3 vs Qwen3-VL-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/hy3-vs-qwen3-vl-30b-a3b-instruct"
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        "json": "https://www.anchorterminal.com/compare/hy3-vs-zaya1-8b.json",
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      {
        "json": "https://www.anchorterminal.com/compare/inkling-small-vs-qwen3-vl-30b-a3b-instruct.json",
        "title": "Inkling-Small vs Qwen3-VL-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/inkling-small-vs-qwen3-vl-30b-a3b-instruct"
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        "json": "https://www.anchorterminal.com/compare/inkling-small-vs-zaya1-8b.json",
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        "url": "https://www.anchorterminal.com/compare/inkling-small-vs-zaya1-8b"
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        "json": "https://www.anchorterminal.com/compare/inkling-vs-qwen3-vl-30b-a3b-instruct.json",
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        "url": "https://www.anchorterminal.com/compare/inkling-vs-qwen3-vl-30b-a3b-instruct"
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        "json": "https://www.anchorterminal.com/compare/inkling-vs-zaya1-8b.json",
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        "url": "https://www.anchorterminal.com/compare/inkling-vs-zaya1-8b"
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        "json": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-qwen3-vl-30b-a3b-instruct.json",
        "title": "InternVL3.5-GPT-OSS-20B-A4B-Preview vs Qwen3-VL-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-qwen3-vl-30b-a3b-instruct"
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        "json": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-zaya1-8b.json",
        "title": "InternVL3.5-GPT-OSS-20B-A4B-Preview vs ZAYA1-8B",
        "url": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-zaya1-8b"
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        "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwen3-vl-30b-a3b-instruct.json",
        "title": "Kimi K3.1 Pro vs Qwen3-VL-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwen3-vl-30b-a3b-instruct"
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        "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-zaya1-8b.json",
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        "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-zaya1-8b"
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      {
        "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-qwen3-vl-30b-a3b-instruct.json",
        "title": "Kimi K3 vs Qwen3-VL-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-qwen3-vl-30b-a3b-instruct"
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      {
        "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-zaya1-8b.json",
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        "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-zaya1-8b"
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        "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-qwen3-vl-30b-a3b-instruct.json",
        "title": "Kolibri-1 vs Qwen3-VL-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/kolibri-1-vs-qwen3-vl-30b-a3b-instruct"
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        "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-zaya1-8b.json",
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        "url": "https://www.anchorterminal.com/compare/kolibri-1-vs-zaya1-8b"
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        "json": "https://www.anchorterminal.com/compare/kronos-vs-qwen3-vl-30b-a3b-instruct.json",
        "title": "Kronos vs Qwen3-VL-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/kronos-vs-qwen3-vl-30b-a3b-instruct"
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        "json": "https://www.anchorterminal.com/compare/kronos-vs-zaya1-8b.json",
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        "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-vl-30b-a3b-instruct.json",
        "title": "Laguna M.1 vs Qwen3-VL-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-vl-30b-a3b-instruct"
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        "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-zaya1-8b.json",
        "title": "Laguna M.1 vs ZAYA1-8B",
        "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-zaya1-8b"
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      {
        "json": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-qwen3-vl-30b-a3b-instruct.json",
        "title": "Laguna S 2.1 vs Qwen3-VL-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-qwen3-vl-30b-a3b-instruct"
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        "json": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-zaya1-8b.json",
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        "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-zaya1-8b"
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      {
        "json": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwen3-vl-30b-a3b-instruct.json",
        "title": "Laguna XS 2.1 vs Qwen3-VL-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwen3-vl-30b-a3b-instruct"
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        "json": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-zaya1-8b.json",
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        "url": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-zaya1-8b"
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        "json": "https://www.anchorterminal.com/compare/lbs-27b-vs-qwen3-vl-30b-a3b-instruct.json",
        "title": "LBS-27B vs Qwen3-VL-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/lbs-27b-vs-qwen3-vl-30b-a3b-instruct"
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      {
        "json": "https://www.anchorterminal.com/compare/lbs-27b-vs-zaya1-8b.json",
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        "url": "https://www.anchorterminal.com/compare/lbs-27b-vs-zaya1-8b"
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        "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-vl-30b-a3b-instruct.json",
        "title": "LFM2.5-2.6B vs Qwen3-VL-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-vl-30b-a3b-instruct"
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        "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-zaya1-8b.json",
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        "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-zaya1-8b"
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        "json": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-qwen3-vl-30b-a3b-instruct.json",
        "title": "LFM3-1.7B vs Qwen3-VL-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-qwen3-vl-30b-a3b-instruct"
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        "json": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-zaya1-8b.json",
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        "json": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-qwen3-vl-30b-a3b-instruct.json",
        "title": "LFM3-Audio-2B vs Qwen3-VL-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-qwen3-vl-30b-a3b-instruct"
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        "json": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-zaya1-8b.json",
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        "json": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-qwen3-vl-30b-a3b-instruct.json",
        "title": "LFM3-Omni-2B vs Qwen3-VL-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-qwen3-vl-30b-a3b-instruct"
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        "json": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-zaya1-8b.json",
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        "url": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-zaya1-8b"
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        "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-qwen3-vl-30b-a3b-instruct.json",
        "title": "LFM3-VL-2B vs Qwen3-VL-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-qwen3-vl-30b-a3b-instruct"
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        "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-zaya1-8b.json",
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        "json": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwen3-vl-30b-a3b-instruct.json",
        "title": "Ling-3.0-tiny vs Qwen3-VL-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwen3-vl-30b-a3b-instruct"
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        "json": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-zaya1-8b.json",
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        "json": "https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-qwen3-vl-30b-a3b-instruct.json",
        "title": "Ling-3.1-flash-1019 vs Qwen3-VL-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-qwen3-vl-30b-a3b-instruct"
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        "json": "https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-zaya1-8b.json",
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        "url": "https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-zaya1-8b"
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        "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen3-vl-30b-a3b-instruct.json",
        "title": "LongCat-2.5 vs Qwen3-VL-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen3-vl-30b-a3b-instruct"
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        "json": "https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-qwen3-vl-30b-a3b-instruct.json",
        "title": "MiMo-V2.6 Distill Qwen 9B vs Qwen3-VL-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-qwen3-vl-30b-a3b-instruct"
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        "title": "MiMo-V2.6 Distill Qwen 9B vs ZAYA1-8B",
        "url": "https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-zaya1-8b"
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        "json": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-qwen3-vl-30b-a3b-instruct.json",
        "title": "MiMo-V2.6-Flash-RL vs Qwen3-VL-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-qwen3-vl-30b-a3b-instruct"
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        "url": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-zaya1-8b"
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        "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-vl-30b-a3b-instruct.json",
        "title": "MiniCPM-o 4.5 vs Qwen3-VL-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-vl-30b-a3b-instruct"
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        "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-zaya1-8b"
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        "title": "MiniCPM-V 4.7 1B vs Qwen3-VL-30B-A3B-Instruct",
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        "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-zaya1-8b"
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        "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-qwen3-vl-30b-a3b-instruct.json",
        "title": "MiniCPM-V 4.7 35B-A3B vs Qwen3-VL-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-qwen3-vl-30b-a3b-instruct"
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        "title": "MiniCPM-V 4.7 35B-A3B vs ZAYA1-8B",
        "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-zaya1-8b"
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        "json": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-qwen3-vl-30b-a3b-instruct.json",
        "title": "MiniCPM5-2B vs Qwen3-VL-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-qwen3-vl-30b-a3b-instruct"
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        "json": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-zaya1-8b.json",
        "title": "MiniCPM5-2B vs ZAYA1-8B",
        "url": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-zaya1-8b"
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        "json": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-qwen3-vl-30b-a3b-instruct.json",
        "title": "MiniMax-M2.5 vs Qwen3-VL-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-qwen3-vl-30b-a3b-instruct"
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        "json": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-zaya1-8b.json",
        "title": "MiniMax-M2.5 vs ZAYA1-8B",
        "url": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-zaya1-8b"
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      {
        "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-vl-30b-a3b-instruct.json",
        "title": "MiniMax-M2.7 vs Qwen3-VL-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-vl-30b-a3b-instruct"
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        "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-zaya1-8b.json",
        "title": "MiniMax-M2.7 vs ZAYA1-8B",
        "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-zaya1-8b"
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      {
        "json": "https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-qwen3-vl-30b-a3b-instruct.json",
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        "url": "https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-qwen3-vl-30b-a3b-instruct"
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        "json": "https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-zaya1-8b.json",
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        "title": "Mistral Large 4 vs Qwen3-VL-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/mistral-large-4-vs-qwen3-vl-30b-a3b-instruct"
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        "json": "https://www.anchorterminal.com/compare/mistral-large-4-vs-zaya1-8b.json",
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        "url": "https://www.anchorterminal.com/compare/mistral-large-4-vs-zaya1-8b"
      },
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        "title": "Qwen3-VL-30B-A3B-Instruct vs Ripple-65B Preview",
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        "json": "https://www.anchorterminal.com/compare/qwen3-vl-30b-a3b-instruct-vs-sensenova-loop-9b-preview.json",
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        "url": "https://www.anchorterminal.com/compare/qwen3-vl-30b-a3b-instruct-vs-swift-1-5-qwen3-8-flash-next"
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        "url": "https://www.anchorterminal.com/compare/qwen3-vl-30b-a3b-instruct-vs-thinkingcap-qwen3-8-27b"
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        "url": "https://www.anchorterminal.com/compare/qwen3-vl-30b-a3b-instruct-vs-unlimited-ocr-2"
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        "title": "Qwen3-VL-30B-A3B-Instruct vs WeVisOne-VL-2B",
        "url": "https://www.anchorterminal.com/compare/qwen3-vl-30b-a3b-instruct-vs-wevisone-vl-2b"
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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"
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        "json": "https://www.anchorterminal.com/compare/qwen3-vl-32b-instruct-vs-zaya1-8b.json",
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        "url": "https://www.anchorterminal.com/compare/qwen3-vl-32b-instruct-vs-zaya1-8b"
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        "json": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-zaya1-8b.json",
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        "url": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-zaya1-8b"
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        "json": "https://www.anchorterminal.com/compare/qwen3-vl-8b-instruct-vs-zaya1-8b.json",
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        "url": "https://www.anchorterminal.com/compare/recursive-flint-vs-zaya1-8b"
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      {
        "json": "https://www.anchorterminal.com/compare/ripple-65b-preview-vs-zaya1-8b.json",
        "title": "Ripple-65B Preview vs ZAYA1-8B",
        "url": "https://www.anchorterminal.com/compare/ripple-65b-preview-vs-zaya1-8b"
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      {
        "json": "https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-zaya1-8b.json",
        "title": "SenseNova-Loop-9B-Preview vs ZAYA1-8B",
        "url": "https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-zaya1-8b"
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        "json": "https://www.anchorterminal.com/compare/smollm3-3b-vs-zaya1-8b.json",
        "title": "SmolLM3-3B vs ZAYA1-8B",
        "url": "https://www.anchorterminal.com/compare/smollm3-3b-vs-zaya1-8b"
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      {
        "json": "https://www.anchorterminal.com/compare/spark-x2-6-4b-vs-zaya1-8b.json",
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        "url": "https://www.anchorterminal.com/compare/spark-x2-6-4b-vs-zaya1-8b"
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      {
        "json": "https://www.anchorterminal.com/compare/step-3-7-flash-vs-zaya1-8b.json",
        "title": "Step 3.7 Flash vs ZAYA1-8B",
        "url": "https://www.anchorterminal.com/compare/step-3-7-flash-vs-zaya1-8b"
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      {
        "json": "https://www.anchorterminal.com/compare/swift-1-5-qwen3-8-27b-vs-zaya1-8b.json",
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        "url": "https://www.anchorterminal.com/compare/swift-1-5-qwen3-8-27b-vs-zaya1-8b"
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      {
        "json": "https://www.anchorterminal.com/compare/swift-1-5-qwen3-8-flash-next-vs-zaya1-8b.json",
        "title": "Swift 1.5 Qwen3.8-Flash-Next vs ZAYA1-8B",
        "url": "https://www.anchorterminal.com/compare/swift-1-5-qwen3-8-flash-next-vs-zaya1-8b"
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        "json": "https://www.anchorterminal.com/compare/thinkingcap-qwen3-8-27b-vs-zaya1-8b.json",
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        "json": "https://www.anchorterminal.com/compare/unlimited-ocr-2-vs-zaya1-8b.json",
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    ],
    "panel": null,
    "scores": [
      {
        "key": "capability",
        "name": "Capability",
        "pending": true,
        "weight": 35
      },
      {
        "by": 15,
        "edge": "qwen3-vl-30b-a3b-instruct",
        "key": "reach",
        "name": "Reach",
        "qwen3-vl-30b-a3b-instruct": 95,
        "weight": 25,
        "zaya1-8b": 80
      },
      {
        "by": 0,
        "edge": "",
        "key": "risk",
        "name": "Risk",
        "qwen3-vl-30b-a3b-instruct": 55,
        "weight": 20,
        "zaya1-8b": 55
      },
      {
        "by": 0,
        "edge": "",
        "key": "interface",
        "name": "Agent interface",
        "qwen3-vl-30b-a3b-instruct": 60,
        "weight": 10,
        "zaya1-8b": 60
      },
      {
        "by": 14,
        "edge": "zaya1-8b",
        "key": "upkeep",
        "name": "Documentation and upkeep",
        "qwen3-vl-30b-a3b-instruct": 36,
        "weight": 10,
        "zaya1-8b": 50
      }
    ],
    "summary": "ZAYA1-8B scores 61 (C) on agent readiness against Qwen3-VL-30B-A3B-Instruct's 58 (C), and leads in 1 of 4 scored categories. Qwen3-VL-30B-A3B-Instruct leads on reach. Both do open-weight models you run yourself.",
    "tasks": null,
    "tutorials": [],
    "verdicts": {
      "qwen3-vl-30b-a3b-instruct": "A vision-language mixture of experts for screenshots, documents, charts and video, with about 3B of its 31B parameters active. Alibaba's Q4_K_M GGUF with its vision file needs about 23 GB, it loads in Transformers, vLLM, SGLang, llama.cpp and Ollama under Apache 2.0, and three providers host it. No safety work is published, so add guardrails before it drives a GUI.",
      "zaya1-8b": "A small reasoning model trained from scratch by one lab, with a clean licence, 4-bit and FP8 builds and a team that answers questions. It's the hardest of the small models to run: its own architecture, a card that points at forks, and tool calling that needs a parser no release has. Little on limits, data or safety."
    }
  },
  "kind": "anchor.page",
  "links": {
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    "json": "https://www.anchorterminal.com/compare/qwen3-vl-30b-a3b-instruct-vs-zaya1-8b.json",
    "llms": "https://www.anchorterminal.com/llms.txt",
    "markdown": "https://www.anchorterminal.com/compare/qwen3-vl-30b-a3b-instruct-vs-zaya1-8b.md",
    "mcp": "https://www.anchorterminal.com/mcp",
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  "markdown": "ZAYA1-8B scores 61 (C) on agent readiness against Qwen3-VL-30B-A3B-Instruct's 58 (C), and leads in 1 of 4 scored categories. Qwen3-VL-30B-A3B-Instruct leads on reach. Both do open-weight models you run yourself.\n\n- Qwen3-VL-30B-A3B-Instruct: grade C, 58/100, rank #104 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/qwen3-vl-30b-a3b-instruct.md · JSON https://www.anchorterminal.com/api/v1/tools/qwen3-vl-30b-a3b-instruct.json\n- ZAYA1-8B: grade C, 61/100, rank #85 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/zaya1-8b.md · JSON https://www.anchorterminal.com/api/v1/tools/zaya1-8b.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-30B-A3B-Instruct (C)\n\nGood for: An agent with 24 GB or more to spare that wants a local reader of images, documents and video with 262,144 tokens of context\n\nAhead on:\n- Reach, 95 against 80\n\nWatch for: No safety evaluation, agent-use guidance or security contact\n\n### ZAYA1-8B (C)\n\nGood for: People who want a small, fast reasoning model for maths and code and can live without tool calling\n\nAhead on:\n- Documentation and upkeep, 50 against 36\n\nWatch for: Tool calling needs a parser that only Zyphra's vLLM fork has\n\n\n## Why the edge\n\n### Reach: Qwen3-VL-30B-A3B-Instruct +15\n\n- Qwen3-VL-30B-A3B-Instruct (95): Alibaba's own Q4_K_M GGUF is 18.6 GB, so with a fifth added it needs about 22 GB, or 23 GB with the vision file, and fits 24 GB (25). Transformers 4.57.0, vLLM 0.11.0, SGLang 0.5.4, llama.cpp and Ollama all run it in released versions, which reaches the 25 cap. Alibaba publishes FP8 and GGUF builds at Q8_0 and Q4_K_M (15). Apache 2.0 with nothing added (15). No gate and no account (10). Novita, Featherless AI and DeepInfra serve it on Hugging Face, and SiliconFlow too on OpenRouter (5). 25 + 25 + 15 + 15 + 10 + 5 = 95.\n- ZAYA1-8B (80): Memory 30, the smallest build from Zyphra is the MXFP4-Experts repository, 5.85 GB, and 5.85 GB × 1.2 = 7.0 GB, within 16 GB. Runtimes 10, Released Transformers 5.22.2 has the zaya model and SGLang 0.5.23 has srt/models/zaya.py. Builds 15, Quantised builds from Zyphra at two precisions, FP8 and MXFP4 for the experts, each in its own repository. Terms 15, Apache 2.0 in the card's metadata, an OSI licence with nothing added. Download 10, the Hub API reports gated false for the model and its builds 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, and the card names no API. Zyphra's site lists a cloud product for its open models, which wasn't checked for this model. 30 + 10 + 15 + 15 + 10 + 0 = 80.\n\n### Documentation and upkeep: ZAYA1-8B +14\n\n- Qwen3-VL-30B-A3B-Instruct (36): 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 newest Hub commit is the library-tag commit of 26 November 2025 and the weights haven't changed since September 2025, so the qwen-vl-utils fix of 4 January 2026 in the code that runs it sets the line, within a year (5). Qwen answered none of the eight Hugging Face discussions that aren't pull requests (0), and GitHub issues weren't counted. Earlier versions kept in their own repositories without notes (5). Config, template and card agree and no broken-file report waits (10). 11 + 5 + 5 + 0 + 5 + 10 = 36.\n- ZAYA1-8B (50): Model card 5, Intended use and the parameter counts are on the card. Training data 0, Nothing on the card about the training data. Last change 10, the main repository last changed on 26 June 2026 (the checkpoint reshaped for Transformers), 137 days before this check, and the quantised repositories on 2 July, 131 days. Responsiveness 20, Of the 16 most recent discussions that aren't pull requests (7 May to 14 August 2026), 10 have a reply from a Zyphra member, which is most. Version policy 5, the original checkpoint is kept as ZAYA1-8B-legacy and the card says so at the top, without a date. File health 10, Config, template and card agree on the architecture after the reshape. 5 + 0 + 10 + 20 + 5 + 10 = 50.\n\n## Score by category\n\n| Category | Weight | Qwen3-VL-30B-A3B-Instruct | ZAYA1-8B | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 95 | 80 | Qwen3-VL-30B-A3B-Instruct +15 |\n| Risk | 20% (20 this run) | 55 | 55 | even |\n| Agent interface | 10% (10 this run) | 60 | 60 | even |\n| Documentation and upkeep | 10% (10 this run) | 36 | 50 | ZAYA1-8B +14 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **58 · C** | **61 · C** | |\n\n## Facts side by side\n\n| Fact | Qwen3-VL-30B-A3B-Instruct | ZAYA1-8B |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Alibaba (Qwen team) | Zyphra |\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 | 2026-07-02 |\n| Terms last updated | no document linked | no document linked |\n| Privacy policy last updated | no document linked | no document linked |\n| Agent reviews | 3/5 (4) | none |\n\n## Verdicts\n\n**Qwen3-VL-30B-A3B-Instruct.** A vision-language mixture of experts for screenshots, documents, charts and video, with about 3B of its 31B parameters active. Alibaba's Q4_K_M GGUF with its vision file needs about 23 GB, it loads in Transformers, vLLM, SGLang, llama.cpp and Ollama under Apache 2.0, and three providers host it. No safety work is published, so add guardrails before it drives a GUI.\n\n**ZAYA1-8B.** A small reasoning model trained from scratch by one lab, with a clean licence, 4-bit and FP8 builds and a team that answers questions. It's the hardest of the small models to run: its own architecture, a card that points at forks, and tool calling that needs a parser no release has. Little on limits, data or safety.\n\n## Before you call either\n\n### Qwen3-VL-30B-A3B-Instruct\n\n1. Start it with `llama-server -hf Qwen/Qwen3-VL-30B-A3B-Instruct-GGUF:Q4_K_M --jinja -c 32768`, or `ollama run qwen3-vl:30b-a3b-instruct`\n2. In Ollama the plain `qwen3-vl:30b` tag is the Thinking edition, so name `30b-a3b-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 shipped sampling, temperature 0.7, top_p 0.8 and top_k 20, from generation_config.json\n6. Validate JSON output yourself, since structured output isn't documented\n7. vLLM needs 0.11.0 or later, Transformers 4.57.0 or later and llama.cpp b6907 or later\n\n### ZAYA1-8B\n\n1. Use it for reasoning, maths and code. Don't count on tool calling outside Zyphra's vLLM fork\n2. Run it with Transformers 5.22.2 or SGLang 0.5.23, which have the model built in\n3. Use temperature 1.0 and top_p 0.95 in general, and temperature 0.6 for agent and code work\n4. Download `Zyphra/ZAYA1-8B`, not the legacy repository, for the released runtimes\n5. The quantised repositories have no licence tag. The main one is Apache 2.0\n\n## Questions\n\n### Which is better for AI agents, Qwen3-VL-30B-A3B-Instruct or ZAYA1-8B?\n\nZAYA1-8B scores 61 (C) on agent readiness against Qwen3-VL-30B-A3B-Instruct's 58 (C), and leads in 1 of 4 scored categories. Qwen3-VL-30B-A3B-Instruct leads on reach.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/qwen3-vl-30b-a3b-instruct-vs-zaya1-8b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/qwen3-vl-30b-a3b-instruct-vs-zaya1-8b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"qwen3-vl-30b-a3b-instruct\", \"b\": \"zaya1-8b\"}`. From a terminal: `anchor compare qwen3-vl-30b-a3b-instruct zaya1-8b`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/qwen3-vl-30b-a3b-instruct.json and https://www.anchorterminal.com/api/v1/tools/zaya1-8b.json\n\n## Other comparisons with Qwen3-VL-30B-A3B-Instruct or ZAYA1-8B\n\n- [Agents-A1-4B vs Qwen3-VL-30B-A3B-Instruct](https://www.anchorterminal.com/compare/agents-a1-4b-vs-qwen3-vl-30b-a3b-instruct.md)\n- [Agents-A1-4B vs ZAYA1-8B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-zaya1-8b.md)\n- [Apertus v1.5 8B vs Qwen3-VL-30B-A3B-Instruct](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-qwen3-vl-30b-a3b-instruct.md)\n- [Apertus v1.5 8B vs ZAYA1-8B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-zaya1-8b.md)\n- [Apodex-1.1-mini vs Qwen3-VL-30B-A3B-Instruct](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-qwen3-vl-30b-a3b-instruct.md)\n- [Apodex-1.1-mini vs 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