{
  "data": {
    "a": {
      "slug": "granite-4-2-30b",
      "name": "Granite 4.2 30B",
      "vendor": "IBM",
      "vendorUrl": "https://www.ibm.com/granite",
      "kind": "weights",
      "category": "open-weights",
      "summary": "Granite 4.2 30B is IBM's open-weight dense reasoning model of 29.3B parameters, post-trained from Granite 4.1 30B Base, with switchable thinking, tool calling and a 131,072-token context, published in BF16, FP8, FP4, GGUF and MLX under Apache 2.0.",
      "url": "https://www.anchorterminal.com/tools/granite-4-2-30b",
      "markdownUrl": "https://www.anchorterminal.com/tools/granite-4-2-30b.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/granite-4-2-30b.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/granite-4-2-30b.json",
      "dns": "granite-4-2-30b.grade.anchorterminal.com",
      "repo": "https://huggingface.co/ibm-granite/granite-4.2-30b",
      "license": "Apache-2.0",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "ibm-granite/granite-4.2-30b"
        },
        {
          "registry": "huggingface",
          "name": "ibm-granite/granite-4.2-30b-GGUF"
        },
        {
          "registry": "huggingface",
          "name": "ibm-granite/granite-4.2-30b-fp8"
        },
        {
          "registry": "huggingface",
          "name": "ibm-granite/granite-4.2-30b-mxfp4"
        },
        {
          "registry": "huggingface",
          "name": "ibm-granite/granite-4.2-30b-nvfp4"
        },
        {
          "registry": "huggingface",
          "name": "ibm-granite/granite-4.2-30b-q4-mlx"
        },
        {
          "registry": "ollama",
          "name": "granite4.2:30b"
        }
      ],
      "auth": "none",
      "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The BF16 repository and IBM's FP8, MXFP4, NVFP4, GGUF and MLX repositories are not gated (checked 2026-11-10).",
      "pricing": "free",
      "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0. Running it yourself costs the hardware, 58.5 GB for the BF16 files, 17.7 GB for IBM's Q4_K_M GGUF or 16.5 GB for its 4-bit MLX build (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/ibm-granite/granite-4.2-30b",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "dense",
        "reasoning",
        "tool-calling",
        "multilingual",
        "apache-2.0",
        "safetensors",
        "fp8",
        "fp4",
        "gguf",
        "mlx",
        "signed",
        "self-hosted"
      ],
      "lastRelease": "2026-09-04",
      "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": 52,
        "methodology": "0.7",
        "run": "2026-10-01",
        "scores": {
          "interface": 85,
          "reach": 90,
          "risk": 55,
          "upkeep": 87
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "low",
          "date": "2026-11-10"
        },
        "negative": 0,
        "verdict": "IBM's largest reasoning cousin, with the same strengths as the 3B. It runs in every runtime we count from IBM's own builds on a 24 GB card, is signed, plain Apache 2.0, and comes with a disclosure of its training data. No safety results are published and structured output isn't documented.",
        "bestFor": "Agents that want a documented, signed, Apache 2.0 reasoning model with tool calling on a 24 GB card",
        "strengths": [
          "Runs in llama.cpp, Ollama, vLLM, SGLang and Transformers from IBM's own FP8, FP4, GGUF and MLX builds",
          "Plain Apache 2.0, ungated, signed with sigstore",
          "The card documents the tool-calling format, the parser to use and three thinking modes",
          "IBM publishes a machine-readable disclosure of the training datasets",
          "IBM answers most questions on the repository"
        ],
        "weaknesses": [
          "No safety results published for it",
          "Structured output isn't documented",
          "The card's vLLM command still loads a Python plugin from the repository, which released vLLM no longer needs",
          "No knowledge cut-off on the card",
          "One inference provider, so an agent without the hardware has little choice"
        ],
        "agentNotes": [
          "Start it with `ollama run granite4.2:30b`, or `llama-server -hf ibm-granite/granite-4.2-30b-GGUF:Q4_K_M --jinja`",
          "With vLLM 0.32.0 or later leave out `--reasoning-parser-plugin`, since the parser is built in, and use `--tool-call-parser qwen3_coder`",
          "Thinking is on by default. Pass `enable_thinking` false to turn it off or `low_effort` true for short reasoning",
          "The context is 131,072 tokens. The card's 512K extension isn't documented",
          "Validate JSON output yourself, since structured output isn't documented",
          "IBM recommends Granite Guardian beside it to flag risky inputs and outputs"
        ],
        "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": 85,
          "reach": 90,
          "risk": 55,
          "upkeep": 87
        },
        "provenanceScore": 88,
        "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": "ollama run granite4.2:30b\n# or, with llama.cpp and IBM's own GGUF:\nllama-server -hf ibm-granite/granite-4.2-30b-GGUF:Q4_K_M --jinja\n# or, with vLLM 0.32.0 or later:\nvllm serve ibm-granite/granite-4.2-30b --max-model-len 131072 \\\n  --reasoning-parser granite_thinking_parser \\\n  --tool-call-parser qwen3_coder --enable-auto-tool-choice",
        "http": "curl http://localhost:11434/api/chat \\\n  -d '{\"model\": \"granite4.2:30b\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/granite-4-2-30b"
      },
      "sameCompany": [
        "granite-embedding-small-english-r2",
        "granite-guardian",
        "granite-speech-5-0-2b",
        "ibm-watson-stt",
        "granite-switch",
        "granite-4-2-3b",
        "granite-4-2-8b"
      ],
      "weights": {
        "trainedBy": "IBM",
        "builtOn": "ibm-granite/granite-4.1-30b-base (IBM's own base checkpoint, post-trained; named on the card and in its metadata)",
        "params": "29.3B",
        "context": 131072,
        "goodFor": "IBM's largest dense reasoning model for tool calling, coding and multilingual chat, with thinking that is on, off or short per request, on a 24 GB card at 4 bits.",
        "runtimes": [
          "llama.cpp",
          "Ollama",
          "vLLM",
          "SGLang",
          "Transformers"
        ],
        "builds": [
          {
            "name": "granite-4.2-30b-nvfp4",
            "url": "https://huggingface.co/ibm-granite/granite-4.2-30b-nvfp4",
            "who": "publisher",
            "precision": "nvfp4",
            "sizeGB": 17.65,
            "memoryGB": 24,
            "format": "safetensors",
            "runsOn": [
              "vLLM"
            ]
          },
          {
            "name": "granite-4.2-30b-mxfp4",
            "url": "https://huggingface.co/ibm-granite/granite-4.2-30b-mxfp4",
            "who": "publisher",
            "precision": "mxfp4",
            "sizeGB": 16.76,
            "memoryGB": 24,
            "format": "safetensors",
            "runsOn": [
              "vLLM"
            ]
          },
          {
            "name": "granite-4.2-30b-Q4_K_M",
            "url": "https://huggingface.co/ibm-granite/granite-4.2-30b-GGUF",
            "who": "publisher",
            "precision": "q4_k_m",
            "sizeGB": 17.72,
            "memoryGB": 24,
            "format": "gguf",
            "runsOn": [
              "llama.cpp"
            ],
            "note": "IBM's GGUF repository also holds Q4_0 (16.58 GB), Q6_K and BF16 files among others"
          },
          {
            "name": "granite-4.2-30b-q4-mlx",
            "url": "https://huggingface.co/ibm-granite/granite-4.2-30b-q4-mlx",
            "who": "publisher",
            "precision": "q4",
            "sizeGB": 16.47,
            "memoryGB": 24,
            "format": "mlx",
            "runsOn": [
              "mlx-lm"
            ],
            "note": "The smallest build at 4-bit or better. IBM also publishes q8-mlx (31.11 GB) and bf16-mlx"
          },
          {
            "name": "ollama granite4.2:30b",
            "url": "https://ollama.com/library/granite4.2:30b",
            "who": "ollama",
            "precision": "q4_k_m",
            "sizeGB": 18,
            "memoryGB": 24,
            "format": "ollama",
            "runsOn": [
              "Ollama"
            ],
            "note": "Ollama's library build, size as its tags page shows it on 10 November 2026"
          },
          {
            "name": "granite-4.2-30b-fp8",
            "url": "https://huggingface.co/ibm-granite/granite-4.2-30b-fp8",
            "who": "publisher",
            "precision": "fp8",
            "sizeGB": 30.11,
            "memoryGB": 48,
            "format": "safetensors",
            "runsOn": [
              "vLLM"
            ]
          },
          {
            "name": "granite-4.2-30b-Q8_0",
            "url": "https://huggingface.co/ibm-granite/granite-4.2-30b-GGUF",
            "who": "publisher",
            "precision": "q8_0",
            "sizeGB": 31.11,
            "memoryGB": 48,
            "format": "gguf",
            "runsOn": [
              "llama.cpp"
            ]
          },
          {
            "name": "granite-4.2-30b",
            "url": "https://huggingface.co/ibm-granite/granite-4.2-30b",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 58.55,
            "memoryGB": 96,
            "format": "safetensors",
            "runsOn": [
              "vLLM",
              "SGLang",
              "Transformers"
            ]
          }
        ],
        "run": "ollama run granite4.2:30b",
        "runBuild": "ollama granite4.2:30b",
        "checked": "2026-11-10",
        "minMemoryGB": 24
      },
      "family": {
        "lab": "IBM (Granite)",
        "family": "Granite 4 (4.0, 4.1, 4.2)",
        "url": "https://www.anchorterminal.com/open-weights/#ibm",
        "others": [
          {
            "name": "granite-4.2-3b",
            "url": "https://huggingface.co/ibm-granite/granite-4.2-3b",
            "params": "3B (Hub safetensors count 3.66B)",
            "listing": "https://www.anchorterminal.com/tools/granite-4-2-3b"
          },
          {
            "name": "granite-4.2-8b",
            "url": "https://huggingface.co/ibm-granite/granite-4.2-8b",
            "params": "8B (Hub safetensors count 8.79B)",
            "listing": "https://www.anchorterminal.com/tools/granite-4-2-8b"
          },
          {
            "name": "granite-4.1-8b-base",
            "url": "https://huggingface.co/ibm-granite/granite-4.1-8b-base",
            "params": "8B (Hub safetensors count 8.38B)"
          },
          {
            "name": "granite-4.1-8b",
            "url": "https://huggingface.co/ibm-granite/granite-4.1-8b",
            "params": "8B (Hub safetensors count 8.79B)"
          },
          {
            "name": "granite-4.1-3b-base",
            "url": "https://huggingface.co/ibm-granite/granite-4.1-3b-base",
            "params": "3B (Hub safetensors count 3.40B)"
          },
          {
            "name": "granite-4.1-3b",
            "url": "https://huggingface.co/ibm-granite/granite-4.1-3b",
            "params": "3B (Hub safetensors count 3.40B)"
          },
          {
            "name": "granite-4.1-30b-base",
            "url": "https://huggingface.co/ibm-granite/granite-4.1-30b-base",
            "params": "30B (Hub safetensors count 28.87B)"
          },
          {
            "name": "granite-4.1-30b",
            "url": "https://huggingface.co/ibm-granite/granite-4.1-30b",
            "params": "30B (Hub safetensors count 28.87B)"
          },
          {
            "name": "granite-4.0-h-350m-base",
            "url": "https://huggingface.co/ibm-granite/granite-4.0-h-350m-base",
            "params": "350M (Hub safetensors count 340M)"
          },
          {
            "name": "granite-4.0-h-350m",
            "url": "https://huggingface.co/ibm-granite/granite-4.0-h-350m",
            "params": "350M (Hub safetensors count 340M)"
          },
          {
            "name": "granite-4.0-h-1b-base",
            "url": "https://huggingface.co/ibm-granite/granite-4.0-h-1b-base",
            "params": "1B (Hub safetensors count 1.46B)"
          },
          {
            "name": "granite-4.0-h-1b",
            "url": "https://huggingface.co/ibm-granite/granite-4.0-h-1b",
            "params": "1B (Hub safetensors count 1.46B)"
          },
          {
            "name": "granite-4.0-350m-base",
            "url": "https://huggingface.co/ibm-granite/granite-4.0-350m-base",
            "params": "350M (Hub safetensors count 352M)"
          },
          {
            "name": "granite-4.0-350m",
            "url": "https://huggingface.co/ibm-granite/granite-4.0-350m",
            "params": "350M (Hub safetensors count 352M)"
          },
          {
            "name": "granite-4.0-1b-base",
            "url": "https://huggingface.co/ibm-granite/granite-4.0-1b-base",
            "params": "1B (Hub safetensors count 1.63B)"
          },
          {
            "name": "granite-4.0-1b",
            "url": "https://huggingface.co/ibm-granite/granite-4.0-1b",
            "params": "1B (Hub safetensors count 1.63B)"
          },
          {
            "name": "granite-4.0-micro-base",
            "url": "https://huggingface.co/ibm-granite/granite-4.0-micro-base",
            "params": "3.40B (Hub safetensors count; the name states none)"
          },
          {
            "name": "granite-4.0-micro",
            "url": "https://huggingface.co/ibm-granite/granite-4.0-micro",
            "params": "3.40B (Hub safetensors count; the name states none)"
          },
          {
            "name": "granite-4.0-h-tiny-base",
            "url": "https://huggingface.co/ibm-granite/granite-4.0-h-tiny-base",
            "params": "6.94B (Hub safetensors count; the name states none)"
          },
          {
            "name": "granite-4.0-h-tiny",
            "url": "https://huggingface.co/ibm-granite/granite-4.0-h-tiny",
            "params": "6.94B (Hub safetensors count; the name states none)"
          },
          {
            "name": "granite-4.0-h-small-base",
            "url": "https://huggingface.co/ibm-granite/granite-4.0-h-small-base",
            "params": "32.21B (Hub safetensors count; the name states none)"
          },
          {
            "name": "granite-4.0-h-small",
            "url": "https://huggingface.co/ibm-granite/granite-4.0-h-small",
            "params": "32.21B (Hub safetensors count; the name states none)"
          },
          {
            "name": "granite-4.0-h-micro-base",
            "url": "https://huggingface.co/ibm-granite/granite-4.0-h-micro-base",
            "params": "3.19B (Hub safetensors count; the name states none)"
          },
          {
            "name": "granite-4.0-h-micro",
            "url": "https://huggingface.co/ibm-granite/granite-4.0-h-micro",
            "params": "3.19B (Hub safetensors count; the name states none)"
          },
          {
            "name": "granite-4.0-tiny-preview",
            "url": "https://huggingface.co/ibm-granite/granite-4.0-tiny-preview",
            "params": "6.67B (Hub safetensors count; the name states none)"
          },
          {
            "name": "granite-4.0-tiny-base-preview",
            "url": "https://huggingface.co/ibm-granite/granite-4.0-tiny-base-preview",
            "params": "6.67B (Hub safetensors count; the name states none)"
          }
        ],
        "olderCount": 64
      },
      "area": "models",
      "provenance": {
        "legalEntity": "International Business Machines Corporation (per IBM's naming guidance, as read for granite-guardian)",
        "domain": "ibm.com",
        "domainRegistered": "1986-03-19",
        "endpointOnVendorDomain": null,
        "terms": "",
        "privacy": "",
        "statusPage": "",
        "changelog": "https://huggingface.co/ibm-granite/granite-4.2-30b/commits/main",
        "securityTxt": "valid",
        "checked": "2026-11-10",
        "notes": [
          "IBM's Granite docs page for 4.2 links the ibm-granite collection on Hugging Face and the GitHub repository, and the docs site says it is no longer updated. The domain date is carried from the granite-guardian check.",
          "The ibm-granite organisation (209 members) isn't verified on the Hub, which the calibration doesn't require.",
          "Nothing is hosted for this listing, so the Apache 2.0 licence stands in for terms of service and no privacy policy is scored.",
          "The changelog is the Hugging Face commit history, 15 commits from 7 August to 20 October 2026, most titled only \"granite-4.2\".",
          "www.ibm.com/.well-known/security.txt is present with PSIRT contacts and expires on 21 November 2026.",
          "Released on 25 August 2026 by the card and IBM's blog, which firstReleased uses. The repository and its weight files date from 7 August 2026, the date BATCH.md and the IBM family page used before 21 October."
        ],
        "score": 88
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/granite-4-2-30b.json"
    },
    "answer": "Granite 4.2 30B and Qwen3.5-9B score within a point of each other on agent readiness, 61 (C) and 60.5 (C).",
    "b": {
      "slug": "qwen3-5-9b",
      "name": "Qwen3.5-9B",
      "vendor": "Alibaba (Qwen team)",
      "vendorUrl": "https://qwen.ai",
      "kind": "weights",
      "category": "open-weights",
      "summary": "Qwen3.5-9B is Alibaba's open-weight dense vision-language model of 9B parameters, with thinking on by default and a 262,144-token context, published on Hugging Face in BF16 under Apache 2.0.",
      "url": "https://www.anchorterminal.com/tools/qwen3-5-9b",
      "markdownUrl": "https://www.anchorterminal.com/tools/qwen3-5-9b.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/qwen3-5-9b.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/qwen3-5-9b.json",
      "dns": "qwen3-5-9b.grade.anchorterminal.com",
      "repo": "https://huggingface.co/Qwen/Qwen3.5-9B",
      "license": "Apache-2.0",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "Qwen/Qwen3.5-9B"
        },
        {
          "registry": "ollama",
          "name": "qwen3.5:9b"
        }
      ],
      "auth": "none",
      "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The repository is not gated (checked 2026-10-21).",
      "pricing": "free",
      "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0. Running it yourself costs the hardware, 19.3 GB for the BF16 files and 6.6 GB for Ollama's default build (checked 2026-10-21).",
      "priceSummary": "Free",
      "where": "local",
      "x402": {
        "level": "no",
        "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-10-21).",
        "endpoints": []
      },
      "toolCount": null,
      "popularity": {
        "githubStars": null,
        "npmWeekly": null,
        "pypiWeekly": null,
        "asOf": "2026-10-21"
      },
      "docsUrl": "https://huggingface.co/Qwen/Qwen3.5-9B",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "dense",
        "multimodal",
        "reasoning",
        "tool-calling",
        "apache-2.0",
        "safetensors",
        "self-hosted"
      ],
      "lastRelease": "2026-03-02",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 60.5,
        "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": 69,
        "methodology": "0.7",
        "run": "2026-10-01",
        "scores": {
          "capability": 51,
          "interface": 70,
          "reach": 85,
          "risk": 55,
          "upkeep": 34
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "high",
          "date": "2026-11-04"
        },
        "negative": 0,
        "verdict": "A small dense vision-language model with trained tool calling, thinking that turns off per request and a 262,144-token context. Ollama's 4-bit build is 6.6 GB and the BF16 files 19.3 GB, and every major runtime loads it. The files haven't changed since 2 March 2026, Qwen answered none of the 40 latest discussions, and no safety results are published.",
        "bestFor": "Agents that want a small multimodal tool-calling model on a laptop or a 16 GB GPU, under a plain licence",
        "strengths": [
          "Runs in vLLM, SGLang, llama.cpp, Ollama, LM Studio and released Transformers",
          "Ollama's 4-bit build is 6.6 GB and the BF16 files 19.3 GB",
          "Plain Apache 2.0, no gate, no account",
          "262,144 tokens native, and thinking that turns off per request",
          "Safetensors only, with a SHA-256 for every shard and a chat template in the repository",
          "Four inference providers serve it for agents without the hardware"
        ],
        "weaknesses": [
          "No quantised build from Qwen itself",
          "No safety evaluation, agent guidance or security contact",
          "No reply from Qwen in any of the 40 most recent discussions",
          "No change to the files or card since 2 March 2026",
          "The tool-call format and structured output aren't documented beyond the template",
          "No limitations, knowledge cut-off or training data detail on the card"
        ],
        "agentNotes": [
          "Smallest start is `ollama run qwen3.5:9b` (6.6 GB, q4_k_m)",
          "On vLLM run `vllm serve Qwen/Qwen3.5-9B --port 8000 --max-model-len 262144 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder`, or tool calls come back as text",
          "Tool calls use the XML style `\u003ctool_call\u003e\u003cfunction=name\u003e\u003cparameter=x\u003e`, not Hermes JSON, so use the qwen3_coder parser and not hermes",
          "Thinking is on by default; pass `chat_template_kwargs` with `enable_thinking` false for short tasks, since `/nothink` isn't supported",
          "There's no generation_config.json, so pass sampling yourself, temperature 1.0, top_p 0.95, top_k 20, presence_penalty 1.5 when thinking, or 0.7, 0.8, 20, 1.5 without",
          "Keep at least 128K of context when thinking, and turn on YaRN only beyond 262,144 tokens",
          "Validate JSON output yourself or use the runtime's guided decoding"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 4,
        "avgRating": 2.5,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "high",
            "grade": "C",
            "methodology": "0.7",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 60.5
          }
        ],
        "editorialScores": {
          "capability": 51,
          "interface": 70,
          "reach": 85,
          "risk": 55,
          "upkeep": 34
        },
        "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 qwen3.5:9b\n# or, with the card's vLLM command:\nvllm serve Qwen/Qwen3.5-9B --port 8000 --max-model-len 262144 --reasoning-parser qwen3 \\\n  --enable-auto-tool-choice --tool-call-parser qwen3_coder\n# or, with llama.cpp and LM Studio's GGUF:\nllama-server -hf lmstudio-community/Qwen3.5-9B-GGUF:Q4_K_M --jinja",
        "http": "curl http://localhost:11434/api/chat \\\n  -d '{\"model\": \"qwen3.5:9b\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/qwen3-5-9b"
      },
      "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-vl-8b-instruct",
        "qwen3-5-0-8b",
        "qwen3-5-122b-a10b",
        "qwen3-5-2b",
        "qwen3-5-397b-a17b",
        "qwen3-5-4b",
        "qwen3-6-27b",
        "qwen3-6-35b-a3b",
        "qwen3-8-27b",
        "qwen3-8-flash-next"
      ],
      "weights": {
        "trainedBy": "Alibaba (Qwen team)",
        "builtOn": "Qwen/Qwen3.5-9B-Base",
        "params": "9B",
        "context": 262144,
        "goodFor": "Dense vision-language model for local agents: text, images and video in, tool calling, thinking on by default.",
        "runtimes": [
          "llama.cpp",
          "Ollama",
          "vLLM",
          "SGLang",
          "LM Studio",
          "Transformers"
        ],
        "builds": [
          {
            "name": "ollama qwen3.5:9b",
            "url": "https://ollama.com/library/qwen3.5:9b",
            "who": "ollama",
            "precision": "q4_k_m",
            "sizeGB": 6.6,
            "memoryGB": 8,
            "format": "ollama",
            "runsOn": [
              "Ollama"
            ],
            "note": "Q4_K_M with a BF16 vision projector (digest 56671c2ab938), also the smallest non-MLX Ollama build. Size as Ollama's tags page shows it, rounded to whole GB above 10 GB; the Linux build is the llama.cpp variant"
          },
          {
            "name": "lmstudio-community Qwen3.5-9B-GGUF (Q4_K_M)",
            "url": "https://huggingface.co/lmstudio-community/Qwen3.5-9B-GGUF",
            "who": "lmstudio",
            "precision": "q4_k_m",
            "sizeGB": 6.55,
            "memoryGB": 8,
            "format": "gguf",
            "runsOn": [
              "LM Studio",
              "llama.cpp"
            ],
            "note": "From lmstudio-community, LM Studio's organisation on the Hub. 5.63 GB of Q4_K_M weights plus the 0.92 GB BF16 vision projector, from the Hub's model information on 10 November 2026. Recorded after the last grading, so Reach doesn't count it yet"
          },
          {
            "name": "lmstudio-community Qwen3.5-9B-GGUF (Q6_K)",
            "url": "https://huggingface.co/lmstudio-community/Qwen3.5-9B-GGUF",
            "who": "lmstudio",
            "precision": "q6_k",
            "sizeGB": 8.28,
            "memoryGB": 12,
            "format": "gguf",
            "runsOn": [
              "LM Studio",
              "llama.cpp"
            ],
            "note": "From lmstudio-community, LM Studio's organisation on the Hub. 7.36 GB of Q6_K weights plus the 0.92 GB BF16 vision projector, from the Hub's model information on 10 November 2026. Recorded after the last grading, so Reach doesn't count it yet"
          },
          {
            "name": "lmstudio-community Qwen3.5-9B-GGUF (Q8_0)",
            "url": "https://huggingface.co/lmstudio-community/Qwen3.5-9B-GGUF",
            "who": "lmstudio",
            "precision": "q8_0",
            "sizeGB": 10.45,
            "memoryGB": 16,
            "format": "gguf",
            "runsOn": [
              "LM Studio",
              "llama.cpp"
            ],
            "note": "From lmstudio-community, LM Studio's organisation on the Hub. 9.53 GB of Q8_0 weights plus the 0.92 GB BF16 vision projector, from the Hub's model information on 10 November 2026. Recorded after the last grading, so Reach doesn't count it yet"
          },
          {
            "name": "Qwen3.5-9B",
            "url": "https://huggingface.co/Qwen/Qwen3.5-9B",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 19.3,
            "memoryGB": 24,
            "format": "safetensors",
            "runsOn": [
              "vLLM (ROCm)",
              "SGLang (ROCm)"
            ]
          }
        ],
        "run": "vllm serve Qwen/Qwen3.5-9B --port 8000 --max-model-len 262144 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder",
        "runBuild": "Qwen3.5-9B",
        "checked": "2026-10-21",
        "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-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": "Qwen3.5-0.8B",
            "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 (named in the licence's copyright line)",
        "domain": "qwen.ai",
        "domainRegistered": "2023-08-27",
        "endpointOnVendorDomain": null,
        "terms": "",
        "privacy": "",
        "statusPage": "",
        "changelog": "",
        "securityTxt": "none",
        "checked": "2026-10-21",
        "notes": [
          "The LICENSE file reads Copyright 2026 Alibaba Cloud. The registrant (Alibaba Cloud (Singapore) Private Limited) and registration date of qwen.ai are per the RDAP reading of 20 October 2026; rdap.org didn't answer 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 commit history's messages are uploads and README updates that don't say what changed.",
          "qwen.ai redirects /.well-known/security.txt and /robots.txt to its home page, so there is no security.txt."
        ],
        "score": 54
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/qwen3-5-9b.json",
      "live": {
        "slug": "qwen3-5-9b",
        "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"
      }
    },
    "edges": [
      {
        "by": 53,
        "category": "upkeep",
        "leads": "granite-4-2-30b",
        "name": "Documentation and upkeep",
        "reasons": {
          "a": "Model card 17, Intended use (best for), limitations (ethical considerations and limitations), architecture, parameters (30B), context length (128K native) and 12 tested languages are on the card. Training data 20, IBM's disclosure file for the 30B lists the datasets used in pre-training and post-training as machine-readable metadata, and the card describes the three training stages. Last change 15, the last commit to the repository was on 4 September 2026, 67 days before this check. Responsiveness 20, Of the 4 discussions that aren't pull requests (28 August to 3 November 2026), 3 have a reply from an ibm-granite member. Version policy 5, Granite 4.1 30B stays in its own repository and 4.2 came out in new ones. File health 10, Config, template and card agree on the architecture, the 131,072-token context and the thinking switch. 17 + 20 + 15 + 20 + 5 + 10 = 87.",
          "b": "Model card 14, it covers intended use, architecture, parameters, context and 201 languages but gives no limitations and no knowledge cut-off. Training data 5, a general line about trillions of multimodal tokens with no cut-off. Last change 5, the files and card last changed on 2 March 2026 (c202236), 247 days before 4 November, within a year. Responsiveness 0, no Qwen member replied in any of the 40 most recent of the 45 discussions that aren't pull requests (2 March to 29 September 2026). Version policy 5, a first release that was never replaced in place, with no dated notes across generations. File health 5, two reports about the files and card (15 and 51) are still open with no reply. 14 + 5 + 5 + 0 + 5 + 5 = 34. The card's highlights mention sparse Mixture-of-Experts for the series while this 9B is dense, but its own overview matches config.json, so it's not counted as a contradiction."
        },
        "scores": {
          "a": 87,
          "b": 34
        }
      },
      {
        "by": 15,
        "category": "interface",
        "leads": "granite-4-2-30b",
        "name": "Agent interface",
        "reasons": {
          "a": "Chat template 20, chat_template.jinja in the repository, with roles, tools, tool responses, the thinking switch and low effort. Tool-calling format 25, the card has tool-calling sections for vLLM and SGLang, declares tools with OpenAI's function schema and names the parser to use, qwen3_coder, which both runtimes ship in the released versions read. Trained tool calling 20, the template loops over every entry in an assistant turn's tool_calls, so it renders several calls in one turn, and the card names tool calling and agentic workflows as what it's best for. Structured output 0, the card doesn't document JSON or JSON Schema output or how to ask for it. Reasoning control 10, the card documents thinking on by default, non-thinking and low-effort modes per request through the template (enable_thinking and low_effort), with the reasoning returned apart from the answer. Context 10, 131,072 tokens natively per the card and config.json. 20 + 25 + 20 + 0 + 10 + 10 = 85.",
          "b": "Chat template 20, a Jinja file in the repository. Tool-calling format 10, the card names the qwen3_coder parser for vLLM and SGLang but leaves the declaration and return format to the template, and Qwen's own function-calling guide still describes Qwen3's Hermes format. Trained tool calling 20, the template renders several calls in one turn. Structured output 0, nothing documents asking the weights for JSON. Reasoning control 10, thinking turns off per request with enable_thinking false and the reasoning comes back in think tags that the qwen3 parser splits out. Context 10, 262,144 tokens native."
        },
        "scores": {
          "a": 85,
          "b": 70
        }
      },
      {
        "by": 5,
        "category": "reach",
        "leads": "granite-4-2-30b",
        "name": "Reach",
        "reasons": {
          "a": "Memory 25, IBM's own 4-bit MLX build granite-4.2-30b-q4-mlx is 16.47 GB, and 16.47 GB × 1.2 = 19.8 GB, which fits 24 GB but not 16 GB. Its Q4_0 GGUF is 16.58 GB and Ollama's library build granite4.2:30b 18 GB, the same band. Runtimes 25, Released versions run the granite architecture in llama.cpp (granite, b12242), vLLM (GraniteForCausalLM in the v0.33.0 registry), SGLang (granite.py at v0.5.23), Transformers (v5.22.2) and Ollama (its own granite4.2 library entry, 30b tag). Builds 15, IBM publishes FP8, MXFP4 and NVFP4 safetensors builds, a GGUF repository with Q4_0 to Q8_0 and BF16 files, and 4-bit, 8-bit and BF16 MLX builds, so quantised builds at two precisions or more. Terms 15, Apache 2.0, an OSI-approved licence, with no use policy or other terms added on the card. Download 10, the Hub API reports gated false for the BF16, FP8, MXFP4, NVFP4, GGUF and MLX repositories, so the files download with no account and nothing to accept. Hosted 0, Hugging Face maps one inference provider (DeepInfra, live), short of the two the line needs. 25 + 25 + 15 + 15 + 10 + 0 = 90.",
          "b": "Memory 30, Qwen publishes no quantised 9B, so the smallest 4-bit build from a runtime's own library is Ollama's qwen3.5:9b at 6.6 GB, about 7.9 GB with the fifth added, well under 16 GB. Runtimes 25, the cap, with vLLM listed in its stable docs, Transformers at released tags, llama.cpp, Ollama's library, LM Studio's own catalogue and SGLang all running the architecture. Builds 0, Qwen's FP8 and GPTQ repositories cover only the 27B and larger Qwen3.5 models, so every quantisation of the 9B is someone else's. Terms 15, plain Apache 2.0. Download 10, the repository isn't gated. Hosted 5, Together, Featherless AI, OVHcloud and DeepInfra are live for it on its Hugging Face page. 30 + 25 + 0 + 15 + 10 + 5 = 85."
        },
        "scores": {
          "a": 90,
          "b": 85
        }
      }
    ],
    "facts": [
      {
        "a": "Open-weight model",
        "b": "Open-weight model",
        "name": "Kind"
      },
      {
        "a": "IBM",
        "b": "Alibaba (Qwen team)",
        "name": "Vendor"
      },
      {
        "a": "no (local only)",
        "b": "no (local only)",
        "name": "Hosted endpoint"
      },
      {
        "a": "",
        "b": "",
        "name": "Transports"
      },
      {
        "a": "None",
        "b": "None",
        "name": "Auth"
      },
      {
        "a": "Free",
        "b": "Free",
        "name": "Pricing"
      },
      {
        "a": "no",
        "b": "no",
        "name": "x402"
      },
      {
        "a": "Apache-2.0",
        "b": "Apache-2.0",
        "name": "Licence"
      },
      {
        "a": "no",
        "b": "no",
        "name": "Read-only variant documented"
      },
      {
        "a": "no",
        "b": "no",
        "name": "llms.txt"
      },
      {
        "a": "2026-09-04",
        "b": "2026-03-02",
        "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": "none",
        "b": "2.5/5 (4)",
        "name": "Agent reviews"
      }
    ],
    "faq": [
      {
        "answer": "Granite 4.2 30B and Qwen3.5-9B score within a point of each other on agent readiness, 61 (C) and 60.5 (C).",
        "question": "Which is better for AI agents, Granite 4.2 30B or Qwen3.5-9B?"
      }
    ],
    "goodFor": [
      {
        "aheadOn": [
          "Reach, 90 against 85",
          "Agent interface, 85 against 70",
          "Documentation and upkeep, 87 against 34"
        ],
        "also": null,
        "goodFor": "Agents that want a documented, signed, Apache 2.0 reasoning model with tool calling on a 24 GB card",
        "slug": "granite-4-2-30b",
        "watchFor": "No safety results published for it"
      },
      {
        "aheadOn": null,
        "also": null,
        "goodFor": "Agents that want a small multimodal tool-calling model on a laptop or a 16 GB GPU, under a plain licence",
        "slug": "qwen3-5-9b",
        "watchFor": "No quantised build from Qwen itself"
      }
    ],
    "history": null,
    "job": {
      "capability": "model.open-weights",
      "name": "Open-weight models you run yourself"
    },
    "others": [
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-granite-4-2-30b.json",
        "title": "Agents-A1-4B vs Granite 4.2 30B",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-granite-4-2-30b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-qwen3-5-9b.json",
        "title": "Agents-A1-4B vs Qwen3.5-9B",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-qwen3-5-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-granite-4-2-30b.json",
        "title": "Apertus v1.5 8B vs Granite 4.2 30B",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-granite-4-2-30b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-qwen3-5-9b.json",
        "title": "Apertus v1.5 8B vs Qwen3.5-9B",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-qwen3-5-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-granite-4-2-30b.json",
        "title": "Apodex-1.1-mini vs Granite 4.2 30B",
        "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-granite-4-2-30b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-qwen3-5-9b.json",
        "title": "Apodex-1.1-mini vs Qwen3.5-9B",
        "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-qwen3-5-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-granite-4-2-30b.json",
        "title": "Arete-27B vs Granite 4.2 30B",
        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-granite-4-2-30b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-qwen3-5-9b.json",
        "title": "Arete-27B vs Qwen3.5-9B",
        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-qwen3-5-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-granite-4-2-30b.json",
        "title": "BigBang-v1 vs Granite 4.2 30B",
        "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-granite-4-2-30b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-qwen3-5-9b.json",
        "title": "BigBang-v1 vs Qwen3.5-9B",
        "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-qwen3-5-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/carbon-x-vs-granite-4-2-30b.json",
        "title": "Carbon-X vs Granite 4.2 30B",
        "url": "https://www.anchorterminal.com/compare/carbon-x-vs-granite-4-2-30b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/carbon-x-vs-qwen3-5-9b.json",
        "title": "Carbon-X vs Qwen3.5-9B",
        "url": "https://www.anchorterminal.com/compare/carbon-x-vs-qwen3-5-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cola2-vs-granite-4-2-30b.json",
        "title": "Cola2 vs Granite 4.2 30B",
        "url": "https://www.anchorterminal.com/compare/cola2-vs-granite-4-2-30b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cola2-vs-qwen3-5-9b.json",
        "title": "Cola2 vs Qwen3.5-9B",
        "url": "https://www.anchorterminal.com/compare/cola2-vs-qwen3-5-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-granite-4-2-30b.json",
        "title": "Cosmos-Reason2-2B vs Granite 4.2 30B",
        "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-granite-4-2-30b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-qwen3-5-9b.json",
        "title": "Cosmos-Reason2-2B vs Qwen3.5-9B",
        "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-qwen3-5-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-granite-4-2-30b.json",
        "title": "Darwin-36B-Opus vs Granite 4.2 30B",
        "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-granite-4-2-30b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-qwen3-5-9b.json",
        "title": "Darwin-36B-Opus vs Qwen3.5-9B",
        "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-qwen3-5-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-granite-4-2-30b.json",
        "title": "DeepSeek-V3.2 vs Granite 4.2 30B",
        "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-granite-4-2-30b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-qwen3-5-9b.json",
        "title": "DeepSeek-V3.2 vs Qwen3.5-9B",
        "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-qwen3-5-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-granite-4-2-30b.json",
        "title": "DeepSeek V4.1 Flash vs Granite 4.2 30B",
        "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-granite-4-2-30b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-5-9b.json",
        "title": "DeepSeek V4.1 Flash vs Qwen3.5-9B",
        "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-5-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-granite-4-2-30b.json",
        "title": "DeepSeek-V4-Flash-0731 vs Granite 4.2 30B",
        "url": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-granite-4-2-30b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-qwen3-5-9b.json",
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        "url": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-qwen3-5-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/devstral-small-2-vs-granite-4-2-30b.json",
        "title": "Devstral Small 2 vs Granite 4.2 30B",
        "url": "https://www.anchorterminal.com/compare/devstral-small-2-vs-granite-4-2-30b"
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      {
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        "json": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-occamy-1-0.json",
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        "url": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-occamy-1-0"
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        "json": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-olmo-3-7b-think.json",
        "title": "Granite 4.2 30B vs Olmo 3 7B Think",
        "url": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-olmo-3-7b-think"
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        "json": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-ornith-1-5-122b.json",
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        "url": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-ornith-1-5-122b"
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        "json": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-ornith-1-5-35b-a3b.json",
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        "url": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-ornith-1-5-35b-a3b"
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        "url": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-ornith-1-5-397b"
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        "json": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-ornith-1-5-9b.json",
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        "url": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-ornith-1-5-9b"
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        "json": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-otel-2-0-llm-31b-it.json",
        "title": "Granite 4.2 30B vs OTel-2.0-LLM-31B-IT",
        "url": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-otel-2-0-llm-31b-it"
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        "json": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-qwen-agentworld-35b-a3b.json",
        "title": "Granite 4.2 30B vs Qwen-AgentWorld-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-qwen-agentworld-35b-a3b"
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        "json": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-qwen3-30b-a3b-instruct-2507.json",
        "title": "Granite 4.2 30B vs Qwen3-30B-A3B-Instruct-2507",
        "url": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-qwen3-30b-a3b-instruct-2507"
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        "json": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-qwen3-4b-instruct-2507.json",
        "title": "Granite 4.2 30B vs Qwen3-4B-Instruct-2507",
        "url": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-qwen3-4b-instruct-2507"
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        "json": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-qwen3-5-0-8b.json",
        "title": "Granite 4.2 30B vs Qwen3.5-0.8B",
        "url": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-qwen3-5-0-8b"
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        "json": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-qwen3-5-122b-a10b.json",
        "title": "Granite 4.2 30B vs Qwen3.5-122B-A10B",
        "url": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-qwen3-5-122b-a10b"
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        "json": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-qwen3-5-2b.json",
        "title": "Granite 4.2 30B vs Qwen3.5-2B",
        "url": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-qwen3-5-2b"
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      {
        "json": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-qwen3-5-397b-a17b.json",
        "title": "Granite 4.2 30B vs Qwen3.5-397B-A17B",
        "url": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-qwen3-5-397b-a17b"
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        "json": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-qwen3-5-4b.json",
        "title": "Granite 4.2 30B vs Qwen3.5-4B",
        "url": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-qwen3-5-4b"
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        "json": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-qwen3-6-27b.json",
        "title": "Granite 4.2 30B vs Qwen3.6-27B",
        "url": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-qwen3-6-27b"
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        "json": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-qwen3-6-35b-a3b.json",
        "title": "Granite 4.2 30B vs Qwen3.6-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-qwen3-6-35b-a3b"
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        "json": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-qwen3-8-27b.json",
        "title": "Granite 4.2 30B vs Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-qwen3-8-27b"
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        "json": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-qwen3-8-flash-next.json",
        "title": "Granite 4.2 30B vs Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-qwen3-8-flash-next"
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        "json": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-qwen3-coder-30b-a3b-instruct.json",
        "title": "Granite 4.2 30B vs Qwen3-Coder-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-qwen3-coder-30b-a3b-instruct"
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        "json": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-qwen3-coder-next.json",
        "title": "Granite 4.2 30B vs Qwen3-Coder-Next",
        "url": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-qwen3-coder-next"
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        "json": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-qwen3-omni-30b-a3b-instruct.json",
        "title": "Granite 4.2 30B vs Qwen3-Omni-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-qwen3-omni-30b-a3b-instruct"
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        "json": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-qwen3-vl-2b-instruct.json",
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        "url": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-qwen3-vl-2b-instruct"
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        "json": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-qwen3-vl-30b-a3b-instruct.json",
        "title": "Granite 4.2 30B vs Qwen3-VL-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-qwen3-vl-30b-a3b-instruct"
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        "json": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-qwen3-vl-32b-instruct.json",
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        "url": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-qwen3-vl-32b-instruct"
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        "json": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-qwen3-vl-4b-instruct.json",
        "title": "Granite 4.2 30B vs Qwen3-VL-4B-Instruct",
        "url": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-qwen3-vl-4b-instruct"
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      {
        "json": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-qwen3-vl-8b-instruct.json",
        "title": "Granite 4.2 30B vs Qwen3-VL-8B-Instruct",
        "url": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-qwen3-vl-8b-instruct"
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        "json": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-qwopus3-8-27b-flash-v2.json",
        "title": "Granite 4.2 30B vs Qwopus3.8-27B-Flash-V2",
        "url": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-qwopus3-8-27b-flash-v2"
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        "json": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-recursive-flint.json",
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        "url": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-recursive-flint"
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        "json": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-ripple-65b-preview.json",
        "title": "Granite 4.2 30B vs Ripple-65B Preview",
        "url": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-ripple-65b-preview"
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        "json": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-sensenova-loop-9b-preview.json",
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        "url": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-sensenova-loop-9b-preview"
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        "json": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-smollm3-3b.json",
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        "url": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-smollm3-3b"
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        "json": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-spark-x2-6-4b.json",
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        "url": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-spark-x2-6-4b"
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        "url": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-step-3-7-flash"
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        "url": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-swift-1-5-qwen3-8-27b"
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        "json": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-swift-1-5-qwen3-8-flash-next.json",
        "title": "Granite 4.2 30B vs Swift 1.5 Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-swift-1-5-qwen3-8-flash-next"
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        "url": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-unlimited-ocr-2"
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        "url": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-wevisone-vl-2b"
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        "url": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-wevisone-vl-4b"
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        "url": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-zaya1-8b"
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        "title": "Granite 4.2 3B vs Qwen3.5-9B",
        "url": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-qwen3-5-9b"
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        "json": "https://www.anchorterminal.com/compare/granite-4-2-8b-vs-qwen3-5-9b.json",
        "title": "Granite 4.2 8B vs Qwen3.5-9B",
        "url": "https://www.anchorterminal.com/compare/granite-4-2-8b-vs-qwen3-5-9b"
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        "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-5-9b.json",
        "title": "Hy-Memory-30B-A3B vs Qwen3.5-9B",
        "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-5-9b"
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        "json": "https://www.anchorterminal.com/compare/hy3-vs-qwen3-5-9b.json",
        "title": "Hy3 vs Qwen3.5-9B",
        "url": "https://www.anchorterminal.com/compare/hy3-vs-qwen3-5-9b"
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        "json": "https://www.anchorterminal.com/compare/inkling-small-vs-qwen3-5-9b.json",
        "title": "Inkling-Small vs Qwen3.5-9B",
        "url": "https://www.anchorterminal.com/compare/inkling-small-vs-qwen3-5-9b"
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        "json": "https://www.anchorterminal.com/compare/inkling-vs-qwen3-5-9b.json",
        "title": "Inkling vs Qwen3.5-9B",
        "url": "https://www.anchorterminal.com/compare/inkling-vs-qwen3-5-9b"
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        "json": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-qwen3-5-9b.json",
        "title": "InternVL3.5-GPT-OSS-20B-A4B-Preview vs Qwen3.5-9B",
        "url": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-qwen3-5-9b"
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        "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwen3-5-9b.json",
        "title": "Kimi K3.1 Pro vs Qwen3.5-9B",
        "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwen3-5-9b"
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      {
        "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-qwen3-5-9b.json",
        "title": "Kimi K3 vs Qwen3.5-9B",
        "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-qwen3-5-9b"
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      {
        "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-qwen3-5-9b.json",
        "title": "Kolibri-1 vs Qwen3.5-9B",
        "url": "https://www.anchorterminal.com/compare/kolibri-1-vs-qwen3-5-9b"
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      {
        "json": "https://www.anchorterminal.com/compare/kronos-vs-qwen3-5-9b.json",
        "title": "Kronos vs Qwen3.5-9B",
        "url": "https://www.anchorterminal.com/compare/kronos-vs-qwen3-5-9b"
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      {
        "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-5-9b.json",
        "title": "Laguna M.1 vs Qwen3.5-9B",
        "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-5-9b"
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      {
        "json": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-qwen3-5-9b.json",
        "title": "Laguna S 2.1 vs Qwen3.5-9B",
        "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-qwen3-5-9b"
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      {
        "json": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwen3-5-9b.json",
        "title": "Laguna XS 2.1 vs Qwen3.5-9B",
        "url": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwen3-5-9b"
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      {
        "json": "https://www.anchorterminal.com/compare/lbs-27b-vs-qwen3-5-9b.json",
        "title": "LBS-27B vs Qwen3.5-9B",
        "url": "https://www.anchorterminal.com/compare/lbs-27b-vs-qwen3-5-9b"
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        "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-5-9b.json",
        "title": "LFM2.5-2.6B vs Qwen3.5-9B",
        "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-5-9b"
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        "json": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-qwen3-5-9b.json",
        "title": "LFM3-1.7B vs Qwen3.5-9B",
        "url": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-qwen3-5-9b"
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      {
        "json": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-qwen3-5-9b.json",
        "title": "LFM3-Audio-2B vs Qwen3.5-9B",
        "url": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-qwen3-5-9b"
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      {
        "json": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-qwen3-5-9b.json",
        "title": "LFM3-Omni-2B vs Qwen3.5-9B",
        "url": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-qwen3-5-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-qwen3-5-9b.json",
        "title": "LFM3-VL-2B vs Qwen3.5-9B",
        "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-qwen3-5-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwen3-5-9b.json",
        "title": "Ling-3.0-tiny vs Qwen3.5-9B",
        "url": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwen3-5-9b"
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      {
        "json": "https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-qwen3-5-9b.json",
        "title": "Ling-3.1-flash-1019 vs Qwen3.5-9B",
        "url": "https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-qwen3-5-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen3-5-9b.json",
        "title": "LongCat-2.5 vs Qwen3.5-9B",
        "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen3-5-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-qwen3-5-9b.json",
        "title": "MiMo-V2.6 Distill Qwen 9B vs Qwen3.5-9B",
        "url": "https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-qwen3-5-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-qwen3-5-9b.json",
        "title": "MiMo-V2.6-Flash-RL vs Qwen3.5-9B",
        "url": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-qwen3-5-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-5-9b.json",
        "title": "MiniCPM-o 4.5 vs Qwen3.5-9B",
        "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-5-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-qwen3-5-9b.json",
        "title": "MiniCPM-V 4.7 1B vs Qwen3.5-9B",
        "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-qwen3-5-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-qwen3-5-9b.json",
        "title": "MiniCPM-V 4.7 35B-A3B vs Qwen3.5-9B",
        "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-qwen3-5-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-qwen3-5-9b.json",
        "title": "MiniCPM5-2B vs Qwen3.5-9B",
        "url": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-qwen3-5-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-qwen3-5-9b.json",
        "title": "MiniMax-M2.5 vs Qwen3.5-9B",
        "url": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-qwen3-5-9b"
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      {
        "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-5-9b.json",
        "title": "MiniMax-M2.7 vs Qwen3.5-9B",
        "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-5-9b"
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      {
        "json": "https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-qwen3-5-9b.json",
        "title": "MiniMax-M3.1-Flash vs Qwen3.5-9B",
        "url": "https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-qwen3-5-9b"
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      {
        "json": "https://www.anchorterminal.com/compare/mistral-large-4-vs-qwen3-5-9b.json",
        "title": "Mistral Large 4 vs Qwen3.5-9B",
        "url": "https://www.anchorterminal.com/compare/mistral-large-4-vs-qwen3-5-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-5-9b.json",
        "title": "Muse Glimmer 30B vs Qwen3.5-9B",
        "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-5-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-qwen3-5-9b.json",
        "title": "Nanbeige4.2-3B vs Qwen3.5-9B",
        "url": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-qwen3-5-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-qwen3-5-9b.json",
        "title": "Nanbeige4.3-33B vs Qwen3.5-9B",
        "url": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-qwen3-5-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-qwen3-5-9b.json",
        "title": "NVIDIA Nemotron 3.5 Bolt E4B 4GB vs Qwen3.5-9B",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-qwen3-5-9b"
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      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-qwen3-5-9b.json",
        "title": "Nemotron 3.5 Lightning 30B A3B vs Qwen3.5-9B",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-qwen3-5-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-qwen3-5-9b.json",
        "title": "Nemotron 3 Nano 30B A3B vs Qwen3.5-9B",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-qwen3-5-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-qwen3-5-9b.json",
        "title": "Nemotron 3 Nano 4B vs Qwen3.5-9B",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-qwen3-5-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-qwen3-5-9b.json",
        "title": "Nemotron 3 Nano Omni 30B A3B Reasoning vs Qwen3.5-9B",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-qwen3-5-9b"
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      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-qwen3-5-9b.json",
        "title": "Nemotron 3 Super 120B A12B vs Qwen3.5-9B",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-qwen3-5-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-qwen3-5-9b.json",
        "title": "Nemotron 3 Ultra 550B A55B vs Qwen3.5-9B",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-qwen3-5-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-qwen3-5-9b.json",
        "title": "North Mini Code 1.0 vs Qwen3.5-9B",
        "url": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-qwen3-5-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-5-9b.json",
        "title": "occamy-1.0 vs Qwen3.5-9B",
        "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-5-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-5-9b.json",
        "title": "Olmo 3 7B Think vs Qwen3.5-9B",
        "url": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-5-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwen3-5-9b.json",
        "title": "Ornith-1.5-122B vs Qwen3.5-9B",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwen3-5-9b"
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      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-5-9b.json",
        "title": "Ornith-1.5-35B-A3B vs Qwen3.5-9B",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-5-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-5-9b.json",
        "title": "Ornith-1.5-397B vs Qwen3.5-9B",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-5-9b"
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      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-5-9b.json",
        "title": "Ornith-1.5-9B vs Qwen3.5-9B",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-5-9b"
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      {
        "json": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-qwen3-5-9b.json",
        "title": "OTel-2.0-LLM-31B-IT vs Qwen3.5-9B",
        "url": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-qwen3-5-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-5-9b.json",
        "title": "Qwen-AgentWorld-35B-A3B vs Qwen3.5-9B",
        "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-5-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-30b-a3b-instruct-2507-vs-qwen3-5-9b.json",
        "title": "Qwen3-30B-A3B-Instruct-2507 vs Qwen3.5-9B",
        "url": "https://www.anchorterminal.com/compare/qwen3-30b-a3b-instruct-2507-vs-qwen3-5-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-4b-instruct-2507-vs-qwen3-5-9b.json",
        "title": "Qwen3-4B-Instruct-2507 vs Qwen3.5-9B",
        "url": "https://www.anchorterminal.com/compare/qwen3-4b-instruct-2507-vs-qwen3-5-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-qwen3-5-9b.json",
        "title": "Qwen3.5-0.8B vs Qwen3.5-9B",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-qwen3-5-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-qwen3-5-9b.json",
        "title": "Qwen3.5-122B-A10B vs Qwen3.5-9B",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-qwen3-5-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-5-2b-vs-qwen3-5-9b.json",
        "title": "Qwen3.5-2B vs Qwen3.5-9B",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-2b-vs-qwen3-5-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-qwen3-5-9b.json",
        "title": "Qwen3.5-397B-A17B vs Qwen3.5-9B",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-qwen3-5-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-5-4b-vs-qwen3-5-9b.json",
        "title": "Qwen3.5-4B vs Qwen3.5-9B",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-4b-vs-qwen3-5-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-qwen3-6-27b.json",
        "title": "Qwen3.5-9B vs Qwen3.6-27B",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-qwen3-6-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-qwen3-6-35b-a3b.json",
        "title": "Qwen3.5-9B vs Qwen3.6-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-qwen3-6-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-qwen3-8-27b.json",
        "title": "Qwen3.5-9B vs Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-qwen3-8-flash-next.json",
        "title": "Qwen3.5-9B vs Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-qwen3-coder-30b-a3b-instruct.json",
        "title": "Qwen3.5-9B vs Qwen3-Coder-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-qwen3-coder-30b-a3b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-qwen3-coder-next.json",
        "title": "Qwen3.5-9B vs Qwen3-Coder-Next",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-qwen3-coder-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-qwen3-omni-30b-a3b-instruct.json",
        "title": "Qwen3.5-9B vs Qwen3-Omni-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-qwen3-omni-30b-a3b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-qwen3-vl-2b-instruct.json",
        "title": "Qwen3.5-9B vs Qwen3-VL-2B-Instruct",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-qwen3-vl-2b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-qwen3-vl-30b-a3b-instruct.json",
        "title": "Qwen3.5-9B vs Qwen3-VL-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-qwen3-vl-30b-a3b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-qwen3-vl-32b-instruct.json",
        "title": "Qwen3.5-9B vs Qwen3-VL-32B-Instruct",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-qwen3-vl-32b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-qwen3-vl-4b-instruct.json",
        "title": "Qwen3.5-9B vs Qwen3-VL-4B-Instruct",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-qwen3-vl-4b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-qwen3-vl-8b-instruct.json",
        "title": "Qwen3.5-9B vs Qwen3-VL-8B-Instruct",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-qwen3-vl-8b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-qwopus3-8-27b-flash-v2.json",
        "title": "Qwen3.5-9B vs Qwopus3.8-27B-Flash-V2",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-qwopus3-8-27b-flash-v2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-recursive-flint.json",
        "title": "Qwen3.5-9B vs Recursive Flint",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-recursive-flint"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-ripple-65b-preview.json",
        "title": "Qwen3.5-9B vs Ripple-65B Preview",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-ripple-65b-preview"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-sensenova-loop-9b-preview.json",
        "title": "Qwen3.5-9B vs SenseNova-Loop-9B-Preview",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-sensenova-loop-9b-preview"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-smollm3-3b.json",
        "title": "Qwen3.5-9B vs SmolLM3-3B",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-smollm3-3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-spark-x2-6-4b.json",
        "title": "Qwen3.5-9B vs Spark-X2.6-4B",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-spark-x2-6-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-step-3-7-flash.json",
        "title": "Qwen3.5-9B vs Step 3.7 Flash",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-step-3-7-flash"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-swift-1-5-qwen3-8-27b.json",
        "title": "Qwen3.5-9B vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-swift-1-5-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-swift-1-5-qwen3-8-flash-next.json",
        "title": "Qwen3.5-9B vs Swift 1.5 Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-swift-1-5-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-thinkingcap-qwen3-8-27b.json",
        "title": "Qwen3.5-9B vs ThinkingCap (Qwen3.8 27B)",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-thinkingcap-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-unlimited-ocr-2.json",
        "title": "Qwen3.5-9B vs Baidu Unlimited-OCR-2",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-unlimited-ocr-2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-wevisone-vl-2b.json",
        "title": "Qwen3.5-9B vs WeVisOne-VL-2B",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-wevisone-vl-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-wevisone-vl-4b.json",
        "title": "Qwen3.5-9B vs WeVisOne-VL-4B",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-wevisone-vl-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-zaya1-8b.json",
        "title": "Qwen3.5-9B vs ZAYA1-8B",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-zaya1-8b"
      }
    ],
    "panel": null,
    "scores": [
      {
        "key": "capability",
        "name": "Capability",
        "pending": true,
        "weight": 35
      },
      {
        "by": 5,
        "edge": "granite-4-2-30b",
        "granite-4-2-30b": 90,
        "key": "reach",
        "name": "Reach",
        "qwen3-5-9b": 85,
        "weight": 25
      },
      {
        "by": 0,
        "edge": "",
        "granite-4-2-30b": 55,
        "key": "risk",
        "name": "Risk",
        "qwen3-5-9b": 55,
        "weight": 20
      },
      {
        "by": 15,
        "edge": "granite-4-2-30b",
        "granite-4-2-30b": 85,
        "key": "interface",
        "name": "Agent interface",
        "qwen3-5-9b": 70,
        "weight": 10
      },
      {
        "by": 53,
        "edge": "granite-4-2-30b",
        "granite-4-2-30b": 87,
        "key": "upkeep",
        "name": "Documentation and upkeep",
        "qwen3-5-9b": 34,
        "weight": 10
      }
    ],
    "summary": "Granite 4.2 30B and Qwen3.5-9B score within a point of each other on agent readiness, 61 (C) and 60.5 (C). Both do open-weight models you run yourself.",
    "tasks": null,
    "tutorials": [],
    "verdicts": {
      "granite-4-2-30b": "IBM's largest reasoning cousin, with the same strengths as the 3B. It runs in every runtime we count from IBM's own builds on a 24 GB card, is signed, plain Apache 2.0, and comes with a disclosure of its training data. No safety results are published and structured output isn't documented.",
      "qwen3-5-9b": "A small dense vision-language model with trained tool calling, thinking that turns off per request and a 262,144-token context. Ollama's 4-bit build is 6.6 GB and the BF16 files 19.3 GB, and every major runtime loads it. The files haven't changed since 2 March 2026, Qwen answered none of the 40 latest discussions, and no safety results are published."
    }
  },
  "kind": "anchor.page",
  "links": {
    "api": "https://www.anchorterminal.com/api/v1/index.json",
    "html": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-qwen3-5-9b",
    "json": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-qwen3-5-9b.json",
    "llms": "https://www.anchorterminal.com/llms.txt",
    "markdown": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-qwen3-5-9b.md",
    "mcp": "https://www.anchorterminal.com/mcp",
    "slim": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-qwen3-5-9b.min.md"
  },
  "markdown": "Granite 4.2 30B and Qwen3.5-9B score within a point of each other on agent readiness, 61 (C) and 60.5 (C). Both do open-weight models you run yourself.\n\n- Granite 4.2 30B: grade C, 61/100, rank #65 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/granite-4-2-30b.md · JSON https://www.anchorterminal.com/api/v1/tools/granite-4-2-30b.json\n- Qwen3.5-9B: grade C, 60.5/100, rank #87 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/qwen3-5-9b.md · JSON https://www.anchorterminal.com/api/v1/tools/qwen3-5-9b.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### Granite 4.2 30B (C)\n\nGood for: Agents that want a documented, signed, Apache 2.0 reasoning model with tool calling on a 24 GB card\n\nAhead on:\n- Reach, 90 against 85\n- Agent interface, 85 against 70\n- Documentation and upkeep, 87 against 34\n\nWatch for: No safety results published for it\n\n### Qwen3.5-9B (C)\n\nGood for: Agents that want a small multimodal tool-calling model on a laptop or a 16 GB GPU, under a plain licence\n\nWatch for: No quantised build from Qwen itself\n\n\n## Why the edge\n\n### Documentation and upkeep: Granite 4.2 30B +53\n\n- Granite 4.2 30B (87): Model card 17, Intended use (best for), limitations (ethical considerations and limitations), architecture, parameters (30B), context length (128K native) and 12 tested languages are on the card. Training data 20, IBM's disclosure file for the 30B lists the datasets used in pre-training and post-training as machine-readable metadata, and the card describes the three training stages. Last change 15, the last commit to the repository was on 4 September 2026, 67 days before this check. Responsiveness 20, Of the 4 discussions that aren't pull requests (28 August to 3 November 2026), 3 have a reply from an ibm-granite member. Version policy 5, Granite 4.1 30B stays in its own repository and 4.2 came out in new ones. File health 10, Config, template and card agree on the architecture, the 131,072-token context and the thinking switch. 17 + 20 + 15 + 20 + 5 + 10 = 87.\n- Qwen3.5-9B (34): Model card 14, it covers intended use, architecture, parameters, context and 201 languages but gives no limitations and no knowledge cut-off. Training data 5, a general line about trillions of multimodal tokens with no cut-off. Last change 5, the files and card last changed on 2 March 2026 (c202236), 247 days before 4 November, within a year. Responsiveness 0, no Qwen member replied in any of the 40 most recent of the 45 discussions that aren't pull requests (2 March to 29 September 2026). Version policy 5, a first release that was never replaced in place, with no dated notes across generations. File health 5, two reports about the files and card (15 and 51) are still open with no reply. 14 + 5 + 5 + 0 + 5 + 5 = 34. The card's highlights mention sparse Mixture-of-Experts for the series while this 9B is dense, but its own overview matches config.json, so it's not counted as a contradiction.\n\n### Agent interface: Granite 4.2 30B +15\n\n- Granite 4.2 30B (85): Chat template 20, chat_template.jinja in the repository, with roles, tools, tool responses, the thinking switch and low effort. Tool-calling format 25, the card has tool-calling sections for vLLM and SGLang, declares tools with OpenAI's function schema and names the parser to use, qwen3_coder, which both runtimes ship in the released versions read. Trained tool calling 20, the template loops over every entry in an assistant turn's tool_calls, so it renders several calls in one turn, and the card names tool calling and agentic workflows as what it's best for. Structured output 0, the card doesn't document JSON or JSON Schema output or how to ask for it. Reasoning control 10, the card documents thinking on by default, non-thinking and low-effort modes per request through the template (enable_thinking and low_effort), with the reasoning returned apart from the answer. Context 10, 131,072 tokens natively per the card and config.json. 20 + 25 + 20 + 0 + 10 + 10 = 85.\n- Qwen3.5-9B (70): Chat template 20, a Jinja file in the repository. Tool-calling format 10, the card names the qwen3_coder parser for vLLM and SGLang but leaves the declaration and return format to the template, and Qwen's own function-calling guide still describes Qwen3's Hermes format. Trained tool calling 20, the template renders several calls in one turn. Structured output 0, nothing documents asking the weights for JSON. Reasoning control 10, thinking turns off per request with enable_thinking false and the reasoning comes back in think tags that the qwen3 parser splits out. Context 10, 262,144 tokens native.\n\n### Reach: Granite 4.2 30B +5\n\n- Granite 4.2 30B (90): Memory 25, IBM's own 4-bit MLX build granite-4.2-30b-q4-mlx is 16.47 GB, and 16.47 GB × 1.2 = 19.8 GB, which fits 24 GB but not 16 GB. Its Q4_0 GGUF is 16.58 GB and Ollama's library build granite4.2:30b 18 GB, the same band. Runtimes 25, Released versions run the granite architecture in llama.cpp (granite, b12242), vLLM (GraniteForCausalLM in the v0.33.0 registry), SGLang (granite.py at v0.5.23), Transformers (v5.22.2) and Ollama (its own granite4.2 library entry, 30b tag). Builds 15, IBM publishes FP8, MXFP4 and NVFP4 safetensors builds, a GGUF repository with Q4_0 to Q8_0 and BF16 files, and 4-bit, 8-bit and BF16 MLX builds, so quantised builds at two precisions or more. Terms 15, Apache 2.0, an OSI-approved licence, with no use policy or other terms added on the card. Download 10, the Hub API reports gated false for the BF16, FP8, MXFP4, NVFP4, GGUF and MLX repositories, so the files download with no account and nothing to accept. Hosted 0, Hugging Face maps one inference provider (DeepInfra, live), short of the two the line needs. 25 + 25 + 15 + 15 + 10 + 0 = 90.\n- Qwen3.5-9B (85): Memory 30, Qwen publishes no quantised 9B, so the smallest 4-bit build from a runtime's own library is Ollama's qwen3.5:9b at 6.6 GB, about 7.9 GB with the fifth added, well under 16 GB. Runtimes 25, the cap, with vLLM listed in its stable docs, Transformers at released tags, llama.cpp, Ollama's library, LM Studio's own catalogue and SGLang all running the architecture. Builds 0, Qwen's FP8 and GPTQ repositories cover only the 27B and larger Qwen3.5 models, so every quantisation of the 9B is someone else's. Terms 15, plain Apache 2.0. Download 10, the repository isn't gated. Hosted 5, Together, Featherless AI, OVHcloud and DeepInfra are live for it on its Hugging Face page. 30 + 25 + 0 + 15 + 10 + 5 = 85.\n\n## Score by category\n\n| Category | Weight | Granite 4.2 30B | Qwen3.5-9B | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 90 | 85 | Granite 4.2 30B +5 |\n| Risk | 20% (20 this run) | 55 | 55 | even |\n| Agent interface | 10% (10 this run) | 85 | 70 | Granite 4.2 30B +15 |\n| Documentation and upkeep | 10% (10 this run) | 87 | 34 | Granite 4.2 30B +53 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **61 · C** | **60.5 · C** | |\n\n## Facts side by side\n\n| Fact | Granite 4.2 30B | Qwen3.5-9B |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | IBM | Alibaba (Qwen team) |\n| Hosted endpoint | no (local only) | no (local only) |\n| Transports |  |  |\n| Auth | None | None |\n| Pricing | Free | Free |\n| x402 | no | no |\n| Licence | Apache-2.0 | Apache-2.0 |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-09-04 | 2026-03-02 |\n| Terms last updated | no document linked | no document linked |\n| Privacy policy last updated | no document linked | no document linked |\n| Agent reviews | none | 2.5/5 (4) |\n\n## Verdicts\n\n**Granite 4.2 30B.** IBM's largest reasoning cousin, with the same strengths as the 3B. It runs in every runtime we count from IBM's own builds on a 24 GB card, is signed, plain Apache 2.0, and comes with a disclosure of its training data. No safety results are published and structured output isn't documented.\n\n**Qwen3.5-9B.** A small dense vision-language model with trained tool calling, thinking that turns off per request and a 262,144-token context. Ollama's 4-bit build is 6.6 GB and the BF16 files 19.3 GB, and every major runtime loads it. The files haven't changed since 2 March 2026, Qwen answered none of the 40 latest discussions, and no safety results are published.\n\n## Before you call either\n\n### Granite 4.2 30B\n\n1. Start it with `ollama run granite4.2:30b`, or `llama-server -hf ibm-granite/granite-4.2-30b-GGUF:Q4_K_M --jinja`\n2. With vLLM 0.32.0 or later leave out `--reasoning-parser-plugin`, since the parser is built in, and use `--tool-call-parser qwen3_coder`\n3. Thinking is on by default. Pass `enable_thinking` false to turn it off or `low_effort` true for short reasoning\n4. The context is 131,072 tokens. The card's 512K extension isn't documented\n5. Validate JSON output yourself, since structured output isn't documented\n6. IBM recommends Granite Guardian beside it to flag risky inputs and outputs\n\n### Qwen3.5-9B\n\n1. Smallest start is `ollama run qwen3.5:9b` (6.6 GB, q4_k_m)\n2. On vLLM run `vllm serve Qwen/Qwen3.5-9B --port 8000 --max-model-len 262144 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder`, or tool calls come back as text\n3. Tool calls use the XML style `\u003ctool_call\u003e\u003cfunction=name\u003e\u003cparameter=x\u003e`, not Hermes JSON, so use the qwen3_coder parser and not hermes\n4. Thinking is on by default; pass `chat_template_kwargs` with `enable_thinking` false for short tasks, since `/nothink` isn't supported\n5. There's no generation_config.json, so pass sampling yourself, temperature 1.0, top_p 0.95, top_k 20, presence_penalty 1.5 when thinking, or 0.7, 0.8, 20, 1.5 without\n6. Keep at least 128K of context when thinking, and turn on YaRN only beyond 262,144 tokens\n7. Validate JSON output yourself or use the runtime's guided decoding\n\n## Questions\n\n### Which is better for AI agents, Granite 4.2 30B or Qwen3.5-9B?\n\nGranite 4.2 30B and Qwen3.5-9B score within a point of each other on agent readiness, 61 (C) and 60.5 (C).\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/granite-4-2-30b-vs-qwen3-5-9b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/granite-4-2-30b-vs-qwen3-5-9b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"granite-4-2-30b\", \"b\": \"qwen3-5-9b\"}`. From a terminal: `anchor compare granite-4-2-30b qwen3-5-9b`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/granite-4-2-30b.json and https://www.anchorterminal.com/api/v1/tools/qwen3-5-9b.json\n\n## Other comparisons with Granite 4.2 30B or Qwen3.5-9B\n\n- [Agents-A1-4B vs Granite 4.2 30B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-granite-4-2-30b.md)\n- [Agents-A1-4B vs Qwen3.5-9B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-qwen3-5-9b.md)\n- [Apertus v1.5 8B vs Granite 4.2 30B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-granite-4-2-30b.md)\n- [Apertus v1.5 8B vs Qwen3.5-9B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-qwen3-5-9b.md)\n- [Apodex-1.1-mini vs Granite 4.2 30B](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-granite-4-2-30b.md)\n- [Apodex-1.1-mini vs Qwen3.5-9B](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-qwen3-5-9b.md)\n- [Arete-27B vs Granite 4.2 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    "description": "Granite 4.2 30B and Qwen3.5-9B score within a point of each other for open-weight models you run yourself, 61 and 60.5 out of 100. Prices, MCP, x402, uptime and agent notes side by side.",
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    "title": "Granite 4.2 30B vs Qwen3.5-9B for AI agents in 2026: scores and prices",
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    "url": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-qwen3-5-9b"
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