{
  "data": {
    "a": {
      "slug": "ling-3-0-tiny",
      "name": "Ling-3.0-tiny",
      "vendor": "inclusionAI (Ant Group)",
      "vendorUrl": "https://www.inclusion-ai.org",
      "kind": "weights",
      "category": "open-weights",
      "summary": "Ling-3.0-tiny is inclusionAI's open-weight hybrid reasoning mixture of experts with 7.9B parameters and 1.3B active, mixing Kimi Delta Attention and MLA layers 3 to 1, with switchable thinking and a 131,072-token context, in BF16, FP8, INT4 and GGUF under MIT.",
      "url": "https://www.anchorterminal.com/tools/ling-3-0-tiny",
      "markdownUrl": "https://www.anchorterminal.com/tools/ling-3-0-tiny.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/ling-3-0-tiny.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/ling-3-0-tiny.json",
      "repo": "https://huggingface.co/inclusionAI/Ling-3.0-tiny",
      "license": "MIT",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "inclusionAI/Ling-3.0-tiny"
        },
        {
          "registry": "huggingface",
          "name": "inclusionAI/Ling-3.0-tiny-fp8"
        },
        {
          "registry": "huggingface",
          "name": "inclusionAI/Ling-3.0-tiny-int4"
        },
        {
          "registry": "huggingface",
          "name": "inclusionAI/Ling-3.0-tiny-GGUF"
        }
      ],
      "auth": "none",
      "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. None of the four repositories is gated (checked 2026-10-21).",
      "pricing": "free",
      "pricingNotes": "Free to download and use, commercial use included, under MIT (named in the card's metadata; the repositories hold no LICENSE file). Running it yourself costs the hardware, 15.8 GB for the BF16 files, 8.4 GB for the FP8 build, 5.8 GB for INT4 and 4.8 GB for the Q4_K_M GGUF (checked 2026-10-21).",
      "priceSummary": "Free",
      "where": "local",
      "x402": {
        "level": "no",
        "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-10-21).",
        "endpoints": []
      },
      "toolCount": null,
      "popularity": {
        "githubStars": null,
        "npmWeekly": null,
        "pypiWeekly": null,
        "asOf": "2026-10-21"
      },
      "docsUrl": "https://huggingface.co/inclusionAI/Ling-3.0-tiny",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "moe",
        "hybrid-attention",
        "reasoning",
        "tool-calling",
        "mit",
        "safetensors",
        "fp8",
        "int4",
        "gguf",
        "self-hosted"
      ],
      "lastRelease": "2026-08-31",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 55,
        "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": 1197,
        "categoryRank": 65,
        "methodology": "0.6",
        "run": "2026-10-01",
        "scores": {
          "capability": 31,
          "interface": 60,
          "reach": 90,
          "risk": 50,
          "upkeep": 56
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "high",
          "date": "2026-10-21"
        },
        "negative": 0,
        "verdict": "A small, quick hybrid reasoning cousin for local agent work, tool calls and coding, under MIT. It runs in about 6 GB from inclusionAI's 4-bit GGUF in released Transformers, vLLM, SGLang and llama.cpp, with no host. The main risks are a card that doesn't match the files in places, the vLLM route running repository code, and no model safety results.",
        "bestFor": "Agents on a laptop or small GPU that want a fast local model for tool calls and short reasoning, under MIT",
        "strengths": [
          "About 6 GB of memory for inclusionAI's own Q4_K_M GGUF, with FP8, INT4 and three more GGUF precisions",
          "Runs in released Transformers, vLLM, SGLang and llama.cpp",
          "MIT with no gate, no account and no use policy, with checksummed files in inclusionAI's own organisation",
          "Thinking switchable per request, several tool calls a turn and 131,072 tokens of native context",
          "A training-content summary with sources, size and an October 2025 cut-off, and a guardrail probe made for this model"
        ],
        "weaknesses": [
          "No hosted provider today, and Ollama support is only an unmerged pull request",
          "The card's SGLang recipe asks for built-in MTP the files don't have, and the safetensors index misstates the total size",
          "The documented vLLM route needs --trust-remote-code",
          "No safety results for the model, no structured output and no security contact",
          "Card and SGLang disagree on which tool and reasoning parsers to use, and most discussions since August have no reply"
        ],
        "agentNotes": [
          "Start it with `llama-server -hf inclusionAI/Ling-3.0-tiny-GGUF:Q4_K_M --jinja -c 32768`, which needs llama.cpp b11772 or a recent release with bailingmoe3",
          "With vLLM 0.32.0, `vllm serve inclusionAI/Ling-3.0-tiny --trust-remote-code --enable-prefix-caching --mamba-cache-mode align --enable-auto-tool-choice --tool-call-parser ling3 --reasoning-parser ling3`, which runs the repository's config code",
          "With SGLang 0.5.22, its cookbook uses `--reasoning-parser deepseek-r1 --tool-call-parser glm45` for this model, and drop the card's NEXTN flags since tiny has no MTP layer",
          "Thinking is on by default, pass `chat_template_kwargs` with `enable_thinking` false for short tasks",
          "Sampling per the card is temperature 1.0, top_p 0.95 and top_k 20",
          "The template keeps earlier reasoning in the prompt, which grows long agent sessions",
          "Validate JSON output yourself or use the runtime's guided decoding, since structured output isn't documented",
          "For a guardrail, inclusionAI's Ling-3.0-tiny-singprobe scores intent, unsafe output and hallucination per token, but only through SGLang and vLLM integration branches"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 4,
        "avgRating": 2.8,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "high",
            "grade": "C",
            "methodology": "0.6",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 55
          }
        ],
        "editorialScores": {
          "capability": 31,
          "interface": 60,
          "reach": 90,
          "risk": 50,
          "upkeep": 56
        },
        "provenanceScore": 57,
        "checked": {
          "categories": {
            "capability": "2026-10-21",
            "interface": "2026-10-21",
            "reach": "2026-10-21",
            "risk": "2026-10-21",
            "upkeep": "2026-10-21"
          },
          "last": "2026-10-21",
          "oldest": "2026-10-21",
          "stale": false,
          "staleAfterDays": 90
        }
      },
      "connect": {
        "install": "llama-server -hf inclusionAI/Ling-3.0-tiny-GGUF:Q4_K_M --jinja -c 32768\n# or with vLLM 0.32.0, per the card's serve command:\nvllm serve inclusionAI/Ling-3.0-tiny --trust-remote-code --enable-prefix-caching --mamba-cache-mode align \\\n  --enable-auto-tool-choice --tool-call-parser ling3 --reasoning-parser ling3",
        "http": "curl http://localhost:8000/v1/chat/completions \\\n  -H \"Content-Type: application/json\" \\\n  -d '{\"model\": \"inclusionAI/Ling-3.0-tiny\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}], \"chat_template_kwargs\": {\"enable_thinking\": false}, \"temperature\": 1.0, \"top_p\": 0.95, \"top_k\": 20}'"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/ling-3-0-tiny"
      },
      "sameCompany": [
        "ming-3-0-asr-tiny",
        "ming-3-0-audio-flash",
        "ming-3-0-audio-tiny",
        "ming-3-0-tts-tiny"
      ],
      "weights": {
        "trainedBy": "inclusionAI (Ant Group)",
        "params": "7.9B (1.3B active)",
        "context": 131072,
        "goodFor": "Small, fast hybrid reasoning model for local agents, tool calling and coding on a laptop, with thinking switchable per request.",
        "runtimes": [
          "llama.cpp",
          "vLLM",
          "SGLang",
          "Transformers"
        ],
        "builds": [
          {
            "name": "Ling-3.0-tiny-int4",
            "url": "https://huggingface.co/inclusionAI/Ling-3.0-tiny-int4",
            "who": "publisher",
            "precision": "int4",
            "sizeGB": 5.8,
            "memoryGB": 8,
            "format": "safetensors",
            "note": "compressed-tensors, 4-bit weights in groups of 32, with attention and dense layers kept at higher precision"
          },
          {
            "name": "Ling-3.0-tiny-Q6_K",
            "url": "https://huggingface.co/inclusionAI/Ling-3.0-tiny-GGUF/blob/main/Ling-3.0-tiny-Q6_K.gguf",
            "who": "publisher",
            "precision": "q6_k",
            "sizeGB": 6.5,
            "memoryGB": 8,
            "format": "gguf",
            "runsOn": [
              "llama.cpp"
            ]
          },
          {
            "name": "Ling-3.0-tiny-Q5_K_M",
            "url": "https://huggingface.co/inclusionAI/Ling-3.0-tiny-GGUF/blob/main/Ling-3.0-tiny-Q5_K_M.gguf",
            "who": "publisher",
            "precision": "q5_k_m",
            "sizeGB": 5.6,
            "memoryGB": 8,
            "format": "gguf",
            "runsOn": [
              "llama.cpp"
            ]
          },
          {
            "name": "Ling-3.0-tiny-Q4_K_M",
            "url": "https://huggingface.co/inclusionAI/Ling-3.0-tiny-GGUF/blob/main/Ling-3.0-tiny-Q4_K_M.gguf",
            "who": "publisher",
            "precision": "q4_k_m",
            "sizeGB": 4.8,
            "memoryGB": 8,
            "format": "gguf",
            "runsOn": [
              "llama.cpp"
            ]
          },
          {
            "name": "Ling-3.0-tiny-fp8",
            "url": "https://huggingface.co/inclusionAI/Ling-3.0-tiny-fp8",
            "who": "publisher",
            "precision": "fp8",
            "sizeGB": 8.4,
            "memoryGB": 12,
            "format": "safetensors"
          },
          {
            "name": "Ling-3.0-tiny-Q8_0",
            "url": "https://huggingface.co/inclusionAI/Ling-3.0-tiny-GGUF/blob/main/Ling-3.0-tiny-Q8_0.gguf",
            "who": "publisher",
            "precision": "q8_0",
            "sizeGB": 8.4,
            "memoryGB": 12,
            "format": "gguf",
            "runsOn": [
              "llama.cpp"
            ]
          },
          {
            "name": "Ling-3.0-tiny",
            "url": "https://huggingface.co/inclusionAI/Ling-3.0-tiny",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 15.8,
            "memoryGB": 24,
            "format": "safetensors"
          }
        ],
        "run": "llama-server -hf inclusionAI/Ling-3.0-tiny-GGUF:Q4_K_M --jinja -c 32768",
        "runBuild": "Ling-3.0-tiny-Q4_K_M",
        "checked": "2026-10-21",
        "minMemoryGB": 8
      },
      "family": {
        "lab": "inclusionAI (Ant Group)",
        "family": "Ling 3",
        "url": "https://www.anchorterminal.com/open-weights/#inclusionai",
        "others": [
          {
            "name": "Ling-3.0-flash",
            "url": "https://huggingface.co/inclusionAI/Ling-3.0-flash",
            "params": "124B total, 5.1B active"
          },
          {
            "name": "Ling-3.0-flash-base",
            "url": "https://huggingface.co/inclusionAI/Ling-3.0-flash-base",
            "params": "124B total, 5.1B active"
          },
          {
            "name": "Ling-3.0-flash-base-30T",
            "url": "https://huggingface.co/inclusionAI/Ling-3.0-flash-base-30T",
            "params": "124B total, 5.1B active"
          },
          {
            "name": "Ling-3.0-flash-base-midtrain",
            "url": "https://huggingface.co/inclusionAI/Ling-3.0-flash-base-midtrain",
            "params": "124B total, 5.1B active"
          },
          {
            "name": "Ling-3.0-flash-Fin",
            "url": "https://huggingface.co/inclusionAI/Ling-3.0-flash-Fin",
            "params": "124B total, 5.1B active"
          },
          {
            "name": "Ling-3.0-flash-VL",
            "url": "https://huggingface.co/inclusionAI/Ling-3.0-flash-VL",
            "params": "124B total, 5.5B active"
          },
          {
            "name": "Ling-3.0-flash-dspark",
            "url": "https://huggingface.co/inclusionAI/Ling-3.0-flash-dspark",
            "params": "1.36B (draft)"
          },
          {
            "name": "Ling-3.0-flash-singprobe",
            "url": "https://huggingface.co/inclusionAI/Ling-3.0-flash-singprobe",
            "params": "5.18M (probe)"
          },
          {
            "name": "Ling-3.0-tiny-base",
            "url": "https://huggingface.co/inclusionAI/Ling-3.0-tiny-base",
            "params": "7.9B total, 1.3B active"
          },
          {
            "name": "Ling-3.0-tiny-base-30T",
            "url": "https://huggingface.co/inclusionAI/Ling-3.0-tiny-base-30T",
            "params": "7.9B total, 1.3B active"
          },
          {
            "name": "Ling-3.0-tiny-base-midtrain",
            "url": "https://huggingface.co/inclusionAI/Ling-3.0-tiny-base-midtrain",
            "params": "7.9B total, 1.3B active"
          },
          {
            "name": "Ling-3.0-tiny-singprobe",
            "url": "https://huggingface.co/inclusionAI/Ling-3.0-tiny-singprobe",
            "params": "3.22M (probe)"
          },
          {
            "name": "Ling-3.1-flash",
            "url": "https://huggingface.co/inclusionAI/Ling-3.1-flash",
            "params": "560B total, 25B active"
          }
        ],
        "olderCount": 54,
        "unrestrictedCopies": 5
      },
      "area": "models",
      "provenance": {
        "legalEntity": "Ant Intelligence (Hang Zhou) Technology Co., Ltd (named as provider in the training-content summary); inclusionAI, Ant Group in the site footer of inclusion-ai.org",
        "domain": "inclusion-ai.org",
        "domainRegistered": "2025-04-03",
        "endpointOnVendorDomain": null,
        "terms": "",
        "privacy": "",
        "statusPage": "",
        "changelog": "https://huggingface.co/inclusionAI/Ling-3.0-tiny/commits/main",
        "securityTxt": "none",
        "checked": "2026-10-21",
        "notes": [
          "inclusion-ai.org links to huggingface.co/inclusionAI and github.com/inclusionAI. The domain registration date is carried from the RDAP check made for the Ming 3.0 listings on 21 October 2026.",
          "Nothing is hosted, so the MIT licence stands in for terms of service and no privacy policy is scored.",
          "The changelog is the Hugging Face commit history, 17 commits from 10 August to 24 September 2026, mostly README updates, the last adding the training-content summary.",
          "inclusion-ai.org/.well-known/security.txt answered 404 on 21 October 2026, and the inclusionAI .github repository holds only a profile README."
        ],
        "score": 57
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/ling-3-0-tiny.json"
    },
    "answer": "Qwen3.5-9B scores 60.5 (C) on agent readiness against Ling-3.0-tiny's 55 (C), and leads in 3 of 5 scored categories. Ling-3.0-tiny leads on reach and documentation and upkeep.",
    "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",
      "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": 1197,
        "categoryRank": 46,
        "methodology": "0.6",
        "run": "2026-10-01",
        "scores": {
          "capability": 51,
          "interface": 70,
          "reach": 85,
          "risk": 55,
          "upkeep": 34
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "high",
          "date": "2026-10-21"
        },
        "negative": 0,
        "verdict": "A small dense vision-language cousin with trained tool calling, switchable thinking and a 262,144-token context, for local agent subtasks. Ollama's 4-bit build needs about 8 GB and the BF16 files about 24 GB, in every major runtime. The main risk is neglect, with no file changes since March, no replies from Qwen to reports and no safety results.",
        "bestFor": "Agents that want a small multimodal tool-calling cousin 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 fits in about 8 GB, the BF16 files in about 24 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": "0.6",
            "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": 49,
        "checked": {
          "categories": {
            "capability": "2026-10-21",
            "interface": "2026-10-21",
            "reach": "2026-10-21",
            "risk": "2026-10-21",
            "upkeep": "2026-10-21"
          },
          "last": "2026-10-21",
          "oldest": "2026-10-21",
          "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",
        "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",
        "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": "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": 49
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/qwen3-5-9b.json"
    },
    "facts": [
      {
        "a": "Open-weight model",
        "b": "Open-weight model",
        "name": "Kind"
      },
      {
        "a": "inclusionAI (Ant Group)",
        "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": "MIT",
        "b": "Apache-2.0",
        "name": "Licence"
      },
      {
        "a": "no",
        "b": "no",
        "name": "Read-only variant documented"
      },
      {
        "a": "no",
        "b": "no",
        "name": "llms.txt"
      },
      {
        "a": "2026-08-31",
        "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": "",
        "b": "",
        "name": "Customer content may train models"
      },
      {
        "a": "",
        "b": "",
        "name": "Terms restrict automated access"
      },
      {
        "a": "",
        "b": "",
        "name": "Terms restrict benchmarking"
      },
      {
        "a": "",
        "b": "",
        "name": "Terms or service can change without notice"
      },
      {
        "a": "",
        "b": "",
        "name": "Arbitration or class-action waiver"
      },
      {
        "a": "2.8/5 (4)",
        "b": "2.5/5 (4)",
        "name": "Agent reviews"
      }
    ],
    "faq": [
      {
        "answer": "Qwen3.5-9B scores 60.5 (C) on agent readiness against Ling-3.0-tiny's 55 (C), and leads in 3 of 5 scored categories. Ling-3.0-tiny leads on reach and documentation and upkeep.",
        "question": "Which is better for AI agents, Ling-3.0-tiny or Qwen3.5-9B?"
      }
    ],
    "goodFor": [
      {
        "aheadOn": [
          "Reach, 90 against 85",
          "Documentation and upkeep, 56 against 34"
        ],
        "also": null,
        "goodFor": "Agents on a laptop or small GPU that want a fast local model for tool calls and short reasoning, under MIT",
        "slug": "ling-3-0-tiny",
        "watchFor": "No hosted provider today, and Ollama support is only an unmerged pull request"
      },
      {
        "aheadOn": [
          "Capability, 51 against 31",
          "Risk, 55 against 50",
          "Agent interface, 70 against 60"
        ],
        "also": null,
        "goodFor": "Agents that want a small multimodal tool-calling cousin on a laptop or a 16 GB GPU, under a plain licence",
        "slug": "qwen3-5-9b",
        "watchFor": "No quantised build from Qwen itself"
      }
    ],
    "job": {
      "capability": "model.open-weights",
      "name": "Open-weight models you run yourself"
    },
    "others": [
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-ling-3-0-tiny.json",
        "title": "Agents-A1-4B vs Ling-3.0-tiny",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-ling-3-0-tiny"
      },
      {
        "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-ling-3-0-tiny.json",
        "title": "Apertus v1.5 8B vs Ling-3.0-tiny",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-ling-3-0-tiny"
      },
      {
        "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-ling-3-0-tiny.json",
        "title": "Apodex-1.1-mini vs Ling-3.0-tiny",
        "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-ling-3-0-tiny"
      },
      {
        "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/bigbang-v1-vs-ling-3-0-tiny.json",
        "title": "BigBang-v1 vs Ling-3.0-tiny",
        "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-ling-3-0-tiny"
      },
      {
        "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/cosmos-reason2-2b-vs-ling-3-0-tiny.json",
        "title": "Cosmos-Reason2-2B vs Ling-3.0-tiny",
        "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-ling-3-0-tiny"
      },
      {
        "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-ling-3-0-tiny.json",
        "title": "Darwin-36B-Opus vs Ling-3.0-tiny",
        "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-ling-3-0-tiny"
      },
      {
        "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-ling-3-0-tiny.json",
        "title": "DeepSeek-V3.2 vs Ling-3.0-tiny",
        "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-ling-3-0-tiny"
      },
      {
        "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-ling-3-0-tiny.json",
        "title": "DeepSeek V4.1 Flash vs Ling-3.0-tiny",
        "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-ling-3-0-tiny"
      },
      {
        "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-ling-3-0-tiny.json",
        "title": "DeepSeek-V4-Flash-0731 vs Ling-3.0-tiny",
        "url": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-ling-3-0-tiny"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-qwen3-5-9b.json",
        "title": "DeepSeek-V4-Flash-0731 vs Qwen3.5-9B",
        "url": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-qwen3-5-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-ling-3-0-tiny.json",
        "title": "DiffusionGemma 26B A4B IT vs Ling-3.0-tiny",
        "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-ling-3-0-tiny"
      },
      {
        "json": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-qwen3-5-9b.json",
        "title": "DiffusionGemma 26B A4B IT vs Qwen3.5-9B",
        "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-qwen3-5-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-ling-3-0-tiny.json",
        "title": "Gemma 4 12B IT vs Ling-3.0-tiny",
        "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-ling-3-0-tiny"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-qwen3-5-9b.json",
        "title": "Gemma 4 12B IT vs Qwen3.5-9B",
        "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-qwen3-5-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-ling-3-0-tiny.json",
        "title": "Gemma 4 26B A4B IT vs Ling-3.0-tiny",
        "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-ling-3-0-tiny"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-qwen3-5-9b.json",
        "title": "Gemma 4 26B A4B IT vs Qwen3.5-9B",
        "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-qwen3-5-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-ling-3-0-tiny.json",
        "title": "Gemma 4 31B IT vs Ling-3.0-tiny",
        "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-ling-3-0-tiny"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-5-9b.json",
        "title": "Gemma 4 31B IT vs Qwen3.5-9B",
        "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-5-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-ling-3-0-tiny.json",
        "title": "Gemma 4 E2B IT vs Ling-3.0-tiny",
        "url": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-ling-3-0-tiny"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-qwen3-5-9b.json",
        "title": "Gemma 4 E2B IT vs Qwen3.5-9B",
        "url": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-qwen3-5-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-ling-3-0-tiny.json",
        "title": "Gemma 4 E4B IT vs Ling-3.0-tiny",
        "url": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-ling-3-0-tiny"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-qwen3-5-9b.json",
        "title": "Gemma 4 E4B IT vs Qwen3.5-9B",
        "url": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-qwen3-5-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-ling-3-0-tiny.json",
        "title": "GLM-4.5-Air vs Ling-3.0-tiny",
        "url": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-ling-3-0-tiny"
      },
      {
        "json": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-qwen3-5-9b.json",
        "title": "GLM-4.5-Air vs Qwen3.5-9B",
        "url": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-qwen3-5-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-ling-3-0-tiny.json",
        "title": "GLM-4.7-Flash vs Ling-3.0-tiny",
        "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-ling-3-0-tiny"
      },
      {
        "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwen3-5-9b.json",
        "title": "GLM-4.7-Flash vs Qwen3.5-9B",
        "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwen3-5-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-ling-3-0-tiny.json",
        "title": "GLM-5.3-Flash vs Ling-3.0-tiny",
        "url": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-ling-3-0-tiny"
      },
      {
        "json": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-qwen3-5-9b.json",
        "title": "GLM-5.3-Flash vs Qwen3.5-9B",
        "url": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-qwen3-5-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/glm-5-3-vs-ling-3-0-tiny.json",
        "title": "GLM-5.3 vs Ling-3.0-tiny",
        "url": "https://www.anchorterminal.com/compare/glm-5-3-vs-ling-3-0-tiny"
      },
      {
        "json": "https://www.anchorterminal.com/compare/glm-5-3-vs-qwen3-5-9b.json",
        "title": "GLM-5.3 vs Qwen3.5-9B",
        "url": "https://www.anchorterminal.com/compare/glm-5-3-vs-qwen3-5-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-ling-3-0-tiny.json",
        "title": "gpt-oss-120b vs Ling-3.0-tiny",
        "url": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-ling-3-0-tiny"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-qwen3-5-9b.json",
        "title": "gpt-oss-120b vs Qwen3.5-9B",
        "url": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-qwen3-5-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-ling-3-0-tiny.json",
        "title": "gpt-oss-20b vs Ling-3.0-tiny",
        "url": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-ling-3-0-tiny"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-qwen3-5-9b.json",
        "title": "gpt-oss-20b vs Qwen3.5-9B",
        "url": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-qwen3-5-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-ling-3-0-tiny.json",
        "title": "Granite 4.2 3B vs Ling-3.0-tiny",
        "url": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-ling-3-0-tiny"
      },
      {
        "json": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-qwen3-5-9b.json",
        "title": "Granite 4.2 3B vs Qwen3.5-9B",
        "url": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-qwen3-5-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/hy3-vs-ling-3-0-tiny.json",
        "title": "Hy3 vs Ling-3.0-tiny",
        "url": "https://www.anchorterminal.com/compare/hy3-vs-ling-3-0-tiny"
      },
      {
        "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"
      },
      {
        "json": "https://www.anchorterminal.com/compare/inkling-small-vs-ling-3-0-tiny.json",
        "title": "Inkling-Small vs Ling-3.0-tiny",
        "url": "https://www.anchorterminal.com/compare/inkling-small-vs-ling-3-0-tiny"
      },
      {
        "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"
      },
      {
        "json": "https://www.anchorterminal.com/compare/inkling-vs-ling-3-0-tiny.json",
        "title": "Inkling vs Ling-3.0-tiny",
        "url": "https://www.anchorterminal.com/compare/inkling-vs-ling-3-0-tiny"
      },
      {
        "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"
      },
      {
        "json": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-ling-3-0-tiny.json",
        "title": "InternVL3.5-GPT-OSS-20B-A4B-Preview vs Ling-3.0-tiny",
        "url": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-ling-3-0-tiny"
      },
      {
        "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"
      },
      {
        "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-ling-3-0-tiny.json",
        "title": "Kimi K3 vs Ling-3.0-tiny",
        "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-ling-3-0-tiny"
      },
      {
        "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"
      },
      {
        "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-ling-3-0-tiny.json",
        "title": "Kolibri-1 vs Ling-3.0-tiny",
        "url": "https://www.anchorterminal.com/compare/kolibri-1-vs-ling-3-0-tiny"
      },
      {
        "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"
      },
      {
        "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-ling-3-0-tiny.json",
        "title": "Laguna M.1 vs Ling-3.0-tiny",
        "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-ling-3-0-tiny"
      },
      {
        "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"
      },
      {
        "json": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-ling-3-0-tiny.json",
        "title": "Laguna S 2.1 vs Ling-3.0-tiny",
        "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-ling-3-0-tiny"
      },
      {
        "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"
      },
      {
        "json": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-ling-3-0-tiny.json",
        "title": "Laguna XS 2.1 vs Ling-3.0-tiny",
        "url": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-ling-3-0-tiny"
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        "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-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-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"
      }
    ],
    "scores": [
      {
        "by": 20,
        "edge": "qwen3-5-9b",
        "key": "capability",
        "ling-3-0-tiny": 31,
        "name": "Capability",
        "qwen3-5-9b": 51,
        "weight": 35
      },
      {
        "by": 5,
        "edge": "ling-3-0-tiny",
        "key": "reach",
        "ling-3-0-tiny": 90,
        "name": "Reach",
        "qwen3-5-9b": 85,
        "weight": 25
      },
      {
        "by": 5,
        "edge": "qwen3-5-9b",
        "key": "risk",
        "ling-3-0-tiny": 50,
        "name": "Risk",
        "qwen3-5-9b": 55,
        "weight": 20
      },
      {
        "by": 10,
        "edge": "qwen3-5-9b",
        "key": "interface",
        "ling-3-0-tiny": 60,
        "name": "Agent interface",
        "qwen3-5-9b": 70,
        "weight": 10
      },
      {
        "by": 22,
        "edge": "ling-3-0-tiny",
        "key": "upkeep",
        "ling-3-0-tiny": 56,
        "name": "Documentation and upkeep",
        "qwen3-5-9b": 34,
        "weight": 10
      }
    ],
    "summary": "Qwen3.5-9B scores 60.5 (C) on agent readiness against Ling-3.0-tiny's 55 (C), and leads in 3 of 5 scored categories. Ling-3.0-tiny leads on reach and documentation and upkeep. Both do open-weight models you run yourself.",
    "tutorials": [],
    "verdicts": {
      "ling-3-0-tiny": "A small, quick hybrid reasoning cousin for local agent work, tool calls and coding, under MIT. It runs in about 6 GB from inclusionAI's 4-bit GGUF in released Transformers, vLLM, SGLang and llama.cpp, with no host. The main risks are a card that doesn't match the files in places, the vLLM route running repository code, and no model safety results.",
      "qwen3-5-9b": "A small dense vision-language cousin with trained tool calling, switchable thinking and a 262,144-token context, for local agent subtasks. Ollama's 4-bit build needs about 8 GB and the BF16 files about 24 GB, in every major runtime. The main risk is neglect, with no file changes since March, no replies from Qwen to reports and no safety results."
    }
  },
  "kind": "anchor.page",
  "links": {
    "api": "https://www.anchorterminal.com/api/v1/index.json",
    "html": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwen3-5-9b",
    "json": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwen3-5-9b.json",
    "llms": "https://www.anchorterminal.com/llms.txt",
    "markdown": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwen3-5-9b.md",
    "mcp": "https://www.anchorterminal.com/mcp",
    "slim": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwen3-5-9b.min.md"
  },
  "markdown": "Qwen3.5-9B scores 60.5 (C) on agent readiness against Ling-3.0-tiny's 55 (C), and leads in 3 of 5 scored categories. Ling-3.0-tiny leads on reach and documentation and upkeep. Both do open-weight models you run yourself.\n\n- Ling-3.0-tiny: grade C, 55/100, rank #81 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/ling-3-0-tiny.md · JSON https://www.anchorterminal.com/api/v1/tools/ling-3-0-tiny.json\n- Qwen3.5-9B: grade C, 60.5/100, rank #53 of 100 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 2870 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md\n\n## Which one, for what\n\n### Ling-3.0-tiny (C)\n\nGood for: Agents on a laptop or small GPU that want a fast local model for tool calls and short reasoning, under MIT\n\nAhead on:\n- Reach, 90 against 85\n- Documentation and upkeep, 56 against 34\n\nWatch for: No hosted provider today, and Ollama support is only an unmerged pull request\n\n### Qwen3.5-9B (C)\n\nGood for: Agents that want a small multimodal tool-calling cousin on a laptop or a 16 GB GPU, under a plain licence\n\nAhead on:\n- Capability, 51 against 31\n- Risk, 55 against 50\n- Agent interface, 70 against 60\n\nWatch for: No quantised build from Qwen itself\n\n\n## Score by category\n\n| Category | Weight | Ling-3.0-tiny | Qwen3.5-9B | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35% (35 this run) | 31 | 51 | Qwen3.5-9B +20 |\n| Reach | 25% (25 this run) | 90 | 85 | Ling-3.0-tiny +5 |\n| Risk | 20% (20 this run) | 50 | 55 | Qwen3.5-9B +5 |\n| Agent interface | 10% (10 this run) | 60 | 70 | Qwen3.5-9B +10 |\n| Documentation and upkeep | 10% (10 this run) | 56 | 34 | Ling-3.0-tiny +22 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **55 · C** | **60.5 · C** | |\n\n## Facts side by side\n\n| Fact | Ling-3.0-tiny | Qwen3.5-9B |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | inclusionAI (Ant Group) | 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 | MIT | Apache-2.0 |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-08-31 | 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| Customer content may train models |  |  |\n| Terms restrict automated access |  |  |\n| Terms restrict benchmarking |  |  |\n| Terms or service can change without notice |  |  |\n| Arbitration or class-action waiver |  |  |\n| Agent reviews | 2.8/5 (4) | 2.5/5 (4) |\n\n## Verdicts\n\n**Ling-3.0-tiny.** A small, quick hybrid reasoning cousin for local agent work, tool calls and coding, under MIT. It runs in about 6 GB from inclusionAI's 4-bit GGUF in released Transformers, vLLM, SGLang and llama.cpp, with no host. The main risks are a card that doesn't match the files in places, the vLLM route running repository code, and no model safety results.\n\n**Qwen3.5-9B.** A small dense vision-language cousin with trained tool calling, switchable thinking and a 262,144-token context, for local agent subtasks. Ollama's 4-bit build needs about 8 GB and the BF16 files about 24 GB, in every major runtime. The main risk is neglect, with no file changes since March, no replies from Qwen to reports and no safety results.\n\n## Before you call either\n\n### Ling-3.0-tiny\n\n1. Start it with `llama-server -hf inclusionAI/Ling-3.0-tiny-GGUF:Q4_K_M --jinja -c 32768`, which needs llama.cpp b11772 or a recent release with bailingmoe3\n2. With vLLM 0.32.0, `vllm serve inclusionAI/Ling-3.0-tiny --trust-remote-code --enable-prefix-caching --mamba-cache-mode align --enable-auto-tool-choice --tool-call-parser ling3 --reasoning-parser ling3`, which runs the repository's config code\n3. With SGLang 0.5.22, its cookbook uses `--reasoning-parser deepseek-r1 --tool-call-parser glm45` for this model, and drop the card's NEXTN flags since tiny has no MTP layer\n4. Thinking is on by default, pass `chat_template_kwargs` with `enable_thinking` false for short tasks\n5. Sampling per the card is temperature 1.0, top_p 0.95 and top_k 20\n6. The template keeps earlier reasoning in the prompt, which grows long agent sessions\n7. Validate JSON output yourself or use the runtime's guided decoding, since structured output isn't documented\n8. For a guardrail, inclusionAI's Ling-3.0-tiny-singprobe scores intent, unsafe output and hallucination per token, but only through SGLang and vLLM integration branches\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, Ling-3.0-tiny or Qwen3.5-9B?\n\nQwen3.5-9B scores 60.5 (C) on agent readiness against Ling-3.0-tiny's 55 (C), and leads in 3 of 5 scored categories. Ling-3.0-tiny leads on reach and documentation and upkeep.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwen3-5-9b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwen3-5-9b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"ling-3-0-tiny\", \"b\": \"qwen3-5-9b\"}`. From a terminal: `anchor compare ling-3-0-tiny qwen3-5-9b`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/ling-3-0-tiny.json and https://www.anchorterminal.com/api/v1/tools/qwen3-5-9b.json\n\n## Other comparisons with Ling-3.0-tiny or Qwen3.5-9B\n\n- [Agents-A1-4B vs Ling-3.0-tiny](https://www.anchorterminal.com/compare/agents-a1-4b-vs-ling-3-0-tiny.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 Ling-3.0-tiny](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-ling-3-0-tiny.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 Ling-3.0-tiny](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-ling-3-0-tiny.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- [BigBang-v1 vs Ling-3.0-tiny](https://www.anchorterminal.com/compare/bigbang-v1-vs-ling-3-0-tiny.md)\n- [BigBang-v1 vs Qwen3.5-9B](https://www.anchorterminal.com/compare/bigbang-v1-vs-qwen3-5-9b.md)\n- [Cosmos-Reason2-2B vs Ling-3.0-tiny](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-ling-3-0-tiny.md)\n- [Cosmos-Reason2-2B vs Qwen3.5-9B](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-qwen3-5-9b.md)\n- [Darwin-36B-Opus vs Ling-3.0-tiny](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-ling-3-0-tiny.md)\n- [Darwin-36B-Opus vs Qwen3.5-9B](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-qwen3-5-9b.md)\n- [DeepSeek-V3.2 vs Ling-3.0-tiny](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-ling-3-0-tiny.md)\n- [DeepSeek-V3.2 vs Qwen3.5-9B](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-qwen3-5-9b.md)\n- [DeepSeek V4.1 Flash vs Ling-3.0-tiny](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-ling-3-0-tiny.md)\n- [DeepSeek V4.1 Flash vs 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