{
  "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-Coder-Next scores 57.6 (C) on agent readiness against Ling-3.0-tiny's 55 (C), and leads in 2 of 5 scored categories. Ling-3.0-tiny leads on reach and documentation and upkeep.",
    "b": {
      "slug": "qwen3-coder-next",
      "name": "Qwen3-Coder-Next",
      "vendor": "Alibaba (Qwen team)",
      "vendorUrl": "https://qwen.ai",
      "kind": "weights",
      "category": "open-weights",
      "summary": "Qwen3-Coder-Next is Alibaba's open-weight coding-agent model, an 80B mixture-of-experts with 3B active and no thinking mode, trained for tool calling across many agent scaffolds with a 262,144-token context, published on Hugging Face in BF16, FP8 and GGUF under Apache 2.0.",
      "url": "https://www.anchorterminal.com/tools/qwen3-coder-next",
      "markdownUrl": "https://www.anchorterminal.com/tools/qwen3-coder-next.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/qwen3-coder-next.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/qwen3-coder-next.json",
      "repo": "https://huggingface.co/Qwen/Qwen3-Coder-Next",
      "license": "Apache-2.0",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "Qwen/Qwen3-Coder-Next"
        },
        {
          "registry": "huggingface",
          "name": "Qwen/Qwen3-Coder-Next-FP8"
        },
        {
          "registry": "huggingface",
          "name": "Qwen/Qwen3-Coder-Next-GGUF"
        },
        {
          "registry": "ollama",
          "name": "qwen3-coder-next"
        }
      ],
      "auth": "none",
      "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. None of the three repositories is gated (checked 2026-10-21).",
      "pricing": "free",
      "pricingNotes": "Free to download and use, commercial use included, under the Apache 2.0 licence the card's metadata names, though no LICENSE file is in the repositories. Featherless AI and Parasail serve it per token. Running it yourself costs the hardware, 159.4 GB for the BF16 files, 80.4 GB in FP8 and 48.4 GB for Alibaba's own Q4_K_M GGUF (checked 2026-10-21).",
      "priceSummary": "Free",
      "where": "local",
      "x402": {
        "level": "no",
        "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-10-21).",
        "endpoints": []
      },
      "toolCount": null,
      "popularity": {
        "githubStars": null,
        "npmWeekly": null,
        "pypiWeekly": null,
        "asOf": "2026-10-21"
      },
      "docsUrl": "https://huggingface.co/Qwen/Qwen3-Coder-Next",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "moe",
        "coding",
        "tool-calling",
        "apache-2.0",
        "safetensors",
        "fp8",
        "gguf",
        "self-hosted"
      ],
      "lastRelease": "2026-02-03",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 57.6,
        "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": 56,
        "methodology": "0.6",
        "run": "2026-10-01",
        "scores": {
          "capability": 42,
          "interface": 75,
          "reach": 85,
          "risk": 50,
          "upkeep": 41
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "high",
          "date": "2026-10-21"
        },
        "negative": 0,
        "verdict": "A fast coding cousin for agent harnesses, with 3B active parameters and tool calling trained across many scaffold formats for Claude Code, Cline or Qwen Code loops. Alibaba's 4-bit GGUF needs about 58 GB, fitting one 64 GB machine, and two providers host it. The main risk is neglect, with no change since February, no replies and no safety results.",
        "bestFor": "Agents that want a local coding model for long tool-calling loops on a single 64 GB to 128 GB machine.",
        "strengths": [
          "Tool-calling format documented by Alibaba, with the parser named and shipped",
          "Trained on 21 tool chat templates, several calls per turn and 262,144 tokens native",
          "Alibaba's own FP8 and GGUF builds, the Q4_K_M about 58 GB in use",
          "Released support in llama.cpp, Ollama, vLLM, SGLang, LM Studio and Transformers",
          "A technical report with data sources, a cut-off and filtering",
          "No account or gate, safetensors and GGUF only"
        ],
        "weaknesses": [
          "No LICENSE file anywhere and a broken licence link on the card",
          "No safety results, agent-use guidance or reporting route for flaws",
          "No reply from Alibaba to any discussion, including a broken shipped parser",
          "No change to the files since 3 February 2026",
          "No thinking mode and no documented structured output"
        ],
        "agentNotes": [
          "Quickest start is `ollama run qwen3-coder-next` (Q4_K_M, 52 GB of files)",
          "On our reference machine Alibaba's Q8_0 GGUF (84.8 GB, about 102 GB in use) also fits, run with `llama-server -m Qwen3-Coder-Next-Q8_0/Qwen3-Coder-Next-Q8_0-00001-of-00004.gguf --jinja -ngl 99 -fa on -c 131072`",
          "Serve with `vllm serve Qwen/Qwen3-Coder-Next --enable-auto-tool-choice --tool-call-parser qwen3_coder` (vLLM 0.15.0 or later), using the built-in parser rather than the repository's qwen3coder_tool_parser_vllm.py, which fails to import on newer vLLM per discussion 36",
          "The card's commands use tensor parallel 2 though the text says 4 GPUs, and it suggests cutting the context to 32,768 if the server fails to start",
          "There's no thinking mode, so don't pass `enable_thinking` or wait for reasoning_content",
          "Sampling per the card is temperature 1.0, top_p 0.95 and top_k 40",
          "The GGUF card's llama.cpp example path omits the quant from the file name; the real files are named like Qwen3-Coder-Next-Q4_K_M-00001-of-00004.gguf",
          "Validate any JSON it returns, since structured output isn't documented"
        ],
        "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": 57.6
          }
        ],
        "editorialScores": {
          "capability": 42,
          "interface": 75,
          "reach": 85,
          "risk": 50,
          "upkeep": 41
        },
        "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-coder-next\n# or, with the card's vLLM command:\nvllm serve Qwen/Qwen3-Coder-Next-FP8 --port 8000 --tensor-parallel-size 2 \\\n  --enable-auto-tool-choice --tool-call-parser qwen3_coder",
        "http": "curl http://localhost:11434/api/chat \\\n  -d '{\"model\": \"qwen3-coder-next\", \"messages\": [{\"role\": \"user\", \"content\": \"Write a quick sort in Go.\"}]}'"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/qwen3-coder-next"
      },
      "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-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",
        "qwen3-5-9b",
        "qwen3-6-27b",
        "qwen3-6-35b-a3b",
        "qwen3-8-27b",
        "qwen3-8-flash-next"
      ],
      "weights": {
        "trainedBy": "Alibaba (Qwen team)",
        "builtOn": "Qwen3-Next-80B-A3B-Base (named in github.com/QwenLM/Qwen3-Coder, not public on the Hub)",
        "params": "80B total, 3B active",
        "context": 262144,
        "goodFor": "Fast coding-agent model for local development, text only, trained for tool calling across many agent harness formats, no thinking mode.",
        "runtimes": [
          "llama.cpp",
          "Ollama",
          "LM Studio",
          "vLLM",
          "SGLang",
          "Transformers"
        ],
        "builds": [
          {
            "name": "Qwen3-Coder-Next-GGUF Q4_K_M",
            "url": "https://huggingface.co/Qwen/Qwen3-Coder-Next-GGUF",
            "who": "publisher",
            "precision": "q4_k_m",
            "sizeGB": 48.4,
            "memoryGB": 64,
            "format": "gguf",
            "runsOn": [
              "llama.cpp"
            ],
            "note": "Four split files in the Qwen3-Coder-Next-Q4_K_M folder, summed from the Hub's file list. The same repository has Q5_0 (55.0 GB), Q5_K_M (56.7 GB), Q6_K (65.5 GB) and F16 (159.5 GB)"
          },
          {
            "name": "ollama qwen3-coder-next:latest",
            "url": "https://ollama.com/library/qwen3-coder-next:latest",
            "who": "ollama",
            "precision": "q4_k_m",
            "sizeGB": 52,
            "memoryGB": 64,
            "format": "ollama",
            "runsOn": [
              "Ollama"
            ],
            "note": "Q4_K_M (digest ca06e9e4087c), also the q4_K_M tag. The q8_0 tag is 85 GB. Size as Ollama's tags page shows it, rounded to whole GB above 10 GB"
          },
          {
            "name": "lmstudio-community Qwen3-Coder-Next-MLX-4bit",
            "url": "https://huggingface.co/lmstudio-community/Qwen3-Coder-Next-MLX-4bit",
            "who": "lmstudio",
            "precision": "mlx-4bit",
            "sizeGB": 44.9,
            "memoryGB": 64,
            "format": "mlx",
            "runsOn": [
              "LM Studio (Apple silicon)"
            ],
            "note": "The smallest build at 4 bits or better from the publisher or a runtime's library, summed from the Hub's file list. LM Studio's catalogue entry states a 42 GB minimum. lmstudio-community's GGUF Q4_K_M is 48.5 GB"
          },
          {
            "name": "Qwen3-Coder-Next-FP8",
            "url": "https://huggingface.co/Qwen/Qwen3-Coder-Next-FP8",
            "who": "publisher",
            "precision": "fp8",
            "sizeGB": 80.4,
            "memoryGB": 128,
            "format": "safetensors"
          },
          {
            "name": "Qwen3-Coder-Next-GGUF Q8_0",
            "url": "https://huggingface.co/Qwen/Qwen3-Coder-Next-GGUF",
            "who": "publisher",
            "precision": "q8_0",
            "sizeGB": 84.8,
            "memoryGB": 128,
            "format": "gguf",
            "runsOn": [
              "llama.cpp"
            ],
            "note": "Four split files in the Qwen3-Coder-Next-Q8_0 folder. 84.8 GB × 1.2 = 101.8 GB, within the reference machine's 107.4 GB"
          },
          {
            "name": "Qwen3-Coder-Next",
            "url": "https://huggingface.co/Qwen/Qwen3-Coder-Next",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 159.4,
            "memoryGB": 192,
            "format": "safetensors",
            "runsOn": [
              "vLLM (ROCm)",
              "SGLang (ROCm)"
            ]
          }
        ],
        "run": "hf download Qwen/Qwen3-Coder-Next-GGUF --include \"Qwen3-Coder-Next-Q4_K_M/*\" --local-dir . \u0026\u0026 llama-server -m Qwen3-Coder-Next-Q4_K_M/Qwen3-Coder-Next-Q4_K_M-00001-of-00004.gguf --jinja -ngl 99 -fa on -c 131072 --temp 1.0 --top-k 40 --top-p 0.95 --min-p 0",
        "runBuild": "Qwen3-Coder-Next-GGUF Q4_K_M",
        "checked": "2026-10-21",
        "minMemoryGB": 64
      },
      "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-Base",
            "url": "https://huggingface.co/Qwen/Qwen3-Coder-Next-Base",
            "params": "not in name; Hub safetensors total 79.7B"
          },
          {
            "name": "WebWorld-14B",
            "url": "https://huggingface.co/Qwen/WebWorld-14B",
            "params": "14B"
          },
          {
            "name": "WebWorld-32B",
            "url": "https://huggingface.co/Qwen/WebWorld-32B",
            "params": "32B"
          },
          {
            "name": "WebWorld-8B",
            "url": "https://huggingface.co/Qwen/WebWorld-8B",
            "params": "8B"
          },
          {
            "name": "Qwen3.5-397B-A17B",
            "url": "https://huggingface.co/Qwen/Qwen3.5-397B-A17B",
            "params": "397B-A17B",
            "listing": "https://www.anchorterminal.com/tools/qwen3-5-397b-a17b"
          },
          {
            "name": "Qwen3.5-122B-A10B",
            "url": "https://huggingface.co/Qwen/Qwen3.5-122B-A10B",
            "params": "122B-A10B",
            "listing": "https://www.anchorterminal.com/tools/qwen3-5-122b-a10b"
          },
          {
            "name": "Qwen3.5-27B",
            "url": "https://huggingface.co/Qwen/Qwen3.5-27B",
            "params": "27B"
          },
          {
            "name": "Qwen3.5-35B-A3B",
            "url": "https://huggingface.co/Qwen/Qwen3.5-35B-A3B",
            "params": "35B-A3B"
          },
          {
            "name": "Qwen3.5-35B-A3B-Base",
            "url": "https://huggingface.co/Qwen/Qwen3.5-35B-A3B-Base",
            "params": "35B-A3B"
          },
          {
            "name": "Qwen3.5-9B-Base",
            "url": "https://huggingface.co/Qwen/Qwen3.5-9B-Base",
            "params": "9B"
          },
          {
            "name": "Qwen3.5-4B",
            "url": "https://huggingface.co/Qwen/Qwen3.5-4B",
            "params": "4B",
            "listing": "https://www.anchorterminal.com/tools/qwen3-5-4b"
          },
          {
            "name": "Qwen3.5-4B-Base",
            "url": "https://huggingface.co/Qwen/Qwen3.5-4B-Base",
            "params": "4B"
          },
          {
            "name": "Qwen3.5-9B",
            "url": "https://huggingface.co/Qwen/Qwen3.5-9B",
            "params": "9B",
            "listing": "https://www.anchorterminal.com/tools/qwen3-5-9b"
          },
          {
            "name": "Qwen3.5-0.8B",
            "url": "https://huggingface.co/Qwen/Qwen3.5-0.8B",
            "params": "0.8B",
            "listing": "https://www.anchorterminal.com/tools/qwen3-5-0-8b"
          },
          {
            "name": "Qwen3.5-0.8B-Base",
            "url": "https://huggingface.co/Qwen/Qwen3.5-0.8B-Base",
            "params": "0.8B"
          },
          {
            "name": "Qwen3.5-2B",
            "url": "https://huggingface.co/Qwen/Qwen3.5-2B",
            "params": "2B",
            "listing": "https://www.anchorterminal.com/tools/qwen3-5-2b"
          },
          {
            "name": "Qwen3.5-2B-Base",
            "url": "https://huggingface.co/Qwen/Qwen3.5-2B-Base",
            "params": "2B"
          },
          {
            "name": "Qwen3.6-35B-A3B",
            "url": "https://huggingface.co/Qwen/Qwen3.6-35B-A3B",
            "params": "35B-A3B",
            "listing": "https://www.anchorterminal.com/tools/qwen3-6-35b-a3b"
          },
          {
            "name": "Qwen3.6-27B",
            "url": "https://huggingface.co/Qwen/Qwen3.6-27B",
            "params": "27B",
            "listing": "https://www.anchorterminal.com/tools/qwen3-6-27b"
          },
          {
            "name": "Qwen-AgentWorld-35B-A3B",
            "url": "https://huggingface.co/Qwen/Qwen-AgentWorld-35B-A3B",
            "params": "35B-A3B"
          },
          {
            "name": "Qwen3.8-27B",
            "url": "https://huggingface.co/Qwen/Qwen3.8-27B",
            "params": "27B",
            "listing": "https://www.anchorterminal.com/tools/qwen3-8-27b"
          },
          {
            "name": "Qwen3.8-2.4T-A95B",
            "url": "https://huggingface.co/Qwen/Qwen3.8-2.4T-A95B",
            "params": "2.4T-A95B"
          },
          {
            "name": "Qwen3.8-Flash-Next",
            "url": "https://huggingface.co/Qwen/Qwen3.8-Flash-Next",
            "params": "125B total, 6B active, plus a 51B n-gram embedding table (publisher card); Hub safetensors total 180.0B",
            "listing": "https://www.anchorterminal.com/tools/qwen3-8-flash-next"
          },
          {
            "name": "Qwen-Drive-1.0-4B",
            "url": "https://huggingface.co/Qwen/Qwen-Drive-1.0-4B",
            "params": "4B"
          }
        ],
        "olderCount": 109,
        "unrestrictedCopies": 5
      },
      "area": "models",
      "provenance": {
        "legalEntity": "Alibaba Cloud (the Qwen team's employer; no LICENSE file names a copyright holder for this model)",
        "domain": "qwen.ai",
        "domainRegistered": "2023-08-27",
        "endpointOnVendorDomain": null,
        "terms": "",
        "privacy": "",
        "statusPage": "",
        "changelog": "",
        "securityTxt": "none",
        "checked": "2026-10-21",
        "notes": [
          "The repositories hold no LICENSE file, so no copyright line names the entity. The other Qwen repositories' licences read Copyright 2026 Alibaba Cloud. The domain's registration date is per the 20 October 2026 RDAP check for qwen3-8-27b.",
          "Nothing is hosted, so the Apache 2.0 licence named in the metadata stands in for terms of service and no privacy policy is scored.",
          "No changelog for the files. The commits are uploads and README updates, the last on 3 February 2026 (4 February in the GGUF repository). github.com/QwenLM/Qwen3-Coder records the release on 3 February 2026 and a tech report update on 4 February.",
          "qwen.ai answers /.well-known/security.txt with a redirect to its home page, so there is no security.txt."
        ],
        "score": 49
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/qwen3-coder-next.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": "2026-02-03",
        "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": "2.8/5 (4)",
        "name": "Agent reviews"
      }
    ],
    "faq": [
      {
        "answer": "Qwen3-Coder-Next scores 57.6 (C) on agent readiness against Ling-3.0-tiny's 55 (C), and leads in 2 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-Coder-Next?"
      }
    ],
    "goodFor": [
      {
        "aheadOn": [
          "Reach, 90 against 85",
          "Documentation and upkeep, 56 against 41"
        ],
        "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, 42 against 31",
          "Agent interface, 75 against 60"
        ],
        "also": null,
        "goodFor": "Agents that want a local coding model for long tool-calling loops on a single 64 GB to 128 GB machine.",
        "slug": "qwen3-coder-next",
        "watchFor": "No LICENSE file anywhere and a broken licence link on the card"
      }
    ],
    "job": {
      "capability": "model.open-weights",
      "name": "Open-weight models you run yourself"
    },
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        "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-coder-next.json",
        "title": "Agents-A1-4B vs Qwen3-Coder-Next",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-qwen3-coder-next"
      },
      {
        "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"
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      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-qwen3-coder-next.json",
        "title": "Apertus v1.5 8B vs Qwen3-Coder-Next",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-qwen3-coder-next"
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      {
        "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"
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      {
        "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-qwen3-coder-next.json",
        "title": "Apodex-1.1-mini vs Qwen3-Coder-Next",
        "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-qwen3-coder-next"
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      {
        "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"
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      {
        "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-qwen3-coder-next.json",
        "title": "BigBang-v1 vs Qwen3-Coder-Next",
        "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-qwen3-coder-next"
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        "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"
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      {
        "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-qwen3-coder-next.json",
        "title": "Cosmos-Reason2-2B vs Qwen3-Coder-Next",
        "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-qwen3-coder-next"
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      {
        "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"
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      {
        "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-qwen3-coder-next.json",
        "title": "Darwin-36B-Opus vs Qwen3-Coder-Next",
        "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-qwen3-coder-next"
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      {
        "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"
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      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-qwen3-coder-next.json",
        "title": "DeepSeek-V3.2 vs Qwen3-Coder-Next",
        "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-qwen3-coder-next"
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      {
        "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"
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      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-coder-next.json",
        "title": "DeepSeek V4.1 Flash vs Qwen3-Coder-Next",
        "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-coder-next"
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      {
        "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"
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      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-qwen3-coder-next.json",
        "title": "DeepSeek-V4-Flash-0731 vs Qwen3-Coder-Next",
        "url": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-qwen3-coder-next"
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      {
        "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-coder-next.json",
        "title": "DiffusionGemma 26B A4B IT vs Qwen3-Coder-Next",
        "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-qwen3-coder-next"
      },
      {
        "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-coder-next.json",
        "title": "Gemma 4 12B IT vs Qwen3-Coder-Next",
        "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-qwen3-coder-next"
      },
      {
        "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-coder-next.json",
        "title": "Gemma 4 26B A4B IT vs Qwen3-Coder-Next",
        "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-qwen3-coder-next"
      },
      {
        "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-coder-next.json",
        "title": "Gemma 4 31B IT vs Qwen3-Coder-Next",
        "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-coder-next"
      },
      {
        "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-coder-next.json",
        "title": "Gemma 4 E2B IT vs Qwen3-Coder-Next",
        "url": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-qwen3-coder-next"
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      {
        "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-coder-next.json",
        "title": "Gemma 4 E4B IT vs Qwen3-Coder-Next",
        "url": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-qwen3-coder-next"
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      {
        "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"
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      {
        "json": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-qwen3-coder-next.json",
        "title": "GLM-4.5-Air vs Qwen3-Coder-Next",
        "url": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-qwen3-coder-next"
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      {
        "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-coder-next.json",
        "title": "GLM-4.7-Flash vs Qwen3-Coder-Next",
        "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwen3-coder-next"
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      {
        "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-coder-next.json",
        "title": "GLM-5.3-Flash vs Qwen3-Coder-Next",
        "url": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-qwen3-coder-next"
      },
      {
        "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"
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      {
        "json": "https://www.anchorterminal.com/compare/glm-5-3-vs-qwen3-coder-next.json",
        "title": "GLM-5.3 vs Qwen3-Coder-Next",
        "url": "https://www.anchorterminal.com/compare/glm-5-3-vs-qwen3-coder-next"
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      {
        "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"
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      {
        "json": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-qwen3-coder-next.json",
        "title": "gpt-oss-120b vs Qwen3-Coder-Next",
        "url": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-qwen3-coder-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-ling-3-0-tiny.json",
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        "url": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-ling-3-0-tiny"
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      {
        "json": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-qwen3-coder-next.json",
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        "url": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-qwen3-coder-next"
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      {
        "json": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-ling-3-0-tiny.json",
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        "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-coder-next.json",
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        "url": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-qwen3-coder-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/hy3-vs-ling-3-0-tiny.json",
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        "url": "https://www.anchorterminal.com/compare/hy3-vs-ling-3-0-tiny"
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        "json": "https://www.anchorterminal.com/compare/hy3-vs-qwen3-coder-next.json",
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        "url": "https://www.anchorterminal.com/compare/hy3-vs-qwen3-coder-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/inkling-small-vs-ling-3-0-tiny.json",
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        "url": "https://www.anchorterminal.com/compare/inkling-small-vs-ling-3-0-tiny"
      },
      {
        "json": "https://www.anchorterminal.com/compare/inkling-small-vs-qwen3-coder-next.json",
        "title": "Inkling-Small vs Qwen3-Coder-Next",
        "url": "https://www.anchorterminal.com/compare/inkling-small-vs-qwen3-coder-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/inkling-vs-ling-3-0-tiny.json",
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        "url": "https://www.anchorterminal.com/compare/inkling-vs-ling-3-0-tiny"
      },
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        "json": "https://www.anchorterminal.com/compare/inkling-vs-qwen3-coder-next.json",
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        "url": "https://www.anchorterminal.com/compare/inkling-vs-qwen3-coder-next"
      },
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        "json": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-coder-next.json",
        "title": "Ornith-1.5-35B-A3B vs Qwen3-Coder-Next",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-coder-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-coder-next.json",
        "title": "Ornith-1.5-397B vs Qwen3-Coder-Next",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-coder-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-coder-next.json",
        "title": "Ornith-1.5-9B vs Qwen3-Coder-Next",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-coder-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-30b-a3b-instruct-2507-vs-qwen3-coder-next.json",
        "title": "Qwen3-30B-A3B-Instruct-2507 vs Qwen3-Coder-Next",
        "url": "https://www.anchorterminal.com/compare/qwen3-30b-a3b-instruct-2507-vs-qwen3-coder-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-4b-instruct-2507-vs-qwen3-coder-next.json",
        "title": "Qwen3-4B-Instruct-2507 vs Qwen3-Coder-Next",
        "url": "https://www.anchorterminal.com/compare/qwen3-4b-instruct-2507-vs-qwen3-coder-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-qwen3-coder-next.json",
        "title": "Qwen3.5-0.8B vs Qwen3-Coder-Next",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-qwen3-coder-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-qwen3-coder-next.json",
        "title": "Qwen3.5-122B-A10B vs Qwen3-Coder-Next",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-qwen3-coder-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-5-2b-vs-qwen3-coder-next.json",
        "title": "Qwen3.5-2B vs Qwen3-Coder-Next",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-2b-vs-qwen3-coder-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-qwen3-coder-next.json",
        "title": "Qwen3.5-397B-A17B vs Qwen3-Coder-Next",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-qwen3-coder-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-5-4b-vs-qwen3-coder-next.json",
        "title": "Qwen3.5-4B vs Qwen3-Coder-Next",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-4b-vs-qwen3-coder-next"
      },
      {
        "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/qwen3-6-27b-vs-qwen3-coder-next.json",
        "title": "Qwen3.6-27B vs Qwen3-Coder-Next",
        "url": "https://www.anchorterminal.com/compare/qwen3-6-27b-vs-qwen3-coder-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-qwen3-coder-next.json",
        "title": "Qwen3.6-35B-A3B vs Qwen3-Coder-Next",
        "url": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-qwen3-coder-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-8-27b-vs-qwen3-coder-next.json",
        "title": "Qwen3.8-27B vs Qwen3-Coder-Next",
        "url": "https://www.anchorterminal.com/compare/qwen3-8-27b-vs-qwen3-coder-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-qwen3-coder-next.json",
        "title": "Qwen3.8-Flash-Next vs Qwen3-Coder-Next",
        "url": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-qwen3-coder-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-coder-30b-a3b-instruct-vs-qwen3-coder-next.json",
        "title": "Qwen3-Coder-30B-A3B-Instruct vs Qwen3-Coder-Next",
        "url": "https://www.anchorterminal.com/compare/qwen3-coder-30b-a3b-instruct-vs-qwen3-coder-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-coder-next-vs-qwen3-omni-30b-a3b-instruct.json",
        "title": "Qwen3-Coder-Next vs Qwen3-Omni-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/qwen3-coder-next-vs-qwen3-omni-30b-a3b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-coder-next-vs-qwen3-vl-2b-instruct.json",
        "title": "Qwen3-Coder-Next vs Qwen3-VL-2B-Instruct",
        "url": "https://www.anchorterminal.com/compare/qwen3-coder-next-vs-qwen3-vl-2b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-coder-next-vs-qwen3-vl-30b-a3b-instruct.json",
        "title": "Qwen3-Coder-Next vs Qwen3-VL-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/qwen3-coder-next-vs-qwen3-vl-30b-a3b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-coder-next-vs-qwen3-vl-32b-instruct.json",
        "title": "Qwen3-Coder-Next vs Qwen3-VL-32B-Instruct",
        "url": "https://www.anchorterminal.com/compare/qwen3-coder-next-vs-qwen3-vl-32b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-coder-next-vs-qwen3-vl-4b-instruct.json",
        "title": "Qwen3-Coder-Next vs Qwen3-VL-4B-Instruct",
        "url": "https://www.anchorterminal.com/compare/qwen3-coder-next-vs-qwen3-vl-4b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-coder-next-vs-qwen3-vl-8b-instruct.json",
        "title": "Qwen3-Coder-Next vs Qwen3-VL-8B-Instruct",
        "url": "https://www.anchorterminal.com/compare/qwen3-coder-next-vs-qwen3-vl-8b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-coder-next-vs-qwopus3-8-27b-flash-v2.json",
        "title": "Qwen3-Coder-Next vs Qwopus3.8-27B-Flash-V2",
        "url": "https://www.anchorterminal.com/compare/qwen3-coder-next-vs-qwopus3-8-27b-flash-v2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-coder-next-vs-smollm3-3b.json",
        "title": "Qwen3-Coder-Next vs SmolLM3-3B",
        "url": "https://www.anchorterminal.com/compare/qwen3-coder-next-vs-smollm3-3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-coder-next-vs-spark-x2-6-4b.json",
        "title": "Qwen3-Coder-Next vs Spark-X2.6-4B",
        "url": "https://www.anchorterminal.com/compare/qwen3-coder-next-vs-spark-x2-6-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-coder-next-vs-swift-1-5-qwen3-8-27b.json",
        "title": "Qwen3-Coder-Next vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/qwen3-coder-next-vs-swift-1-5-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-coder-next-vs-swift-1-5-qwen3-8-flash-next.json",
        "title": "Qwen3-Coder-Next vs Swift 1.5 Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/qwen3-coder-next-vs-swift-1-5-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-coder-next-vs-thinkingcap-qwen3-8-27b.json",
        "title": "Qwen3-Coder-Next vs ThinkingCap (Qwen3.8 27B)",
        "url": "https://www.anchorterminal.com/compare/qwen3-coder-next-vs-thinkingcap-qwen3-8-27b"
      }
    ],
    "scores": [
      {
        "by": 11,
        "edge": "qwen3-coder-next",
        "key": "capability",
        "ling-3-0-tiny": 31,
        "name": "Capability",
        "qwen3-coder-next": 42,
        "weight": 35
      },
      {
        "by": 5,
        "edge": "ling-3-0-tiny",
        "key": "reach",
        "ling-3-0-tiny": 90,
        "name": "Reach",
        "qwen3-coder-next": 85,
        "weight": 25
      },
      {
        "by": 0,
        "edge": "",
        "key": "risk",
        "ling-3-0-tiny": 50,
        "name": "Risk",
        "qwen3-coder-next": 50,
        "weight": 20
      },
      {
        "by": 15,
        "edge": "qwen3-coder-next",
        "key": "interface",
        "ling-3-0-tiny": 60,
        "name": "Agent interface",
        "qwen3-coder-next": 75,
        "weight": 10
      },
      {
        "by": 15,
        "edge": "ling-3-0-tiny",
        "key": "upkeep",
        "ling-3-0-tiny": 56,
        "name": "Documentation and upkeep",
        "qwen3-coder-next": 41,
        "weight": 10
      }
    ],
    "summary": "Qwen3-Coder-Next scores 57.6 (C) on agent readiness against Ling-3.0-tiny's 55 (C), and leads in 2 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-coder-next": "A fast coding cousin for agent harnesses, with 3B active parameters and tool calling trained across many scaffold formats for Claude Code, Cline or Qwen Code loops. Alibaba's 4-bit GGUF needs about 58 GB, fitting one 64 GB machine, and two providers host it. The main risk is neglect, with no change since February, no replies 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-coder-next",
    "json": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwen3-coder-next.json",
    "llms": "https://www.anchorterminal.com/llms.txt",
    "markdown": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwen3-coder-next.md",
    "mcp": "https://www.anchorterminal.com/mcp",
    "slim": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwen3-coder-next.min.md"
  },
  "markdown": "Qwen3-Coder-Next scores 57.6 (C) on agent readiness against Ling-3.0-tiny's 55 (C), and leads in 2 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-Coder-Next: grade C, 57.6/100, rank #70 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/qwen3-coder-next.md · JSON https://www.anchorterminal.com/api/v1/tools/qwen3-coder-next.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 41\n\nWatch for: No hosted provider today, and Ollama support is only an unmerged pull request\n\n### Qwen3-Coder-Next (C)\n\nGood for: Agents that want a local coding model for long tool-calling loops on a single 64 GB to 128 GB machine.\n\nAhead on:\n- Capability, 42 against 31\n- Agent interface, 75 against 60\n\nWatch for: No LICENSE file anywhere and a broken licence link on the card\n\n\n## Score by category\n\n| Category | Weight | Ling-3.0-tiny | Qwen3-Coder-Next | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35% (35 this run) | 31 | 42 | Qwen3-Coder-Next +11 |\n| Reach | 25% (25 this run) | 90 | 85 | Ling-3.0-tiny +5 |\n| Risk | 20% (20 this run) | 50 | 50 | even |\n| Agent interface | 10% (10 this run) | 60 | 75 | Qwen3-Coder-Next +15 |\n| Documentation and upkeep | 10% (10 this run) | 56 | 41 | Ling-3.0-tiny +15 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **55 · C** | **57.6 · C** | |\n\n## Facts side by side\n\n| Fact | Ling-3.0-tiny | Qwen3-Coder-Next |\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 | 2026-02-03 |\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) | 2.8/5 (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-Coder-Next.** A fast coding cousin for agent harnesses, with 3B active parameters and tool calling trained across many scaffold formats for Claude Code, Cline or Qwen Code loops. Alibaba's 4-bit GGUF needs about 58 GB, fitting one 64 GB machine, and two providers host it. The main risk is neglect, with no change since February, no replies 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-Coder-Next\n\n1. Quickest start is `ollama run qwen3-coder-next` (Q4_K_M, 52 GB of files)\n2. On our reference machine Alibaba's Q8_0 GGUF (84.8 GB, about 102 GB in use) also fits, run with `llama-server -m Qwen3-Coder-Next-Q8_0/Qwen3-Coder-Next-Q8_0-00001-of-00004.gguf --jinja -ngl 99 -fa on -c 131072`\n3. Serve with `vllm serve Qwen/Qwen3-Coder-Next --enable-auto-tool-choice --tool-call-parser qwen3_coder` (vLLM 0.15.0 or later), using the built-in parser rather than the repository's qwen3coder_tool_parser_vllm.py, which fails to import on newer vLLM per discussion 36\n4. The card's commands use tensor parallel 2 though the text says 4 GPUs, and it suggests cutting the context to 32,768 if the server fails to start\n5. There's no thinking mode, so don't pass `enable_thinking` or wait for reasoning_content\n6. Sampling per the card is temperature 1.0, top_p 0.95 and top_k 40\n7. The GGUF card's llama.cpp example path omits the quant from the file name; the real files are named like Qwen3-Coder-Next-Q4_K_M-00001-of-00004.gguf\n8. Validate any JSON it returns, since structured output isn't documented\n\n## Questions\n\n### Which is better for AI agents, Ling-3.0-tiny or Qwen3-Coder-Next?\n\nQwen3-Coder-Next scores 57.6 (C) on agent readiness against Ling-3.0-tiny's 55 (C), and leads in 2 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-coder-next.json, and with the fewest tokens: https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwen3-coder-next.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"ling-3-0-tiny\", \"b\": \"qwen3-coder-next\"}`. From a terminal: `anchor compare ling-3-0-tiny qwen3-coder-next`\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-coder-next.json\n\n## Other comparisons with Ling-3.0-tiny or Qwen3-Coder-Next\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-Coder-Next](https://www.anchorterminal.com/compare/agents-a1-4b-vs-qwen3-coder-next.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-Coder-Next](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-qwen3-coder-next.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-Coder-Next](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-qwen3-coder-next.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-Coder-Next](https://www.anchorterminal.com/compare/bigbang-v1-vs-qwen3-coder-next.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-Coder-Next](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-qwen3-coder-next.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-Coder-Next](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-qwen3-coder-next.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-Coder-Next](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-qwen3-coder-next.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 Qwen3-Coder-Next](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-coder-next.md)\n- [DeepSeek-V4-Flash-0731 vs Ling-3.0-tiny](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-ling-3-0-tiny.md)\n- [DeepSeek-V4-Flash-0731 vs Qwen3-Coder-Next](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-qwen3-coder-next.md)\n- [DiffusionGemma 26B A4B IT vs Ling-3.0-tiny](https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-ling-3-0-tiny.md)\n- [DiffusionGemma 26B A4B IT vs Qwen3-Coder-Next](https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-qwen3-coder-next.md)\n- [Gemma 4 12B IT vs Ling-3.0-tiny](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-ling-3-0-tiny.md)\n- [Gemma 4 12B IT vs Qwen3-Coder-Next](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-qwen3-coder-next.md)\n- [Gemma 4 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