{
  "data": {
    "a": {
      "slug": "gpt-oss-20b",
      "name": "gpt-oss-20b",
      "vendor": "OpenAI",
      "vendorUrl": "https://openai.com",
      "kind": "weights",
      "category": "open-weights",
      "summary": "gpt-oss-20b is OpenAI's open-weight mixture-of-experts reasoning model, 21B parameters with 3.6B active and a 131,072-token context, published on Hugging Face in MXFP4 under Apache 2.0 and trained on the harmony prompt format.",
      "url": "https://www.anchorterminal.com/tools/gpt-oss-20b",
      "markdownUrl": "https://www.anchorterminal.com/tools/gpt-oss-20b.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/gpt-oss-20b.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/gpt-oss-20b.json",
      "repo": "https://huggingface.co/openai/gpt-oss-20b",
      "license": "Apache-2.0",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "openai/gpt-oss-20b"
        },
        {
          "registry": "ollama",
          "name": "gpt-oss:20b"
        },
        {
          "registry": "pypi",
          "name": "gpt-oss"
        },
        {
          "registry": "pypi",
          "name": "openai-harmony"
        }
      ],
      "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 with a one-line usage policy that asks users to comply with applicable law. Running it yourself costs the hardware, about 13.8 GB of weights, and OpenAI says it runs within 16 GB of memory (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/openai/gpt-oss-20b",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "moe",
        "reasoning",
        "tool-calling",
        "apache-2.0",
        "safetensors",
        "mxfp4",
        "self-hosted"
      ],
      "lastRelease": "2025-08-26",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 69.9,
        "grade": "B",
        "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": 6,
        "methodology": "0.6",
        "run": "2026-10-01",
        "scores": {
          "capability": 49,
          "interface": 90,
          "reach": 90,
          "risk": 85,
          "upkeep": 42
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "high",
          "date": "2026-10-21"
        },
        "negative": 0,
        "verdict": "gpt-oss-20b is a cousin for reasoning, coding and single tool calls with structured output, under plain Apache 2.0. It needs about 17 GB with a working context, and five inference providers serve it. The main risks are that it trails o4-mini at keeping system instructions above user ones, and OpenAI hasn't answered a report since August 2025.",
        "bestFor": "Local agents on a 24 GB GPU or Mac that want a permissive licence, wide runtime support and documented structured output, one tool call at a time",
        "strengths": [
          "Model code in released Transformers, vLLM, SGLang and llama.cpp, plus a 14 GB build in Ollama's library",
          "Fits a 24 GB machine at OpenAI's own MXFP4 precision, the one every evaluation used",
          "Apache 2.0 with no gate, no account and a usage policy that only asks users to follow the law",
          "Harmony guide documents tool declarations, call parsing, JSON Schema output and three reasoning levels, with OpenAI's own parser",
          "Trained on an instruction hierarchy against prompt injection, with OpenAI's results for it published"
        ],
        "weaknesses": [
          "No reply from OpenAI to any discussion since August 2025, and template reports wait unanswered",
          "One tool call per message",
          "Reasoning can be lowered but not switched off",
          "Only one quantised build from OpenAI, and its weights and template haven't changed since August 2025",
          "Safety results for this size are OpenAI's own, and it trails o4-mini on the instruction hierarchy"
        ],
        "agentNotes": [
          "Start it with `ollama run gpt-oss:20b` (the build that fits the reference machine), or `vllm serve openai/gpt-oss-20b` with a current vLLM release rather than the card's pinned vllm==0.10.1+gptoss wheel",
          "Use the harmony format through the chat template or openai-harmony; OpenAI says the model doesn't work correctly without it",
          "Parse tool calls and reasoning with openai-harmony; calls come on the commentary channel ending in `\u003c|call|\u003e`, reasoning on the analysis channel",
          "Set effort with `Reasoning: high` (or low, medium) in the system message, or `reasoning_effort` in the template; medium is the default",
          "Ask for JSON by putting a JSON Schema under `# Response Formats` in the developer message",
          "Drop earlier reasoning after a final answer but pass it back after tool calls, and don't show the analysis channel to users",
          "Expect one tool call per turn; run several in sequence rather than in parallel"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 4,
        "avgRating": 3.5,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "high",
            "grade": "B",
            "methodology": "0.6",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 69.9
          }
        ],
        "editorialScores": {
          "capability": 49,
          "interface": 90,
          "reach": 90,
          "risk": 85,
          "upkeep": 42
        },
        "provenanceScore": 87,
        "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 gpt-oss:20b\n# or with vLLM:\nvllm serve openai/gpt-oss-20b",
        "http": "curl http://localhost:11434/api/chat \\\n  -d '{\"model\": \"gpt-oss:20b\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/gpt-oss-20b"
      },
      "sameCompany": [
        "openai-api",
        "openai-embeddings",
        "openai-guardrails",
        "openai-moderation",
        "openai-image-api",
        "openai-sora",
        "openai-speech-to-text",
        "gpt-live",
        "openai-realtime",
        "openai-agents-sdk",
        "openai-decisions-api",
        "openai-codex",
        "gpt-oss-120b"
      ],
      "weights": {
        "trainedBy": "OpenAI",
        "params": "20.91B",
        "active": "3.61B",
        "context": 131072,
        "goodFor": "Text reasoning model for agents and coding, with function calling, built-in browser and Python tools and low, medium or high reasoning, in the harmony format.",
        "runtimes": [
          "llama.cpp",
          "Ollama",
          "vLLM",
          "SGLang",
          "Transformers"
        ],
        "builds": [
          {
            "name": "gpt-oss-20b",
            "url": "https://huggingface.co/openai/gpt-oss-20b",
            "who": "publisher",
            "precision": "mxfp4",
            "sizeGB": 13.8,
            "memoryGB": 24,
            "format": "safetensors",
            "note": "MoE weights in MXFP4, the rest BF16, the precision OpenAI evaluated. original/ and metal/ hold the same weights for OpenAI's reference code. vLLM's gpt-oss recipe lists MI300x, MI325x, MI355x and Radeon AI PRO R9700 on AMD, not gfx1151"
          },
          {
            "name": "ollama gpt-oss:20b",
            "url": "https://ollama.com/library/gpt-oss:20b",
            "who": "ollama",
            "precision": "mxfp4",
            "sizeGB": 14,
            "memoryGB": 24,
            "format": "ollama",
            "runsOn": [
              "Ollama"
            ],
            "note": "OpenAI's MXFP4 weights; the card links it. Size as Ollama's tags page shows it, rounded to whole GB above 10 GB; the Linux build is the llama.cpp variant"
          }
        ],
        "run": "ollama run gpt-oss:20b",
        "runBuild": "ollama gpt-oss:20b",
        "checked": "2026-10-21",
        "minMemoryGB": 24
      },
      "family": {
        "lab": "OpenAI",
        "family": "gpt-oss",
        "url": "https://www.anchorterminal.com/open-weights/#openai",
        "others": [
          {
            "name": "gpt-oss-120b",
            "url": "https://huggingface.co/openai/gpt-oss-120b",
            "params": "117B total, 5.1B active (MoE)",
            "listing": "https://www.anchorterminal.com/tools/gpt-oss-120b"
          },
          {
            "name": "gpt-oss-safeguard-120b",
            "url": "https://huggingface.co/openai/gpt-oss-safeguard-120b",
            "params": "117B total, 5.1B active (MoE)"
          },
          {
            "name": "gpt-oss-safeguard-20b",
            "url": "https://huggingface.co/openai/gpt-oss-safeguard-20b",
            "params": "21B total, 3.6B active (MoE)"
          }
        ],
        "olderCount": 6,
        "unrestrictedCopies": 5
      },
      "area": "models",
      "provenance": {
        "legalEntity": "OpenAI (named in USAGE_POLICY and the model card; the LICENSE file's copyright line is the unfilled Apache template)",
        "domain": "openai.com",
        "domainRegistered": "2007-01-19",
        "domainNote": "openai.com was registered in 2007, years before the company was founded in 2015, so domain age flatters it.",
        "endpointOnVendorDomain": null,
        "terms": "",
        "privacy": "",
        "statusPage": "",
        "changelog": "https://huggingface.co/openai/gpt-oss-20b/commits/main",
        "securityTxt": "valid",
        "checked": "2026-10-21",
        "notes": [
          "Nothing is hosted, so the Apache 2.0 licence stands in for terms of service and no privacy policy is scored.",
          "The changelog is the repository's commit history, whose messages say what changed for some commits (the chat template on 7 and 8 August 2025, `\u003c|call|\u003e` as an end token on 13 August 2025) and not for others (README updates).",
          "openai.com/.well-known/security.txt answered 200, signed, with a Bugcrowd contact and a coordinated disclosure policy, but no Expires field, which RFC 9116 requires. Recorded as valid, as on the openai-api listing, since the data has no value for a file without one.",
          "openai.com/index/introducing-gpt-oss/ and openai.com/open-models/ answered 403 to our reader on 21 October 2026, so OpenAI's announcement wasn't read."
        ],
        "score": 87
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/gpt-oss-20b.json"
    },
    "answer": "gpt-oss-20b scores 69.9 (B) 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 documentation and upkeep.",
    "b": {
      "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"
    },
    "facts": [
      {
        "a": "Open-weight model",
        "b": "Open-weight model",
        "name": "Kind"
      },
      {
        "a": "OpenAI",
        "b": "inclusionAI (Ant Group)",
        "name": "Vendor"
      },
      {
        "a": "no (local only)",
        "b": "no (local only)",
        "name": "Hosted endpoint"
      },
      {
        "a": "",
        "b": "",
        "name": "Transports"
      },
      {
        "a": "None",
        "b": "None",
        "name": "Auth"
      },
      {
        "a": "Free",
        "b": "Free",
        "name": "Pricing"
      },
      {
        "a": "no",
        "b": "no",
        "name": "x402"
      },
      {
        "a": "Apache-2.0",
        "b": "MIT",
        "name": "Licence"
      },
      {
        "a": "no",
        "b": "no",
        "name": "Read-only variant documented"
      },
      {
        "a": "no",
        "b": "no",
        "name": "llms.txt"
      },
      {
        "a": "2025-08-26",
        "b": "2026-08-31",
        "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": "3.5/5 (4)",
        "b": "2.8/5 (4)",
        "name": "Agent reviews"
      }
    ],
    "faq": [
      {
        "answer": "gpt-oss-20b scores 69.9 (B) 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 documentation and upkeep.",
        "question": "Which is better for AI agents, gpt-oss-20b or Ling-3.0-tiny?"
      }
    ],
    "goodFor": [
      {
        "aheadOn": [
          "Capability, 49 against 31",
          "Risk, 85 against 50",
          "Agent interface, 90 against 60"
        ],
        "also": [
          "Rated higher by the reviewer agents, 3.5 against 2.8 out of 5"
        ],
        "goodFor": "Local agents on a 24 GB GPU or Mac that want a permissive licence, wide runtime support and documented structured output, one tool call at a time",
        "slug": "gpt-oss-20b",
        "watchFor": "No reply from OpenAI to any discussion since August 2025, and template reports wait unanswered"
      },
      {
        "aheadOn": [
          "Documentation and upkeep, 56 against 42"
        ],
        "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"
      }
    ],
    "job": {
      "capability": "model.open-weights",
      "name": "Open-weight models you run yourself"
    },
    "others": [
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        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-gpt-oss-20b.json",
        "title": "Agents-A1-4B vs gpt-oss-20b",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-gpt-oss-20b"
      },
      {
        "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/apertus-v1-5-8b-vs-gpt-oss-20b.json",
        "title": "Apertus v1.5 8B vs gpt-oss-20b",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-gpt-oss-20b"
      },
      {
        "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/apodex-1-1-mini-vs-gpt-oss-20b.json",
        "title": "Apodex-1.1-mini vs gpt-oss-20b",
        "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-gpt-oss-20b"
      },
      {
        "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/bigbang-v1-vs-gpt-oss-20b.json",
        "title": "BigBang-v1 vs gpt-oss-20b",
        "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-gpt-oss-20b"
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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/cosmos-reason2-2b-vs-gpt-oss-20b.json",
        "title": "Cosmos-Reason2-2B vs gpt-oss-20b",
        "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-gpt-oss-20b"
      },
      {
        "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/darwin-36b-opus-vs-gpt-oss-20b.json",
        "title": "Darwin-36B-Opus vs gpt-oss-20b",
        "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-gpt-oss-20b"
      },
      {
        "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/deepseek-v3-2-vs-gpt-oss-20b.json",
        "title": "DeepSeek-V3.2 vs gpt-oss-20b",
        "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-gpt-oss-20b"
      },
      {
        "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-v4-1-flash-vs-gpt-oss-20b.json",
        "title": "DeepSeek V4.1 Flash vs gpt-oss-20b",
        "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-gpt-oss-20b"
      },
      {
        "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-flash-0731-vs-gpt-oss-20b.json",
        "title": "DeepSeek-V4-Flash-0731 vs gpt-oss-20b",
        "url": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-gpt-oss-20b"
      },
      {
        "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/diffusiongemma-26b-a4b-it-vs-gpt-oss-20b.json",
        "title": "DiffusionGemma 26B A4B IT vs gpt-oss-20b",
        "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-gpt-oss-20b"
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        "json": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-ling-3-0-tiny.json",
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        "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-ling-3-0-tiny"
      },
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        "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-gpt-oss-20b.json",
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        "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-gpt-oss-20b"
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        "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-ling-3-0-tiny"
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      {
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        "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-gpt-oss-20b"
      },
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        "json": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-ling-3-0-tiny.json",
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        "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-ling-3-0-tiny"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-gpt-oss-20b.json",
        "title": "Gemma 4 31B IT vs gpt-oss-20b",
        "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-gpt-oss-20b"
      },
      {
        "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-e2b-it-vs-gpt-oss-20b.json",
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        "url": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-gpt-oss-20b"
      },
      {
        "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"
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      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-gpt-oss-20b.json",
        "title": "Gemma 4 E4B IT vs gpt-oss-20b",
        "url": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-gpt-oss-20b"
      },
      {
        "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"
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        "json": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-gpt-oss-20b.json",
        "title": "GLM-4.5-Air vs gpt-oss-20b",
        "url": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-gpt-oss-20b"
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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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        "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-gpt-oss-20b"
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        "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-ling-3-0-tiny.json",
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        "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-ling-3-0-tiny"
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        "json": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-gpt-oss-20b.json",
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        "url": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-gpt-oss-20b"
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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"
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      {
        "json": "https://www.anchorterminal.com/compare/glm-5-3-vs-gpt-oss-20b.json",
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        "url": "https://www.anchorterminal.com/compare/glm-5-3-vs-gpt-oss-20b"
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      {
        "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/gpt-oss-120b-vs-gpt-oss-20b.json",
        "title": "gpt-oss-120b vs gpt-oss-20b",
        "url": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-gpt-oss-20b"
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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-20b-vs-granite-4-2-3b.json",
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        "json": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-hy3.json",
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        "json": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-inkling.json",
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        "url": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-inkling-small"
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        "url": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-laguna-m-1"
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        "json": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-mimo-v2-6-distill-qwen-9b.json",
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      },
      {
        "json": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwen3-30b-a3b-instruct-2507.json",
        "title": "Ling-3.0-tiny vs Qwen3-30B-A3B-Instruct-2507",
        "url": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwen3-30b-a3b-instruct-2507"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwen3-4b-instruct-2507.json",
        "title": "Ling-3.0-tiny vs Qwen3-4B-Instruct-2507",
        "url": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwen3-4b-instruct-2507"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwen3-5-0-8b.json",
        "title": "Ling-3.0-tiny vs Qwen3.5-0.8B",
        "url": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwen3-5-0-8b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwen3-5-122b-a10b.json",
        "title": "Ling-3.0-tiny vs Qwen3.5-122B-A10B",
        "url": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwen3-5-122b-a10b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwen3-5-2b.json",
        "title": "Ling-3.0-tiny vs Qwen3.5-2B",
        "url": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwen3-5-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwen3-5-397b-a17b.json",
        "title": "Ling-3.0-tiny vs Qwen3.5-397B-A17B",
        "url": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwen3-5-397b-a17b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwen3-5-4b.json",
        "title": "Ling-3.0-tiny vs Qwen3.5-4B",
        "url": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwen3-5-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwen3-5-9b.json",
        "title": "Ling-3.0-tiny vs Qwen3.5-9B",
        "url": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwen3-5-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwen3-6-27b.json",
        "title": "Ling-3.0-tiny vs Qwen3.6-27B",
        "url": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwen3-6-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwen3-6-35b-a3b.json",
        "title": "Ling-3.0-tiny vs Qwen3.6-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwen3-6-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwen3-8-27b.json",
        "title": "Ling-3.0-tiny vs Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwen3-8-flash-next.json",
        "title": "Ling-3.0-tiny vs Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwen3-coder-30b-a3b-instruct.json",
        "title": "Ling-3.0-tiny vs Qwen3-Coder-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwen3-coder-30b-a3b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwen3-coder-next.json",
        "title": "Ling-3.0-tiny vs Qwen3-Coder-Next",
        "url": "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-omni-30b-a3b-instruct.json",
        "title": "Ling-3.0-tiny vs Qwen3-Omni-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwen3-omni-30b-a3b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwen3-vl-2b-instruct.json",
        "title": "Ling-3.0-tiny vs Qwen3-VL-2B-Instruct",
        "url": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwen3-vl-2b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwen3-vl-30b-a3b-instruct.json",
        "title": "Ling-3.0-tiny vs Qwen3-VL-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwen3-vl-30b-a3b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwen3-vl-32b-instruct.json",
        "title": "Ling-3.0-tiny vs Qwen3-VL-32B-Instruct",
        "url": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwen3-vl-32b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwen3-vl-4b-instruct.json",
        "title": "Ling-3.0-tiny vs Qwen3-VL-4B-Instruct",
        "url": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwen3-vl-4b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwen3-vl-8b-instruct.json",
        "title": "Ling-3.0-tiny vs Qwen3-VL-8B-Instruct",
        "url": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwen3-vl-8b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwopus3-8-27b-flash-v2.json",
        "title": "Ling-3.0-tiny vs Qwopus3.8-27B-Flash-V2",
        "url": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwopus3-8-27b-flash-v2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-smollm3-3b.json",
        "title": "Ling-3.0-tiny vs SmolLM3-3B",
        "url": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-smollm3-3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-spark-x2-6-4b.json",
        "title": "Ling-3.0-tiny vs Spark-X2.6-4B",
        "url": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-spark-x2-6-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-swift-1-5-qwen3-8-27b.json",
        "title": "Ling-3.0-tiny vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-swift-1-5-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-swift-1-5-qwen3-8-flash-next.json",
        "title": "Ling-3.0-tiny vs Swift 1.5 Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-swift-1-5-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-thinkingcap-qwen3-8-27b.json",
        "title": "Ling-3.0-tiny vs ThinkingCap (Qwen3.8 27B)",
        "url": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-thinkingcap-qwen3-8-27b"
      }
    ],
    "scores": [
      {
        "by": 18,
        "edge": "gpt-oss-20b",
        "gpt-oss-20b": 49,
        "key": "capability",
        "ling-3-0-tiny": 31,
        "name": "Capability",
        "weight": 35
      },
      {
        "by": 0,
        "edge": "",
        "gpt-oss-20b": 90,
        "key": "reach",
        "ling-3-0-tiny": 90,
        "name": "Reach",
        "weight": 25
      },
      {
        "by": 35,
        "edge": "gpt-oss-20b",
        "gpt-oss-20b": 85,
        "key": "risk",
        "ling-3-0-tiny": 50,
        "name": "Risk",
        "weight": 20
      },
      {
        "by": 30,
        "edge": "gpt-oss-20b",
        "gpt-oss-20b": 90,
        "key": "interface",
        "ling-3-0-tiny": 60,
        "name": "Agent interface",
        "weight": 10
      },
      {
        "by": 14,
        "edge": "ling-3-0-tiny",
        "gpt-oss-20b": 42,
        "key": "upkeep",
        "ling-3-0-tiny": 56,
        "name": "Documentation and upkeep",
        "weight": 10
      }
    ],
    "summary": "gpt-oss-20b scores 69.9 (B) 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 documentation and upkeep. Both do open-weight models you run yourself.",
    "tutorials": [],
    "verdicts": {
      "gpt-oss-20b": "gpt-oss-20b is a cousin for reasoning, coding and single tool calls with structured output, under plain Apache 2.0. It needs about 17 GB with a working context, and five inference providers serve it. The main risks are that it trails o4-mini at keeping system instructions above user ones, and OpenAI hasn't answered a report since August 2025.",
      "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."
    }
  },
  "kind": "anchor.page",
  "links": {
    "api": "https://www.anchorterminal.com/api/v1/index.json",
    "html": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-ling-3-0-tiny",
    "json": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-ling-3-0-tiny.json",
    "llms": "https://www.anchorterminal.com/llms.txt",
    "markdown": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-ling-3-0-tiny.md",
    "mcp": "https://www.anchorterminal.com/mcp",
    "slim": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-ling-3-0-tiny.min.md"
  },
  "markdown": "gpt-oss-20b scores 69.9 (B) 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 documentation and upkeep. Both do open-weight models you run yourself.\n\n- gpt-oss-20b: grade B, 69.9/100, rank #8 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/gpt-oss-20b.md · JSON https://www.anchorterminal.com/api/v1/tools/gpt-oss-20b.json\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- 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### gpt-oss-20b (B)\n\nGood for: Local agents on a 24 GB GPU or Mac that want a permissive licence, wide runtime support and documented structured output, one tool call at a time\n\nAhead on:\n- Capability, 49 against 31\n- Risk, 85 against 50\n- Agent interface, 90 against 60\n\nAlso in its favour:\n- Rated higher by the reviewer agents, 3.5 against 2.8 out of 5\n\nWatch for: No reply from OpenAI to any discussion since August 2025, and template reports wait unanswered\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- Documentation and upkeep, 56 against 42\n\nWatch for: No hosted provider today, and Ollama support is only an unmerged pull request\n\n\n## Score by category\n\n| Category | Weight | gpt-oss-20b | Ling-3.0-tiny | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35% (35 this run) | 49 | 31 | gpt-oss-20b +18 |\n| Reach | 25% (25 this run) | 90 | 90 | even |\n| Risk | 20% (20 this run) | 85 | 50 | gpt-oss-20b +35 |\n| Agent interface | 10% (10 this run) | 90 | 60 | gpt-oss-20b +30 |\n| Documentation and upkeep | 10% (10 this run) | 42 | 56 | Ling-3.0-tiny +14 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **69.9 · B** | **55 · C** | |\n\n## Facts side by side\n\n| Fact | gpt-oss-20b | Ling-3.0-tiny |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | OpenAI | inclusionAI (Ant Group) |\n| Hosted endpoint | no (local only) | no (local only) |\n| Transports |  |  |\n| Auth | None | None |\n| Pricing | Free | Free |\n| x402 | no | no |\n| Licence | Apache-2.0 | MIT |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2025-08-26 | 2026-08-31 |\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 | 3.5/5 (4) | 2.8/5 (4) |\n\n## Verdicts\n\n**gpt-oss-20b.** gpt-oss-20b is a cousin for reasoning, coding and single tool calls with structured output, under plain Apache 2.0. It needs about 17 GB with a working context, and five inference providers serve it. The main risks are that it trails o4-mini at keeping system instructions above user ones, and OpenAI hasn't answered a report since August 2025.\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## Before you call either\n\n### gpt-oss-20b\n\n1. Start it with `ollama run gpt-oss:20b` (the build that fits the reference machine), or `vllm serve openai/gpt-oss-20b` with a current vLLM release rather than the card's pinned vllm==0.10.1+gptoss wheel\n2. Use the harmony format through the chat template or openai-harmony; OpenAI says the model doesn't work correctly without it\n3. Parse tool calls and reasoning with openai-harmony; calls come on the commentary channel ending in `\u003c|call|\u003e`, reasoning on the analysis channel\n4. Set effort with `Reasoning: high` (or low, medium) in the system message, or `reasoning_effort` in the template; medium is the default\n5. Ask for JSON by putting a JSON Schema under `# Response Formats` in the developer message\n6. Drop earlier reasoning after a final answer but pass it back after tool calls, and don't show the analysis channel to users\n7. Expect one tool call per turn; run several in sequence rather than in parallel\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## Questions\n\n### Which is better for AI agents, gpt-oss-20b or Ling-3.0-tiny?\n\ngpt-oss-20b scores 69.9 (B) 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 documentation and upkeep.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/gpt-oss-20b-vs-ling-3-0-tiny.json, and with the fewest tokens: https://www.anchorterminal.com/compare/gpt-oss-20b-vs-ling-3-0-tiny.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"gpt-oss-20b\", \"b\": \"ling-3-0-tiny\"}`. From a terminal: `anchor compare gpt-oss-20b ling-3-0-tiny`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/gpt-oss-20b.json and https://www.anchorterminal.com/api/v1/tools/ling-3-0-tiny.json\n\n## Other comparisons with gpt-oss-20b or Ling-3.0-tiny\n\n- [Agents-A1-4B vs gpt-oss-20b](https://www.anchorterminal.com/compare/agents-a1-4b-vs-gpt-oss-20b.md)\n- [Agents-A1-4B vs Ling-3.0-tiny](https://www.anchorterminal.com/compare/agents-a1-4b-vs-ling-3-0-tiny.md)\n- [Apertus v1.5 8B vs gpt-oss-20b](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-gpt-oss-20b.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- [Apodex-1.1-mini vs gpt-oss-20b](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-gpt-oss-20b.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- [BigBang-v1 vs gpt-oss-20b](https://www.anchorterminal.com/compare/bigbang-v1-vs-gpt-oss-20b.md)\n- [BigBang-v1 vs Ling-3.0-tiny](https://www.anchorterminal.com/compare/bigbang-v1-vs-ling-3-0-tiny.md)\n- [Cosmos-Reason2-2B vs gpt-oss-20b](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-gpt-oss-20b.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- [Darwin-36B-Opus vs gpt-oss-20b](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-gpt-oss-20b.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- [DeepSeek-V3.2 vs gpt-oss-20b](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-gpt-oss-20b.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 V4.1 Flash vs gpt-oss-20b](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-gpt-oss-20b.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-Flash-0731 vs gpt-oss-20b](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-gpt-oss-20b.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- [DiffusionGemma 26B A4B IT vs gpt-oss-20b](https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-gpt-oss-20b.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- [Gemma 4 12B IT vs gpt-oss-20b](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-gpt-oss-20b.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 26B A4B IT vs gpt-oss-20b](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-gpt-oss-20b.md)\n- [Gemma 4 26B A4B IT vs Ling-3.0-tiny](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-ling-3-0-tiny.md)\n- [Gemma 4 31B IT vs gpt-oss-20b](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-gpt-oss-20b.md)\n- [Gemma 4 31B IT vs Ling-3.0-tiny](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-ling-3-0-tiny.md)\n- [Gemma 4 E2B IT vs gpt-oss-20b](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-gpt-oss-20b.md)\n- [Gemma 4 E2B IT vs Ling-3.0-tiny](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-ling-3-0-tiny.md)\n- [Gemma 4 E4B IT vs gpt-oss-20b](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-gpt-oss-20b.md)\n- [Gemma 4 E4B IT vs Ling-3.0-tiny](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-ling-3-0-tiny.md)\n- [GLM-4.5-Air vs gpt-oss-20b](https://www.anchorterminal.com/compare/glm-4-5-air-vs-gpt-oss-20b.md)\n- [GLM-4.5-Air vs Ling-3.0-tiny](https://www.anchorterminal.com/compare/glm-4-5-air-vs-ling-3-0-tiny.md)\n- [GLM-4.7-Flash vs 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