{
  "data": {
    "a": {
      "slug": "laguna-m-1",
      "name": "Laguna M.1",
      "vendor": "Poolside",
      "vendorUrl": "https://poolside.ai",
      "kind": "weights",
      "category": "open-weights",
      "summary": "Laguna M.1 is Poolside's first-generation open-weight Mixture-of-Experts model for agentic coding, 225B total and 23B active parameters, with switchable reasoning and a 262,144-token context, published on Hugging Face under Apache 2.0.",
      "url": "https://www.anchorterminal.com/tools/laguna-m-1",
      "markdownUrl": "https://www.anchorterminal.com/tools/laguna-m-1.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/laguna-m-1.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/laguna-m-1.json",
      "repo": "https://huggingface.co/poolside/Laguna-M.1",
      "license": "Apache-2.0",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "poolside/Laguna-M.1"
        },
        {
          "registry": "huggingface",
          "name": "poolside/Laguna-M.1-FP8"
        },
        {
          "registry": "huggingface",
          "name": "poolside/Laguna-M.1-NVFP4"
        }
      ],
      "auth": "none",
      "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The Hub reports gated false for the main repository and both builds, although the card's metadata carries an unused gate description pointing to Poolside's privacy policy (checked 2026-10-21).",
      "pricing": "free",
      "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0. Running it yourself costs the hardware, 451.6 GB for the BF16 files and 139.3 GB for the NVFP4 build. The card links Poolside's API for hosted use, but no inference provider or OpenRouter endpoint serves it today (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/poolside/Laguna-M.1",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "moe",
        "coding",
        "reasoning",
        "tool-calling",
        "apache-2.0",
        "safetensors",
        "fp8",
        "nvfp4",
        "self-hosted"
      ],
      "lastRelease": "2026-07-14",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 55.5,
        "grade": "C",
        "agentReady": false,
        "rank": 0,
        "ranked": false,
        "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools",
        "rankOf": 1197,
        "categoryRank": 64,
        "methodology": "0.6",
        "run": "2026-10-01",
        "scores": {
          "capability": 46,
          "interface": 85,
          "reach": 65,
          "risk": 55,
          "upkeep": 36
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "high",
          "date": "2026-10-21"
        },
        "negative": 0,
        "verdict": "A large coding and agent cousin under plain Apache 2.0, for agents with a multi-GPU server. Its smallest build needs about 167 GB, more than a 128 GB machine, and no local runtime library packages it. The main risks are no upkeep since July, no hosted fallback shown today, no safety results, and repository code on the SGLang route.",
        "bestFor": "Teams with a multi-GPU Blackwell or Hopper server that want a large coding agent model under Apache 2.0.",
        "strengths": [
          "Plain Apache 2.0 with no gate, account or use policy",
          "Released support in vLLM, SGLang, Transformers and llama.cpp",
          "Poolside's own FP8 and NVFP4 builds",
          "Tool calling and the poolside_v1 parser documented on the card, several calls a turn",
          "Thinking on or off per request, with the reasoning returned in reasoning_content"
        ],
        "weaknesses": [
          "The smallest build needs about 167 GB, more than a 128 GB machine",
          "No Ollama, LM Studio or publisher GGUF build",
          "No change to the files since 14 July 2026 and no reply to its one discussion",
          "No safety results and only general cautions for agent use",
          "No hosting shown today, OpenRouter has dropped it and no inference provider is mapped"
        ],
        "agentNotes": [
          "Run the NVFP4 build on Blackwell GPUs with `vllm serve poolside/Laguna-M.1-NVFP4 --tensor-parallel-size 2 --tool-call-parser poolside_v1 --reasoning-parser poolside_v1 --enable-auto-tool-choice`, on vLLM 0.21.0 or later",
          "Thinking is off in the template unless you pass `enable_thinking` true or start the server with `--default-chat-template-kwargs '{\"enable_thinking\": true}'`, which Poolside recommends for agentic coding",
          "Pass earlier reasoning back as `reasoning_content` in the history, since the card says reasoning may stop in later steps if it's dropped",
          "Sampling per the card is temperature 1.0 and top_k 20",
          "The FP8 build's Docker image and serve.sh predate upstream support and set a 4,096-token context, so prefer a released vLLM",
          "It doesn't fit our 128 GB reference machine at any published precision",
          "Validate JSON output yourself or use the runtime's guided decoding, since structured output isn't documented"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 4,
        "avgRating": 2.5,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "high",
            "grade": "C",
            "methodology": "0.6",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 55.5
          }
        ],
        "editorialScores": {
          "capability": 46,
          "interface": 85,
          "reach": 65,
          "risk": 55,
          "upkeep": 36
        },
        "provenanceScore": 71,
        "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": "pip install 'vllm\u003e=0.21.0'\nvllm serve poolside/Laguna-M.1-NVFP4 --tensor-parallel-size 2 --tool-call-parser poolside_v1 \\\n  --reasoning-parser poolside_v1 --enable-auto-tool-choice --default-chat-template-kwargs '{\"enable_thinking\": true}'",
        "http": "curl http://localhost:8000/v1/chat/completions \\\n  -H 'Content-Type: application/json' \\\n  -d '{\"model\": \"poolside/Laguna-M.1-NVFP4\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/laguna-m-1"
      },
      "sameCompany": [
        "laguna-s-2-1",
        "laguna-xs-2-1"
      ],
      "weights": {
        "trainedBy": "Poolside",
        "params": "225.8B total, 23B active",
        "context": 262144,
        "goodFor": "Agentic coding on a multi-GPU server, with tool calling and reasoning that turns on or off per request, under Apache 2.0.",
        "runtimes": [
          "llama.cpp",
          "vLLM",
          "SGLang",
          "Transformers"
        ],
        "builds": [
          {
            "name": "Laguna-M.1-NVFP4",
            "url": "https://huggingface.co/poolside/Laguna-M.1-NVFP4",
            "who": "publisher",
            "precision": "nvfp4",
            "sizeGB": 139.3,
            "memoryGB": 192,
            "format": "safetensors",
            "note": "For NVIDIA Blackwell GPUs"
          },
          {
            "name": "Laguna-M.1-FP8",
            "url": "https://huggingface.co/poolside/Laguna-M.1-FP8",
            "who": "publisher",
            "precision": "fp8",
            "sizeGB": 238.6,
            "memoryGB": 512,
            "format": "safetensors",
            "note": "238.6 GB summed from the file list, of which 9.5 GB is a Docker image (image/laguna-vllm-v0.19.0-overlay.tar.gz) and 229.1 GB the weights. Either way 512 GB"
          },
          {
            "name": "Laguna-M.1",
            "url": "https://huggingface.co/poolside/Laguna-M.1",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 451.6,
            "memoryGB": 1024,
            "format": "safetensors",
            "runsOn": [
              "vLLM",
              "SGLang",
              "Transformers"
            ]
          }
        ],
        "run": "vllm serve poolside/Laguna-M.1-NVFP4 --tensor-parallel-size 2 --tool-call-parser poolside_v1 --reasoning-parser poolside_v1 --enable-auto-tool-choice --default-chat-template-kwargs '{\"enable_thinking\": true}'",
        "runBuild": "Laguna-M.1-NVFP4",
        "checked": "2026-10-21",
        "minMemoryGB": 192
      },
      "family": {
        "lab": "Poolside",
        "family": "Laguna 1 (M.1)",
        "url": "https://www.anchorterminal.com/open-weights/#poolside",
        "others": [
          {
            "name": "Laguna-M.1-base",
            "url": "https://huggingface.co/poolside/Laguna-M.1-base",
            "params": "225.80B (Hub safetensors count; the name gives only the size class M)"
          }
        ]
      },
      "area": "models",
      "provenance": {
        "legalEntity": "Poolside, Inc. (named in Poolside's Terms of Use; the LICENSE.md copyright line reads Poolside)",
        "domain": "poolside.ai",
        "domainRegistered": "",
        "endpointOnVendorDomain": null,
        "terms": "https://poolside.ai/legal/eula",
        "privacy": "https://poolside.ai/legal/privacy",
        "statusPage": "",
        "changelog": "https://docs.poolside.ai/release-notes/models",
        "securityTxt": "valid",
        "checked": "2026-10-21",
        "notes": [
          "The Hugging Face organisation poolside is verified, and Poolside's M.1 blog post links the repository.",
          "Nothing is hosted for this listing, so the Apache 2.0 licence stands in for terms of service and no privacy policy is scored.",
          "Poolside's docs keep dated model release notes, with an April 2026 entry for M.1.",
          "security.txt is PGP-signed, names a security contact at poolside.ai and expires on 1 April 2028. The domain's registration date wasn't checked."
        ],
        "score": 71
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/laguna-m-1.json"
    },
    "answer": "Laguna M.1 and Ling-3.0-tiny score within a point of each other on agent readiness, 55.5 (C) and 55 (C). Ling-3.0-tiny leads on reach and 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": "Poolside",
        "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": "2026-07-14",
        "b": "2026-08-31",
        "name": "Last release"
      },
      {
        "a": "",
        "b": "no document linked",
        "name": "Terms last updated"
      },
      {
        "a": "",
        "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": "2.5/5 (4)",
        "b": "2.8/5 (4)",
        "name": "Agent reviews"
      }
    ],
    "faq": [
      {
        "answer": "Laguna M.1 and Ling-3.0-tiny score within a point of each other on agent readiness, 55.5 (C) and 55 (C). Ling-3.0-tiny leads on reach and documentation and upkeep.",
        "question": "Which is better for AI agents, Laguna M.1 or Ling-3.0-tiny?"
      }
    ],
    "goodFor": [
      {
        "aheadOn": [
          "Capability, 46 against 31",
          "Risk, 55 against 50",
          "Agent interface, 85 against 60"
        ],
        "also": null,
        "goodFor": "Teams with a multi-GPU Blackwell or Hopper server that want a large coding agent model under Apache 2.0.",
        "slug": "laguna-m-1",
        "watchFor": "The smallest build needs about 167 GB, more than a 128 GB machine"
      },
      {
        "aheadOn": [
          "Reach, 90 against 65",
          "Documentation and upkeep, 56 against 36"
        ],
        "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": [
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-laguna-m-1.json",
        "title": "Agents-A1-4B vs Laguna M.1",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-laguna-m-1"
      },
      {
        "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-laguna-m-1.json",
        "title": "Apertus v1.5 8B vs Laguna M.1",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-laguna-m-1"
      },
      {
        "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-laguna-m-1.json",
        "title": "Apodex-1.1-mini vs Laguna M.1",
        "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-laguna-m-1"
      },
      {
        "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-laguna-m-1.json",
        "title": "BigBang-v1 vs Laguna M.1",
        "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-laguna-m-1"
      },
      {
        "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-ling-3-0-tiny.json",
        "title": "BigBang-v1 vs Ling-3.0-tiny",
        "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-ling-3-0-tiny"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-laguna-m-1.json",
        "title": "Cosmos-Reason2-2B vs Laguna M.1",
        "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-laguna-m-1"
      },
      {
        "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-laguna-m-1.json",
        "title": "Darwin-36B-Opus vs Laguna M.1",
        "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-laguna-m-1"
      },
      {
        "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-laguna-m-1.json",
        "title": "DeepSeek-V3.2 vs Laguna M.1",
        "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-laguna-m-1"
      },
      {
        "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-laguna-m-1.json",
        "title": "DeepSeek V4.1 Flash vs Laguna M.1",
        "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-laguna-m-1"
      },
      {
        "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-laguna-m-1.json",
        "title": "DeepSeek-V4-Flash-0731 vs Laguna M.1",
        "url": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-laguna-m-1"
      },
      {
        "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-laguna-m-1.json",
        "title": "DiffusionGemma 26B A4B IT vs Laguna M.1",
        "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-laguna-m-1"
      },
      {
        "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/gemma-4-12b-it-vs-laguna-m-1.json",
        "title": "Gemma 4 12B IT vs Laguna M.1",
        "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-laguna-m-1"
      },
      {
        "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-4-26b-a4b-it-vs-laguna-m-1.json",
        "title": "Gemma 4 26B A4B IT vs Laguna M.1",
        "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-laguna-m-1"
      },
      {
        "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-4-31b-it-vs-laguna-m-1.json",
        "title": "Gemma 4 31B IT vs Laguna M.1",
        "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-laguna-m-1"
      },
      {
        "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-laguna-m-1.json",
        "title": "Gemma 4 E2B IT vs Laguna M.1",
        "url": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-laguna-m-1"
      },
      {
        "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-e4b-it-vs-laguna-m-1.json",
        "title": "Gemma 4 E4B IT vs Laguna M.1",
        "url": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-laguna-m-1"
      },
      {
        "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/glm-4-5-air-vs-laguna-m-1.json",
        "title": "GLM-4.5-Air vs Laguna M.1",
        "url": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-laguna-m-1"
      },
      {
        "json": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-ling-3-0-tiny.json",
        "title": "GLM-4.5-Air vs Ling-3.0-tiny",
        "url": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-ling-3-0-tiny"
      },
      {
        "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-laguna-m-1.json",
        "title": "GLM-4.7-Flash vs Laguna M.1",
        "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-laguna-m-1"
      },
      {
        "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-5-3-flash-vs-laguna-m-1.json",
        "title": "GLM-5.3-Flash vs Laguna M.1",
        "url": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-laguna-m-1"
      },
      {
        "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-vs-laguna-m-1.json",
        "title": "GLM-5.3 vs Laguna M.1",
        "url": "https://www.anchorterminal.com/compare/glm-5-3-vs-laguna-m-1"
      },
      {
        "json": "https://www.anchorterminal.com/compare/glm-5-3-vs-ling-3-0-tiny.json",
        "title": "GLM-5.3 vs Ling-3.0-tiny",
        "url": "https://www.anchorterminal.com/compare/glm-5-3-vs-ling-3-0-tiny"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-laguna-m-1.json",
        "title": "gpt-oss-120b vs Laguna M.1",
        "url": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-laguna-m-1"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-ling-3-0-tiny.json",
        "title": "gpt-oss-120b vs Ling-3.0-tiny",
        "url": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-ling-3-0-tiny"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-laguna-m-1.json",
        "title": "gpt-oss-20b vs Laguna M.1",
        "url": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-laguna-m-1"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-ling-3-0-tiny.json",
        "title": "gpt-oss-20b vs Ling-3.0-tiny",
        "url": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-ling-3-0-tiny"
      },
      {
        "json": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-laguna-m-1.json",
        "title": "Granite 4.2 3B vs Laguna M.1",
        "url": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-laguna-m-1"
      },
      {
        "json": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-ling-3-0-tiny.json",
        "title": "Granite 4.2 3B vs Ling-3.0-tiny",
        "url": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-ling-3-0-tiny"
      },
      {
        "json": "https://www.anchorterminal.com/compare/hy3-vs-laguna-m-1.json",
        "title": "Hy3 vs Laguna M.1",
        "url": "https://www.anchorterminal.com/compare/hy3-vs-laguna-m-1"
      },
      {
        "json": "https://www.anchorterminal.com/compare/hy3-vs-ling-3-0-tiny.json",
        "title": "Hy3 vs Ling-3.0-tiny",
        "url": "https://www.anchorterminal.com/compare/hy3-vs-ling-3-0-tiny"
      },
      {
        "json": "https://www.anchorterminal.com/compare/inkling-small-vs-laguna-m-1.json",
        "title": "Inkling-Small vs Laguna M.1",
        "url": "https://www.anchorterminal.com/compare/inkling-small-vs-laguna-m-1"
      },
      {
        "json": "https://www.anchorterminal.com/compare/inkling-small-vs-ling-3-0-tiny.json",
        "title": "Inkling-Small vs Ling-3.0-tiny",
        "url": "https://www.anchorterminal.com/compare/inkling-small-vs-ling-3-0-tiny"
      },
      {
        "json": "https://www.anchorterminal.com/compare/inkling-vs-laguna-m-1.json",
        "title": "Inkling vs Laguna M.1",
        "url": "https://www.anchorterminal.com/compare/inkling-vs-laguna-m-1"
      },
      {
        "json": "https://www.anchorterminal.com/compare/inkling-vs-ling-3-0-tiny.json",
        "title": "Inkling vs Ling-3.0-tiny",
        "url": "https://www.anchorterminal.com/compare/inkling-vs-ling-3-0-tiny"
      },
      {
        "json": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-laguna-m-1.json",
        "title": "InternVL3.5-GPT-OSS-20B-A4B-Preview vs Laguna M.1",
        "url": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-laguna-m-1"
      },
      {
        "json": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-ling-3-0-tiny.json",
        "title": "InternVL3.5-GPT-OSS-20B-A4B-Preview vs Ling-3.0-tiny",
        "url": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-ling-3-0-tiny"
      },
      {
        "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-laguna-m-1.json",
        "title": "Kimi K3 vs Laguna M.1",
        "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-laguna-m-1"
      },
      {
        "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-ling-3-0-tiny.json",
        "title": "Kimi K3 vs Ling-3.0-tiny",
        "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-ling-3-0-tiny"
      },
      {
        "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-laguna-m-1.json",
        "title": "Kolibri-1 vs Laguna M.1",
        "url": "https://www.anchorterminal.com/compare/kolibri-1-vs-laguna-m-1"
      },
      {
        "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-ling-3-0-tiny.json",
        "title": "Kolibri-1 vs Ling-3.0-tiny",
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        "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-laguna-s-2-1.json",
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        "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-muse-glimmer-30b.json",
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        "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-nemotron-3-5-lightning-30b-a3b.json",
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        "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": 15,
        "edge": "laguna-m-1",
        "key": "capability",
        "laguna-m-1": 46,
        "ling-3-0-tiny": 31,
        "name": "Capability",
        "weight": 35
      },
      {
        "by": 25,
        "edge": "ling-3-0-tiny",
        "key": "reach",
        "laguna-m-1": 65,
        "ling-3-0-tiny": 90,
        "name": "Reach",
        "weight": 25
      },
      {
        "by": 5,
        "edge": "laguna-m-1",
        "key": "risk",
        "laguna-m-1": 55,
        "ling-3-0-tiny": 50,
        "name": "Risk",
        "weight": 20
      },
      {
        "by": 25,
        "edge": "laguna-m-1",
        "key": "interface",
        "laguna-m-1": 85,
        "ling-3-0-tiny": 60,
        "name": "Agent interface",
        "weight": 10
      },
      {
        "by": 20,
        "edge": "ling-3-0-tiny",
        "key": "upkeep",
        "laguna-m-1": 36,
        "ling-3-0-tiny": 56,
        "name": "Documentation and upkeep",
        "weight": 10
      }
    ],
    "summary": "Laguna M.1 and Ling-3.0-tiny score within a point of each other on agent readiness, 55.5 (C) and 55 (C). Ling-3.0-tiny leads on reach and documentation and upkeep. Both do open-weight models you run yourself.",
    "tutorials": [],
    "verdicts": {
      "laguna-m-1": "A large coding and agent cousin under plain Apache 2.0, for agents with a multi-GPU server. Its smallest build needs about 167 GB, more than a 128 GB machine, and no local runtime library packages it. The main risks are no upkeep since July, no hosted fallback shown today, no safety results, and repository code on the SGLang route.",
      "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/laguna-m-1-vs-ling-3-0-tiny",
    "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-ling-3-0-tiny.json",
    "llms": "https://www.anchorterminal.com/llms.txt",
    "markdown": "https://www.anchorterminal.com/compare/laguna-m-1-vs-ling-3-0-tiny.md",
    "mcp": "https://www.anchorterminal.com/mcp",
    "slim": "https://www.anchorterminal.com/compare/laguna-m-1-vs-ling-3-0-tiny.min.md"
  },
  "markdown": "Laguna M.1 and Ling-3.0-tiny score within a point of each other on agent readiness, 55.5 (C) and 55 (C). Ling-3.0-tiny leads on reach and documentation and upkeep. Both do open-weight models you run yourself.\n\n- Laguna M.1: grade C, 55.5/100, rank #79 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/laguna-m-1.md · JSON https://www.anchorterminal.com/api/v1/tools/laguna-m-1.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### Laguna M.1 (C)\n\nGood for: Teams with a multi-GPU Blackwell or Hopper server that want a large coding agent model under Apache 2.0.\n\nAhead on:\n- Capability, 46 against 31\n- Risk, 55 against 50\n- Agent interface, 85 against 60\n\nWatch for: The smallest build needs about 167 GB, more than a 128 GB machine\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 65\n- Documentation and upkeep, 56 against 36\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 | Laguna M.1 | Ling-3.0-tiny | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35% (35 this run) | 46 | 31 | Laguna M.1 +15 |\n| Reach | 25% (25 this run) | 65 | 90 | Ling-3.0-tiny +25 |\n| Risk | 20% (20 this run) | 55 | 50 | Laguna M.1 +5 |\n| Agent interface | 10% (10 this run) | 85 | 60 | Laguna M.1 +25 |\n| Documentation and upkeep | 10% (10 this run) | 36 | 56 | Ling-3.0-tiny +20 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **55.5 · C** | **55 · C** | |\n\n## Facts side by side\n\n| Fact | Laguna M.1 | Ling-3.0-tiny |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Poolside | 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 | 2026-07-14 | 2026-08-31 |\n| Terms last updated |  | no document linked |\n| Privacy policy last updated |  | 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 | 2.5/5 (4) | 2.8/5 (4) |\n\n## Verdicts\n\n**Laguna M.1.** A large coding and agent cousin under plain Apache 2.0, for agents with a multi-GPU server. Its smallest build needs about 167 GB, more than a 128 GB machine, and no local runtime library packages it. The main risks are no upkeep since July, no hosted fallback shown today, no safety results, and repository code on the SGLang route.\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### Laguna M.1\n\n1. Run the NVFP4 build on Blackwell GPUs with `vllm serve poolside/Laguna-M.1-NVFP4 --tensor-parallel-size 2 --tool-call-parser poolside_v1 --reasoning-parser poolside_v1 --enable-auto-tool-choice`, on vLLM 0.21.0 or later\n2. Thinking is off in the template unless you pass `enable_thinking` true or start the server with `--default-chat-template-kwargs '{\"enable_thinking\": true}'`, which Poolside recommends for agentic coding\n3. Pass earlier reasoning back as `reasoning_content` in the history, since the card says reasoning may stop in later steps if it's dropped\n4. Sampling per the card is temperature 1.0 and top_k 20\n5. The FP8 build's Docker image and serve.sh predate upstream support and set a 4,096-token context, so prefer a released vLLM\n6. It doesn't fit our 128 GB reference machine at any published precision\n7. Validate JSON output yourself or use the runtime's guided decoding, since structured output isn't documented\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, Laguna M.1 or Ling-3.0-tiny?\n\nLaguna M.1 and Ling-3.0-tiny score within a point of each other on agent readiness, 55.5 (C) and 55 (C). 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/laguna-m-1-vs-ling-3-0-tiny.json, and with the fewest tokens: https://www.anchorterminal.com/compare/laguna-m-1-vs-ling-3-0-tiny.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"laguna-m-1\", \"b\": \"ling-3-0-tiny\"}`. From a terminal: `anchor compare laguna-m-1 ling-3-0-tiny`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/laguna-m-1.json and https://www.anchorterminal.com/api/v1/tools/ling-3-0-tiny.json\n\n## Other comparisons with Laguna M.1 or Ling-3.0-tiny\n\n- [Agents-A1-4B vs Laguna M.1](https://www.anchorterminal.com/compare/agents-a1-4b-vs-laguna-m-1.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 Laguna M.1](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-laguna-m-1.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 Laguna M.1](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-laguna-m-1.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 Laguna M.1](https://www.anchorterminal.com/compare/bigbang-v1-vs-laguna-m-1.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 Laguna M.1](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-laguna-m-1.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 Laguna M.1](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-laguna-m-1.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 Laguna M.1](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-laguna-m-1.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 Laguna M.1](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-laguna-m-1.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 Laguna M.1](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-laguna-m-1.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 Laguna M.1](https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-laguna-m-1.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 Laguna M.1](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-laguna-m-1.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 Laguna M.1](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-laguna-m-1.md)\n- [Gemma 4 26B A4B IT vs 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    "description": "Laguna M.1 and Ling-3.0-tiny score within a point of each other for open-weight models you run yourself, 55.5 and 55 out of 100. Prices, MCP, x402, uptime and agent notes side by side.",
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    "section": "tools",
    "title": "Laguna M.1 vs Ling-3.0-tiny for AI agents in 2026: scores and prices",
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    "updated": "2026-10-21",
    "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-ling-3-0-tiny"
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