{
  "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",
      "dns": "laguna-m-1.grade.anchorterminal.com",
      "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 is mapped on the Hub and Poolside's API docs name only Laguna S 2.1 (checked 2026-11-04).",
      "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": 56.7,
        "grade": "C",
        "agentReady": false,
        "rank": 0,
        "ranked": false,
        "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools",
        "rankOf": 1518,
        "categoryRank": 84,
        "methodology": "0.7",
        "run": "2026-10-01",
        "scores": {
          "capability": 46,
          "interface": 85,
          "reach": 70,
          "risk": 55,
          "upkeep": 36
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "high",
          "date": "2026-11-04"
        },
        "negative": 0,
        "verdict": "A large open-weight coding and agent model under plain Apache 2.0, for teams with a multi-GPU server. Its smallest build needs about 167 GB, more than a 128 GB machine. Released llama.cpp, Ollama, vLLM, SGLang and Transformers code runs the architecture. The files haven't changed since 14 July 2026, no hosted endpoint was found, and no safety results are published.",
        "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, llama.cpp and Ollama (through its pinned 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 library or LM Studio catalogue entry, and no publisher GGUF build",
          "No change to the files since 14 July 2026 and no reply from Poolside to its one discussion",
          "No safety results and only general cautions for agent use",
          "No hosting found. No inference provider is mapped on the Hub, and Poolside's API docs name only Laguna S 2.1"
        ],
        "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": "0.7",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 56.7
          }
        ],
        "editorialScores": {
          "capability": 46,
          "interface": 85,
          "reach": 70,
          "risk": 55,
          "upkeep": 36
        },
        "provenanceScore": 69,
        "checked": {
          "categories": {
            "capability": "2026-11-04",
            "interface": "2026-11-04",
            "reach": "2026-11-04",
            "risk": "2026-11-04",
            "upkeep": "2026-11-04"
          },
          "last": "2026-11-04",
          "lastMoved": "2026-11-04",
          "oldest": "2026-11-04",
          "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",
          "Ollama",
          "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": "2026-11-04",
        "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": 69
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/laguna-m-1.json",
      "live": {
        "slug": "laguna-m-1",
        "securityTxt": {
          "url": "https://poolside.ai/.well-known/security.txt",
          "state": "valid",
          "expires": "2028-04-01T12:00:00.000Z",
          "checkedAt": "2026-11-10T16:09:25.614312659Z"
        },
        "domain": {
          "domain": "poolside.ai",
          "registered": "2020-07-07",
          "source": "https://rdap.identitydigital.services/rdap/domain/poolside.ai",
          "checkedAt": "2026-11-08T14:58:52.089559761Z"
        },
        "pages": [
          {
            "url": "https://docs.poolside.ai/release-notes/models",
            "kind": "changelog",
            "status": 200,
            "checkedAt": "2026-11-10T16:18:11.541481401Z",
            "changedAt": "2026-11-01T06:42:30.392417309Z",
            "fingerprint": "41c3a898c1a6"
          },
          {
            "url": "https://poolside.ai/legal/privacy",
            "kind": "privacy",
            "status": 304,
            "checkedAt": "2026-11-10T16:28:14.659109859Z",
            "changedAt": "0001-01-01T00:00:00Z",
            "fingerprint": "95a56164038b"
          },
          {
            "url": "https://poolside.ai/legal/eula",
            "kind": "terms",
            "status": 304,
            "checkedAt": "2026-11-10T16:28:12.585971651Z",
            "changedAt": "0001-01-01T00:00:00Z",
            "fingerprint": "6daf0ef509c0"
          }
        ],
        "updatedAt": "2026-11-10T16:28:14.659109859Z"
      }
    },
    "answer": "Laguna M.1 scores 56.7 (C) on agent readiness against Ling-3.1-flash-1019's 54.8 (C), and leads in 3 of 4 scored categories. Ling-3.1-flash-1019 leads on documentation and upkeep.",
    "b": {
      "slug": "ling-3-1-flash-1019",
      "name": "Ling-3.1-flash-1019",
      "vendor": "inclusionAI (Ant Group)",
      "vendorUrl": "https://www.inclusion-ai.org",
      "kind": "weights",
      "category": "open-weights",
      "summary": "Ling-3.1-flash-1019 is inclusionAI's open-weight hybrid reasoning mixture of experts with 560B parameters and 25B active, a post-trained update of Ling-3.1-flash, with switchable thinking, tool calling and a 262,144-token native context, published in FP8 under MIT.",
      "url": "https://www.anchorterminal.com/tools/ling-3-1-flash-1019",
      "markdownUrl": "https://www.anchorterminal.com/tools/ling-3-1-flash-1019.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/ling-3-1-flash-1019.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/ling-3-1-flash-1019.json",
      "dns": "ling-3-1-flash-1019.grade.anchorterminal.com",
      "repo": "https://huggingface.co/inclusionAI/Ling-3.1-flash-1019",
      "license": "MIT",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "inclusionAI/Ling-3.1-flash-1019"
        }
      ],
      "auth": "none",
      "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The repository is not gated (checked 2026-11-10).",
      "pricing": "free",
      "pricingNotes": "Free to download and use, commercial use included, under MIT (named in the card's metadata; the repository holds no LICENSE file). Running it yourself costs the hardware, 573.8 GB of FP8 files, which the card serves on 8 H200 or 4 GB300 GPUs (checked 2026-11-10).",
      "priceSummary": "Free",
      "where": "local",
      "x402": {
        "level": "no",
        "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-11-10).",
        "endpoints": []
      },
      "toolCount": null,
      "popularity": {
        "githubStars": null,
        "npmWeekly": null,
        "pypiWeekly": null,
        "asOf": "2026-11-10"
      },
      "docsUrl": "https://huggingface.co/inclusionAI/Ling-3.1-flash-1019",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "moe",
        "hybrid-attention",
        "reasoning",
        "tool-calling",
        "long-context",
        "mit",
        "safetensors",
        "fp8",
        "self-hosted"
      ],
      "lastRelease": "2026-11-04",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 54.8,
        "grade": "C",
        "agentReady": false,
        "rank": 0,
        "ranked": false,
        "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools",
        "rankOf": 1518,
        "categoryRank": 94,
        "methodology": "0.7",
        "run": "2026-10-01",
        "scores": {
          "interface": 60,
          "reach": 60,
          "risk": 45,
          "upkeep": 56
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "low",
          "date": "2026-11-10"
        },
        "negative": 0,
        "verdict": "inclusionAI's newest flagship, a 560B hybrid mixture of experts under plain MIT that vLLM, SGLang, Transformers and llama.cpp run in released versions. It ships only as 574 GB of FP8, so it needs a multi-GPU server. The card overstates the context, documents little about tool calls, and has no safety results.",
        "bestFor": "Teams with a multi-GPU server that want an MIT-licensed flagship for long agent runs and coding",
        "strengths": [
          "Plain MIT, ungated, with a hash for every shard",
          "Released vLLM, SGLang, Transformers and llama.cpp all have the architecture",
          "A 262,144-token native context, and 1M with YaRN per SGLang's cookbook",
          "A training-content summary that names source categories, the size band and a collection date",
          "Updated a week before this check"
        ],
        "weaknesses": [
          "574 GB in FP8 with no smaller build from inclusionAI, so no single machine under 690 GB of GPU memory runs it",
          "The card says 1M tokens of context where the files give 262,144",
          "The tool-call format is only in the template",
          "No safety results, no agent guidance and no security contact",
          "No inference provider mapped for this release, only for the earlier one",
          "Neither question on the repository has a reply"
        ],
        "agentNotes": [
          "Serve it with SGLang 0.5.23 or later on 8 H200 or 4 GB300 GPUs, per the card, and use this repository's name as the model path, not the earlier one the card's command shows",
          "With vLLM pass `--trust-remote-code`, `--tool-call-parser ling3` and `--reasoning-parser ling3`",
          "Set `chat_template_kwargs` `enable_thinking` false for direct answers. Reasoning comes back in `reasoning_content` per SGLang's cookbook",
          "Plan on 262,144 tokens of context. The 1M figure needs the YaRN override in SGLang's cookbook",
          "Use temperature 1.0, top_p 0.95 and top_k 20, per the card",
          "Validate JSON output yourself, since structured output isn't documented"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 0,
        "avgRating": 0,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "low",
            "grade": "C",
            "methodology": "0.7",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 54.8
          }
        ],
        "editorialScores": {
          "interface": 60,
          "reach": 60,
          "risk": 45,
          "upkeep": 56
        },
        "provenanceScore": 61,
        "checked": {
          "categories": {
            "interface": "2026-11-10",
            "reach": "2026-11-10",
            "risk": "2026-11-10",
            "upkeep": "2026-11-10"
          },
          "last": "2026-11-10",
          "oldest": "2026-11-10",
          "stale": false,
          "staleAfterDays": 90
        }
      },
      "connect": {
        "install": "# with SGLang 0.5.23 or later, on 8 H200 GPUs:\npython3 -m sglang.launch_server --model-path inclusionAI/Ling-3.1-flash-1019 \\\n  --tp 8 --ep 8 --speculative-algorithm NEXTN --host 0.0.0.0 --port 30000\n# or with vLLM 0.33.0, per the card's serve command:\nvllm serve inclusionAI/Ling-3.1-flash-1019 --trust-remote-code --tensor-parallel-size 8 \\\n  --enable-prefix-caching --enable-expert-parallel --mamba-cache-mode align \\\n  --enable-auto-tool-choice --tool-call-parser ling3 --reasoning-parser ling3\n# or, with llama.cpp and bartowski's GGUF, a third party's conversion:\nllama-server -hf bartowski/Ling-3.1-flash-1019-GGUF:Q4_K_M --jinja",
        "http": "curl http://localhost:30000/v1/chat/completions \\\n  -H \"Content-Type: application/json\" \\\n  -d '{\"model\": \"inclusionAI/Ling-3.1-flash-1019\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}], \"chat_template_kwargs\": {\"enable_thinking\": false}}'"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/ling-3-1-flash-1019"
      },
      "sameCompany": [
        "ming-3-0-asr-tiny",
        "ming-3-0-audio-flash",
        "ming-3-0-audio-tiny",
        "ming-3-0-tts-tiny",
        "lbs-27b",
        "ling-3-0-tiny"
      ],
      "weights": {
        "trainedBy": "inclusionAI (Ant Group)",
        "builtOn": "inclusionAI/Ling-3.1-flash, post-trained further (named on the card; the metadata names no base model)",
        "params": "560B (25B active)",
        "context": 262144,
        "goodFor": "Large hybrid reasoning model for agents, office work and software engineering on a multi-GPU server, with thinking switchable per request. It needs about 690 GB of GPU memory in FP8.",
        "runtimes": [
          "llama.cpp",
          "vLLM",
          "SGLang",
          "Transformers"
        ],
        "builds": [
          {
            "name": "bartowski Ling-3.1-flash-1019-GGUF (Q4_K_M)",
            "url": "https://huggingface.co/bartowski/Ling-3.1-flash-1019-GGUF",
            "who": "bartowski",
            "precision": "q4_k_m",
            "sizeGB": 357.97,
            "memoryGB": 512,
            "format": "gguf",
            "runsOn": [
              "llama.cpp"
            ],
            "note": "From bartowski, a third party's conversion and not inclusionAI's. 357.97 GB of Q4_K_M weights, from the Hub's model information on 10 November 2026. Reach doesn't count it"
          },
          {
            "name": "Ling-3.1-flash-1019",
            "url": "https://huggingface.co/inclusionAI/Ling-3.1-flash-1019",
            "who": "publisher",
            "precision": "fp8",
            "sizeGB": 573.78,
            "memoryGB": 1024,
            "format": "safetensors",
            "runsOn": [
              "vLLM",
              "SGLang",
              "Transformers"
            ],
            "note": "The release itself, FP8 E4M3 weights in 128×128 blocks. inclusionAI publishes no smaller build"
          }
        ],
        "run": "python3 -m sglang.launch_server --model-path inclusionAI/Ling-3.1-flash-1019 --tp 8 --ep 8 --speculative-algorithm NEXTN",
        "runBuild": "Ling-3.1-flash-1019",
        "checked": "2026-11-10",
        "minMemoryGB": 512
      },
      "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",
            "url": "https://huggingface.co/inclusionAI/Ling-3.0-tiny",
            "params": "7.9B total, 1.3B active",
            "listing": "https://www.anchorterminal.com/tools/ling-3-0-tiny"
          },
          {
            "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"
          },
          {
            "name": "Ling-3.1-flash-base",
            "url": "https://huggingface.co/inclusionAI/Ling-3.1-flash-base",
            "params": "560B total, 25B active"
          }
        ],
        "olderCount": 54
      },
      "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-3.1-flash-1019/commits/main",
        "securityTxt": "none",
        "checked": "2026-11-10",
        "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 by the publisher, so the MIT licence stands in for terms of service and no privacy policy is scored.",
          "The changelog is the Hugging Face commit history, 8 commits from 27 October to 4 November 2026, the last three card edits.",
          "www.inclusion-ai.org/.well-known/security.txt answered 404 on 10 November 2026."
        ],
        "score": 61
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/ling-3-1-flash-1019.json"
    },
    "edges": [
      {
        "by": 25,
        "category": "interface",
        "leads": "laguna-m-1",
        "name": "Agent interface",
        "reasons": {
          "a": "Chat template 20, a Jinja file in the repository. Tool-calling format 25, the card shows how tools go in and calls come back and names the poolside_v1 parser. Trained tool calling 20, several calls a turn. Structured output 0, not documented. Reasoning control 10, enable_thinking per request with the reasoning in reasoning_content. Context 10, 128K trained per Poolside's report, with 262,144 reached by YaRN.",
          "b": "Chat template 20, chat_template.jinja in the repository (6,042 characters), which handles tools, the thinking switch and preserved reasoning. Tool-calling format 10, only the template says how tools are declared (JSON in \u003ctools\u003e) and how calls come back (\u003ctool_call\u003ename with \u003carg_key\u003e and \u003carg_value\u003e pairs). Trained tool calling 20, the template tells the model it may call one or more functions and loops over every entry in an assistant message's tool_calls, and the card reports gains on agentic benchmarks, AA-Briefcase and AutomationBench-AA among them. Structured output 0, the card doesn't document JSON or JSON Schema output. Reasoning control 0, the card documents turning thinking on or off per request with chat_template_kwargs enable_thinking, and names the ling3 reasoning parser, but doesn't say where the reasoning text comes back. Context 10, 262,144 tokens natively in config.json (max_position_embeddings 262144, rope_scaling null) and per SGLang's cookbook. 20 + 10 + 20 + 0 + 0 + 10 = 60."
        },
        "scores": {
          "a": 85,
          "b": 60
        }
      },
      {
        "by": 20,
        "category": "upkeep",
        "leads": "ling-3-1-flash-1019",
        "name": "Documentation and upkeep",
        "reasons": {
          "a": "Model card 11 of 20, intended use, architecture, parameters and context are there, limitations, languages and a cut-off aren't. Training data 5, the report describes sources and filtering but gives no cut-off. Last change 10, 14 July 2026, 113 days ago. Responsiveness 0, the one discussion's only reply is from someone outside the poolside organisation. Version policy 5, a first open release with nothing replaced. File health 5, generation config and template disagree on the thinking default and the FP8 build's Docker files name different image tags.",
          "b": "Model card 11, Intended use (complex reasoning, multi-step tasks and agentic workflows), architecture, parameters (560B total, 25B active) and context (1M claimed) are on the card. Training data 15, the training-content summary the card links names the source categories (Common Crawl and other public datasets, commercially licensed data, the Ant Spider crawler, synthetic data), the size band (over 10 trillion text tokens), data collected up to about July 2026 and the filtering, without listing datasets with sizes. Last change 20, the newest commit, on 4 November 2026, edited the card, six days before this check, and the files went up on 27 October. Responsiveness 0, Neither of the 2 discussions that aren't pull requests (both opened on 27 October 2026) has a reply from an inclusionAI member. Version policy 5, the update went into a repository of its own and Ling-3.1-flash and Ling 3.0 stay in theirs. File health 5, the card states a 1M-token context while config.json gives 262,144 with no rope scaling, and its serve commands name the earlier repository as the model path. 11 + 15 + 20 + 0 + 5 + 5 = 56."
        },
        "scores": {
          "a": 36,
          "b": 56
        }
      },
      {
        "by": 10,
        "category": "reach",
        "leads": "laguna-m-1",
        "name": "Reach",
        "reasons": {
          "a": "Memory 5 of 30, the smallest 4-bit build is Poolside's 139.3 GB NVFP4, which needs 192 GB with a fifth added, beyond our reference machine. Runtimes 25, released llama.cpp, Ollama (through the llama.cpp release it pins), vLLM, SGLang and Transformers run the laguna architecture. Builds 15, FP8 and NVFP4 from Poolside. Terms 15, Apache 2.0 with nothing binding added. Download 10, nothing is gated. Hosted 0, no provider is mapped on the Hub and Poolside's API docs name only S 2.1.",
          "b": "Memory 0, inclusionAI's only build is the FP8 release at 573.78 GB, × 1.2 = 688.5 GB, above 256 GB, and no runtime's library has a build. Runtimes 25, Released model code in Transformers 5.22.2 (bailing_hybrid), vLLM 0.33.0 (BailingMoeV3ForCausalLM, with ling3 tool and reasoning parsers), SGLang 0.5.23 (bailing_moe_v3.py, its bailing_hybrid config and MTP module) and llama.cpp b12242 (bailingmoe3), and Ollama, credited through its pinned llama.cpp as on the Ling-3.0-tiny listing: v0.40.16 sets LLAMA_CPP_VERSION to b12177. Builds 10, the release is FP8, the one quantised precision inclusionAI publishes for it. Terms 15, MIT in the card's metadata, with no use policy or other terms added on the card or in the repository. Download 10, the Hub API's model info gives gated false on 10 November 2026, so the files download with no account and no terms to accept. Hosted 0, the Hub API's provider mapping is empty for this repository on 10 November 2026. 0 + 25 + 10 + 15 + 10 + 0 = 60."
        },
        "scores": {
          "a": 70,
          "b": 60
        }
      },
      {
        "by": 10,
        "category": "risk",
        "leads": "laguna-m-1",
        "name": "Risk",
        "reasons": {
          "a": "File safety 15 of 25, safetensors only, but the repository ships model code behind auto_map and the card's SGLang and TensorRT-LLM instructions use trust_remote_code. Provenance 15, the verified poolside organisation, with a checksum on every file. Safety evaluation 0, none published. Agent guidance 5, general cautions only. Licence risk 15, plain Apache 2.0. Security programme 5, a signed security.txt and a security contact on the card, with no public advisories found.",
          "b": "File safety 15, safetensors only, no .bin, .pt or .pth files (25), less 10 because the card's documented vLLM command passes --trust-remote-code, which runs the repository's configuration_bailing_moe_v3.py, since vLLM 0.33.0 registers no bailing_hybrid config of its own. Provenance 15, files in the inclusionAI organisation that inclusion-ai.org links to, per the Ling-3.0-tiny listing (10). Safety evaluation 0, no safety results for the model on its card or in the training-content summary. Agent guidance 0, the card has nothing on prompt injection, refusals or safeguards, and inclusionAI publishes no guardrail probe for this model as it did for Ling 3.0. Licence risk 15, MIT with no use policy, no field-of-use limits, no threshold and no clause letting inclusionAI change or revoke it. Security programme 0, www.inclusion-ai.org answers 404 for security.txt on 10 November 2026, and the Ling-3.0-tiny listing found no SECURITY.md or disclosure policy in the inclusionAI .github repository. 15 + 15 + 0 + 0 + 15 + 0 = 45."
        },
        "scores": {
          "a": 55,
          "b": 45
        }
      }
    ],
    "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": "2026-11-04",
        "name": "Last release"
      },
      {
        "a": "2026-07-21",
        "b": "no document linked",
        "name": "Terms last updated"
      },
      {
        "a": "2026-07-21",
        "b": "no document linked",
        "name": "Privacy policy last updated"
      },
      {
        "a": "yes, with an opt-out",
        "b": "not read yet",
        "name": "Customer content may train models"
      },
      {
        "a": "not found in the text",
        "b": "not read yet",
        "name": "Terms restrict automated access"
      },
      {
        "a": "not found in the text",
        "b": "not read yet",
        "name": "Terms restrict benchmarking"
      },
      {
        "a": "yes",
        "b": "not read yet",
        "name": "Terms or service can change without notice"
      },
      {
        "a": "yes",
        "b": "not read yet",
        "name": "Arbitration or class-action waiver"
      },
      {
        "a": "2.5/5 (4)",
        "b": "none",
        "name": "Agent reviews"
      }
    ],
    "faq": [
      {
        "answer": "Laguna M.1 scores 56.7 (C) on agent readiness against Ling-3.1-flash-1019's 54.8 (C), and leads in 3 of 4 scored categories. Ling-3.1-flash-1019 leads on documentation and upkeep.",
        "question": "Which is better for AI agents, Laguna M.1 or Ling-3.1-flash-1019?"
      }
    ],
    "goodFor": [
      {
        "aheadOn": [
          "Reach, 70 against 60",
          "Risk, 55 against 45",
          "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": [
          "Documentation and upkeep, 56 against 36"
        ],
        "also": null,
        "goodFor": "Teams with a multi-GPU server that want an MIT-licensed flagship for long agent runs and coding",
        "slug": "ling-3-1-flash-1019",
        "watchFor": "574 GB in FP8 with no smaller build from inclusionAI, so no single machine under 690 GB of GPU memory runs it"
      }
    ],
    "history": {
      "laguna-m-1": [
        {
          "causes": [
            "correction"
          ],
          "date": "2026-11-04",
          "delta": 1.2,
          "from": 55.5,
          "fromGrade": "C",
          "to": 56.7,
          "toGrade": "C"
        }
      ],
      "ling-3-1-flash-1019": null
    },
    "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-3-1-flash-1019.json",
        "title": "Agents-A1-4B vs Ling-3.1-flash-1019",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-ling-3-1-flash-1019"
      },
      {
        "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-3-1-flash-1019.json",
        "title": "Apertus v1.5 8B vs Ling-3.1-flash-1019",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-ling-3-1-flash-1019"
      },
      {
        "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-3-1-flash-1019.json",
        "title": "Apodex-1.1-mini vs Ling-3.1-flash-1019",
        "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-ling-3-1-flash-1019"
      },
      {
        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-laguna-m-1.json",
        "title": "Arete-27B vs Laguna M.1",
        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-laguna-m-1"
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      {
        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-ling-3-1-flash-1019.json",
        "title": "Arete-27B vs Ling-3.1-flash-1019",
        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-ling-3-1-flash-1019"
      },
      {
        "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-3-1-flash-1019.json",
        "title": "BigBang-v1 vs Ling-3.1-flash-1019",
        "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-ling-3-1-flash-1019"
      },
      {
        "json": "https://www.anchorterminal.com/compare/carbon-x-vs-laguna-m-1.json",
        "title": "Carbon-X vs Laguna M.1",
        "url": "https://www.anchorterminal.com/compare/carbon-x-vs-laguna-m-1"
      },
      {
        "json": "https://www.anchorterminal.com/compare/carbon-x-vs-ling-3-1-flash-1019.json",
        "title": "Carbon-X vs Ling-3.1-flash-1019",
        "url": "https://www.anchorterminal.com/compare/carbon-x-vs-ling-3-1-flash-1019"
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      {
        "json": "https://www.anchorterminal.com/compare/cola2-vs-laguna-m-1.json",
        "title": "Cola2 vs Laguna M.1",
        "url": "https://www.anchorterminal.com/compare/cola2-vs-laguna-m-1"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cola2-vs-ling-3-1-flash-1019.json",
        "title": "Cola2 vs Ling-3.1-flash-1019",
        "url": "https://www.anchorterminal.com/compare/cola2-vs-ling-3-1-flash-1019"
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        "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-laguna-m-1"
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        "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-ling-3-1-flash-1019"
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        "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"
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      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-laguna-m-1.json",
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        "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-laguna-m-1"
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      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-ling-3-1-flash-1019.json",
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      {
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        "url": "https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-nemotron-3-ultra-550b-a55b"
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        "title": "Ling-3.1-flash-1019 vs North Mini Code 1.0",
        "url": "https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-north-mini-code-1-0"
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        "url": "https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-occamy-1-0"
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        "title": "Ling-3.1-flash-1019 vs Olmo 3 7B Think",
        "url": "https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-olmo-3-7b-think"
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        "title": "Ling-3.1-flash-1019 vs Ornith-1.5-122B",
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        "url": "https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-ornith-1-5-35b-a3b"
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        "json": "https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-ornith-1-5-397b.json",
        "title": "Ling-3.1-flash-1019 vs Ornith-1.5-397B",
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        "url": "https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-otel-2-0-llm-31b-it"
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        "url": "https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-qwen-agentworld-35b-a3b"
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      {
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        "title": "Ling-3.1-flash-1019 vs Qwen3-4B-Instruct-2507",
        "url": "https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-qwen3-4b-instruct-2507"
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      {
        "json": "https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-qwen3-5-0-8b.json",
        "title": "Ling-3.1-flash-1019 vs Qwen3.5-0.8B",
        "url": "https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-qwen3-5-0-8b"
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      {
        "json": "https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-qwen3-5-122b-a10b.json",
        "title": "Ling-3.1-flash-1019 vs Qwen3.5-122B-A10B",
        "url": "https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-qwen3-5-122b-a10b"
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      {
        "json": "https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-qwen3-5-2b.json",
        "title": "Ling-3.1-flash-1019 vs Qwen3.5-2B",
        "url": "https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-qwen3-5-2b"
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        "title": "Ling-3.1-flash-1019 vs Qwen3.5-397B-A17B",
        "url": "https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-qwen3-5-397b-a17b"
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        "url": "https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-qwen3-5-4b"
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        "title": "Ling-3.1-flash-1019 vs Qwen3.5-9B",
        "url": "https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-qwen3-5-9b"
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      {
        "json": "https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-qwen3-6-27b.json",
        "title": "Ling-3.1-flash-1019 vs Qwen3.6-27B",
        "url": "https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-qwen3-6-27b"
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        "json": "https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-qwen3-6-35b-a3b.json",
        "title": "Ling-3.1-flash-1019 vs Qwen3.6-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-qwen3-6-35b-a3b"
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        "json": "https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-qwen3-8-27b.json",
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        "url": "https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-qwen3-8-27b"
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      {
        "json": "https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-qwen3-8-flash-next.json",
        "title": "Ling-3.1-flash-1019 vs Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-qwen3-8-flash-next"
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        "json": "https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-qwen3-coder-30b-a3b-instruct.json",
        "title": "Ling-3.1-flash-1019 vs Qwen3-Coder-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-qwen3-coder-30b-a3b-instruct"
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        "json": "https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-qwen3-coder-next.json",
        "title": "Ling-3.1-flash-1019 vs Qwen3-Coder-Next",
        "url": "https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-qwen3-coder-next"
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        "json": "https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-qwen3-omni-30b-a3b-instruct.json",
        "title": "Ling-3.1-flash-1019 vs Qwen3-Omni-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-qwen3-omni-30b-a3b-instruct"
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        "json": "https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-qwen3-vl-2b-instruct.json",
        "title": "Ling-3.1-flash-1019 vs Qwen3-VL-2B-Instruct",
        "url": "https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-qwen3-vl-2b-instruct"
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        "title": "Ling-3.1-flash-1019 vs Qwen3-VL-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-qwen3-vl-30b-a3b-instruct"
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        "json": "https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-qwen3-vl-32b-instruct.json",
        "title": "Ling-3.1-flash-1019 vs Qwen3-VL-32B-Instruct",
        "url": "https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-qwen3-vl-32b-instruct"
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      {
        "json": "https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-qwen3-vl-4b-instruct.json",
        "title": "Ling-3.1-flash-1019 vs Qwen3-VL-4B-Instruct",
        "url": "https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-qwen3-vl-4b-instruct"
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      {
        "json": "https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-qwen3-vl-8b-instruct.json",
        "title": "Ling-3.1-flash-1019 vs Qwen3-VL-8B-Instruct",
        "url": "https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-qwen3-vl-8b-instruct"
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      {
        "json": "https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-qwopus3-8-27b-flash-v2.json",
        "title": "Ling-3.1-flash-1019 vs Qwopus3.8-27B-Flash-V2",
        "url": "https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-qwopus3-8-27b-flash-v2"
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        "json": "https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-recursive-flint.json",
        "title": "Ling-3.1-flash-1019 vs Recursive Flint",
        "url": "https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-recursive-flint"
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      {
        "json": "https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-ripple-65b-preview.json",
        "title": "Ling-3.1-flash-1019 vs Ripple-65B Preview",
        "url": "https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-ripple-65b-preview"
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        "json": "https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-sensenova-loop-9b-preview.json",
        "title": "Ling-3.1-flash-1019 vs SenseNova-Loop-9B-Preview",
        "url": "https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-sensenova-loop-9b-preview"
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      {
        "json": "https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-smollm3-3b.json",
        "title": "Ling-3.1-flash-1019 vs SmolLM3-3B",
        "url": "https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-smollm3-3b"
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      {
        "json": "https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-spark-x2-6-4b.json",
        "title": "Ling-3.1-flash-1019 vs Spark-X2.6-4B",
        "url": "https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-spark-x2-6-4b"
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      {
        "json": "https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-step-3-7-flash.json",
        "title": "Ling-3.1-flash-1019 vs Step 3.7 Flash",
        "url": "https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-step-3-7-flash"
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      {
        "json": "https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-swift-1-5-qwen3-8-27b.json",
        "title": "Ling-3.1-flash-1019 vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-swift-1-5-qwen3-8-27b"
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        "json": "https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-swift-1-5-qwen3-8-flash-next.json",
        "title": "Ling-3.1-flash-1019 vs Swift 1.5 Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-swift-1-5-qwen3-8-flash-next"
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      {
        "json": "https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-thinkingcap-qwen3-8-27b.json",
        "title": "Ling-3.1-flash-1019 vs ThinkingCap (Qwen3.8 27B)",
        "url": "https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-thinkingcap-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-unlimited-ocr-2.json",
        "title": "Ling-3.1-flash-1019 vs Baidu Unlimited-OCR-2",
        "url": "https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-unlimited-ocr-2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-wevisone-vl-2b.json",
        "title": "Ling-3.1-flash-1019 vs WeVisOne-VL-2B",
        "url": "https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-wevisone-vl-2b"
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      {
        "json": "https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-wevisone-vl-4b.json",
        "title": "Ling-3.1-flash-1019 vs WeVisOne-VL-4B",
        "url": "https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-wevisone-vl-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-zaya1-8b.json",
        "title": "Ling-3.1-flash-1019 vs ZAYA1-8B",
        "url": "https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-zaya1-8b"
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    ],
    "panel": null,
    "scores": [
      {
        "key": "capability",
        "name": "Capability",
        "pending": true,
        "weight": 35
      },
      {
        "by": 10,
        "edge": "laguna-m-1",
        "key": "reach",
        "laguna-m-1": 70,
        "ling-3-1-flash-1019": 60,
        "name": "Reach",
        "weight": 25
      },
      {
        "by": 10,
        "edge": "laguna-m-1",
        "key": "risk",
        "laguna-m-1": 55,
        "ling-3-1-flash-1019": 45,
        "name": "Risk",
        "weight": 20
      },
      {
        "by": 25,
        "edge": "laguna-m-1",
        "key": "interface",
        "laguna-m-1": 85,
        "ling-3-1-flash-1019": 60,
        "name": "Agent interface",
        "weight": 10
      },
      {
        "by": 20,
        "edge": "ling-3-1-flash-1019",
        "key": "upkeep",
        "laguna-m-1": 36,
        "ling-3-1-flash-1019": 56,
        "name": "Documentation and upkeep",
        "weight": 10
      }
    ],
    "summary": "Laguna M.1 scores 56.7 (C) on agent readiness against Ling-3.1-flash-1019's 54.8 (C), and leads in 3 of 4 scored categories. Ling-3.1-flash-1019 leads on documentation and upkeep. Both do open-weight models you run yourself.",
    "tasks": null,
    "tutorials": [],
    "verdicts": {
      "laguna-m-1": "A large open-weight coding and agent model under plain Apache 2.0, for teams with a multi-GPU server. Its smallest build needs about 167 GB, more than a 128 GB machine. Released llama.cpp, Ollama, vLLM, SGLang and Transformers code runs the architecture. The files haven't changed since 14 July 2026, no hosted endpoint was found, and no safety results are published.",
      "ling-3-1-flash-1019": "inclusionAI's newest flagship, a 560B hybrid mixture of experts under plain MIT that vLLM, SGLang, Transformers and llama.cpp run in released versions. It ships only as 574 GB of FP8, so it needs a multi-GPU server. The card overstates the context, documents little about tool calls, and has no 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-3-1-flash-1019",
    "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-ling-3-1-flash-1019.json",
    "llms": "https://www.anchorterminal.com/llms.txt",
    "markdown": "https://www.anchorterminal.com/compare/laguna-m-1-vs-ling-3-1-flash-1019.md",
    "mcp": "https://www.anchorterminal.com/mcp",
    "slim": "https://www.anchorterminal.com/compare/laguna-m-1-vs-ling-3-1-flash-1019.min.md"
  },
  "markdown": "Laguna M.1 scores 56.7 (C) on agent readiness against Ling-3.1-flash-1019's 54.8 (C), and leads in 3 of 4 scored categories. Ling-3.1-flash-1019 leads on documentation and upkeep. Both do open-weight models you run yourself.\n\n- Laguna M.1: grade C, 56.7/100, rank #112 of 160 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-3.1-flash-1019: grade C, 54.8/100, rank #126 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/ling-3-1-flash-1019.md · JSON https://www.anchorterminal.com/api/v1/tools/ling-3-1-flash-1019.json\n- Best open-weight models you download and run yourself for AI agents: https://www.anchorterminal.com/best/open-weights/index.md\n- All 6359 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md\n\n## Which one, for what\n\n### 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- Reach, 70 against 60\n- Risk, 55 against 45\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-3.1-flash-1019 (C)\n\nGood for: Teams with a multi-GPU server that want an MIT-licensed flagship for long agent runs and coding\n\nAhead on:\n- Documentation and upkeep, 56 against 36\n\nWatch for: 574 GB in FP8 with no smaller build from inclusionAI, so no single machine under 690 GB of GPU memory runs it\n\n\n## Why the edge\n\n### Agent interface: Laguna M.1 +25\n\n- Laguna M.1 (85): Chat template 20, a Jinja file in the repository. Tool-calling format 25, the card shows how tools go in and calls come back and names the poolside_v1 parser. Trained tool calling 20, several calls a turn. Structured output 0, not documented. Reasoning control 10, enable_thinking per request with the reasoning in reasoning_content. Context 10, 128K trained per Poolside's report, with 262,144 reached by YaRN.\n- Ling-3.1-flash-1019 (60): Chat template 20, chat_template.jinja in the repository (6,042 characters), which handles tools, the thinking switch and preserved reasoning. Tool-calling format 10, only the template says how tools are declared (JSON in \u003ctools\u003e) and how calls come back (\u003ctool_call\u003ename with \u003carg_key\u003e and \u003carg_value\u003e pairs). Trained tool calling 20, the template tells the model it may call one or more functions and loops over every entry in an assistant message's tool_calls, and the card reports gains on agentic benchmarks, AA-Briefcase and AutomationBench-AA among them. Structured output 0, the card doesn't document JSON or JSON Schema output. Reasoning control 0, the card documents turning thinking on or off per request with chat_template_kwargs enable_thinking, and names the ling3 reasoning parser, but doesn't say where the reasoning text comes back. Context 10, 262,144 tokens natively in config.json (max_position_embeddings 262144, rope_scaling null) and per SGLang's cookbook. 20 + 10 + 20 + 0 + 0 + 10 = 60.\n\n### Documentation and upkeep: Ling-3.1-flash-1019 +20\n\n- Laguna M.1 (36): Model card 11 of 20, intended use, architecture, parameters and context are there, limitations, languages and a cut-off aren't. Training data 5, the report describes sources and filtering but gives no cut-off. Last change 10, 14 July 2026, 113 days ago. Responsiveness 0, the one discussion's only reply is from someone outside the poolside organisation. Version policy 5, a first open release with nothing replaced. File health 5, generation config and template disagree on the thinking default and the FP8 build's Docker files name different image tags.\n- Ling-3.1-flash-1019 (56): Model card 11, Intended use (complex reasoning, multi-step tasks and agentic workflows), architecture, parameters (560B total, 25B active) and context (1M claimed) are on the card. Training data 15, the training-content summary the card links names the source categories (Common Crawl and other public datasets, commercially licensed data, the Ant Spider crawler, synthetic data), the size band (over 10 trillion text tokens), data collected up to about July 2026 and the filtering, without listing datasets with sizes. Last change 20, the newest commit, on 4 November 2026, edited the card, six days before this check, and the files went up on 27 October. Responsiveness 0, Neither of the 2 discussions that aren't pull requests (both opened on 27 October 2026) has a reply from an inclusionAI member. Version policy 5, the update went into a repository of its own and Ling-3.1-flash and Ling 3.0 stay in theirs. File health 5, the card states a 1M-token context while config.json gives 262,144 with no rope scaling, and its serve commands name the earlier repository as the model path. 11 + 15 + 20 + 0 + 5 + 5 = 56.\n\n### Reach: Laguna M.1 +10\n\n- Laguna M.1 (70): Memory 5 of 30, the smallest 4-bit build is Poolside's 139.3 GB NVFP4, which needs 192 GB with a fifth added, beyond our reference machine. Runtimes 25, released llama.cpp, Ollama (through the llama.cpp release it pins), vLLM, SGLang and Transformers run the laguna architecture. Builds 15, FP8 and NVFP4 from Poolside. Terms 15, Apache 2.0 with nothing binding added. Download 10, nothing is gated. Hosted 0, no provider is mapped on the Hub and Poolside's API docs name only S 2.1.\n- Ling-3.1-flash-1019 (60): Memory 0, inclusionAI's only build is the FP8 release at 573.78 GB, × 1.2 = 688.5 GB, above 256 GB, and no runtime's library has a build. Runtimes 25, Released model code in Transformers 5.22.2 (bailing_hybrid), vLLM 0.33.0 (BailingMoeV3ForCausalLM, with ling3 tool and reasoning parsers), SGLang 0.5.23 (bailing_moe_v3.py, its bailing_hybrid config and MTP module) and llama.cpp b12242 (bailingmoe3), and Ollama, credited through its pinned llama.cpp as on the Ling-3.0-tiny listing: v0.40.16 sets LLAMA_CPP_VERSION to b12177. Builds 10, the release is FP8, the one quantised precision inclusionAI publishes for it. Terms 15, MIT in the card's metadata, with no use policy or other terms added on the card or in the repository. Download 10, the Hub API's model info gives gated false on 10 November 2026, so the files download with no account and no terms to accept. Hosted 0, the Hub API's provider mapping is empty for this repository on 10 November 2026. 0 + 25 + 10 + 15 + 10 + 0 = 60.\n\n### Risk: Laguna M.1 +10\n\n- Laguna M.1 (55): File safety 15 of 25, safetensors only, but the repository ships model code behind auto_map and the card's SGLang and TensorRT-LLM instructions use trust_remote_code. Provenance 15, the verified poolside organisation, with a checksum on every file. Safety evaluation 0, none published. Agent guidance 5, general cautions only. Licence risk 15, plain Apache 2.0. Security programme 5, a signed security.txt and a security contact on the card, with no public advisories found.\n- Ling-3.1-flash-1019 (45): File safety 15, safetensors only, no .bin, .pt or .pth files (25), less 10 because the card's documented vLLM command passes --trust-remote-code, which runs the repository's configuration_bailing_moe_v3.py, since vLLM 0.33.0 registers no bailing_hybrid config of its own. Provenance 15, files in the inclusionAI organisation that inclusion-ai.org links to, per the Ling-3.0-tiny listing (10). Safety evaluation 0, no safety results for the model on its card or in the training-content summary. Agent guidance 0, the card has nothing on prompt injection, refusals or safeguards, and inclusionAI publishes no guardrail probe for this model as it did for Ling 3.0. Licence risk 15, MIT with no use policy, no field-of-use limits, no threshold and no clause letting inclusionAI change or revoke it. Security programme 0, www.inclusion-ai.org answers 404 for security.txt on 10 November 2026, and the Ling-3.0-tiny listing found no SECURITY.md or disclosure policy in the inclusionAI .github repository. 15 + 15 + 0 + 0 + 15 + 0 = 45.\n\n## Score by category\n\n| Category | Weight | Laguna M.1 | Ling-3.1-flash-1019 | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 70 | 60 | Laguna M.1 +10 |\n| Risk | 20% (20 this run) | 55 | 45 | Laguna M.1 +10 |\n| Agent interface | 10% (10 this run) | 85 | 60 | Laguna M.1 +25 |\n| Documentation and upkeep | 10% (10 this run) | 36 | 56 | Ling-3.1-flash-1019 +20 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **56.7 · C** | **54.8 · C** | |\n\n## Facts side by side\n\n| Fact | Laguna M.1 | Ling-3.1-flash-1019 |\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 | 2026-11-04 |\n| Terms last updated | 2026-07-21 | no document linked |\n| Privacy policy last updated | 2026-07-21 | no document linked |\n| Customer content may train models | yes, with an opt-out | not read yet |\n| Terms restrict automated access | not found in the text | not read yet |\n| Terms restrict benchmarking | not found in the text | not read yet |\n| Terms or service can change without notice | yes | not read yet |\n| Arbitration or class-action waiver | yes | not read yet |\n| Agent reviews | 2.5/5 (4) | none |\n\n## How the scores have moved\n\n### Laguna M.1\n\n- +1.2 on 4 November 2026: correction\n\n### Ling-3.1-flash-1019\n\nNo score change since it was listed.\n\n## Verdicts\n\n**Laguna M.1.** A large open-weight coding and agent model under plain Apache 2.0, for teams with a multi-GPU server. Its smallest build needs about 167 GB, more than a 128 GB machine. Released llama.cpp, Ollama, vLLM, SGLang and Transformers code runs the architecture. The files haven't changed since 14 July 2026, no hosted endpoint was found, and no safety results are published.\n\n**Ling-3.1-flash-1019.** inclusionAI's newest flagship, a 560B hybrid mixture of experts under plain MIT that vLLM, SGLang, Transformers and llama.cpp run in released versions. It ships only as 574 GB of FP8, so it needs a multi-GPU server. The card overstates the context, documents little about tool calls, and has no 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-3.1-flash-1019\n\n1. Serve it with SGLang 0.5.23 or later on 8 H200 or 4 GB300 GPUs, per the card, and use this repository's name as the model path, not the earlier one the card's command shows\n2. With vLLM pass `--trust-remote-code`, `--tool-call-parser ling3` and `--reasoning-parser ling3`\n3. Set `chat_template_kwargs` `enable_thinking` false for direct answers. Reasoning comes back in `reasoning_content` per SGLang's cookbook\n4. Plan on 262,144 tokens of context. The 1M figure needs the YaRN override in SGLang's cookbook\n5. Use temperature 1.0, top_p 0.95 and top_k 20, per the card\n6. Validate JSON output yourself, since structured output isn't documented\n\n## Questions\n\n### Which is better for AI agents, Laguna M.1 or Ling-3.1-flash-1019?\n\nLaguna M.1 scores 56.7 (C) on agent readiness against Ling-3.1-flash-1019's 54.8 (C), and leads in 3 of 4 scored categories. Ling-3.1-flash-1019 leads on documentation and upkeep.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/laguna-m-1-vs-ling-3-1-flash-1019.json, and with the fewest tokens: https://www.anchorterminal.com/compare/laguna-m-1-vs-ling-3-1-flash-1019.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"laguna-m-1\", \"b\": \"ling-3-1-flash-1019\"}`. From a terminal: `anchor compare laguna-m-1 ling-3-1-flash-1019`\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-3-1-flash-1019.json\n\n## Other comparisons with Laguna M.1 or Ling-3.1-flash-1019\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-3.1-flash-1019](https://www.anchorterminal.com/compare/agents-a1-4b-vs-ling-3-1-flash-1019.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-3.1-flash-1019](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-ling-3-1-flash-1019.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-3.1-flash-1019](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-ling-3-1-flash-1019.md)\n- [Arete-27B vs 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