{
  "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 XS 2.1 scores 61 (C) on agent readiness against Laguna M.1's 55.5 (C), and leads in 2 of 4 scored categories.",
    "b": {
      "slug": "laguna-xs-2-1",
      "name": "Laguna XS 2.1",
      "vendor": "Poolside",
      "vendorUrl": "https://poolside.ai",
      "kind": "weights",
      "category": "open-weights",
      "summary": "Laguna XS 2.1 is Poolside's open-weight Mixture-of-Experts coding model, 33B total and 3B active parameters, with switchable reasoning and a 262,144-token context, published on Hugging Face under OpenMDW-1.1.",
      "url": "https://www.anchorterminal.com/tools/laguna-xs-2-1",
      "markdownUrl": "https://www.anchorterminal.com/tools/laguna-xs-2-1.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/laguna-xs-2-1.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/laguna-xs-2-1.json",
      "repo": "https://huggingface.co/poolside/Laguna-XS-2.1",
      "license": "OpenMDW-1.1",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "poolside/Laguna-XS-2.1"
        },
        {
          "registry": "huggingface",
          "name": "poolside/Laguna-XS-2.1-FP8"
        },
        {
          "registry": "huggingface",
          "name": "poolside/Laguna-XS-2.1-INT4"
        },
        {
          "registry": "huggingface",
          "name": "poolside/Laguna-XS-2.1-NVFP4"
        },
        {
          "registry": "huggingface",
          "name": "poolside/Laguna-XS-2.1-NVFP4-mlx"
        },
        {
          "registry": "huggingface",
          "name": "poolside/Laguna-XS-2.1-GGUF"
        },
        {
          "registry": "ollama",
          "name": "laguna-xs-2.1"
        }
      ],
      "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 every build, 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 OpenMDW-1.1. Running it yourself costs the hardware, 66.9 GB for the BF16 files and 20.3 GB for Poolside's Q4_K_M GGUF, which is also Ollama's default build. Poolside serves it on OpenRouter with a free and a paid endpoint, both marked to expire on 31 October 2026 (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-XS-2.1",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "moe",
        "coding",
        "reasoning",
        "tool-calling",
        "openmdw-1.1",
        "safetensors",
        "gguf",
        "fp8",
        "nvfp4",
        "self-hosted"
      ],
      "lastRelease": "2026-08-19",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 61,
        "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": 33,
        "methodology": "0.6",
        "run": "2026-10-01",
        "scores": {
          "interface": 85,
          "reach": 85,
          "risk": 55,
          "upkeep": 46
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "low",
          "date": "2026-10-21"
        },
        "negative": 0,
        "verdict": "A small coding and agent cousin with 3B active parameters, so it's quick on modest hardware. Poolside's 4-bit GGUF, also Ollama's default, needs about 24 GB, and every major runtime runs it. The main risks are that nobody at Poolside answers its discussions, there are no safety results, and the SGLang route runs repository code.",
        "bestFor": "Agents that want a fast local coding model on a 32 GB GPU or Mac, with tool calling and a permissive licence.",
        "strengths": [
          "Released support in llama.cpp, Ollama, vLLM, SGLang, Transformers and mlx-lm",
          "Poolside's own Q4_K_M, FP8, INT4, NVFP4 and MLX builds, the Q4_K_M at 20.3 GB",
          "Permissive OpenMDW-1.1 licence with no gate, account or use policy",
          "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": [
          "No reply from Poolside to any discussion, including reports about the tokenizer and remote code",
          "No safety results and only general cautions for agent use",
          "The SGLang and TensorRT-LLM commands run the repository's Python with trust_remote_code",
          "The template defaults thinking off while generation_config.json turns it on",
          "No structured output documentation, and no limitations, languages or cut-off on the card"
        ],
        "agentNotes": [
          "Quickest start is `ollama run laguna-xs-2.1`, 20 GB of files, the same Q4_K_M as Poolside's GGUF",
          "On our reference machine use `llama-server -hf poolside/Laguna-XS-2.1-GGUF:Q4_K_M --jinja -c 131072`, from a llama.cpp release with the laguna architecture (b11772 has it), or BF16 with vLLM (66.9 GB)",
          "With vLLM use `--tool-call-parser poolside_v1 --reasoning-parser poolside_v1 --enable-auto-tool-choice`, on a build with vllm-project/vllm#47311, or `--tool-call-parser glm47` on older builds",
          "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 card warns that Ollama on Apple Silicon may return empty output until llama.cpp#25389 ships",
          "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": 2.8,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "low",
            "grade": "C",
            "methodology": "0.6",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 61
          }
        ],
        "editorialScores": {
          "interface": 85,
          "reach": 85,
          "risk": 55,
          "upkeep": 46
        },
        "provenanceScore": 71,
        "checked": {
          "categories": {
            "interface": "2026-10-21",
            "reach": "2026-10-21",
            "risk": "2026-10-21",
            "upkeep": "2026-10-21"
          },
          "last": "2026-10-21",
          "oldest": "2026-10-21",
          "stale": false,
          "staleAfterDays": 90
        }
      },
      "connect": {
        "install": "ollama run laguna-xs-2.1\n# or, with vLLM 0.21.0 or later (a build with vllm-project/vllm#47311):\nvllm serve poolside/Laguna-XS-2.1 --tool-call-parser poolside_v1 --reasoning-parser poolside_v1 \\\n  --enable-auto-tool-choice --default-chat-template-kwargs '{\"enable_thinking\": true}'",
        "http": "curl http://localhost:11434/api/chat \\\n  -d '{\"model\": \"laguna-xs-2.1\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/laguna-xs-2-1"
      },
      "sameCompany": [
        "laguna-m-1",
        "laguna-s-2-1"
      ],
      "weights": {
        "trainedBy": "Poolside",
        "params": "33.4B total, 3B active",
        "context": 262144,
        "goodFor": "Agentic coding and long-horizon terminal work on one GPU or a 32 GB machine, with tool calling and reasoning that turns on or off per request.",
        "runtimes": [
          "llama.cpp",
          "Ollama",
          "vLLM",
          "SGLang",
          "LM Studio",
          "Transformers"
        ],
        "builds": [
          {
            "name": "Laguna-XS-2.1-INT4",
            "url": "https://huggingface.co/poolside/Laguna-XS-2.1-INT4",
            "who": "publisher",
            "precision": "int4 (w4a16)",
            "sizeGB": 23.9,
            "memoryGB": 32,
            "format": "safetensors"
          },
          {
            "name": "Laguna-XS-2.1-NVFP4",
            "url": "https://huggingface.co/poolside/Laguna-XS-2.1-NVFP4",
            "who": "publisher",
            "precision": "nvfp4",
            "sizeGB": 21.6,
            "memoryGB": 32,
            "format": "safetensors",
            "note": "For NVIDIA Blackwell GPUs"
          },
          {
            "name": "Laguna-XS-2.1-NVFP4-mlx",
            "url": "https://huggingface.co/poolside/Laguna-XS-2.1-NVFP4-mlx",
            "who": "publisher",
            "precision": "nvfp4",
            "sizeGB": 21.6,
            "memoryGB": 32,
            "format": "mlx",
            "runsOn": [
              "MLX"
            ]
          },
          {
            "name": "Laguna-XS-2.1-GGUF Q4_K_M",
            "url": "https://huggingface.co/poolside/Laguna-XS-2.1-GGUF",
            "who": "publisher",
            "precision": "q4_k_m",
            "sizeGB": 20.3,
            "memoryGB": 32,
            "format": "gguf",
            "runsOn": [
              "llama.cpp",
              "Ollama"
            ],
            "note": "Laguna-XS-2.1-Q4_K_M.gguf, 20,274,300,032 bytes. The repository also holds a BF16 GGUF of 66.9 GB (96 GB of memory)"
          },
          {
            "name": "ollama laguna-xs-2.1:latest",
            "url": "https://ollama.com/library/laguna-xs-2.1:latest",
            "who": "ollama",
            "precision": "q4_k_m",
            "sizeGB": 20.3,
            "memoryGB": 32,
            "format": "ollama",
            "runsOn": [
              "Ollama"
            ],
            "note": "Ollama shows 20 GB. Its model blob (sha256 1ac7079101fc) is Poolside's Q4_K_M GGUF, so the exact size is Poolside's. The library also has q8_0 (36 GB), bf16 (67 GB) and MLX tags (nvfp4 shown as 20 GB, exact size unpublished, mxfp8 40 GB, mlx-bf16 68 GB). The card warns that ollama run may return empty output on Apple Silicon until llama.cpp#25389 ships"
          },
          {
            "name": "Laguna-XS-2.1-FP8",
            "url": "https://huggingface.co/poolside/Laguna-XS-2.1-FP8",
            "who": "publisher",
            "precision": "fp8",
            "sizeGB": 35.3,
            "memoryGB": 48,
            "format": "safetensors"
          },
          {
            "name": "Laguna-XS-2.1",
            "url": "https://huggingface.co/poolside/Laguna-XS-2.1",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 66.9,
            "memoryGB": 96,
            "format": "safetensors",
            "runsOn": [
              "vLLM",
              "SGLang",
              "Transformers"
            ]
          }
        ],
        "run": "llama-server -hf poolside/Laguna-XS-2.1-GGUF:Q4_K_M --jinja -c 131072 --port 8000",
        "runBuild": "Laguna-XS-2.1-GGUF Q4_K_M",
        "checked": "2026-10-21",
        "minMemoryGB": 32
      },
      "family": {
        "lab": "Poolside",
        "family": "Laguna 2 (XS.2, XS 2.1, S 2.1)",
        "url": "https://www.anchorterminal.com/open-weights/#poolside",
        "others": [
          {
            "name": "Laguna-S-2.1",
            "url": "https://huggingface.co/poolside/Laguna-S-2.1",
            "params": "117.56B (Hub safetensors count; the name gives only the size class S)",
            "listing": "https://www.anchorterminal.com/tools/laguna-s-2-1"
          },
          {
            "name": "Laguna-S-2.1-base",
            "url": "https://huggingface.co/poolside/Laguna-S-2.1-base",
            "params": "not stated (no safetensors count on the Hub; the instruct model is 117.56B)"
          },
          {
            "name": "Laguna-XS.2",
            "url": "https://huggingface.co/poolside/Laguna-XS.2",
            "params": "33.44B (Hub safetensors count; the name gives only the size class XS)"
          }
        ],
        "unrestrictedCopies": 1
      },
      "area": "models",
      "provenance": {
        "legalEntity": "Poolside, Inc. (named in Poolside's Terms of Use)",
        "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 blog post for this release links its collection there.",
          "Nothing is hosted for this listing, so the OpenMDW-1.1 licence stands in for terms of service and no privacy policy is scored.",
          "Poolside's docs keep dated model release notes. The June 2026 entry calls XS 2.1 an incremental update to Laguna XS.2.",
          "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-xs-2-1.json"
    },
    "facts": [
      {
        "a": "Open-weight model",
        "b": "Open-weight model",
        "name": "Kind"
      },
      {
        "a": "Poolside",
        "b": "Poolside",
        "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": "OpenMDW-1.1",
        "name": "Licence"
      },
      {
        "a": "no",
        "b": "no",
        "name": "Read-only variant documented"
      },
      {
        "a": "no",
        "b": "no",
        "name": "llms.txt"
      },
      {
        "a": "2026-07-14",
        "b": "2026-08-19",
        "name": "Last release"
      },
      {
        "a": "",
        "b": "",
        "name": "Terms last updated"
      },
      {
        "a": "",
        "b": "",
        "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 XS 2.1 scores 61 (C) on agent readiness against Laguna M.1's 55.5 (C), and leads in 2 of 4 scored categories.",
        "question": "Which is better for AI agents, Laguna M.1 or Laguna XS 2.1?"
      }
    ],
    "goodFor": [
      {
        "aheadOn": null,
        "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, 85 against 65",
          "Documentation and upkeep, 46 against 36"
        ],
        "also": null,
        "goodFor": "Agents that want a fast local coding model on a 32 GB GPU or Mac, with tool calling and a permissive licence.",
        "slug": "laguna-xs-2-1",
        "watchFor": "No reply from Poolside to any discussion, including reports about the tokenizer and remote code"
      }
    ],
    "job": {
      "capability": "model.open-weights",
      "name": "Open-weight models you run yourself"
    },
    "others": [
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        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-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-laguna-xs-2-1.json",
        "title": "Agents-A1-4B vs Laguna XS 2.1",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-laguna-xs-2-1"
      },
      {
        "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"
      },
      {
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        "title": "Apertus v1.5 8B vs Laguna XS 2.1",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-laguna-xs-2-1"
      },
      {
        "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"
      },
      {
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        "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-laguna-xs-2-1"
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        "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-laguna-m-1"
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        "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-laguna-xs-2-1"
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        "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"
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      },
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        "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-laguna-m-1"
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        "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-laguna-m-1"
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        "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-minicpm5-2b"
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        "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-minimax-m2-5.json",
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        "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-minimax-m2-5"
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        "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-minimax-m2-7.json",
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        "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-minimax-m2-7"
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        "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-muse-glimmer-30b.json",
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        "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-muse-glimmer-30b"
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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/laguna-m-1-vs-nemotron-3-nano-30b-a3b.json",
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        "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-nemotron-3-nano-4b.json",
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        "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-nemotron-3-nano-4b"
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        "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-nemotron-3-nano-omni-30b-a3b-reasoning.json",
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        "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-nemotron-3-nano-omni-30b-a3b-reasoning"
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        "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-nemotron-3-super-120b-a12b.json",
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        "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-nemotron-3-ultra-550b-a55b.json",
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        "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-occamy-1-0.json",
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        "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-olmo-3-7b-think.json",
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        "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-ornith-1-5-35b-a3b.json",
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        "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-ornith-1-5-9b.json",
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        "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-30b-a3b-instruct-2507.json",
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        "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-4b-instruct-2507.json",
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        "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-5-0-8b.json",
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        "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-5-0-8b"
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        "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-5-122b-a10b.json",
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        "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-5-2b.json",
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        "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-5-397b-a17b.json",
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        "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-5-4b.json",
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        "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-5-9b.json",
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        "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-6-27b.json",
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        "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-6-35b-a3b.json",
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        "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-8-27b.json",
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        "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-8-flash-next.json",
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        "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-coder-30b-a3b-instruct.json",
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        "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-omni-30b-a3b-instruct.json",
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      },
      {
        "json": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwen3-8-27b.json",
        "title": "Laguna XS 2.1 vs Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwen3-8-flash-next.json",
        "title": "Laguna XS 2.1 vs Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwen3-coder-30b-a3b-instruct.json",
        "title": "Laguna XS 2.1 vs Qwen3-Coder-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwen3-coder-30b-a3b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwen3-coder-next.json",
        "title": "Laguna XS 2.1 vs Qwen3-Coder-Next",
        "url": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwen3-coder-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwen3-omni-30b-a3b-instruct.json",
        "title": "Laguna XS 2.1 vs Qwen3-Omni-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwen3-omni-30b-a3b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwen3-vl-2b-instruct.json",
        "title": "Laguna XS 2.1 vs Qwen3-VL-2B-Instruct",
        "url": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwen3-vl-2b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwen3-vl-30b-a3b-instruct.json",
        "title": "Laguna XS 2.1 vs Qwen3-VL-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwen3-vl-30b-a3b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwen3-vl-32b-instruct.json",
        "title": "Laguna XS 2.1 vs Qwen3-VL-32B-Instruct",
        "url": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwen3-vl-32b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwen3-vl-4b-instruct.json",
        "title": "Laguna XS 2.1 vs Qwen3-VL-4B-Instruct",
        "url": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwen3-vl-4b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwen3-vl-8b-instruct.json",
        "title": "Laguna XS 2.1 vs Qwen3-VL-8B-Instruct",
        "url": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwen3-vl-8b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwopus3-8-27b-flash-v2.json",
        "title": "Laguna XS 2.1 vs Qwopus3.8-27B-Flash-V2",
        "url": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwopus3-8-27b-flash-v2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-smollm3-3b.json",
        "title": "Laguna XS 2.1 vs SmolLM3-3B",
        "url": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-smollm3-3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-spark-x2-6-4b.json",
        "title": "Laguna XS 2.1 vs Spark-X2.6-4B",
        "url": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-spark-x2-6-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-swift-1-5-qwen3-8-27b.json",
        "title": "Laguna XS 2.1 vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-swift-1-5-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-swift-1-5-qwen3-8-flash-next.json",
        "title": "Laguna XS 2.1 vs Swift 1.5 Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-swift-1-5-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-thinkingcap-qwen3-8-27b.json",
        "title": "Laguna XS 2.1 vs ThinkingCap (Qwen3.8 27B)",
        "url": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-thinkingcap-qwen3-8-27b"
      }
    ],
    "scores": [
      {
        "key": "capability",
        "name": "Capability",
        "pending": true,
        "weight": 35
      },
      {
        "by": 20,
        "edge": "laguna-xs-2-1",
        "key": "reach",
        "laguna-m-1": 65,
        "laguna-xs-2-1": 85,
        "name": "Reach",
        "weight": 25
      },
      {
        "by": 0,
        "edge": "",
        "key": "risk",
        "laguna-m-1": 55,
        "laguna-xs-2-1": 55,
        "name": "Risk",
        "weight": 20
      },
      {
        "by": 0,
        "edge": "",
        "key": "interface",
        "laguna-m-1": 85,
        "laguna-xs-2-1": 85,
        "name": "Agent interface",
        "weight": 10
      },
      {
        "by": 10,
        "edge": "laguna-xs-2-1",
        "key": "upkeep",
        "laguna-m-1": 36,
        "laguna-xs-2-1": 46,
        "name": "Documentation and upkeep",
        "weight": 10
      }
    ],
    "summary": "Laguna XS 2.1 scores 61 (C) on agent readiness against Laguna M.1's 55.5 (C), and leads in 2 of 4 scored categories. 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.",
      "laguna-xs-2-1": "A small coding and agent cousin with 3B active parameters, so it's quick on modest hardware. Poolside's 4-bit GGUF, also Ollama's default, needs about 24 GB, and every major runtime runs it. The main risks are that nobody at Poolside answers its discussions, there are no safety results, and the SGLang route runs repository code."
    }
  },
  "kind": "anchor.page",
  "links": {
    "api": "https://www.anchorterminal.com/api/v1/index.json",
    "html": "https://www.anchorterminal.com/compare/laguna-m-1-vs-laguna-xs-2-1",
    "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-laguna-xs-2-1.json",
    "llms": "https://www.anchorterminal.com/llms.txt",
    "markdown": "https://www.anchorterminal.com/compare/laguna-m-1-vs-laguna-xs-2-1.md",
    "mcp": "https://www.anchorterminal.com/mcp",
    "slim": "https://www.anchorterminal.com/compare/laguna-m-1-vs-laguna-xs-2-1.min.md"
  },
  "markdown": "Laguna XS 2.1 scores 61 (C) on agent readiness against Laguna M.1's 55.5 (C), and leads in 2 of 4 scored categories. 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- Laguna XS 2.1: grade C, 61/100, rank #40 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/laguna-xs-2-1.md · JSON https://www.anchorterminal.com/api/v1/tools/laguna-xs-2-1.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\nWatch for: The smallest build needs about 167 GB, more than a 128 GB machine\n\n### Laguna XS 2.1 (C)\n\nGood for: Agents that want a fast local coding model on a 32 GB GPU or Mac, with tool calling and a permissive licence.\n\nAhead on:\n- Reach, 85 against 65\n- Documentation and upkeep, 46 against 36\n\nWatch for: No reply from Poolside to any discussion, including reports about the tokenizer and remote code\n\n\n## Score by category\n\n| Category | Weight | Laguna M.1 | Laguna XS 2.1 | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 65 | 85 | Laguna XS 2.1 +20 |\n| Risk | 20% (20 this run) | 55 | 55 | even |\n| Agent interface | 10% (10 this run) | 85 | 85 | even |\n| Documentation and upkeep | 10% (10 this run) | 36 | 46 | Laguna XS 2.1 +10 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **55.5 · C** | **61 · C** | |\n\n## Facts side by side\n\n| Fact | Laguna M.1 | Laguna XS 2.1 |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Poolside | Poolside |\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 | OpenMDW-1.1 |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-07-14 | 2026-08-19 |\n| Terms last updated |  |  |\n| Privacy policy last updated |  |  |\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**Laguna XS 2.1.** A small coding and agent cousin with 3B active parameters, so it's quick on modest hardware. Poolside's 4-bit GGUF, also Ollama's default, needs about 24 GB, and every major runtime runs it. The main risks are that nobody at Poolside answers its discussions, there are no safety results, and the SGLang route runs repository code.\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### Laguna XS 2.1\n\n1. Quickest start is `ollama run laguna-xs-2.1`, 20 GB of files, the same Q4_K_M as Poolside's GGUF\n2. On our reference machine use `llama-server -hf poolside/Laguna-XS-2.1-GGUF:Q4_K_M --jinja -c 131072`, from a llama.cpp release with the laguna architecture (b11772 has it), or BF16 with vLLM (66.9 GB)\n3. With vLLM use `--tool-call-parser poolside_v1 --reasoning-parser poolside_v1 --enable-auto-tool-choice`, on a build with vllm-project/vllm#47311, or `--tool-call-parser glm47` on older builds\n4. 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\n5. Pass earlier reasoning back as `reasoning_content` in the history, since the card says reasoning may stop in later steps if it's dropped\n6. Sampling per the card is temperature 1.0 and top_k 20\n7. The card warns that Ollama on Apple Silicon may return empty output until llama.cpp#25389 ships\n8. Validate JSON output yourself or use the runtime's guided decoding, since structured output isn't documented\n\n## Questions\n\n### Which is better for AI agents, Laguna M.1 or Laguna XS 2.1?\n\nLaguna XS 2.1 scores 61 (C) on agent readiness against Laguna M.1's 55.5 (C), and leads in 2 of 4 scored categories.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/laguna-m-1-vs-laguna-xs-2-1.json, and with the fewest tokens: https://www.anchorterminal.com/compare/laguna-m-1-vs-laguna-xs-2-1.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"laguna-m-1\", \"b\": \"laguna-xs-2-1\"}`. From a terminal: `anchor compare laguna-m-1 laguna-xs-2-1`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/laguna-m-1.json and https://www.anchorterminal.com/api/v1/tools/laguna-xs-2-1.json\n\n## Other comparisons with Laguna M.1 or Laguna XS 2.1\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 Laguna XS 2.1](https://www.anchorterminal.com/compare/agents-a1-4b-vs-laguna-xs-2-1.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 Laguna XS 2.1](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-laguna-xs-2-1.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 Laguna XS 2.1](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-laguna-xs-2-1.md)\n- [BigBang-v1 vs Laguna M.1](https://www.anchorterminal.com/compare/bigbang-v1-vs-laguna-m-1.md)\n- [BigBang-v1 vs Laguna XS 2.1](https://www.anchorterminal.com/compare/bigbang-v1-vs-laguna-xs-2-1.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 Laguna XS 2.1](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-laguna-xs-2-1.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 Laguna XS 2.1](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-laguna-xs-2-1.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 Laguna XS 2.1](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-laguna-xs-2-1.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 Laguna XS 2.1](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-laguna-xs-2-1.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 Laguna XS 2.1](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-laguna-xs-2-1.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 Laguna XS 2.1](https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-laguna-xs-2-1.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 Laguna XS 2.1](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-laguna-xs-2-1.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 Laguna XS 2.1](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-laguna-xs-2-1.md)\n- [Gemma 4 31B IT vs Laguna M.1](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-laguna-m-1.md)\n- [Gemma 4 31B IT vs Laguna XS 2.1](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-laguna-xs-2-1.md)\n- [Gemma 4 E2B IT vs Laguna M.1](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-laguna-m-1.md)\n- [Gemma 4 E2B IT vs Laguna XS 2.1](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-laguna-xs-2-1.md)\n- [Gemma 4 E4B IT vs Laguna M.1](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-laguna-m-1.md)\n- [Gemma 4 E4B IT vs Laguna XS 2.1](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-laguna-xs-2-1.md)\n- [GLM-4.5-Air vs Laguna M.1](https://www.anchorterminal.com/compare/glm-4-5-air-vs-laguna-m-1.md)\n- [GLM-4.5-Air vs Laguna XS 2.1](https://www.anchorterminal.com/compare/glm-4-5-air-vs-laguna-xs-2-1.md)\n- [GLM-4.7-Flash vs Laguna M.1](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-laguna-m-1.md)\n- [GLM-4.7-Flash vs Laguna XS 2.1](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-laguna-xs-2-1.md)\n- [GLM-5.3-Flash vs Laguna M.1](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-laguna-m-1.md)\n- [GLM-5.3-Flash vs Laguna XS 2.1](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-laguna-xs-2-1.md)\n- [GLM-5.3 vs Laguna M.1](https://www.anchorterminal.com/compare/glm-5-3-vs-laguna-m-1.md)\n- [GLM-5.3 vs Laguna XS 2.1](https://www.anchorterminal.com/compare/glm-5-3-vs-laguna-xs-2-1.md)\n- [gpt-oss-120b vs Laguna M.1](https://www.anchorterminal.com/compare/gpt-oss-120b-vs-laguna-m-1.md)\n- [gpt-oss-120b vs Laguna XS 2.1](https://www.anchorterminal.com/compare/gpt-oss-120b-vs-laguna-xs-2-1.md)\n- [gpt-oss-20b vs Laguna M.1](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-laguna-m-1.md)\n- [gpt-oss-20b vs Laguna XS 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    "title": "Laguna M.1 vs Laguna XS 2.1 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-laguna-xs-2-1"
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