{
  "data": {
    "a": {
      "slug": "gemma-4-26b-a4b-it",
      "name": "Gemma 4 26B A4B IT",
      "vendor": "Google DeepMind",
      "vendorUrl": "https://gemma.ai",
      "kind": "weights",
      "category": "open-weights",
      "summary": "Gemma 4 26B A4B IT is Google DeepMind's open-weight instruction-tuned mixture-of-experts model, 25.2B parameters with 3.8B active, taking text and images with a 262,144-token context, published on Hugging Face under Apache 2.0.",
      "url": "https://www.anchorterminal.com/tools/gemma-4-26b-a4b-it",
      "markdownUrl": "https://www.anchorterminal.com/tools/gemma-4-26b-a4b-it.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/gemma-4-26b-a4b-it.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/gemma-4-26b-a4b-it.json",
      "repo": "https://huggingface.co/google/gemma-4-26B-A4B-it",
      "license": "Apache-2.0",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "google/gemma-4-26B-A4B-it"
        },
        {
          "registry": "huggingface",
          "name": "google/gemma-4-26B-A4B-it-qat-q4_0-gguf"
        },
        {
          "registry": "ollama",
          "name": "gemma4:26b"
        }
      ],
      "auth": "none",
      "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The repository is not gated (checked 2026-10-21).",
      "pricing": "free",
      "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0. Google also hosts it as gemma-4-26b-a4b-it on the Gemini API, which needs an API key. Running it yourself costs the hardware, 57.7 GB at BF16 and 14.4 GB at Q4_0 per Google (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://gemma.ai/models/gemma-4/",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "moe",
        "multimodal",
        "reasoning",
        "tool-calling",
        "apache-2.0",
        "safetensors",
        "gguf",
        "self-hosted"
      ],
      "lastRelease": "2026-07-20",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 72.2,
        "grade": "BB",
        "agentReady": true,
        "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": 2,
        "methodology": "0.6",
        "run": "2026-10-01",
        "scores": {
          "capability": 59,
          "interface": 85,
          "reach": 90,
          "risk": 70,
          "upkeep": 65
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "high",
          "date": "2026-10-21"
        },
        "negative": 0,
        "verdict": "A fast mixture-of-experts cousin for general agent work with tools, optional thinking and image input, under Apache 2.0. Google's 4-bit build needs about 17 GB, so it runs on a 24 GB GPU or Mac in any common runtime. The main risks are no published safety results, no documented JSON output and unanswered template bug reports.",
        "bestFor": "Agents on a 24 GB or larger GPU or Mac that want a fast, permissively licensed tool-calling model with images and long context",
        "strengths": [
          "Plain Apache 2.0, no account or gate, safetensors and GGUF only with a checksum on every file",
          "Google's own 4-bit QAT GGUF needs about 17 GB with working space",
          "Released support in vLLM, SGLang, llama.cpp, Ollama, LM Studio and Transformers",
          "Tool declaration and call format documented by Google, with parallel calls and named parsers",
          "262,144 tokens of native context and thinking that turns on or off per request",
          "Also served by the Gemini API and four inference providers"
        ],
        "weaknesses": [
          "No safety evaluation figures published",
          "No documented way to ask for JSON or schema output",
          "Only one quantised precision from Google, no FP8",
          "Files unchanged since 20 July 2026, with chat-template bug reports unanswered",
          "Training data described only by type and cut-off"
        ],
        "agentNotes": [
          "On the reference machine run `llama-server -hf google/gemma-4-26B-A4B-it-qat-q4_0-gguf --hf-file gemma-4-26B_q4_0-it.gguf --jinja -c 65536`, and add the mmproj file for images",
          "With Ollama run `ollama run gemma4:26b` and raise num_ctx or OLLAMA_CONTEXT_LENGTH, since Google's guide says it defaults to 4K below 24 GiB of VRAM and agents want at least 64K",
          "In vLLM or SGLang pass `--tool-call-parser gemma4 --reasoning-parser gemma4` (vLLM also `--enable-auto-tool-choice`), or tool calls come back as text",
          "Thinking is off by default; pass enable_thinking=True or start the system prompt with \u003c|think|\u003e, and drop earlier turns' thinking from history except on tool-call turns",
          "Validate tool names and arguments before running them, and check any JSON yourself"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 4,
        "avgRating": 3.8,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "high",
            "grade": "BB",
            "methodology": "0.6",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 72.2
          }
        ],
        "editorialScores": {
          "capability": 59,
          "interface": 85,
          "reach": 90,
          "risk": 70,
          "upkeep": 65
        },
        "provenanceScore": 68,
        "checked": {
          "categories": {
            "capability": "2026-10-21",
            "interface": "2026-10-21",
            "reach": "2026-10-21",
            "risk": "2026-10-21",
            "upkeep": "2026-10-21"
          },
          "last": "2026-10-21",
          "oldest": "2026-10-21",
          "stale": false,
          "staleAfterDays": 90
        }
      },
      "connect": {
        "install": "ollama run gemma4:26b\n# or, with vLLM:\nvllm serve google/gemma-4-26B-A4B-it --max-model-len 262144 --reasoning-parser gemma4 \\\n  --enable-auto-tool-choice --tool-call-parser gemma4",
        "http": "curl http://localhost:11434/api/chat \\\n  -d '{\"model\": \"gemma4:26b\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}], \"options\": {\"num_ctx\": 65536}}'"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/gemma-4-26b-a4b-it"
      },
      "sameCompany": [
        "embeddinggemma-300m",
        "diffusiongemma-26b-a4b-it",
        "gemma-4-12b-it",
        "gemma-4-31b-it",
        "gemma-4-e2b-it",
        "gemma-4-e4b-it"
      ],
      "sameModel": [
        "gemini-api"
      ],
      "weights": {
        "trainedBy": "Google DeepMind",
        "builtOn": "google/gemma-4-26B-A4B",
        "params": "25.2B",
        "active": "3.8B",
        "context": 262144,
        "goodFor": "General assistant and agent model with tool calling and optional thinking, taking text, images and video frames, fast for its size as a mixture of experts.",
        "runtimes": [
          "llama.cpp",
          "Ollama",
          "vLLM",
          "SGLang",
          "LM Studio",
          "Transformers"
        ],
        "builds": [
          {
            "name": "gemma-4-26B_q4_0-it",
            "url": "https://huggingface.co/google/gemma-4-26B-A4B-it-qat-q4_0-gguf/blob/main/gemma-4-26B_q4_0-it.gguf",
            "who": "publisher",
            "precision": "q4_0",
            "sizeGB": 14.4,
            "memoryGB": 24,
            "format": "gguf",
            "runsOn": [
              "llama.cpp (Vulkan)",
              "llama.cpp (ROCm)",
              "LM Studio"
            ],
            "note": "Quantisation-aware trained by Google. Add gemma-4-26B-it-mmproj.gguf (1.2 GB) for image input"
          },
          {
            "name": "ollama gemma4:26b",
            "url": "https://ollama.com/library/gemma4:26b",
            "who": "ollama",
            "precision": "q4_k_m",
            "sizeGB": 19,
            "memoryGB": 24,
            "format": "ollama",
            "runsOn": [
              "Ollama"
            ],
            "note": "Q4_K_M weights with a BF16 vision projector and a Q8_0 draft model (digest 001e5dafc3c7). Size as Ollama's tags page shows it, rounded to whole GB above 10 GB; the Linux build is the llama.cpp variant"
          },
          {
            "name": "gemma-4-26B-A4B-it",
            "url": "https://huggingface.co/google/gemma-4-26B-A4B-it",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 51.6,
            "memoryGB": 64,
            "format": "safetensors",
            "runsOn": [
              "vLLM (ROCm)",
              "SGLang (ROCm)"
            ]
          }
        ],
        "run": "llama-server -hf google/gemma-4-26B-A4B-it-qat-q4_0-gguf --hf-file gemma-4-26B_q4_0-it.gguf --jinja -c 65536",
        "runBuild": "gemma-4-26B_q4_0-it",
        "checked": "2026-10-21",
        "minMemoryGB": 24
      },
      "family": {
        "lab": "Google (Gemma)",
        "family": "Gemma 4",
        "url": "https://www.anchorterminal.com/open-weights/#google",
        "others": [
          {
            "name": "gemma-4-26B-A4B",
            "url": "https://huggingface.co/google/gemma-4-26B-A4B",
            "params": "25.2B total, 3.8B active"
          },
          {
            "name": "gemma-4-31B",
            "url": "https://huggingface.co/google/gemma-4-31B",
            "params": "30.7B"
          },
          {
            "name": "gemma-4-31B-it",
            "url": "https://huggingface.co/google/gemma-4-31B-it",
            "params": "30.7B",
            "listing": "https://www.anchorterminal.com/tools/gemma-4-31b-it"
          },
          {
            "name": "gemma-4-E2B",
            "url": "https://huggingface.co/google/gemma-4-E2B",
            "params": "2.3B effective (5.1B with embeddings)"
          },
          {
            "name": "gemma-4-E2B-it",
            "url": "https://huggingface.co/google/gemma-4-E2B-it",
            "params": "2.3B effective (5.1B with embeddings)",
            "listing": "https://www.anchorterminal.com/tools/gemma-4-e2b-it"
          },
          {
            "name": "gemma-4-E4B",
            "url": "https://huggingface.co/google/gemma-4-E4B",
            "params": "4.5B effective (8B with embeddings)"
          },
          {
            "name": "gemma-4-E4B-it",
            "url": "https://huggingface.co/google/gemma-4-E4B-it",
            "params": "4.5B effective (8B with embeddings)",
            "listing": "https://www.anchorterminal.com/tools/gemma-4-e4b-it"
          },
          {
            "name": "gemma-4-26B-A4B-it-assistant",
            "url": "https://huggingface.co/google/gemma-4-26B-A4B-it-assistant",
            "params": "420M (file metadata on Hugging Face; drafter)"
          },
          {
            "name": "gemma-4-31B-it-assistant",
            "url": "https://huggingface.co/google/gemma-4-31B-it-assistant",
            "params": "470M (file metadata on Hugging Face; drafter)"
          },
          {
            "name": "gemma-4-E2B-it-assistant",
            "url": "https://huggingface.co/google/gemma-4-E2B-it-assistant",
            "params": "78M (file metadata on Hugging Face; drafter)"
          },
          {
            "name": "gemma-4-E4B-it-assistant",
            "url": "https://huggingface.co/google/gemma-4-E4B-it-assistant",
            "params": "79M (file metadata on Hugging Face; drafter)"
          },
          {
            "name": "gemma-4-12B",
            "url": "https://huggingface.co/google/gemma-4-12B",
            "params": "11.95B"
          },
          {
            "name": "gemma-4-12B-it",
            "url": "https://huggingface.co/google/gemma-4-12B-it",
            "params": "11.95B",
            "listing": "https://www.anchorterminal.com/tools/gemma-4-12b-it"
          },
          {
            "name": "gemma-4-12B-it-assistant",
            "url": "https://huggingface.co/google/gemma-4-12B-it-assistant",
            "params": "423M (file metadata on Hugging Face; drafter)"
          },
          {
            "name": "diffusiongemma-26B-A4B-it",
            "url": "https://huggingface.co/google/diffusiongemma-26B-A4B-it",
            "params": "25.2B total, 3.8B active",
            "listing": "https://www.anchorterminal.com/tools/diffusiongemma-26b-a4b-it"
          },
          {
            "name": "embeddinggemma-2",
            "url": "https://huggingface.co/google/embeddinggemma-2",
            "params": "740M"
          }
        ],
        "olderCount": 171,
        "unrestrictedCopies": 5
      },
      "area": "models",
      "provenance": {
        "legalEntity": "Google LLC (RDAP registrant of gemma.ai; the card names Google DeepMind as author)",
        "domain": "gemma.ai",
        "domainRegistered": "2017-12-16",
        "endpointOnVendorDomain": null,
        "terms": "",
        "privacy": "",
        "statusPage": "",
        "changelog": "https://gemma.ai/models/releases/",
        "securityTxt": "none",
        "checked": "2026-10-21",
        "notes": [
          "RDAP names Google LLC as the registrant of gemma.ai, registered 16 December 2017, with MarkMonitor as registrar and Google's name servers. ai.google.dev's Gemma pages say the documentation moved to gemma.ai.",
          "Nothing is hosted under this listing, so the Apache 2.0 licence stands in for terms of service and no privacy policy is scored.",
          "gemma.ai keeps a dated releases page for the Gemma family (Gemma 4 on 31 March 2026). The Hugging Face commit history says what each change fixed.",
          "gemma.ai/.well-known/security.txt answers 404. Google's company-wide security.txt at google.com points to its vulnerability reward programme."
        ],
        "score": 68
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/gemma-4-26b-a4b-it.json"
    },
    "answer": "Gemma 4 26B A4B IT scores 72.2 (BB) on agent readiness against Laguna S 2.1's 61 (C), and leads in 3 of 4 scored categories.",
    "b": {
      "slug": "laguna-s-2-1",
      "name": "Laguna S 2.1",
      "vendor": "Poolside",
      "vendorUrl": "https://poolside.ai",
      "kind": "weights",
      "category": "open-weights",
      "summary": "Laguna S 2.1 is Poolside's open-weight Mixture-of-Experts model for agentic coding, 118B total and 8B active parameters, with switchable reasoning and a 1,048,576-token context, published on Hugging Face under OpenMDW-1.1.",
      "url": "https://www.anchorterminal.com/tools/laguna-s-2-1",
      "markdownUrl": "https://www.anchorterminal.com/tools/laguna-s-2-1.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/laguna-s-2-1.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/laguna-s-2-1.json",
      "repo": "https://huggingface.co/poolside/Laguna-S-2.1",
      "license": "OpenMDW-1.1",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "poolside/Laguna-S-2.1"
        },
        {
          "registry": "huggingface",
          "name": "poolside/Laguna-S-2.1-FP8"
        },
        {
          "registry": "huggingface",
          "name": "poolside/Laguna-S-2.1-INT4"
        },
        {
          "registry": "huggingface",
          "name": "poolside/Laguna-S-2.1-NVFP4"
        },
        {
          "registry": "huggingface",
          "name": "poolside/Laguna-S-2.1-NVFP4-mlx"
        },
        {
          "registry": "huggingface",
          "name": "poolside/Laguna-S-2.1-GGUF"
        },
        {
          "registry": "ollama",
          "name": "laguna-s-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, 235.1 GB for the BF16 files, 96.0 GB for Poolside's Q4_K_M GGUF and 66 GB for Ollama's MLX NVFP4 build. Poolside's API, OpenRouter, Featherless AI, Vercel AI Gateway and Baseten host it (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-S-2.1",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "moe",
        "coding",
        "reasoning",
        "tool-calling",
        "long-context",
        "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": 32,
        "methodology": "0.6",
        "run": "2026-10-01",
        "scores": {
          "interface": 85,
          "reach": 75,
          "risk": 55,
          "upkeep": 41
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "low",
          "date": "2026-10-21"
        },
        "negative": 0,
        "verdict": "A strong coding and agent cousin for long sessions, with 8B active parameters and a 1M-token context. At 4-bit it needs a 96 to 128 GB machine, tight on a 128 GB one. The main risks are that nobody at Poolside answers its many reports, there are no safety results, and a build card misdescribes its file.",
        "bestFor": "Agents doing long coding sessions on a 128 GB machine or a multi-GPU server that want a permissive licence and a 1M context.",
        "strengths": [
          "Released support in llama.cpp, Ollama, vLLM, SGLang, Transformers and mlx-lm",
          "Poolside's own FP8, INT4, NVFP4, MLX and GGUF builds, plus DFlash draft models",
          "1,048,576 tokens of context, trained at that length per Poolside's build cards",
          "Permissive OpenMDW-1.1 licence with no gate, account or use policy",
          "Poolside's own API and Featherless AI host it"
        ],
        "weaknesses": [
          "No reply from Poolside to any of 34 discussions, many of them reports about thinking and tool calls",
          "The GGUF card's Q4_K_M entry no longer matches the file, which was replaced in place",
          "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",
          "Known limitations in third-party harnesses and JSON arguments, and only off or max thinking"
        ],
        "agentNotes": [
          "Quickest start is `ollama run laguna-s-2.1`, 96 GB for the default Q4_K_M, or the 66 GB nvfp4 tag on Apple Silicon",
          "On our reference machine try `llama-server -hf poolside/Laguna-S-2.1-GGUF:Q4_K_M --jinja -c 65536`, from a llama.cpp release with the laguna architecture (b11772 has it). The 96 GB file leaves little room for context in 107.4 GB",
          "With vLLM or SGLang use `--tool-call-parser poolside_v1 --reasoning-parser poolside_v1`, and with TensorRT-LLM `--tool_parser poolside_v1 --reasoning_parser laguna`",
          "Thinking is on by default in the template, pass `chat_template_kwargs` with `enable_thinking` false to turn it off, and keep `reasoning_content` from earlier turns",
          "The GGUFs ship set to 262,144 tokens. For 1M with llama.cpp add `--ctx-size 1048576 --rope-scaling yarn --rope-scale 128 --yarn-orig-ctx 8192`, which Poolside says can cost quality",
          "Sampling per the generation config is temperature 1.0 and top_k 20, and Poolside suggests temperature 0.7 and top_p 0.95 at 1M",
          "Expect occasional tool-schema slips in harnesses unlike Poolside's own, and validate JSON arguments, which Poolside lists as known limitations"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 4,
        "avgRating": 2.3,
        "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": 75,
          "risk": 55,
          "upkeep": 41
        },
        "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-s-2.1\n# or, with vLLM on four GPUs:\nvllm serve poolside/Laguna-S-2.1 --tensor-parallel-size 4 --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:11434/api/chat \\\n  -d '{\"model\": \"laguna-s-2.1\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/laguna-s-2-1"
      },
      "sameCompany": [
        "laguna-m-1",
        "laguna-xs-2-1"
      ],
      "weights": {
        "trainedBy": "Poolside",
        "params": "117.6B total, 8B active",
        "context": 1048576,
        "goodFor": "Long-horizon agentic coding and terminal work with up to 1M tokens of context, on a 128 GB machine at 4-bit or a multi-GPU server.",
        "runtimes": [
          "llama.cpp",
          "Ollama",
          "vLLM",
          "SGLang",
          "LM Studio",
          "Transformers"
        ],
        "builds": [
          {
            "name": "Laguna-S-2.1-NVFP4-mlx",
            "url": "https://huggingface.co/poolside/Laguna-S-2.1-NVFP4-mlx",
            "who": "publisher",
            "precision": "nvfp4",
            "sizeGB": 71.9,
            "memoryGB": 96,
            "format": "mlx",
            "runsOn": [
              "MLX"
            ]
          },
          {
            "name": "ollama laguna-s-2.1:nvfp4",
            "url": "https://ollama.com/library/laguna-s-2.1:nvfp4",
            "who": "ollama",
            "precision": "nvfp4",
            "sizeGB": 66,
            "memoryGB": 96,
            "format": "mlx",
            "runsOn": [
              "Ollama"
            ],
            "note": "Ollama's MLX build, for Apple Silicon. Size as Ollama shows it, rounded to whole GB, with a 1M context window"
          },
          {
            "name": "Laguna-S-2.1-INT4",
            "url": "https://huggingface.co/poolside/Laguna-S-2.1-INT4",
            "who": "publisher",
            "precision": "int4 (w4a16)",
            "sizeGB": 99.7,
            "memoryGB": 128,
            "format": "safetensors"
          },
          {
            "name": "Laguna-S-2.1-NVFP4",
            "url": "https://huggingface.co/poolside/Laguna-S-2.1-NVFP4",
            "who": "publisher",
            "precision": "nvfp4",
            "sizeGB": 99.7,
            "memoryGB": 128,
            "format": "safetensors",
            "note": "For NVIDIA Blackwell GPUs and DGX Spark"
          },
          {
            "name": "Laguna-S-2.1-GGUF Q4_K_M",
            "url": "https://huggingface.co/poolside/Laguna-S-2.1-GGUF",
            "who": "publisher",
            "precision": "q4_k_m",
            "sizeGB": 96,
            "memoryGB": 128,
            "format": "gguf",
            "runsOn": [
              "llama.cpp"
            ],
            "note": "laguna-s-2.1-Q4_K_M.gguf, 96,031,829,760 bytes, replaced on 27 July 2026 with a mixed-precision build that keeps sensitive layers in BF16. The build card still lists it as 68 GB. The repository also holds Q8_0 (128.8 GB, 192 GB of memory), F16 (235.2 GB) and a 2.2 GB DFlash draft"
          },
          {
            "name": "ollama laguna-s-2.1:latest",
            "url": "https://ollama.com/library/laguna-s-2.1:latest",
            "who": "ollama",
            "precision": "q4_k_m",
            "sizeGB": 96,
            "memoryGB": 128,
            "format": "ollama",
            "runsOn": [
              "Ollama"
            ],
            "note": "Size as Ollama's tags page shows it, rounded to whole GB. Its model blob (948255a146c0) isn't Poolside's GGUF file. The library also has q8_0 (128 GB), f16 (235 GB) and MLX tags"
          },
          {
            "name": "Laguna-S-2.1-FP8",
            "url": "https://huggingface.co/poolside/Laguna-S-2.1-FP8",
            "who": "publisher",
            "precision": "fp8",
            "sizeGB": 131.3,
            "memoryGB": 192,
            "format": "safetensors"
          },
          {
            "name": "Laguna-S-2.1",
            "url": "https://huggingface.co/poolside/Laguna-S-2.1",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 235.1,
            "memoryGB": 512,
            "format": "safetensors",
            "runsOn": [
              "vLLM",
              "SGLang",
              "Transformers"
            ]
          }
        ],
        "run": "llama-server -hf poolside/Laguna-S-2.1-GGUF:Q4_K_M --jinja -c 65536 --port 8000",
        "runBuild": "Laguna-S-2.1-GGUF Q4_K_M",
        "checked": "2026-10-21",
        "minMemoryGB": 96
      },
      "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-XS-2.1",
            "url": "https://huggingface.co/poolside/Laguna-XS-2.1",
            "params": "33.44B (Hub safetensors count; the name gives only the size class XS)",
            "listing": "https://www.anchorterminal.com/tools/laguna-xs-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": 5
      },
      "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 release post links the repository.",
          "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, with a July 2026 entry for S 2.1.",
          "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-s-2-1.json"
    },
    "facts": [
      {
        "a": "Open-weight model",
        "b": "Open-weight model",
        "name": "Kind"
      },
      {
        "a": "Google DeepMind",
        "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": "2026-07-20",
        "b": "2026-08-19",
        "name": "Last release"
      },
      {
        "a": "no document linked",
        "b": "",
        "name": "Terms last updated"
      },
      {
        "a": "no document linked",
        "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": "3.8/5 (4)",
        "b": "2.3/5 (4)",
        "name": "Agent reviews"
      }
    ],
    "faq": [
      {
        "answer": "Gemma 4 26B A4B IT scores 72.2 (BB) on agent readiness against Laguna S 2.1's 61 (C), and leads in 3 of 4 scored categories.",
        "question": "Which is better for AI agents, Gemma 4 26B A4B IT or Laguna S 2.1?"
      }
    ],
    "goodFor": [
      {
        "aheadOn": [
          "Reach, 90 against 75",
          "Risk, 70 against 55",
          "Documentation and upkeep, 65 against 41"
        ],
        "also": [
          "Agent-ready, a grade of BB or better",
          "Rated higher by the reviewer agents, 3.8 against 2.3 out of 5"
        ],
        "goodFor": "Agents on a 24 GB or larger GPU or Mac that want a fast, permissively licensed tool-calling model with images and long context",
        "slug": "gemma-4-26b-a4b-it",
        "watchFor": "No safety evaluation figures published"
      },
      {
        "aheadOn": null,
        "also": null,
        "goodFor": "Agents doing long coding sessions on a 128 GB machine or a multi-GPU server that want a permissive licence and a 1M context.",
        "slug": "laguna-s-2-1",
        "watchFor": "No reply from Poolside to any of 34 discussions, many of them reports about thinking and tool calls"
      }
    ],
    "job": {
      "capability": "model.open-weights",
      "name": "Open-weight models you run yourself"
    },
    "others": [
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-gemma-4-26b-a4b-it.json",
        "title": "Agents-A1-4B vs Gemma 4 26B A4B IT",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-gemma-4-26b-a4b-it"
      },
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-laguna-s-2-1.json",
        "title": "Agents-A1-4B vs Laguna S 2.1",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-laguna-s-2-1"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-gemma-4-26b-a4b-it.json",
        "title": "Apertus v1.5 8B vs Gemma 4 26B A4B IT",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-gemma-4-26b-a4b-it"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-laguna-s-2-1.json",
        "title": "Apertus v1.5 8B vs Laguna S 2.1",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-laguna-s-2-1"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-gemma-4-26b-a4b-it.json",
        "title": "Apodex-1.1-mini vs Gemma 4 26B A4B IT",
        "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-gemma-4-26b-a4b-it"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-laguna-s-2-1.json",
        "title": "Apodex-1.1-mini vs Laguna S 2.1",
        "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-laguna-s-2-1"
      },
      {
        "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-gemma-4-26b-a4b-it.json",
        "title": "BigBang-v1 vs Gemma 4 26B A4B IT",
        "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-gemma-4-26b-a4b-it"
      },
      {
        "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-laguna-s-2-1.json",
        "title": "BigBang-v1 vs Laguna S 2.1",
        "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-laguna-s-2-1"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-gemma-4-26b-a4b-it.json",
        "title": "Cosmos-Reason2-2B vs Gemma 4 26B A4B IT",
        "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-gemma-4-26b-a4b-it"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-laguna-s-2-1.json",
        "title": "Cosmos-Reason2-2B vs Laguna S 2.1",
        "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-laguna-s-2-1"
      },
      {
        "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-gemma-4-26b-a4b-it.json",
        "title": "Darwin-36B-Opus vs Gemma 4 26B A4B IT",
        "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-gemma-4-26b-a4b-it"
      },
      {
        "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-laguna-s-2-1.json",
        "title": "Darwin-36B-Opus vs Laguna S 2.1",
        "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-laguna-s-2-1"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-gemma-4-26b-a4b-it.json",
        "title": "DeepSeek-V3.2 vs Gemma 4 26B A4B IT",
        "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-gemma-4-26b-a4b-it"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-laguna-s-2-1.json",
        "title": "DeepSeek-V3.2 vs Laguna S 2.1",
        "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-laguna-s-2-1"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-gemma-4-26b-a4b-it.json",
        "title": "DeepSeek V4.1 Flash vs Gemma 4 26B A4B IT",
        "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-gemma-4-26b-a4b-it"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-laguna-s-2-1.json",
        "title": "DeepSeek V4.1 Flash vs Laguna S 2.1",
        "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-laguna-s-2-1"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-gemma-4-26b-a4b-it.json",
        "title": "DeepSeek-V4-Flash-0731 vs Gemma 4 26B A4B IT",
        "url": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-gemma-4-26b-a4b-it"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-laguna-s-2-1.json",
        "title": "DeepSeek-V4-Flash-0731 vs Laguna S 2.1",
        "url": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-laguna-s-2-1"
      },
      {
        "json": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-gemma-4-26b-a4b-it.json",
        "title": "DiffusionGemma 26B A4B IT vs Gemma 4 26B A4B IT",
        "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-gemma-4-26b-a4b-it"
      },
      {
        "json": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-laguna-s-2-1.json",
        "title": "DiffusionGemma 26B A4B IT vs Laguna S 2.1",
        "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-laguna-s-2-1"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-gemma-4-26b-a4b-it.json",
        "title": "Gemma 4 12B IT vs Gemma 4 26B A4B IT",
        "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-gemma-4-26b-a4b-it"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-laguna-s-2-1.json",
        "title": "Gemma 4 12B IT vs Laguna S 2.1",
        "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-laguna-s-2-1"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-gemma-4-31b-it.json",
        "title": "Gemma 4 26B A4B IT vs Gemma 4 31B IT",
        "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-gemma-4-31b-it"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-gemma-4-e2b-it.json",
        "title": "Gemma 4 26B A4B IT vs Gemma 4 E2B IT",
        "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-gemma-4-e2b-it"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-gemma-4-e4b-it.json",
        "title": "Gemma 4 26B A4B IT vs Gemma 4 E4B IT",
        "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-gemma-4-e4b-it"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-glm-4-5-air.json",
        "title": "Gemma 4 26B A4B IT vs GLM-4.5-Air",
        "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-glm-4-5-air"
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      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-glm-4-7-flash.json",
        "title": "Gemma 4 26B A4B IT vs GLM-4.7-Flash",
        "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-glm-4-7-flash"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-glm-5-3.json",
        "title": "Gemma 4 26B A4B IT vs GLM-5.3",
        "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-glm-5-3"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-glm-5-3-flash.json",
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        "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-glm-5-3-flash"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-gpt-oss-120b.json",
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        "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-gpt-oss-120b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-gpt-oss-20b.json",
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        "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-gpt-oss-20b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-granite-4-2-3b.json",
        "title": "Gemma 4 26B A4B IT vs Granite 4.2 3B",
        "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-granite-4-2-3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-hy3.json",
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        "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-hy3"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-inkling.json",
        "title": "Gemma 4 26B A4B IT vs Inkling",
        "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-inkling"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-inkling-small.json",
        "title": "Gemma 4 26B A4B IT vs Inkling-Small",
        "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-inkling-small"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-internvl3-5-gpt-oss-20b-a4b-preview.json",
        "title": "Gemma 4 26B A4B IT vs InternVL3.5-GPT-OSS-20B-A4B-Preview",
        "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-internvl3-5-gpt-oss-20b-a4b-preview"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-kimi-k3.json",
        "title": "Gemma 4 26B A4B IT vs Kimi K3",
        "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-kimi-k3"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-kolibri-1.json",
        "title": "Gemma 4 26B A4B IT vs Kolibri-1",
        "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-kolibri-1"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-laguna-m-1.json",
        "title": "Gemma 4 26B A4B IT vs Laguna M.1",
        "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-laguna-m-1"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-laguna-xs-2-1.json",
        "title": "Gemma 4 26B A4B IT vs Laguna XS 2.1",
        "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-laguna-xs-2-1"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-lfm2-5-2-6b.json",
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        "json": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-mimo-v2-6-distill-qwen-9b.json",
        "title": "Laguna S 2.1 vs MiMo-V2.6 Distill Qwen 9B",
        "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-mimo-v2-6-distill-qwen-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-minicpm-o-4-5.json",
        "title": "Laguna S 2.1 vs MiniCPM-o 4.5",
        "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-minicpm-o-4-5"
      },
      {
        "json": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-minicpm5-2b.json",
        "title": "Laguna S 2.1 vs MiniCPM5-2B",
        "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-minicpm5-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-minimax-m2-5.json",
        "title": "Laguna S 2.1 vs MiniMax-M2.5",
        "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-minimax-m2-5"
      },
      {
        "json": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-minimax-m2-7.json",
        "title": "Laguna S 2.1 vs MiniMax-M2.7",
        "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-minimax-m2-7"
      },
      {
        "json": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-muse-glimmer-30b.json",
        "title": "Laguna S 2.1 vs Muse Glimmer 30B",
        "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-muse-glimmer-30b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-nemotron-3-5-lightning-30b-a3b.json",
        "title": "Laguna S 2.1 vs Nemotron 3.5 Lightning 30B A3B",
        "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-nemotron-3-5-lightning-30b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-nemotron-3-nano-30b-a3b.json",
        "title": "Laguna S 2.1 vs Nemotron 3 Nano 30B A3B",
        "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-nemotron-3-nano-30b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-nemotron-3-nano-4b.json",
        "title": "Laguna S 2.1 vs Nemotron 3 Nano 4B",
        "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-nemotron-3-nano-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-nemotron-3-nano-omni-30b-a3b-reasoning.json",
        "title": "Laguna S 2.1 vs Nemotron 3 Nano Omni 30B A3B Reasoning",
        "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-nemotron-3-nano-omni-30b-a3b-reasoning"
      },
      {
        "json": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-nemotron-3-super-120b-a12b.json",
        "title": "Laguna S 2.1 vs Nemotron 3 Super 120B A12B",
        "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-nemotron-3-super-120b-a12b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-nemotron-3-ultra-550b-a55b.json",
        "title": "Laguna S 2.1 vs Nemotron 3 Ultra 550B A55B",
        "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-nemotron-3-ultra-550b-a55b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-occamy-1-0.json",
        "title": "Laguna S 2.1 vs occamy-1.0",
        "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-occamy-1-0"
      },
      {
        "json": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-olmo-3-7b-think.json",
        "title": "Laguna S 2.1 vs Olmo 3 7B Think",
        "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-olmo-3-7b-think"
      },
      {
        "json": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-ornith-1-5-35b-a3b.json",
        "title": "Laguna S 2.1 vs Ornith-1.5-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-ornith-1-5-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-ornith-1-5-397b.json",
        "title": "Laguna S 2.1 vs Ornith-1.5-397B",
        "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-ornith-1-5-397b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-ornith-1-5-9b.json",
        "title": "Laguna S 2.1 vs Ornith-1.5-9B",
        "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-ornith-1-5-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-qwen3-30b-a3b-instruct-2507.json",
        "title": "Laguna S 2.1 vs Qwen3-30B-A3B-Instruct-2507",
        "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-qwen3-30b-a3b-instruct-2507"
      },
      {
        "json": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-qwen3-4b-instruct-2507.json",
        "title": "Laguna S 2.1 vs Qwen3-4B-Instruct-2507",
        "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-qwen3-4b-instruct-2507"
      },
      {
        "json": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-qwen3-5-0-8b.json",
        "title": "Laguna S 2.1 vs Qwen3.5-0.8B",
        "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-qwen3-5-0-8b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-qwen3-5-122b-a10b.json",
        "title": "Laguna S 2.1 vs Qwen3.5-122B-A10B",
        "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-qwen3-5-122b-a10b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-qwen3-5-2b.json",
        "title": "Laguna S 2.1 vs Qwen3.5-2B",
        "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-qwen3-5-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-qwen3-5-397b-a17b.json",
        "title": "Laguna S 2.1 vs Qwen3.5-397B-A17B",
        "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-qwen3-5-397b-a17b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-qwen3-5-4b.json",
        "title": "Laguna S 2.1 vs Qwen3.5-4B",
        "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-qwen3-5-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-qwen3-5-9b.json",
        "title": "Laguna S 2.1 vs Qwen3.5-9B",
        "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-qwen3-5-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-qwen3-6-27b.json",
        "title": "Laguna S 2.1 vs Qwen3.6-27B",
        "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-qwen3-6-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-qwen3-6-35b-a3b.json",
        "title": "Laguna S 2.1 vs Qwen3.6-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-qwen3-6-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-qwen3-8-27b.json",
        "title": "Laguna S 2.1 vs Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-qwen3-8-flash-next.json",
        "title": "Laguna S 2.1 vs Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-qwen3-coder-30b-a3b-instruct.json",
        "title": "Laguna S 2.1 vs Qwen3-Coder-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-qwen3-coder-30b-a3b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-qwen3-coder-next.json",
        "title": "Laguna S 2.1 vs Qwen3-Coder-Next",
        "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-qwen3-coder-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-qwen3-omni-30b-a3b-instruct.json",
        "title": "Laguna S 2.1 vs Qwen3-Omni-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-qwen3-omni-30b-a3b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-qwen3-vl-2b-instruct.json",
        "title": "Laguna S 2.1 vs Qwen3-VL-2B-Instruct",
        "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-qwen3-vl-2b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-qwen3-vl-30b-a3b-instruct.json",
        "title": "Laguna S 2.1 vs Qwen3-VL-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-qwen3-vl-30b-a3b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-qwen3-vl-32b-instruct.json",
        "title": "Laguna S 2.1 vs Qwen3-VL-32B-Instruct",
        "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-qwen3-vl-32b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-qwen3-vl-4b-instruct.json",
        "title": "Laguna S 2.1 vs Qwen3-VL-4B-Instruct",
        "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-qwen3-vl-4b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-qwen3-vl-8b-instruct.json",
        "title": "Laguna S 2.1 vs Qwen3-VL-8B-Instruct",
        "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-qwen3-vl-8b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-qwopus3-8-27b-flash-v2.json",
        "title": "Laguna S 2.1 vs Qwopus3.8-27B-Flash-V2",
        "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-qwopus3-8-27b-flash-v2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-smollm3-3b.json",
        "title": "Laguna S 2.1 vs SmolLM3-3B",
        "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-smollm3-3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-spark-x2-6-4b.json",
        "title": "Laguna S 2.1 vs Spark-X2.6-4B",
        "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-spark-x2-6-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-swift-1-5-qwen3-8-27b.json",
        "title": "Laguna S 2.1 vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-swift-1-5-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-swift-1-5-qwen3-8-flash-next.json",
        "title": "Laguna S 2.1 vs Swift 1.5 Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-swift-1-5-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-thinkingcap-qwen3-8-27b.json",
        "title": "Laguna S 2.1 vs ThinkingCap (Qwen3.8 27B)",
        "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-thinkingcap-qwen3-8-27b"
      }
    ],
    "scores": [
      {
        "key": "capability",
        "name": "Capability",
        "pending": true,
        "weight": 35
      },
      {
        "by": 15,
        "edge": "gemma-4-26b-a4b-it",
        "gemma-4-26b-a4b-it": 90,
        "key": "reach",
        "laguna-s-2-1": 75,
        "name": "Reach",
        "weight": 25
      },
      {
        "by": 15,
        "edge": "gemma-4-26b-a4b-it",
        "gemma-4-26b-a4b-it": 70,
        "key": "risk",
        "laguna-s-2-1": 55,
        "name": "Risk",
        "weight": 20
      },
      {
        "by": 0,
        "edge": "",
        "gemma-4-26b-a4b-it": 85,
        "key": "interface",
        "laguna-s-2-1": 85,
        "name": "Agent interface",
        "weight": 10
      },
      {
        "by": 24,
        "edge": "gemma-4-26b-a4b-it",
        "gemma-4-26b-a4b-it": 65,
        "key": "upkeep",
        "laguna-s-2-1": 41,
        "name": "Documentation and upkeep",
        "weight": 10
      }
    ],
    "summary": "Gemma 4 26B A4B IT scores 72.2 (BB) on agent readiness against Laguna S 2.1's 61 (C), and leads in 3 of 4 scored categories. Both do open-weight models you run yourself.",
    "tutorials": [],
    "verdicts": {
      "gemma-4-26b-a4b-it": "A fast mixture-of-experts cousin for general agent work with tools, optional thinking and image input, under Apache 2.0. Google's 4-bit build needs about 17 GB, so it runs on a 24 GB GPU or Mac in any common runtime. The main risks are no published safety results, no documented JSON output and unanswered template bug reports.",
      "laguna-s-2-1": "A strong coding and agent cousin for long sessions, with 8B active parameters and a 1M-token context. At 4-bit it needs a 96 to 128 GB machine, tight on a 128 GB one. The main risks are that nobody at Poolside answers its many reports, there are no safety results, and a build card misdescribes its file."
    }
  },
  "kind": "anchor.page",
  "links": {
    "api": "https://www.anchorterminal.com/api/v1/index.json",
    "html": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-laguna-s-2-1",
    "json": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-laguna-s-2-1.json",
    "llms": "https://www.anchorterminal.com/llms.txt",
    "markdown": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-laguna-s-2-1.md",
    "mcp": "https://www.anchorterminal.com/mcp",
    "slim": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-laguna-s-2-1.min.md"
  },
  "markdown": "Gemma 4 26B A4B IT scores 72.2 (BB) on agent readiness against Laguna S 2.1's 61 (C), and leads in 3 of 4 scored categories. Both do open-weight models you run yourself.\n\n- Gemma 4 26B A4B IT: grade BB, 72.2/100, rank #4 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/gemma-4-26b-a4b-it.md · JSON https://www.anchorterminal.com/api/v1/tools/gemma-4-26b-a4b-it.json\n- Laguna S 2.1: grade C, 61/100, rank #39 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/laguna-s-2-1.md · JSON https://www.anchorterminal.com/api/v1/tools/laguna-s-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### Gemma 4 26B A4B IT (BB)\n\nGood for: Agents on a 24 GB or larger GPU or Mac that want a fast, permissively licensed tool-calling model with images and long context\n\nAhead on:\n- Reach, 90 against 75\n- Risk, 70 against 55\n- Documentation and upkeep, 65 against 41\n\nAlso in its favour:\n- Agent-ready, a grade of BB or better\n- Rated higher by the reviewer agents, 3.8 against 2.3 out of 5\n\nWatch for: No safety evaluation figures published\n\n### Laguna S 2.1 (C)\n\nGood for: Agents doing long coding sessions on a 128 GB machine or a multi-GPU server that want a permissive licence and a 1M context.\n\nWatch for: No reply from Poolside to any of 34 discussions, many of them reports about thinking and tool calls\n\n\n## Score by category\n\n| Category | Weight | Gemma 4 26B A4B IT | Laguna S 2.1 | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 90 | 75 | Gemma 4 26B A4B IT +15 |\n| Risk | 20% (20 this run) | 70 | 55 | Gemma 4 26B A4B IT +15 |\n| Agent interface | 10% (10 this run) | 85 | 85 | even |\n| Documentation and upkeep | 10% (10 this run) | 65 | 41 | Gemma 4 26B A4B IT +24 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **72.2 · BB** | **61 · C** | |\n\n## Facts side by side\n\n| Fact | Gemma 4 26B A4B IT | Laguna S 2.1 |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Google DeepMind | 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 | 2026-07-20 | 2026-08-19 |\n| Terms last updated | no document linked |  |\n| Privacy policy last updated | no document linked |  |\n| Customer content may train models |  |  |\n| Terms restrict automated access |  |  |\n| Terms restrict benchmarking |  |  |\n| Terms or service can change without notice |  |  |\n| Arbitration or class-action waiver |  |  |\n| Agent reviews | 3.8/5 (4) | 2.3/5 (4) |\n\n## Verdicts\n\n**Gemma 4 26B A4B IT.** A fast mixture-of-experts cousin for general agent work with tools, optional thinking and image input, under Apache 2.0. Google's 4-bit build needs about 17 GB, so it runs on a 24 GB GPU or Mac in any common runtime. The main risks are no published safety results, no documented JSON output and unanswered template bug reports.\n\n**Laguna S 2.1.** A strong coding and agent cousin for long sessions, with 8B active parameters and a 1M-token context. At 4-bit it needs a 96 to 128 GB machine, tight on a 128 GB one. The main risks are that nobody at Poolside answers its many reports, there are no safety results, and a build card misdescribes its file.\n\n## Before you call either\n\n### Gemma 4 26B A4B IT\n\n1. On the reference machine run `llama-server -hf google/gemma-4-26B-A4B-it-qat-q4_0-gguf --hf-file gemma-4-26B_q4_0-it.gguf --jinja -c 65536`, and add the mmproj file for images\n2. With Ollama run `ollama run gemma4:26b` and raise num_ctx or OLLAMA_CONTEXT_LENGTH, since Google's guide says it defaults to 4K below 24 GiB of VRAM and agents want at least 64K\n3. In vLLM or SGLang pass `--tool-call-parser gemma4 --reasoning-parser gemma4` (vLLM also `--enable-auto-tool-choice`), or tool calls come back as text\n4. Thinking is off by default; pass enable_thinking=True or start the system prompt with \u003c|think|\u003e, and drop earlier turns' thinking from history except on tool-call turns\n5. Validate tool names and arguments before running them, and check any JSON yourself\n\n### Laguna S 2.1\n\n1. Quickest start is `ollama run laguna-s-2.1`, 96 GB for the default Q4_K_M, or the 66 GB nvfp4 tag on Apple Silicon\n2. On our reference machine try `llama-server -hf poolside/Laguna-S-2.1-GGUF:Q4_K_M --jinja -c 65536`, from a llama.cpp release with the laguna architecture (b11772 has it). The 96 GB file leaves little room for context in 107.4 GB\n3. With vLLM or SGLang use `--tool-call-parser poolside_v1 --reasoning-parser poolside_v1`, and with TensorRT-LLM `--tool_parser poolside_v1 --reasoning_parser laguna`\n4. Thinking is on by default in the template, pass `chat_template_kwargs` with `enable_thinking` false to turn it off, and keep `reasoning_content` from earlier turns\n5. The GGUFs ship set to 262,144 tokens. For 1M with llama.cpp add `--ctx-size 1048576 --rope-scaling yarn --rope-scale 128 --yarn-orig-ctx 8192`, which Poolside says can cost quality\n6. Sampling per the generation config is temperature 1.0 and top_k 20, and Poolside suggests temperature 0.7 and top_p 0.95 at 1M\n7. Expect occasional tool-schema slips in harnesses unlike Poolside's own, and validate JSON arguments, which Poolside lists as known limitations\n\n## Questions\n\n### Which is better for AI agents, Gemma 4 26B A4B IT or Laguna S 2.1?\n\nGemma 4 26B A4B IT scores 72.2 (BB) on agent readiness against Laguna S 2.1's 61 (C), and leads in 3 of 4 scored categories.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-laguna-s-2-1.json, and with the fewest tokens: https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-laguna-s-2-1.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"gemma-4-26b-a4b-it\", \"b\": \"laguna-s-2-1\"}`. From a terminal: `anchor compare gemma-4-26b-a4b-it laguna-s-2-1`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/gemma-4-26b-a4b-it.json and https://www.anchorterminal.com/api/v1/tools/laguna-s-2-1.json\n\n## Other comparisons with Gemma 4 26B A4B IT or Laguna S 2.1\n\n- [Agents-A1-4B vs Gemma 4 26B A4B IT](https://www.anchorterminal.com/compare/agents-a1-4b-vs-gemma-4-26b-a4b-it.md)\n- [Agents-A1-4B vs Laguna S 2.1](https://www.anchorterminal.com/compare/agents-a1-4b-vs-laguna-s-2-1.md)\n- [Apertus v1.5 8B vs Gemma 4 26B A4B IT](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-gemma-4-26b-a4b-it.md)\n- [Apertus v1.5 8B vs Laguna S 2.1](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-laguna-s-2-1.md)\n- [Apodex-1.1-mini vs Gemma 4 26B A4B IT](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-gemma-4-26b-a4b-it.md)\n- [Apodex-1.1-mini vs Laguna S 2.1](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-laguna-s-2-1.md)\n- [BigBang-v1 vs Gemma 4 26B A4B IT](https://www.anchorterminal.com/compare/bigbang-v1-vs-gemma-4-26b-a4b-it.md)\n- [BigBang-v1 vs Laguna S 2.1](https://www.anchorterminal.com/compare/bigbang-v1-vs-laguna-s-2-1.md)\n- [Cosmos-Reason2-2B vs Gemma 4 26B A4B IT](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-gemma-4-26b-a4b-it.md)\n- [Cosmos-Reason2-2B vs Laguna S 2.1](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-laguna-s-2-1.md)\n- [Darwin-36B-Opus vs Gemma 4 26B A4B IT](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-gemma-4-26b-a4b-it.md)\n- [Darwin-36B-Opus vs Laguna S 2.1](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-laguna-s-2-1.md)\n- [DeepSeek-V3.2 vs Gemma 4 26B A4B IT](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-gemma-4-26b-a4b-it.md)\n- [DeepSeek-V3.2 vs Laguna S 2.1](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-laguna-s-2-1.md)\n- [DeepSeek V4.1 Flash vs Gemma 4 26B A4B IT](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-gemma-4-26b-a4b-it.md)\n- [DeepSeek V4.1 Flash vs Laguna S 2.1](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-laguna-s-2-1.md)\n- [DeepSeek-V4-Flash-0731 vs Gemma 4 26B A4B IT](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-gemma-4-26b-a4b-it.md)\n- [DeepSeek-V4-Flash-0731 vs Laguna S 2.1](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-laguna-s-2-1.md)\n- [DiffusionGemma 26B A4B IT vs Gemma 4 26B A4B IT](https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-gemma-4-26b-a4b-it.md)\n- [DiffusionGemma 26B A4B IT vs Laguna S 2.1](https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-laguna-s-2-1.md)\n- [Gemma 4 12B IT vs Gemma 4 26B A4B IT](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-gemma-4-26b-a4b-it.md)\n- [Gemma 4 12B IT vs Laguna S 2.1](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-laguna-s-2-1.md)\n- [Gemma 4 26B A4B IT vs Gemma 4 31B IT](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-gemma-4-31b-it.md)\n- [Gemma 4 26B A4B IT vs Gemma 4 E2B IT](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-gemma-4-e2b-it.md)\n- [Gemma 4 26B A4B IT vs Gemma 4 E4B IT](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-gemma-4-e4b-it.md)\n- [Gemma 4 26B A4B IT vs GLM-4.5-Air](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-glm-4-5-air.md)\n- [Gemma 4 26B A4B IT vs GLM-4.7-Flash](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-glm-4-7-flash.md)\n- [Gemma 4 26B A4B IT vs GLM-5.3](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-glm-5-3.md)\n- [Gemma 4 26B A4B IT vs GLM-5.3-Flash](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-glm-5-3-flash.md)\n- [Gemma 4 26B A4B IT vs gpt-oss-120b](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-gpt-oss-120b.md)\n- [Gemma 4 26B A4B IT vs gpt-oss-20b](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-gpt-oss-20b.md)\n- [Gemma 4 26B A4B IT vs Granite 4.2 3B](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-granite-4-2-3b.md)\n- [Gemma 4 26B A4B IT vs Hy3](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-hy3.md)\n- [Gemma 4 26B A4B IT vs Inkling](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-inkling.md)\n- [Gemma 4 26B A4B IT vs Inkling-Small](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-inkling-small.md)\n- [Gemma 4 26B A4B IT vs InternVL3.5-GPT-OSS-20B-A4B-Preview](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-internvl3-5-gpt-oss-20b-a4b-preview.md)\n- [Gemma 4 26B A4B IT vs Kimi K3](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-kimi-k3.md)\n- [Gemma 4 26B A4B IT vs Kolibri-1](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-kolibri-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 26B A4B IT vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-lfm2-5-2-6b.md)\n- [Gemma 4 26B A4B IT vs Ling-3.0-tiny](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-ling-3-0-tiny.md)\n- [Gemma 4 26B A4B IT vs MiMo-V2.6 Distill Qwen 9B](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-mimo-v2-6-distill-qwen-9b.md)\n- [Gemma 4 26B A4B IT vs MiniCPM-o 4.5](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-minicpm-o-4-5.md)\n- [Gemma 4 26B A4B IT vs MiniCPM5-2B](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-minicpm5-2b.md)\n- [Gemma 4 26B A4B IT vs MiniMax-M2.5](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-minimax-m2-5.md)\n- [Gemma 4 26B A4B IT vs MiniMax-M2.7](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-minimax-m2-7.md)\n- [Gemma 4 26B A4B IT vs Muse Glimmer 30B](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-muse-glimmer-30b.md)\n- [Gemma 4 26B A4B IT vs Nemotron 3.5 Lightning 30B A3B](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-nemotron-3-5-lightning-30b-a3b.md)\n- [Gemma 4 26B A4B IT vs Nemotron 3 Nano 30B A3B](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-nemotron-3-nano-30b-a3b.md)\n- [Gemma 4 26B A4B IT vs Nemotron 3 Nano 4B](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-nemotron-3-nano-4b.md)\n- [Gemma 4 26B A4B IT vs Nemotron 3 Nano Omni 30B A3B Reasoning](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-nemotron-3-nano-omni-30b-a3b-reasoning.md)\n- [Gemma 4 26B A4B IT vs Nemotron 3 Super 120B A12B](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-nemotron-3-super-120b-a12b.md)\n- [Gemma 4 26B A4B IT vs Nemotron 3 Ultra 550B A55B](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-nemotron-3-ultra-550b-a55b.md)\n- [Gemma 4 26B A4B IT vs occamy-1.0](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-occamy-1-0.md)\n- [Gemma 4 26B A4B IT vs Olmo 3 7B Think](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-olmo-3-7b-think.md)\n- [Gemma 4 26B A4B IT vs Ornith-1.5-35B-A3B](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-ornith-1-5-35b-a3b.md)\n- [Gemma 4 26B A4B IT vs Ornith-1.5-397B](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-ornith-1-5-397b.md)\n- [Gemma 4 26B A4B IT vs Ornith-1.5-9B](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-ornith-1-5-9b.md)\n- [Gemma 4 26B A4B IT vs Qwen3-30B-A3B-Instruct-2507](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-qwen3-30b-a3b-instruct-2507.md)\n- [Gemma 4 26B A4B IT vs Qwen3-4B-Instruct-2507](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-qwen3-4b-instruct-2507.md)\n- [Gemma 4 26B A4B IT vs Qwen3.5-0.8B](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-qwen3-5-0-8b.md)\n- [Gemma 4 26B A4B IT vs Qwen3.5-122B-A10B](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-qwen3-5-122b-a10b.md)\n- [Gemma 4 26B A4B IT vs Qwen3.5-2B](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-qwen3-5-2b.md)\n- [Gemma 4 26B A4B IT vs Qwen3.5-397B-A17B](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-qwen3-5-397b-a17b.md)\n- [Gemma 4 26B A4B IT vs Qwen3.5-4B](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-qwen3-5-4b.md)\n- [Gemma 4 26B A4B IT vs Qwen3.5-9B](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-qwen3-5-9b.md)\n- [Gemma 4 26B A4B IT vs Qwen3.6-27B](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-qwen3-6-27b.md)\n- [Gemma 4 26B A4B IT vs Qwen3.6-35B-A3B](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-qwen3-6-35b-a3b.md)\n- [Gemma 4 26B A4B IT vs Qwen3.8-27B](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-qwen3-8-27b.md)\n- [Gemma 4 26B A4B IT vs Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-qwen3-8-flash-next.md)\n- [Gemma 4 26B A4B IT vs Qwen3-Coder-30B-A3B-Instruct](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-qwen3-coder-30b-a3b-instruct.md)\n- [Gemma 4 26B A4B IT vs Qwen3-Coder-Next](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-qwen3-coder-next.md)\n- [Gemma 4 26B A4B IT vs Qwen3-Omni-30B-A3B-Instruct](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-qwen3-omni-30b-a3b-instruct.md)\n- [Gemma 4 26B A4B IT vs Qwen3-VL-2B-Instruct](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-qwen3-vl-2b-instruct.md)\n- [Gemma 4 26B A4B IT vs Qwen3-VL-30B-A3B-Instruct](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-qwen3-vl-30b-a3b-instruct.md)\n- [Gemma 4 26B A4B IT vs Qwen3-VL-32B-Instruct](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-qwen3-vl-32b-instruct.md)\n- [Gemma 4 26B A4B IT vs Qwen3-VL-4B-Instruct](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-qwen3-vl-4b-instruct.md)\n- [Gemma 4 26B A4B IT vs Qwen3-VL-8B-Instruct](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-qwen3-vl-8b-instruct.md)\n- [Gemma 4 26B A4B IT vs Qwopus3.8-27B-Flash-V2](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-qwopus3-8-27b-flash-v2.md)\n- [Gemma 4 26B A4B IT vs SmolLM3-3B](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-smollm3-3b.md)\n- [Gemma 4 26B A4B IT vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-spark-x2-6-4b.md)\n- [Gemma 4 26B A4B IT vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-swift-1-5-qwen3-8-27b.md)\n- [Gemma 4 26B A4B IT vs Swift 1.5 Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-swift-1-5-qwen3-8-flash-next.md)\n- [Gemma 4 26B A4B IT vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-thinkingcap-qwen3-8-27b.md)\n- [Gemma 4 31B IT vs Laguna S 2.1](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-laguna-s-2-1.md)\n- [Gemma 4 E2B IT vs Laguna S 2.1](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-laguna-s-2-1.md)\n- [Gemma 4 E4B IT vs Laguna S 2.1](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-laguna-s-2-1.md)\n- [GLM-4.5-Air vs Laguna S 2.1](https://www.anchorterminal.com/compare/glm-4-5-air-vs-laguna-s-2-1.md)\n- [GLM-4.7-Flash vs Laguna S 2.1](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-laguna-s-2-1.md)\n- [GLM-5.3-Flash vs Laguna S 2.1](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-laguna-s-2-1.md)\n- [GLM-5.3 vs Laguna S 2.1](https://www.anchorterminal.com/compare/glm-5-3-vs-laguna-s-2-1.md)\n- [gpt-oss-120b vs Laguna S 2.1](https://www.anchorterminal.com/compare/gpt-oss-120b-vs-laguna-s-2-1.md)\n- [gpt-oss-20b vs Laguna S 2.1](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-laguna-s-2-1.md)\n- [Granite 4.2 3B vs Laguna S 2.1](https://www.anchorterminal.com/compare/granite-4-2-3b-vs-laguna-s-2-1.md)\n- [Hy3 vs Laguna S 2.1](https://www.anchorterminal.com/compare/hy3-vs-laguna-s-2-1.md)\n- [Inkling-Small vs Laguna S 2.1](https://www.anchorterminal.com/compare/inkling-small-vs-laguna-s-2-1.md)\n- [Inkling vs Laguna S 2.1](https://www.anchorterminal.com/compare/inkling-vs-laguna-s-2-1.md)\n- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs Laguna S 2.1](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-laguna-s-2-1.md)\n- [Kimi K3 vs Laguna S 2.1](https://www.anchorterminal.com/compare/kimi-k3-vs-laguna-s-2-1.md)\n- [Kolibri-1 vs Laguna S 2.1](https://www.anchorterminal.com/compare/kolibri-1-vs-laguna-s-2-1.md)\n- [Laguna M.1 vs Laguna S 2.1](https://www.anchorterminal.com/compare/laguna-m-1-vs-laguna-s-2-1.md)\n- [Laguna S 2.1 vs Laguna XS 2.1](https://www.anchorterminal.com/compare/laguna-s-2-1-vs-laguna-xs-2-1.md)\n- [Laguna S 2.1 vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/laguna-s-2-1-vs-lfm2-5-2-6b.md)\n- [Laguna S 2.1 vs Ling-3.0-tiny](https://www.anchorterminal.com/compare/laguna-s-2-1-vs-ling-3-0-tiny.md)\n- [Laguna S 2.1 vs MiMo-V2.6 Distill Qwen 9B](https://www.anchorterminal.com/compare/laguna-s-2-1-vs-mimo-v2-6-distill-qwen-9b.md)\n- [Laguna S 2.1 vs MiniCPM-o 4.5](https://www.anchorterminal.com/compare/laguna-s-2-1-vs-minicpm-o-4-5.md)\n- [Laguna S 2.1 vs MiniCPM5-2B](https://www.anchorterminal.com/compare/laguna-s-2-1-vs-minicpm5-2b.md)\n- [Laguna S 2.1 vs 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      {
        "name": "Gemma 4 26B A4B IT vs Laguna S 2.1",
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    "description": "Gemma 4 26B A4B IT scores 72.2 (BB) to Laguna S 2.1's 61 (C) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side.",
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    "h1": "Gemma 4 26B A4B IT vs Laguna S 2.1",
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    "path": "/compare/gemma-4-26b-a4b-it-vs-laguna-s-2-1",
    "published": "2026-10-01",
    "section": "tools",
    "title": "Gemma 4 26B A4B IT vs Laguna S 2.1 for AI agents (2026)",
    "toc": null,
    "updated": "2026-10-21",
    "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-laguna-s-2-1"
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