{
  "data": {
    "a": {
      "slug": "exaone-4-5-33b",
      "name": "EXAONE 4.5 33B",
      "vendor": "LG AI Research",
      "vendorUrl": "https://www.lgresearch.ai",
      "kind": "weights",
      "category": "open-weights",
      "summary": "EXAONE 4.5 33B is LG AI Research's open-weight vision-language model, 34B parameters, with tool calling, switchable reasoning and a 256K context, strong on documents and Korean. Its licence allows research and education only, with LG's own GGUF from 18 GB.",
      "url": "https://www.anchorterminal.com/tools/exaone-4-5-33b",
      "markdownUrl": "https://www.anchorterminal.com/tools/exaone-4-5-33b.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/exaone-4-5-33b.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/exaone-4-5-33b.json",
      "dns": "exaone-4-5-33b.grade.anchorterminal.com",
      "repo": "https://huggingface.co/LGAI-EXAONE/EXAONE-4.5-33B",
      "license": "EXAONE AI Model License Agreement 1.2 - NC (research and education only)",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "LGAI-EXAONE/EXAONE-4.5-33B"
        },
        {
          "registry": "huggingface",
          "name": "LGAI-EXAONE/EXAONE-4.5-33B-GGUF"
        },
        {
          "registry": "huggingface",
          "name": "LGAI-EXAONE/EXAONE-4.5-33B-AWQ"
        },
        {
          "registry": "huggingface",
          "name": "LGAI-EXAONE/EXAONE-4.5-33B-FP8"
        }
      ],
      "auth": "none",
      "authNotes": "The weights download from Hugging Face with no account or token. None of the four repositories is gated, though the licence says downloading is agreeing to it (checked 2026-11-10).",
      "pricing": "free",
      "pricingNotes": "Free to download for research and education. The EXAONE AI Model License Agreement 1.2 - NC prohibits commercial use of the model, its derivatives and its output without a separate agreement with LG. Running it yourself takes 20.6 GB at IQ4_XS with its projector, up to 68.7 GB in BF16 (checked 2026-11-10).",
      "priceSummary": "Free",
      "where": "local",
      "x402": {
        "level": "no",
        "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-11-10).",
        "endpoints": []
      },
      "toolCount": null,
      "popularity": {
        "githubStars": null,
        "npmWeekly": null,
        "pypiWeekly": null,
        "asOf": "2026-11-10"
      },
      "docsUrl": "https://github.com/LG-AI-EXAONE/EXAONE-4.5",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "multimodal",
        "vision",
        "documents",
        "korean",
        "reasoning",
        "tool-calling",
        "long-context",
        "non-commercial",
        "safetensors",
        "gguf",
        "awq",
        "fp8",
        "self-hosted"
      ],
      "lastRelease": "2026-07-06",
      "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": 1518,
        "categoryRank": 51,
        "methodology": "0.7",
        "run": "2026-10-01",
        "scores": {
          "interface": 85,
          "reach": 65,
          "risk": 50,
          "upkeep": 50
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "low",
          "date": "2026-11-10"
        },
        "negative": 0,
        "verdict": "A well-documented vision-language model with a full set of builds, tool calling, switchable reasoning, a 256K context and a stated knowledge cut-off. The licence decides whether you can use it: research and education only, output included, and LG can change the terms. Nothing answered on the repository and no safety results.",
        "bestFor": "Researchers and students working on documents, Korean or multimodal agents who don't need commercial rights",
        "strengths": [
          "A complete card, down to a knowledge cut-off",
          "GGUF, AWQ and FP8 builds from LG",
          "Released vLLM, Transformers and llama.cpp run it with no repository code",
          "Tool calling and reasoning control documented with parsers",
          "A 256K context, served on one large GPU"
        ],
        "weaknesses": [
          "Research and education only. No commercial use of the model or its output",
          "LG can amend the licence at any time, and it's revocable",
          "No building models that compete with LG's",
          "No reply from LG on the repository's discussions",
          "Not changed since the summer",
          "No safety results or training data sources on the card"
        ],
        "agentNotes": [
          "Don't use it, or anything it writes, commercially. The licence covers the output too",
          "Serve it with vLLM and `--reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser hermes`",
          "Set `enable_thinking` false for a direct answer. Thinking is on by default",
          "Use temperature 1.0, top_p 0.95 and presence_penalty 1.5 in general, and temperature 0.6 with top_k 20 for documents and Korean",
          "For llama.cpp, load a projector file from LG's GGUF repository with the weights for image input",
          "Its knowledge stops at December 2024"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 0,
        "avgRating": 0,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "low",
            "grade": "C",
            "methodology": "0.7",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 61
          }
        ],
        "editorialScores": {
          "interface": 85,
          "reach": 65,
          "risk": 50,
          "upkeep": 50
        },
        "provenanceScore": 49,
        "checked": {
          "categories": {
            "interface": "2026-11-10",
            "reach": "2026-11-10",
            "risk": "2026-11-10",
            "upkeep": "2026-11-10"
          },
          "last": "2026-11-10",
          "oldest": "2026-11-10",
          "stale": false,
          "staleAfterDays": 90
        }
      },
      "connect": {
        "install": "pip install \"vllm\u003e=0.33.0\" \"transformers\u003e=5.8.0\"\nvllm serve LGAI-EXAONE/EXAONE-4.5-33B --max-model-len 262144 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser hermes",
        "http": "# vLLM serves an OpenAI-compatible API at http://localhost:8000/v1. The card recommends temperature 1.0,\n# top_p 0.95 and presence_penalty 1.5, and enable_thinking false for a direct answer"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/exaone-4-5-33b"
      },
      "weights": {
        "trainedBy": "LG AI Research",
        "params": "34B",
        "context": 262144,
        "goodFor": "Research on document understanding, Korean and agent work with images, where non-commercial terms are acceptable.",
        "runtimes": [
          "llama.cpp",
          "vLLM",
          "Transformers"
        ],
        "builds": [
          {
            "name": "EXAONE-4.5-33B-GGUF IQ4_XS",
            "url": "https://huggingface.co/LGAI-EXAONE/EXAONE-4.5-33B-GGUF/blob/main/EXAONE-4.5-33B-IQ4_XS.gguf",
            "who": "publisher",
            "precision": "iq4_xs",
            "sizeGB": 20.6,
            "memoryGB": 32,
            "format": "gguf",
            "runsOn": [
              "llama.cpp"
            ],
            "note": "18.03 GB with the 2.57 GB F16 projector"
          },
          {
            "name": "EXAONE-4.5-33B-GGUF Q4_K_M",
            "url": "https://huggingface.co/LGAI-EXAONE/EXAONE-4.5-33B-GGUF/blob/main/EXAONE-4.5-33B-Q4_K_M.gguf",
            "who": "publisher",
            "precision": "q4_k_m",
            "sizeGB": 22.62,
            "memoryGB": 32,
            "format": "gguf",
            "runsOn": [
              "llama.cpp"
            ],
            "note": "20.05 GB with the 2.57 GB F16 projector"
          },
          {
            "name": "EXAONE-4.5-33B-AWQ",
            "url": "https://huggingface.co/LGAI-EXAONE/EXAONE-4.5-33B-AWQ",
            "who": "publisher",
            "precision": "awq",
            "sizeGB": 22.75,
            "memoryGB": 32,
            "format": "safetensors",
            "runsOn": [
              "vLLM"
            ],
            "note": "LG's 4-bit AWQ build"
          },
          {
            "name": "EXAONE-4.5-33B-GGUF Q8_0",
            "url": "https://huggingface.co/LGAI-EXAONE/EXAONE-4.5-33B-GGUF/blob/main/EXAONE-4.5-33B-Q8_0.gguf",
            "who": "publisher",
            "precision": "q8_0",
            "sizeGB": 37.71,
            "memoryGB": 48,
            "format": "gguf",
            "runsOn": [
              "llama.cpp"
            ],
            "note": "35.14 GB with the 2.57 GB F16 projector"
          },
          {
            "name": "EXAONE-4.5-33B-FP8",
            "url": "https://huggingface.co/LGAI-EXAONE/EXAONE-4.5-33B-FP8",
            "who": "publisher",
            "precision": "fp8",
            "sizeGB": 41.31,
            "memoryGB": 64,
            "format": "safetensors",
            "runsOn": [
              "vLLM"
            ],
            "note": "LG's FP8 build"
          },
          {
            "name": "EXAONE-4.5-33B (BF16 safetensors)",
            "url": "https://huggingface.co/LGAI-EXAONE/EXAONE-4.5-33B",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 68.7,
            "memoryGB": 96,
            "format": "safetensors",
            "runsOn": [
              "vLLM",
              "Transformers"
            ],
            "note": "Two shards"
          }
        ],
        "run": "llama-server -hf LGAI-EXAONE/EXAONE-4.5-33B-GGUF:Q4_K_M --jinja",
        "runBuild": "EXAONE-4.5-33B-GGUF Q4_K_M",
        "checked": "2026-11-10",
        "minMemoryGB": 32
      },
      "family": {
        "lab": "LG AI Research",
        "family": "EXAONE 4",
        "url": "https://www.anchorterminal.com/open-weights/#lg-ai-research",
        "others": [
          {
            "name": "EXAONE-4.0.1-32B",
            "url": "https://huggingface.co/LGAI-EXAONE/EXAONE-4.0.1-32B"
          },
          {
            "name": "EXAONE-4.0-1.2B",
            "url": "https://huggingface.co/LGAI-EXAONE/EXAONE-4.0-1.2B"
          }
        ]
      },
      "area": "models",
      "provenance": {
        "legalEntity": "LG Management Development Institute Co., Ltd., the licensor named in the model's licence. The card names LG AI Research",
        "domain": "lgresearch.ai",
        "domainRegistered": "",
        "endpointOnVendorDomain": null,
        "terms": "https://huggingface.co/LGAI-EXAONE/EXAONE-4.5-33B/blob/main/LICENSE",
        "privacy": "",
        "statusPage": "",
        "changelog": "https://huggingface.co/LGAI-EXAONE/EXAONE-4.5-33B/commits/main",
        "securityTxt": "none",
        "checked": "2026-11-10",
        "notes": [
          "The card and the Hub organisation name LG AI Research, and the licence names LG Management Development Institute Co., Ltd. as licensor. How the two relate wasn't read first-hand.",
          "www.lgresearch.ai's robots.txt allows reading. Its home page gave little text and its about page a 404, so no people or ownership facts were taken from it.",
          "The domain's registration date wasn't looked up. www.lgresearch.ai has no security.txt (404 on 10 November 2026).",
          "Nothing is hosted under this listing, so the model licence stands in for terms of service and no privacy policy is scored.",
          "The changelog is the commit history, 8 commits from 9 April to 6 July 2026."
        ],
        "score": 49
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/exaone-4-5-33b.json"
    },
    "answer": "EXAONE 4.5 33B and Laguna S 2.1 score within a point of each other on agent readiness, 61 (C) and 61 (C). Laguna S 2.1 leads on reach and risk.",
    "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",
      "dns": "laguna-s-2-1.grade.anchorterminal.com",
      "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 and Featherless AI host it; Poolside's OpenRouter endpoints expired on 31 October 2026 (checked 2026-11-03).",
      "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": 1518,
        "categoryRank": 35,
        "methodology": "0.7",
        "run": "2026-10-01",
        "scores": {
          "interface": 85,
          "reach": 75,
          "risk": 55,
          "upkeep": 41
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "low",
          "date": "2026-11-03"
        },
        "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": "0.7",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 61
          }
        ],
        "editorialScores": {
          "interface": 85,
          "reach": 75,
          "risk": 55,
          "upkeep": 41
        },
        "provenanceScore": 69,
        "checked": {
          "categories": {
            "interface": "2026-11-03",
            "reach": "2026-11-03",
            "risk": "2026-11-03",
            "upkeep": "2026-11-03"
          },
          "last": "2026-11-03",
          "oldest": "2026-11-03",
          "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}'\n# or, with llama.cpp and Unsloth's GGUF, a third party's conversion:\nllama-server -hf unsloth/Laguna-S-2.1-GGUF:UD-Q4_K_M --jinja",
        "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": "unsloth Laguna-S-2.1-GGUF (UD-Q4_K_M)",
            "url": "https://huggingface.co/unsloth/Laguna-S-2.1-GGUF",
            "who": "unsloth",
            "precision": "q4_k_m",
            "sizeGB": 73.12,
            "memoryGB": 96,
            "format": "gguf",
            "runsOn": [
              "llama.cpp"
            ],
            "note": "From Unsloth, a third party's conversion and not Poolside's. 73.12 GB of UD-Q4_K_M weights, from the Hub's model information on 10 November 2026. Recorded after the last grading, so Reach doesn't count it"
          },
          {
            "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": 69
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/laguna-s-2-1.json",
      "live": {
        "slug": "laguna-s-2-1",
        "securityTxt": {
          "url": "https://poolside.ai/.well-known/security.txt",
          "state": "valid",
          "expires": "2028-04-01T12:00:00.000Z",
          "checkedAt": "2026-11-10T16:09:25.614312659Z"
        },
        "domain": {
          "domain": "poolside.ai",
          "registered": "2020-07-07",
          "source": "https://rdap.identitydigital.services/rdap/domain/poolside.ai",
          "checkedAt": "2026-11-08T14:58:52.089559761Z"
        },
        "updatedAt": "2026-11-10T16:09:25.614312659Z"
      }
    },
    "edges": [
      {
        "by": 10,
        "category": "reach",
        "leads": "laguna-s-2-1",
        "name": "Reach",
        "reasons": {
          "a": "Memory 20, the smallest build at 4 bits is LG's IQ4_XS GGUF, 18.03 GB with the 2.57 GB projector, and 20.60 GB × 1.2 = 24.7 GB, within 32 GB. Without the projector, for text only, it would fit 24 GB. Runtimes 15, Released vLLM 0.33.0 has exaone4_5.py, Transformers 5.22.2 has the exaone4_5 model, and llama.cpp b12242 has the architecture, with LG's own GGUF. Three at 5 each. Builds 15, LG publishes GGUF at five quantised precisions, an AWQ build and an FP8 build. Terms 5, Research and educational use only. Download 10, the Hub API reports gated false for all four repositories on 10 November 2026, so the files download with no account and no click-through, though the licence says downloading is acceptance. Hosted 0, Hugging Face maps no inference provider to it, and the card names no API that serves it. 20 + 15 + 15 + 5 + 10 + 0 = 65.",
          "b": "Memory 10 of 30, the smallest 4-bit build is Ollama's 66 GB MLX nvfp4, which needs 96 GB with a fifth added and fits our 128 GB reference machine. Runtimes 25, released llama.cpp, Ollama, vLLM, SGLang and Transformers all run the laguna architecture. Builds 15, Poolside publishes FP8, INT4, NVFP4, MLX and GGUF builds. Terms 10, OpenMDW-1.1 is permissive but not OSI-approved. Download 10, nothing is gated. Hosted 5, Poolside's own API serves it and Featherless AI hosts it, though Poolside's OpenRouter endpoints have expired."
        },
        "scores": {
          "a": 65,
          "b": 75
        }
      },
      {
        "by": 9,
        "category": "upkeep",
        "leads": "exaone-4-5-33b",
        "name": "Documentation and upkeep",
        "reasons": {
          "a": "Model card 20, Uses (the quickstart's question answering, document and agent examples), limitations, the architecture, the parameters, the context, the languages (in the metadata) and a knowledge cut-off of December 2024 are all on the card. Training data 5, A knowledge cut-off and one line on excluding personal, harmful and biased content, with no sources or sizes. Last change 10, the main repository last changed on 6 July 2026 (dependencies moved to official releases), 127 days before this check, and the FP8 repository on 9 August, 93 days. Responsiveness 0, none of the 5 most recent discussions that aren't pull requests (13 April to 4 May 2026) has a reply from an LGAI-EXAONE member, counted on 10 November by author flags and dates with a script, text not read. Version policy 5, EXAONE 4.0 and 4.0.1 stay in their own repositories and 4.5 came out in new ones, with no dated note on the older cards that we read. File health 10, Config, template and card agree on the architecture and the 262,144 tokens, and a July commit moved the dependencies to released versions. 20 + 5 + 10 + 0 + 5 + 10 = 50.",
          "b": "Model card 11 of 20, intended use, architecture, parameters and context are on the card, limitations, languages and a cut-off aren't. Training data 10, the release post describes the data and gives a November 2025 cut-off. Last change 15, 25 August 2026, 70 days back. Responsiveness 0, Poolside answered none of the 34 discussions. Version policy 5, a first release at this size, with GGUF and NVFP4 weights replaced in place without a card note. File health 0, the GGUF card's size and quantisation for Q4_K_M no longer match the file, and several reports wait without a reply."
        },
        "scores": {
          "a": 50,
          "b": 41
        }
      },
      {
        "by": 5,
        "category": "risk",
        "leads": "laguna-s-2-1",
        "name": "Risk",
        "reasons": {
          "a": "File safety 25, safetensors and GGUF only, no .bin, .pt or .pth files and no Python in the repository. Provenance 15, the files sit in the LGAI-EXAONE organisation (LG AI Research on the Hub), which the card's GitHub repository shares a name with and the licence in the repository is LG's (10). Safety evaluation 0, no safety results on the card. Agent guidance 5, General cautions: the limitation section lists inappropriate, biased, incorrect and outdated answers and tells users not to try to induce harmful output. Licence risk 5, 15 less 5 for field-of-use limits beyond the law (no commercial use, no building competing models) and 5 for terms the licensor can change or revoke (LG may amend the agreement at any time, and the licence is revocable). Security programme 0, no way to report a flaw on the card beyond a support contact, and www.lgresearch.ai has no security.txt. 25 + 15 + 0 + 5 + 5 + 0 = 50.",
          "b": "File safety 15 of 25, safetensors and GGUF only, but the repository ships model code behind auto_map and the card's SGLang and TensorRT-LLM commands use trust_remote_code. Provenance 15, the verified poolside organisation, with a checksum on every file. Safety evaluation 0, none published. Agent guidance 5, general cautions only. Licence risk 15, OpenMDW-1.1 has no use policy, threshold or revocation clause. Security programme 5, a signed security.txt and a security contact on the card, with no public advisories found."
        },
        "scores": {
          "a": 50,
          "b": 55
        }
      }
    ],
    "facts": [
      {
        "a": "Open-weight model",
        "b": "Open-weight model",
        "name": "Kind"
      },
      {
        "a": "LG AI Research",
        "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": "EXAONE AI Model License Agreement 1.2 - NC (research and education only)",
        "b": "OpenMDW-1.1",
        "name": "Licence"
      },
      {
        "a": "no",
        "b": "no",
        "name": "Read-only variant documented"
      },
      {
        "a": "no",
        "b": "no",
        "name": "llms.txt"
      },
      {
        "a": "2026-07-06",
        "b": "2026-08-19",
        "name": "Last release"
      },
      {
        "a": "not read yet",
        "b": "2026-07-21",
        "name": "Terms last updated"
      },
      {
        "a": "no document linked",
        "b": "2026-07-21",
        "name": "Privacy policy last updated"
      },
      {
        "a": "not read yet",
        "b": "yes, with an opt-out",
        "name": "Customer content may train models"
      },
      {
        "a": "not read yet",
        "b": "not found in the text",
        "name": "Terms restrict automated access"
      },
      {
        "a": "not read yet",
        "b": "not found in the text",
        "name": "Terms restrict benchmarking"
      },
      {
        "a": "not read yet",
        "b": "yes",
        "name": "Terms or service can change without notice"
      },
      {
        "a": "not read yet",
        "b": "yes",
        "name": "Arbitration or class-action waiver"
      },
      {
        "a": "none",
        "b": "2.3/5 (4)",
        "name": "Agent reviews"
      }
    ],
    "faq": [
      {
        "answer": "EXAONE 4.5 33B and Laguna S 2.1 score within a point of each other on agent readiness, 61 (C) and 61 (C). Laguna S 2.1 leads on reach and risk.",
        "question": "Which is better for AI agents, EXAONE 4.5 33B or Laguna S 2.1?"
      }
    ],
    "goodFor": [
      {
        "aheadOn": [
          "Documentation and upkeep, 50 against 41"
        ],
        "also": null,
        "goodFor": "Researchers and students working on documents, Korean or multimodal agents who don't need commercial rights",
        "slug": "exaone-4-5-33b",
        "watchFor": "Research and education only. No commercial use of the model or its output"
      },
      {
        "aheadOn": [
          "Reach, 75 against 65",
          "Risk, 55 against 50"
        ],
        "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"
      }
    ],
    "history": null,
    "job": {
      "capability": "model.open-weights",
      "name": "Open-weight models you run yourself"
    },
    "others": [
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-exaone-4-5-33b.json",
        "title": "Agents-A1-4B vs EXAONE 4.5 33B",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-exaone-4-5-33b"
      },
      {
        "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-exaone-4-5-33b.json",
        "title": "Apertus v1.5 8B vs EXAONE 4.5 33B",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-exaone-4-5-33b"
      },
      {
        "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-exaone-4-5-33b.json",
        "title": "Apodex-1.1-mini vs EXAONE 4.5 33B",
        "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-exaone-4-5-33b"
      },
      {
        "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/arete-27b-vs-exaone-4-5-33b.json",
        "title": "Arete-27B vs EXAONE 4.5 33B",
        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-exaone-4-5-33b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-laguna-s-2-1.json",
        "title": "Arete-27B vs Laguna S 2.1",
        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-laguna-s-2-1"
      },
      {
        "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-exaone-4-5-33b.json",
        "title": "BigBang-v1 vs EXAONE 4.5 33B",
        "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-exaone-4-5-33b"
      },
      {
        "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/carbon-x-vs-exaone-4-5-33b.json",
        "title": "Carbon-X vs EXAONE 4.5 33B",
        "url": "https://www.anchorterminal.com/compare/carbon-x-vs-exaone-4-5-33b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/carbon-x-vs-laguna-s-2-1.json",
        "title": "Carbon-X vs Laguna S 2.1",
        "url": "https://www.anchorterminal.com/compare/carbon-x-vs-laguna-s-2-1"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cola2-vs-exaone-4-5-33b.json",
        "title": "Cola2 vs EXAONE 4.5 33B",
        "url": "https://www.anchorterminal.com/compare/cola2-vs-exaone-4-5-33b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cola2-vs-laguna-s-2-1.json",
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        "url": "https://www.anchorterminal.com/compare/cola2-vs-laguna-s-2-1"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-exaone-4-5-33b.json",
        "title": "Cosmos-Reason2-2B vs EXAONE 4.5 33B",
        "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-exaone-4-5-33b"
      },
      {
        "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-exaone-4-5-33b.json",
        "title": "Darwin-36B-Opus vs EXAONE 4.5 33B",
        "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-exaone-4-5-33b"
      },
      {
        "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-exaone-4-5-33b.json",
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        "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-exaone-4-5-33b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-laguna-s-2-1.json",
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        "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-laguna-s-2-1"
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      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-exaone-4-5-33b.json",
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        "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-exaone-4-5-33b"
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      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-laguna-s-2-1.json",
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        "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-laguna-s-2-1"
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      {
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        "url": "https://www.anchorterminal.com/compare/devstral-small-2-vs-exaone-4-5-33b"
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        "url": "https://www.anchorterminal.com/compare/devstral-small-2-vs-laguna-s-2-1"
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        "url": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-gemma-4-31b-it"
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      {
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        "title": "Laguna S 2.1 vs NVIDIA Nemotron 3.5 Bolt E4B 4GB",
        "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-nemotron-3-5-bolt-e4b-4gb"
      },
      {
        "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-north-mini-code-1-0.json",
        "title": "Laguna S 2.1 vs North Mini Code 1.0",
        "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-north-mini-code-1-0"
      },
      {
        "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-1-5-122b.json",
        "title": "Laguna S 2.1 vs Ornith-1.5-122B",
        "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-ornith-1-5-122b"
      },
      {
        "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-otel-2-0-llm-31b-it.json",
        "title": "Laguna S 2.1 vs OTel-2.0-LLM-31B-IT",
        "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-otel-2-0-llm-31b-it"
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      {
        "json": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-qwen-agentworld-35b-a3b.json",
        "title": "Laguna S 2.1 vs Qwen-AgentWorld-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-qwen-agentworld-35b-a3b"
      },
      {
        "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"
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      {
        "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"
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      {
        "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-recursive-flint.json",
        "title": "Laguna S 2.1 vs Recursive Flint",
        "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-recursive-flint"
      },
      {
        "json": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-ripple-65b-preview.json",
        "title": "Laguna S 2.1 vs Ripple-65B Preview",
        "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-ripple-65b-preview"
      },
      {
        "json": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-sensenova-loop-9b-preview.json",
        "title": "Laguna S 2.1 vs SenseNova-Loop-9B-Preview",
        "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-sensenova-loop-9b-preview"
      },
      {
        "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-step-3-7-flash.json",
        "title": "Laguna S 2.1 vs Step 3.7 Flash",
        "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-step-3-7-flash"
      },
      {
        "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"
      },
      {
        "json": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-unlimited-ocr-2.json",
        "title": "Laguna S 2.1 vs Baidu Unlimited-OCR-2",
        "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-unlimited-ocr-2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-wevisone-vl-2b.json",
        "title": "Laguna S 2.1 vs WeVisOne-VL-2B",
        "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-wevisone-vl-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-wevisone-vl-4b.json",
        "title": "Laguna S 2.1 vs WeVisOne-VL-4B",
        "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-wevisone-vl-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-zaya1-8b.json",
        "title": "Laguna S 2.1 vs ZAYA1-8B",
        "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-zaya1-8b"
      }
    ],
    "panel": null,
    "scores": [
      {
        "key": "capability",
        "name": "Capability",
        "pending": true,
        "weight": 35
      },
      {
        "by": 10,
        "edge": "laguna-s-2-1",
        "exaone-4-5-33b": 65,
        "key": "reach",
        "laguna-s-2-1": 75,
        "name": "Reach",
        "weight": 25
      },
      {
        "by": 5,
        "edge": "laguna-s-2-1",
        "exaone-4-5-33b": 50,
        "key": "risk",
        "laguna-s-2-1": 55,
        "name": "Risk",
        "weight": 20
      },
      {
        "by": 0,
        "edge": "",
        "exaone-4-5-33b": 85,
        "key": "interface",
        "laguna-s-2-1": 85,
        "name": "Agent interface",
        "weight": 10
      },
      {
        "by": 9,
        "edge": "exaone-4-5-33b",
        "exaone-4-5-33b": 50,
        "key": "upkeep",
        "laguna-s-2-1": 41,
        "name": "Documentation and upkeep",
        "weight": 10
      }
    ],
    "summary": "EXAONE 4.5 33B and Laguna S 2.1 score within a point of each other on agent readiness, 61 (C) and 61 (C). Laguna S 2.1 leads on reach and risk. Both do open-weight models you run yourself.",
    "tasks": null,
    "tutorials": [],
    "verdicts": {
      "exaone-4-5-33b": "A well-documented vision-language model with a full set of builds, tool calling, switchable reasoning, a 256K context and a stated knowledge cut-off. The licence decides whether you can use it: research and education only, output included, and LG can change the terms. Nothing answered on the repository and no safety results.",
      "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/exaone-4-5-33b-vs-laguna-s-2-1",
    "json": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-laguna-s-2-1.json",
    "llms": "https://www.anchorterminal.com/llms.txt",
    "markdown": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-laguna-s-2-1.md",
    "mcp": "https://www.anchorterminal.com/mcp",
    "slim": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-laguna-s-2-1.min.md"
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  "markdown": "EXAONE 4.5 33B and Laguna S 2.1 score within a point of each other on agent readiness, 61 (C) and 61 (C). Laguna S 2.1 leads on reach and risk. Both do open-weight models you run yourself.\n\n- EXAONE 4.5 33B: grade C, 61/100, rank #61 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/exaone-4-5-33b.md · JSON https://www.anchorterminal.com/api/v1/tools/exaone-4-5-33b.json\n- Laguna S 2.1: grade C, 61/100, rank #45 of 160 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 6359 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md\n\n## Which one, for what\n\n### EXAONE 4.5 33B (C)\n\nGood for: Researchers and students working on documents, Korean or multimodal agents who don't need commercial rights\n\nAhead on:\n- Documentation and upkeep, 50 against 41\n\nWatch for: Research and education only. No commercial use of the model or its output\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\nAhead on:\n- Reach, 75 against 65\n- Risk, 55 against 50\n\nWatch for: No reply from Poolside to any of 34 discussions, many of them reports about thinking and tool calls\n\n\n## Why the edge\n\n### Reach: Laguna S 2.1 +10\n\n- EXAONE 4.5 33B (65): Memory 20, the smallest build at 4 bits is LG's IQ4_XS GGUF, 18.03 GB with the 2.57 GB projector, and 20.60 GB × 1.2 = 24.7 GB, within 32 GB. Without the projector, for text only, it would fit 24 GB. Runtimes 15, Released vLLM 0.33.0 has exaone4_5.py, Transformers 5.22.2 has the exaone4_5 model, and llama.cpp b12242 has the architecture, with LG's own GGUF. Three at 5 each. Builds 15, LG publishes GGUF at five quantised precisions, an AWQ build and an FP8 build. Terms 5, Research and educational use only. Download 10, the Hub API reports gated false for all four repositories on 10 November 2026, so the files download with no account and no click-through, though the licence says downloading is acceptance. Hosted 0, Hugging Face maps no inference provider to it, and the card names no API that serves it. 20 + 15 + 15 + 5 + 10 + 0 = 65.\n- Laguna S 2.1 (75): Memory 10 of 30, the smallest 4-bit build is Ollama's 66 GB MLX nvfp4, which needs 96 GB with a fifth added and fits our 128 GB reference machine. Runtimes 25, released llama.cpp, Ollama, vLLM, SGLang and Transformers all run the laguna architecture. Builds 15, Poolside publishes FP8, INT4, NVFP4, MLX and GGUF builds. Terms 10, OpenMDW-1.1 is permissive but not OSI-approved. Download 10, nothing is gated. Hosted 5, Poolside's own API serves it and Featherless AI hosts it, though Poolside's OpenRouter endpoints have expired.\n\n### Documentation and upkeep: EXAONE 4.5 33B +9\n\n- EXAONE 4.5 33B (50): Model card 20, Uses (the quickstart's question answering, document and agent examples), limitations, the architecture, the parameters, the context, the languages (in the metadata) and a knowledge cut-off of December 2024 are all on the card. Training data 5, A knowledge cut-off and one line on excluding personal, harmful and biased content, with no sources or sizes. Last change 10, the main repository last changed on 6 July 2026 (dependencies moved to official releases), 127 days before this check, and the FP8 repository on 9 August, 93 days. Responsiveness 0, none of the 5 most recent discussions that aren't pull requests (13 April to 4 May 2026) has a reply from an LGAI-EXAONE member, counted on 10 November by author flags and dates with a script, text not read. Version policy 5, EXAONE 4.0 and 4.0.1 stay in their own repositories and 4.5 came out in new ones, with no dated note on the older cards that we read. File health 10, Config, template and card agree on the architecture and the 262,144 tokens, and a July commit moved the dependencies to released versions. 20 + 5 + 10 + 0 + 5 + 10 = 50.\n- Laguna S 2.1 (41): Model card 11 of 20, intended use, architecture, parameters and context are on the card, limitations, languages and a cut-off aren't. Training data 10, the release post describes the data and gives a November 2025 cut-off. Last change 15, 25 August 2026, 70 days back. Responsiveness 0, Poolside answered none of the 34 discussions. Version policy 5, a first release at this size, with GGUF and NVFP4 weights replaced in place without a card note. File health 0, the GGUF card's size and quantisation for Q4_K_M no longer match the file, and several reports wait without a reply.\n\n### Risk: Laguna S 2.1 +5\n\n- EXAONE 4.5 33B (50): File safety 25, safetensors and GGUF only, no .bin, .pt or .pth files and no Python in the repository. Provenance 15, the files sit in the LGAI-EXAONE organisation (LG AI Research on the Hub), which the card's GitHub repository shares a name with and the licence in the repository is LG's (10). Safety evaluation 0, no safety results on the card. Agent guidance 5, General cautions: the limitation section lists inappropriate, biased, incorrect and outdated answers and tells users not to try to induce harmful output. Licence risk 5, 15 less 5 for field-of-use limits beyond the law (no commercial use, no building competing models) and 5 for terms the licensor can change or revoke (LG may amend the agreement at any time, and the licence is revocable). Security programme 0, no way to report a flaw on the card beyond a support contact, and www.lgresearch.ai has no security.txt. 25 + 15 + 0 + 5 + 5 + 0 = 50.\n- Laguna S 2.1 (55): File safety 15 of 25, safetensors and GGUF only, but the repository ships model code behind auto_map and the card's SGLang and TensorRT-LLM commands use trust_remote_code. Provenance 15, the verified poolside organisation, with a checksum on every file. Safety evaluation 0, none published. Agent guidance 5, general cautions only. Licence risk 15, OpenMDW-1.1 has no use policy, threshold or revocation clause. Security programme 5, a signed security.txt and a security contact on the card, with no public advisories found.\n\n## Score by category\n\n| Category | Weight | EXAONE 4.5 33B | Laguna S 2.1 | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 65 | 75 | Laguna S 2.1 +10 |\n| Risk | 20% (20 this run) | 50 | 55 | Laguna S 2.1 +5 |\n| Agent interface | 10% (10 this run) | 85 | 85 | even |\n| Documentation and upkeep | 10% (10 this run) | 50 | 41 | EXAONE 4.5 33B +9 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **61 · C** | **61 · C** | |\n\n## Facts side by side\n\n| Fact | EXAONE 4.5 33B | Laguna S 2.1 |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | LG AI Research | 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 | EXAONE AI Model License Agreement 1.2 - NC (research and education only) | OpenMDW-1.1 |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-07-06 | 2026-08-19 |\n| Terms last updated | not read yet | 2026-07-21 |\n| Privacy policy last updated | no document linked | 2026-07-21 |\n| Customer content may train models | not read yet | yes, with an opt-out |\n| Terms restrict automated access | not read yet | not found in the text |\n| Terms restrict benchmarking | not read yet | not found in the text |\n| Terms or service can change without notice | not read yet | yes |\n| Arbitration or class-action waiver | not read yet | yes |\n| Agent reviews | none | 2.3/5 (4) |\n\n## Verdicts\n\n**EXAONE 4.5 33B.** A well-documented vision-language model with a full set of builds, tool calling, switchable reasoning, a 256K context and a stated knowledge cut-off. The licence decides whether you can use it: research and education only, output included, and LG can change the terms. Nothing answered on the repository and no safety results.\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### EXAONE 4.5 33B\n\n1. Don't use it, or anything it writes, commercially. The licence covers the output too\n2. Serve it with vLLM and `--reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser hermes`\n3. Set `enable_thinking` false for a direct answer. Thinking is on by default\n4. Use temperature 1.0, top_p 0.95 and presence_penalty 1.5 in general, and temperature 0.6 with top_k 20 for documents and Korean\n5. For llama.cpp, load a projector file from LG's GGUF repository with the weights for image input\n6. Its knowledge stops at December 2024\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, EXAONE 4.5 33B or Laguna S 2.1?\n\nEXAONE 4.5 33B and Laguna S 2.1 score within a point of each other on agent readiness, 61 (C) and 61 (C). Laguna S 2.1 leads on reach and risk.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-laguna-s-2-1.json, and with the fewest tokens: https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-laguna-s-2-1.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"exaone-4-5-33b\", \"b\": \"laguna-s-2-1\"}`. From a terminal: `anchor compare exaone-4-5-33b laguna-s-2-1`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/exaone-4-5-33b.json and https://www.anchorterminal.com/api/v1/tools/laguna-s-2-1.json\n\n## Other comparisons with EXAONE 4.5 33B or Laguna S 2.1\n\n- [Agents-A1-4B vs EXAONE 4.5 33B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-exaone-4-5-33b.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 EXAONE 4.5 33B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-exaone-4-5-33b.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 EXAONE 4.5 33B](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-exaone-4-5-33b.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- [Arete-27B vs EXAONE 4.5 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