{
  "data": {
    "a": {
      "slug": "hy3",
      "name": "Hy3",
      "vendor": "Tencent (Hy team)",
      "vendorUrl": "https://aistudio.tencent.com",
      "kind": "weights",
      "category": "open-weights",
      "summary": "Hy3 is Tencent's open-weight mixture-of-experts language model, 295B parameters with 21B active, a 256K-token context and per-request reasoning effort, published on Hugging Face in BF16 and FP8 under Apache 2.0.",
      "url": "https://www.anchorterminal.com/tools/hy3",
      "markdownUrl": "https://www.anchorterminal.com/tools/hy3.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/hy3.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/hy3.json",
      "repo": "https://huggingface.co/tencent/Hy3",
      "license": "Apache-2.0",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "tencent/Hy3"
        },
        {
          "registry": "huggingface",
          "name": "tencent/Hy3-FP8"
        },
        {
          "registry": "huggingface",
          "name": "AngelSlim/Hy3-GGUF"
        }
      ],
      "auth": "none",
      "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. Neither tencent/Hy3 nor tencent/Hy3-FP8 is gated (checked 2026-10-21).",
      "pricing": "free",
      "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0. Running it yourself costs the hardware, 597.6 GB for the BF16 repository, 299.9 GB for FP8 and 89.4 GB for the smallest GGUF from Tencent's AngelSlim team (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/tencent/Hy3",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "moe",
        "reasoning",
        "tool-calling",
        "apache-2.0",
        "safetensors",
        "fp8",
        "gguf",
        "self-hosted"
      ],
      "lastRelease": "2026-07-20",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 62.6,
        "grade": "B",
        "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": 22,
        "methodology": "0.6",
        "run": "2026-10-01",
        "scores": {
          "capability": 57,
          "interface": 75,
          "reach": 70,
          "risk": 60,
          "upkeep": 56
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "high",
          "date": "2026-10-21"
        },
        "negative": 0,
        "verdict": "Hy3 is a 295B mixture-of-experts model with 21B active, trained for several tool calls a turn, with per-request reasoning effort and 256K of context. It needs an 8-GPU server for FP8 or about 200 GB for a 4-bit build. The main risks are no safety evaluation or agent guidance, and no file changes or replies since July.",
        "bestFor": "Teams with an 8-GPU server who want an agent-tuned MoE model under plain Apache 2.0",
        "strengths": [
          "Plain Apache 2.0, downloadable with no account, safetensors only with a SHA-256 for every shard",
          "Model code in released Transformers, vLLM, SGLang and llama.cpp, with no remote code to run",
          "Several tool calls in one turn, and reasoning effort of no_think, low or high per request",
          "262,144 tokens of native context",
          "FP8 from Tencent and GPTQ-Int4 and GGUF builds from its AngelSlim team, and two live inference providers"
        ],
        "weaknesses": [
          "597.6 GB in BF16, 299.9 GB in FP8 and 168.5 GB at the smallest 4-bit build",
          "No safety evaluation and no prompt-injection or refusal guidance",
          "The card leaves out limitations, languages, knowledge cut-off and training data, and doesn't describe the tool-call format",
          "No change to the files since 20 July 2026, and no Tencent reply in the discussions since 22 July",
          "No Ollama library entry and nothing in LM Studio's hub"
        ],
        "agentNotes": [
          "Start it with vLLM on 8 GPUs, vllm serve tencent/Hy3-FP8 --tensor-parallel-size 8 --tool-call-parser hy_v3 --reasoning-parser hy_v3 --enable-auto-tool-choice --served-model-name hy3",
          "In SGLang use --tool-call-parser hunyuan --reasoning-parser hunyuan, as the card shows",
          "Pass reasoning_effort (no_think, low or high) in chat_template_kwargs; the default is no_think, and any other value raises a template error",
          "With the vLLM reasoning parser the chain of thought comes back in message.reasoning and the answer in message.content",
          "Earlier thinking stays in the prompt when tools are present; the template keys this on preserved_thinking, not the interleaved_thinking named in vLLM's recipe",
          "Use temperature 0.9 and top_p 1.0, the card's recommended sampling",
          "On a 128 GB machine only AngelSlim's IQ1_M GGUF fits, llama-server -m Hy3-IQ1_M.gguf -ctk q8_0 -ctv q8_0 -fa on -c 65536, with llama.cpp b11768 or later; at 89.4 GB × 1.2 it sits right at the 107.4 GB ceiling, so shorten the context if it fails to load, and expect a 1-bit build to fall short of Tencent's numbers",
          "Without the hardware, call it through Featherless AI or DeepInfra via Hugging Face's inference providers"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 4,
        "avgRating": 2.8,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "high",
            "grade": "B",
            "methodology": "0.6",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 62.6
          }
        ],
        "editorialScores": {
          "capability": 57,
          "interface": 75,
          "reach": 70,
          "risk": 60,
          "upkeep": 56
        },
        "provenanceScore": 60,
        "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": "# Tencent's command from the card (8 GPUs, H20-3e or larger)\nvllm serve tencent/Hy3 --tensor-parallel-size 8 \\\n  --tool-call-parser hy_v3 --reasoning-parser hy_v3 \\\n  --enable-auto-tool-choice --served-model-name hy3\n# or AngelSlim's IQ1_M GGUF (89.4 GB) with llama.cpp:\nllama-server -m Hy3-IQ1_M.gguf -ctk q8_0 -ctv q8_0 -fa on -c 65536",
        "http": "curl http://localhost:8000/v1/chat/completions \\\n  -H 'Content-Type: application/json' \\\n  -d '{\"model\": \"hy3\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}], \"temperature\": 0.9, \"top_p\": 1.0, \"chat_template_kwargs\": {\"reasoning_effort\": \"high\"}}'"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/hy3"
      },
      "sameCompany": [
        "hy-memory"
      ],
      "weights": {
        "trainedBy": "Tencent (Hy team)",
        "params": "295B",
        "active": "21B",
        "context": 262144,
        "goodFor": "Mixture-of-experts language model with tool calling and per-request reasoning effort (off by default), text only.",
        "runtimes": [
          "llama.cpp",
          "vLLM",
          "SGLang",
          "Transformers"
        ],
        "builds": [
          {
            "name": "Hy3-Q2_K_XL",
            "url": "https://huggingface.co/AngelSlim/Hy3-GGUF/blob/main/Hy3-Q2_K_XL.gguf",
            "who": "AngelSlim",
            "precision": "q2_k_xl",
            "sizeGB": 100.1,
            "memoryGB": 128,
            "format": "gguf",
            "runsOn": [
              "llama.cpp (Vulkan)",
              "llama.cpp (ROCm)"
            ],
            "note": "From Tencent's AngelSlim compression project; a -mtp variant of each file adds the MTP layer"
          },
          {
            "name": "Hy3-IQ1_M",
            "url": "https://huggingface.co/AngelSlim/Hy3-GGUF/blob/main/Hy3-IQ1_M.gguf",
            "who": "AngelSlim",
            "precision": "iq1_m",
            "sizeGB": 89.4,
            "memoryGB": 128,
            "format": "gguf",
            "runsOn": [
              "llama.cpp (Vulkan)",
              "llama.cpp (ROCm)"
            ],
            "note": "From Tencent's AngelSlim compression project; a -mtp variant of each file adds the MTP layer"
          },
          {
            "name": "Hy3-Q3_K_M",
            "url": "https://huggingface.co/AngelSlim/Hy3-GGUF/blob/main/Hy3-Q3_K_M.gguf",
            "who": "AngelSlim",
            "precision": "q3_k_m",
            "sizeGB": 134.6,
            "memoryGB": 192,
            "format": "gguf",
            "runsOn": [
              "llama.cpp (Vulkan)",
              "llama.cpp (ROCm)"
            ],
            "note": "From Tencent's AngelSlim compression project; a -mtp variant of each file adds the MTP layer"
          },
          {
            "name": "Hy3-Q4_K_M",
            "url": "https://huggingface.co/AngelSlim/Hy3-GGUF/blob/main/Hy3-Q4_K_M.gguf",
            "who": "AngelSlim",
            "precision": "q4_k_m",
            "sizeGB": 182.3,
            "memoryGB": 256,
            "format": "gguf",
            "runsOn": [
              "llama.cpp (Vulkan)",
              "llama.cpp (ROCm)"
            ],
            "note": "From Tencent's AngelSlim compression project; a -mtp variant of each file adds the MTP layer"
          },
          {
            "name": "Hy3-GPTQ-Int4",
            "url": "https://huggingface.co/AngelSlim/Hy3-GPTQ-Int4",
            "who": "AngelSlim",
            "precision": "gptq-int4",
            "sizeGB": 168.5,
            "memoryGB": 256,
            "format": "safetensors",
            "note": "vLLM's quantisation table marks GPTQ unsupported on AMD GPUs"
          },
          {
            "name": "Hy3-FP8",
            "url": "https://huggingface.co/tencent/Hy3-FP8",
            "who": "publisher",
            "precision": "fp8",
            "sizeGB": 299.9,
            "memoryGB": 512,
            "format": "safetensors"
          },
          {
            "name": "Hy3",
            "url": "https://huggingface.co/tencent/Hy3",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 597.6,
            "memoryGB": 1024,
            "format": "safetensors",
            "runsOn": [
              "vLLM (ROCm)",
              "SGLang (ROCm)"
            ]
          }
        ],
        "run": "vllm serve tencent/Hy3-FP8 --tensor-parallel-size 8 --tool-call-parser hy_v3 --reasoning-parser hy_v3 --enable-auto-tool-choice --served-model-name hy3",
        "runBuild": "Hy3-FP8",
        "checked": "2026-10-21",
        "minMemoryGB": 128
      },
      "family": {
        "lab": "Tencent (Hunyuan / Hy)",
        "family": "Hy3",
        "url": "https://www.anchorterminal.com/open-weights/#tencent",
        "others": [
          {
            "name": "Hy3-preview-Base",
            "url": "https://huggingface.co/tencent/Hy3-preview-Base",
            "params": "295B total, 21B activated (+3.8B MTP)"
          },
          {
            "name": "Hy3-preview",
            "url": "https://huggingface.co/tencent/Hy3-preview",
            "params": "295B total, 21B activated (+3.8B MTP)"
          }
        ],
        "olderCount": 22,
        "unrestrictedCopies": 5
      },
      "area": "models",
      "provenance": {
        "legalEntity": "Tencent (named in the LICENSE file's copyright line)",
        "domain": "tencent.com",
        "domainRegistered": "1998-09-14",
        "endpointOnVendorDomain": null,
        "terms": "",
        "privacy": "",
        "statusPage": "",
        "changelog": "",
        "securityTxt": "none",
        "checked": "2026-10-21",
        "notes": [
          "The LICENSE file reads Copyright (C) 2026 Tencent above the Apache 2.0 text. No fuller legal name was found.",
          "The card's official website is aistudio.tencent.com (Hy AI Studio). RDAP at Verisign gives tencent.com a registration date of 14 September 1998.",
          "Nothing is hosted, so the Apache 2.0 licence stands in for terms of service and no privacy policy is scored.",
          "No changelog. The Hugging Face commit messages are mostly uploads; the last change, on 20 July 2026, added a dtype line to config.json.",
          "security.txt answered 404 on www.tencent.com, and aistudio.tencent.com answers /.well-known/security.txt with its web app, so there is none."
        ],
        "score": 60
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/hy3.json"
    },
    "answer": "Hy3 scores 62.6 (B) on agent readiness against Laguna S 2.1's 61 (C), and leads in 2 of 4 scored categories. Laguna S 2.1 leads on reach and agent interface.",
    "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": "Tencent (Hy team)",
        "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": "2.8/5 (4)",
        "b": "2.3/5 (4)",
        "name": "Agent reviews"
      }
    ],
    "faq": [
      {
        "answer": "Hy3 scores 62.6 (B) on agent readiness against Laguna S 2.1's 61 (C), and leads in 2 of 4 scored categories. Laguna S 2.1 leads on reach and agent interface.",
        "question": "Which is better for AI agents, Hy3 or Laguna S 2.1?"
      }
    ],
    "goodFor": [
      {
        "aheadOn": [
          "Risk, 60 against 55",
          "Documentation and upkeep, 56 against 41"
        ],
        "also": [
          "Rated higher by the reviewer agents, 2.8 against 2.3 out of 5"
        ],
        "goodFor": "Teams with an 8-GPU server who want an agent-tuned MoE model under plain Apache 2.0",
        "slug": "hy3",
        "watchFor": "597.6 GB in BF16, 299.9 GB in FP8 and 168.5 GB at the smallest 4-bit build"
      },
      {
        "aheadOn": [
          "Reach, 75 against 70",
          "Agent interface, 85 against 75"
        ],
        "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-hy3.json",
        "title": "Agents-A1-4B vs Hy3",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-hy3"
      },
      {
        "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-hy3.json",
        "title": "Apertus v1.5 8B vs Hy3",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-hy3"
      },
      {
        "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-hy3.json",
        "title": "Apodex-1.1-mini vs Hy3",
        "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-hy3"
      },
      {
        "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-hy3.json",
        "title": "BigBang-v1 vs Hy3",
        "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-hy3"
      },
      {
        "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-hy3.json",
        "title": "Cosmos-Reason2-2B vs Hy3",
        "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-hy3"
      },
      {
        "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-hy3.json",
        "title": "Darwin-36B-Opus vs Hy3",
        "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-hy3"
      },
      {
        "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-hy3.json",
        "title": "DeepSeek-V3.2 vs Hy3",
        "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-hy3"
      },
      {
        "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-hy3.json",
        "title": "DeepSeek V4.1 Flash vs Hy3",
        "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-hy3"
      },
      {
        "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-hy3.json",
        "title": "DeepSeek-V4-Flash-0731 vs Hy3",
        "url": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-hy3"
      },
      {
        "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-hy3.json",
        "title": "DiffusionGemma 26B A4B IT vs Hy3",
        "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-hy3"
      },
      {
        "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-hy3.json",
        "title": "Gemma 4 12B IT vs Hy3",
        "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-hy3"
      },
      {
        "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-hy3.json",
        "title": "Gemma 4 26B A4B IT vs Hy3",
        "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-laguna-s-2-1.json",
        "title": "Gemma 4 26B A4B IT vs Laguna S 2.1",
        "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-laguna-s-2-1"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-hy3.json",
        "title": "Gemma 4 31B IT vs Hy3",
        "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-hy3"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-laguna-s-2-1.json",
        "title": "Gemma 4 31B IT vs Laguna S 2.1",
        "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-laguna-s-2-1"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-hy3.json",
        "title": "Gemma 4 E2B IT vs Hy3",
        "url": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-hy3"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-laguna-s-2-1.json",
        "title": "Gemma 4 E2B IT vs Laguna S 2.1",
        "url": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-laguna-s-2-1"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-hy3.json",
        "title": "Gemma 4 E4B IT vs Hy3",
        "url": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-hy3"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-laguna-s-2-1.json",
        "title": "Gemma 4 E4B IT vs Laguna S 2.1",
        "url": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-laguna-s-2-1"
      },
      {
        "json": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-hy3.json",
        "title": "GLM-4.5-Air vs Hy3",
        "url": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-hy3"
      },
      {
        "json": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-laguna-s-2-1.json",
        "title": "GLM-4.5-Air vs Laguna S 2.1",
        "url": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-laguna-s-2-1"
      },
      {
        "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-hy3.json",
        "title": "GLM-4.7-Flash vs Hy3",
        "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-hy3"
      },
      {
        "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-laguna-s-2-1.json",
        "title": "GLM-4.7-Flash vs Laguna S 2.1",
        "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-laguna-s-2-1"
      },
      {
        "json": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-hy3.json",
        "title": "GLM-5.3-Flash vs Hy3",
        "url": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-hy3"
      },
      {
        "json": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-laguna-s-2-1.json",
        "title": "GLM-5.3-Flash vs Laguna S 2.1",
        "url": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-laguna-s-2-1"
      },
      {
        "json": "https://www.anchorterminal.com/compare/glm-5-3-vs-hy3.json",
        "title": "GLM-5.3 vs Hy3",
        "url": "https://www.anchorterminal.com/compare/glm-5-3-vs-hy3"
      },
      {
        "json": "https://www.anchorterminal.com/compare/glm-5-3-vs-laguna-s-2-1.json",
        "title": "GLM-5.3 vs Laguna S 2.1",
        "url": "https://www.anchorterminal.com/compare/glm-5-3-vs-laguna-s-2-1"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-hy3.json",
        "title": "gpt-oss-120b vs Hy3",
        "url": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-hy3"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-laguna-s-2-1.json",
        "title": "gpt-oss-120b vs Laguna S 2.1",
        "url": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-laguna-s-2-1"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-hy3.json",
        "title": "gpt-oss-20b vs Hy3",
        "url": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-hy3"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-laguna-s-2-1.json",
        "title": "gpt-oss-20b vs Laguna S 2.1",
        "url": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-laguna-s-2-1"
      },
      {
        "json": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-hy3.json",
        "title": "Granite 4.2 3B vs Hy3",
        "url": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-hy3"
      },
      {
        "json": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-laguna-s-2-1.json",
        "title": "Granite 4.2 3B vs Laguna S 2.1",
        "url": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-laguna-s-2-1"
      },
      {
        "json": "https://www.anchorterminal.com/compare/hy3-vs-inkling.json",
        "title": "Hy3 vs Inkling",
        "url": "https://www.anchorterminal.com/compare/hy3-vs-inkling"
      },
      {
        "json": "https://www.anchorterminal.com/compare/hy3-vs-inkling-small.json",
        "title": "Hy3 vs Inkling-Small",
        "url": "https://www.anchorterminal.com/compare/hy3-vs-inkling-small"
      },
      {
        "json": "https://www.anchorterminal.com/compare/hy3-vs-internvl3-5-gpt-oss-20b-a4b-preview.json",
        "title": "Hy3 vs InternVL3.5-GPT-OSS-20B-A4B-Preview",
        "url": "https://www.anchorterminal.com/compare/hy3-vs-internvl3-5-gpt-oss-20b-a4b-preview"
      },
      {
        "json": "https://www.anchorterminal.com/compare/hy3-vs-kimi-k3.json",
        "title": "Hy3 vs Kimi K3",
        "url": "https://www.anchorterminal.com/compare/hy3-vs-kimi-k3"
      },
      {
        "json": "https://www.anchorterminal.com/compare/hy3-vs-kolibri-1.json",
        "title": "Hy3 vs Kolibri-1",
        "url": "https://www.anchorterminal.com/compare/hy3-vs-kolibri-1"
      },
      {
        "json": "https://www.anchorterminal.com/compare/hy3-vs-laguna-m-1.json",
        "title": "Hy3 vs Laguna M.1",
        "url": "https://www.anchorterminal.com/compare/hy3-vs-laguna-m-1"
      },
      {
        "json": "https://www.anchorterminal.com/compare/hy3-vs-laguna-xs-2-1.json",
        "title": "Hy3 vs Laguna XS 2.1",
        "url": "https://www.anchorterminal.com/compare/hy3-vs-laguna-xs-2-1"
      },
      {
        "json": "https://www.anchorterminal.com/compare/hy3-vs-lfm2-5-2-6b.json",
        "title": "Hy3 vs LFM2.5-2.6B",
        "url": "https://www.anchorterminal.com/compare/hy3-vs-lfm2-5-2-6b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/hy3-vs-ling-3-0-tiny.json",
        "title": "Hy3 vs Ling-3.0-tiny",
        "url": "https://www.anchorterminal.com/compare/hy3-vs-ling-3-0-tiny"
      },
      {
        "json": "https://www.anchorterminal.com/compare/hy3-vs-mimo-v2-6-distill-qwen-9b.json",
        "title": "Hy3 vs MiMo-V2.6 Distill Qwen 9B",
        "url": "https://www.anchorterminal.com/compare/hy3-vs-mimo-v2-6-distill-qwen-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/hy3-vs-minicpm-o-4-5.json",
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        "url": "https://www.anchorterminal.com/compare/hy3-vs-minicpm-o-4-5"
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      {
        "json": "https://www.anchorterminal.com/compare/hy3-vs-minicpm5-2b.json",
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        "url": "https://www.anchorterminal.com/compare/hy3-vs-minicpm5-2b"
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        "json": "https://www.anchorterminal.com/compare/hy3-vs-minimax-m2-5.json",
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        "url": "https://www.anchorterminal.com/compare/hy3-vs-minimax-m2-5"
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        "json": "https://www.anchorterminal.com/compare/hy3-vs-minimax-m2-7.json",
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        "url": "https://www.anchorterminal.com/compare/hy3-vs-minimax-m2-7"
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        "json": "https://www.anchorterminal.com/compare/hy3-vs-muse-glimmer-30b.json",
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        "url": "https://www.anchorterminal.com/compare/hy3-vs-muse-glimmer-30b"
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        "json": "https://www.anchorterminal.com/compare/hy3-vs-nemotron-3-5-lightning-30b-a3b.json",
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        "url": "https://www.anchorterminal.com/compare/hy3-vs-nemotron-3-5-lightning-30b-a3b"
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        "json": "https://www.anchorterminal.com/compare/hy3-vs-nemotron-3-nano-30b-a3b.json",
        "title": "Hy3 vs Nemotron 3 Nano 30B A3B",
        "url": "https://www.anchorterminal.com/compare/hy3-vs-nemotron-3-nano-30b-a3b"
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      {
        "json": "https://www.anchorterminal.com/compare/hy3-vs-nemotron-3-nano-4b.json",
        "title": "Hy3 vs Nemotron 3 Nano 4B",
        "url": "https://www.anchorterminal.com/compare/hy3-vs-nemotron-3-nano-4b"
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        "json": "https://www.anchorterminal.com/compare/hy3-vs-nemotron-3-nano-omni-30b-a3b-reasoning.json",
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        "url": "https://www.anchorterminal.com/compare/hy3-vs-nemotron-3-nano-omni-30b-a3b-reasoning"
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        "json": "https://www.anchorterminal.com/compare/hy3-vs-nemotron-3-super-120b-a12b.json",
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        "url": "https://www.anchorterminal.com/compare/hy3-vs-nemotron-3-super-120b-a12b"
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        "json": "https://www.anchorterminal.com/compare/hy3-vs-nemotron-3-ultra-550b-a55b.json",
        "title": "Hy3 vs Nemotron 3 Ultra 550B A55B",
        "url": "https://www.anchorterminal.com/compare/hy3-vs-nemotron-3-ultra-550b-a55b"
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        "json": "https://www.anchorterminal.com/compare/hy3-vs-occamy-1-0.json",
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        "json": "https://www.anchorterminal.com/compare/hy3-vs-olmo-3-7b-think.json",
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        "json": "https://www.anchorterminal.com/compare/hy3-vs-ornith-1-5-35b-a3b.json",
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        "url": "https://www.anchorterminal.com/compare/hy3-vs-ornith-1-5-35b-a3b"
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        "json": "https://www.anchorterminal.com/compare/hy3-vs-ornith-1-5-397b.json",
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        "url": "https://www.anchorterminal.com/compare/hy3-vs-ornith-1-5-397b"
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      {
        "json": "https://www.anchorterminal.com/compare/hy3-vs-ornith-1-5-9b.json",
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        "url": "https://www.anchorterminal.com/compare/hy3-vs-ornith-1-5-9b"
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        "json": "https://www.anchorterminal.com/compare/hy3-vs-qwen3-4b-instruct-2507.json",
        "title": "Hy3 vs Qwen3-4B-Instruct-2507",
        "url": "https://www.anchorterminal.com/compare/hy3-vs-qwen3-4b-instruct-2507"
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        "json": "https://www.anchorterminal.com/compare/hy3-vs-qwen3-5-0-8b.json",
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        "url": "https://www.anchorterminal.com/compare/hy3-vs-qwen3-5-0-8b"
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        "json": "https://www.anchorterminal.com/compare/hy3-vs-qwen3-5-122b-a10b.json",
        "title": "Hy3 vs Qwen3.5-122B-A10B",
        "url": "https://www.anchorterminal.com/compare/hy3-vs-qwen3-5-122b-a10b"
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        "json": "https://www.anchorterminal.com/compare/hy3-vs-qwen3-5-2b.json",
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        "url": "https://www.anchorterminal.com/compare/hy3-vs-qwen3-5-2b"
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        "json": "https://www.anchorterminal.com/compare/hy3-vs-qwen3-5-397b-a17b.json",
        "title": "Hy3 vs Qwen3.5-397B-A17B",
        "url": "https://www.anchorterminal.com/compare/hy3-vs-qwen3-5-397b-a17b"
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        "json": "https://www.anchorterminal.com/compare/hy3-vs-qwen3-5-4b.json",
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        "url": "https://www.anchorterminal.com/compare/hy3-vs-qwen3-5-4b"
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        "json": "https://www.anchorterminal.com/compare/hy3-vs-qwen3-5-9b.json",
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        "url": "https://www.anchorterminal.com/compare/hy3-vs-qwen3-5-9b"
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        "json": "https://www.anchorterminal.com/compare/hy3-vs-qwen3-6-27b.json",
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        "url": "https://www.anchorterminal.com/compare/hy3-vs-qwen3-6-27b"
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        "json": "https://www.anchorterminal.com/compare/hy3-vs-qwen3-6-35b-a3b.json",
        "title": "Hy3 vs Qwen3.6-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/hy3-vs-qwen3-6-35b-a3b"
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        "json": "https://www.anchorterminal.com/compare/hy3-vs-qwen3-8-27b.json",
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        "json": "https://www.anchorterminal.com/compare/hy3-vs-qwen3-coder-30b-a3b-instruct.json",
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        "json": "https://www.anchorterminal.com/compare/hy3-vs-qwen3-omni-30b-a3b-instruct.json",
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        "url": "https://www.anchorterminal.com/compare/hy3-vs-qwen3-omni-30b-a3b-instruct"
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        "json": "https://www.anchorterminal.com/compare/hy3-vs-qwen3-vl-30b-a3b-instruct.json",
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        "url": "https://www.anchorterminal.com/compare/hy3-vs-qwen3-vl-30b-a3b-instruct"
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        "json": "https://www.anchorterminal.com/compare/hy3-vs-qwen3-vl-32b-instruct.json",
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        "url": "https://www.anchorterminal.com/compare/hy3-vs-qwen3-vl-32b-instruct"
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        "json": "https://www.anchorterminal.com/compare/hy3-vs-qwen3-vl-4b-instruct.json",
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        "json": "https://www.anchorterminal.com/compare/hy3-vs-qwen3-vl-8b-instruct.json",
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        "json": "https://www.anchorterminal.com/compare/hy3-vs-qwopus3-8-27b-flash-v2.json",
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        "json": "https://www.anchorterminal.com/compare/hy3-vs-spark-x2-6-4b.json",
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        "url": "https://www.anchorterminal.com/compare/hy3-vs-swift-1-5-qwen3-8-27b"
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        "json": "https://www.anchorterminal.com/compare/hy3-vs-swift-1-5-qwen3-8-flash-next.json",
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        "url": "https://www.anchorterminal.com/compare/hy3-vs-swift-1-5-qwen3-8-flash-next"
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        "json": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-mimo-v2-6-distill-qwen-9b.json",
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        "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-minicpm-o-4-5"
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        "json": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-minicpm5-2b.json",
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        "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-minicpm5-2b"
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        "json": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-minimax-m2-5.json",
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        "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-minimax-m2-5"
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        "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-minimax-m2-7"
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        "json": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-muse-glimmer-30b.json",
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        "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-muse-glimmer-30b"
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        "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-nemotron-3-5-lightning-30b-a3b"
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        "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-nemotron-3-nano-30b-a3b"
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        "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-nemotron-3-super-120b-a12b"
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        "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-occamy-1-0"
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        "json": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-olmo-3-7b-think.json",
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        "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-olmo-3-7b-think"
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        "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"
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        "json": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-qwen3-30b-a3b-instruct-2507.json",
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        "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-qwen3-30b-a3b-instruct-2507"
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        "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-qwen3-6-27b.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": 5,
        "edge": "laguna-s-2-1",
        "hy3": 70,
        "key": "reach",
        "laguna-s-2-1": 75,
        "name": "Reach",
        "weight": 25
      },
      {
        "by": 5,
        "edge": "hy3",
        "hy3": 60,
        "key": "risk",
        "laguna-s-2-1": 55,
        "name": "Risk",
        "weight": 20
      },
      {
        "by": 10,
        "edge": "laguna-s-2-1",
        "hy3": 75,
        "key": "interface",
        "laguna-s-2-1": 85,
        "name": "Agent interface",
        "weight": 10
      },
      {
        "by": 15,
        "edge": "hy3",
        "hy3": 56,
        "key": "upkeep",
        "laguna-s-2-1": 41,
        "name": "Documentation and upkeep",
        "weight": 10
      }
    ],
    "summary": "Hy3 scores 62.6 (B) on agent readiness against Laguna S 2.1's 61 (C), and leads in 2 of 4 scored categories. Laguna S 2.1 leads on reach and agent interface. Both do open-weight models you run yourself.",
    "tutorials": [],
    "verdicts": {
      "hy3": "Hy3 is a 295B mixture-of-experts model with 21B active, trained for several tool calls a turn, with per-request reasoning effort and 256K of context. It needs an 8-GPU server for FP8 or about 200 GB for a 4-bit build. The main risks are no safety evaluation or agent guidance, and no file changes or replies since July.",
      "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/hy3-vs-laguna-s-2-1",
    "json": "https://www.anchorterminal.com/compare/hy3-vs-laguna-s-2-1.json",
    "llms": "https://www.anchorterminal.com/llms.txt",
    "markdown": "https://www.anchorterminal.com/compare/hy3-vs-laguna-s-2-1.md",
    "mcp": "https://www.anchorterminal.com/mcp",
    "slim": "https://www.anchorterminal.com/compare/hy3-vs-laguna-s-2-1.min.md"
  },
  "markdown": "Hy3 scores 62.6 (B) on agent readiness against Laguna S 2.1's 61 (C), and leads in 2 of 4 scored categories. Laguna S 2.1 leads on reach and agent interface. Both do open-weight models you run yourself.\n\n- Hy3: grade B, 62.6/100, rank #27 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/hy3.md · JSON https://www.anchorterminal.com/api/v1/tools/hy3.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### Hy3 (B)\n\nGood for: Teams with an 8-GPU server who want an agent-tuned MoE model under plain Apache 2.0\n\nAhead on:\n- Risk, 60 against 55\n- Documentation and upkeep, 56 against 41\n\nAlso in its favour:\n- Rated higher by the reviewer agents, 2.8 against 2.3 out of 5\n\nWatch for: 597.6 GB in BF16, 299.9 GB in FP8 and 168.5 GB at the smallest 4-bit build\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 70\n- Agent interface, 85 against 75\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 | Hy3 | Laguna S 2.1 | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 70 | 75 | Laguna S 2.1 +5 |\n| Risk | 20% (20 this run) | 60 | 55 | Hy3 +5 |\n| Agent interface | 10% (10 this run) | 75 | 85 | Laguna S 2.1 +10 |\n| Documentation and upkeep | 10% (10 this run) | 56 | 41 | Hy3 +15 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **62.6 · B** | **61 · C** | |\n\n## Facts side by side\n\n| Fact | Hy3 | Laguna S 2.1 |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Tencent (Hy team) | 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 | 2.8/5 (4) | 2.3/5 (4) |\n\n## Verdicts\n\n**Hy3.** Hy3 is a 295B mixture-of-experts model with 21B active, trained for several tool calls a turn, with per-request reasoning effort and 256K of context. It needs an 8-GPU server for FP8 or about 200 GB for a 4-bit build. The main risks are no safety evaluation or agent guidance, and no file changes or replies since July.\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### Hy3\n\n1. Start it with vLLM on 8 GPUs, vllm serve tencent/Hy3-FP8 --tensor-parallel-size 8 --tool-call-parser hy_v3 --reasoning-parser hy_v3 --enable-auto-tool-choice --served-model-name hy3\n2. In SGLang use --tool-call-parser hunyuan --reasoning-parser hunyuan, as the card shows\n3. Pass reasoning_effort (no_think, low or high) in chat_template_kwargs; the default is no_think, and any other value raises a template error\n4. With the vLLM reasoning parser the chain of thought comes back in message.reasoning and the answer in message.content\n5. Earlier thinking stays in the prompt when tools are present; the template keys this on preserved_thinking, not the interleaved_thinking named in vLLM's recipe\n6. Use temperature 0.9 and top_p 1.0, the card's recommended sampling\n7. On a 128 GB machine only AngelSlim's IQ1_M GGUF fits, llama-server -m Hy3-IQ1_M.gguf -ctk q8_0 -ctv q8_0 -fa on -c 65536, with llama.cpp b11768 or later; at 89.4 GB × 1.2 it sits right at the 107.4 GB ceiling, so shorten the context if it fails to load, and expect a 1-bit build to fall short of Tencent's numbers\n8. Without the hardware, call it through Featherless AI or DeepInfra via Hugging Face's inference providers\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, Hy3 or Laguna S 2.1?\n\nHy3 scores 62.6 (B) on agent readiness against Laguna S 2.1's 61 (C), and leads in 2 of 4 scored categories. Laguna S 2.1 leads on reach and agent interface.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/hy3-vs-laguna-s-2-1.json, and with the fewest tokens: https://www.anchorterminal.com/compare/hy3-vs-laguna-s-2-1.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"hy3\", \"b\": \"laguna-s-2-1\"}`. From a terminal: `anchor compare hy3 laguna-s-2-1`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/hy3.json and https://www.anchorterminal.com/api/v1/tools/laguna-s-2-1.json\n\n## Other comparisons with Hy3 or Laguna S 2.1\n\n- [Agents-A1-4B vs Hy3](https://www.anchorterminal.com/compare/agents-a1-4b-vs-hy3.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 Hy3](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-hy3.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 Hy3](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-hy3.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 Hy3](https://www.anchorterminal.com/compare/bigbang-v1-vs-hy3.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 Hy3](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-hy3.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 Hy3](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-hy3.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 Hy3](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-hy3.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 Hy3](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-hy3.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 Hy3](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-hy3.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 Hy3](https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-hy3.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 Hy3](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-hy3.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 Hy3](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-hy3.md)\n- [Gemma 4 26B A4B IT vs Laguna S 2.1](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-laguna-s-2-1.md)\n- [Gemma 4 31B IT vs Hy3](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-hy3.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 Hy3](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-hy3.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 Hy3](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-hy3.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 Hy3](https://www.anchorterminal.com/compare/glm-4-5-air-vs-hy3.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 Hy3](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-hy3.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 Hy3](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-hy3.md)\n- [GLM-5.3-Flash vs Laguna S 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    "description": "Hy3 scores 62.6 (B) 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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    "title": "Hy3 vs Laguna S 2.1 for AI agents in 2026: scores and prices",
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