{
  "data": {
    "a": {
      "slug": "laguna-xs-2-1",
      "name": "Laguna XS 2.1",
      "vendor": "Poolside",
      "vendorUrl": "https://poolside.ai",
      "kind": "weights",
      "category": "open-weights",
      "summary": "Laguna XS 2.1 is Poolside's open-weight Mixture-of-Experts coding model, 33B total and 3B active parameters, with switchable reasoning and a 262,144-token context, published on Hugging Face under OpenMDW-1.1.",
      "url": "https://www.anchorterminal.com/tools/laguna-xs-2-1",
      "markdownUrl": "https://www.anchorterminal.com/tools/laguna-xs-2-1.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/laguna-xs-2-1.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/laguna-xs-2-1.json",
      "dns": "laguna-xs-2-1.grade.anchorterminal.com",
      "repo": "https://huggingface.co/poolside/Laguna-XS-2.1",
      "license": "OpenMDW-1.1",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "poolside/Laguna-XS-2.1"
        },
        {
          "registry": "huggingface",
          "name": "poolside/Laguna-XS-2.1-FP8"
        },
        {
          "registry": "huggingface",
          "name": "poolside/Laguna-XS-2.1-INT4"
        },
        {
          "registry": "huggingface",
          "name": "poolside/Laguna-XS-2.1-NVFP4"
        },
        {
          "registry": "huggingface",
          "name": "poolside/Laguna-XS-2.1-NVFP4-mlx"
        },
        {
          "registry": "huggingface",
          "name": "poolside/Laguna-XS-2.1-GGUF"
        },
        {
          "registry": "ollama",
          "name": "laguna-xs-2.1"
        }
      ],
      "auth": "none",
      "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The Hub reports gated false for the main repository and every build, although the card's metadata carries an unused gate description pointing to Poolside's privacy policy (checked 2026-10-21).",
      "pricing": "free",
      "pricingNotes": "Free to download and use, commercial use included, under OpenMDW-1.1. Running it yourself costs the hardware, 66.9 GB for the BF16 files and 20.3 GB for Poolside's Q4_K_M GGUF, which is also Ollama's default build. Poolside's OpenRouter endpoints expired on 31 October 2026 and no other host was found (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-XS-2.1",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "moe",
        "coding",
        "reasoning",
        "tool-calling",
        "openmdw-1.1",
        "safetensors",
        "gguf",
        "fp8",
        "nvfp4",
        "self-hosted"
      ],
      "lastRelease": "2026-08-19",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 61,
        "grade": "C",
        "agentReady": false,
        "rank": 0,
        "ranked": false,
        "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools",
        "rankOf": 1518,
        "categoryRank": 36,
        "methodology": "0.7",
        "run": "2026-10-01",
        "scores": {
          "interface": 85,
          "reach": 80,
          "risk": 55,
          "upkeep": 46
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "low",
          "date": "2026-11-03"
        },
        "negative": 0,
        "verdict": "A small coding and agent cousin with 3B active parameters, so it's quick on modest hardware. Poolside's 4-bit GGUF, also Ollama's default, needs about 24 GB, and every major runtime runs it. The main risks are that nobody at Poolside answers its discussions, there are no safety results, and the SGLang route runs repository code.",
        "bestFor": "Agents that want a fast local coding model on a 32 GB GPU or Mac, with tool calling and a permissive licence.",
        "strengths": [
          "Released support in llama.cpp, Ollama, vLLM, SGLang, Transformers and mlx-lm",
          "Poolside's own Q4_K_M, FP8, INT4, NVFP4 and MLX builds, the Q4_K_M at 20.3 GB",
          "Permissive OpenMDW-1.1 licence with no gate, account or use policy",
          "Tool calling and the poolside_v1 parser documented on the card, several calls a turn",
          "Thinking on or off per request, with the reasoning returned in reasoning_content"
        ],
        "weaknesses": [
          "No reply from Poolside to any discussion, including reports about the tokenizer and remote code",
          "No safety results and only general cautions for agent use",
          "The SGLang and TensorRT-LLM commands run the repository's Python with trust_remote_code",
          "The template defaults thinking off while generation_config.json turns it on",
          "No structured output documentation, and no limitations, languages or cut-off on the card",
          "No hosted endpoint since Poolside's OpenRouter endpoints expired on 31 October 2026, so an agent without the hardware can't call it"
        ],
        "agentNotes": [
          "Quickest start is `ollama run laguna-xs-2.1`, 20 GB of files, the same Q4_K_M as Poolside's GGUF",
          "On our reference machine use `llama-server -hf poolside/Laguna-XS-2.1-GGUF:Q4_K_M --jinja -c 131072`, from a llama.cpp release with the laguna architecture (b11772 has it), or BF16 with vLLM (66.9 GB)",
          "With vLLM use `--tool-call-parser poolside_v1 --reasoning-parser poolside_v1 --enable-auto-tool-choice`, on a build with vllm-project/vllm#47311, or `--tool-call-parser glm47` on older builds",
          "Thinking is off in the template unless you pass `enable_thinking` true or start the server with `--default-chat-template-kwargs '{\"enable_thinking\": true}'`, which Poolside recommends for agentic coding",
          "Pass earlier reasoning back as `reasoning_content` in the history, since the card says reasoning may stop in later steps if it's dropped",
          "Sampling per the card is temperature 1.0 and top_k 20",
          "The card warns that Ollama on Apple Silicon may return empty output until llama.cpp#25389 ships",
          "Validate JSON output yourself or use the runtime's guided decoding, since structured output isn't documented"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 4,
        "avgRating": 2.8,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "low",
            "grade": "C",
            "methodology": "0.7",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 61
          }
        ],
        "editorialScores": {
          "interface": 85,
          "reach": 80,
          "risk": 55,
          "upkeep": 46
        },
        "provenanceScore": 69,
        "checked": {
          "categories": {
            "interface": "2026-11-03",
            "reach": "2026-11-03",
            "risk": "2026-11-03",
            "upkeep": "2026-11-03"
          },
          "last": "2026-11-03",
          "lastMoved": "2026-11-03",
          "oldest": "2026-11-03",
          "stale": false,
          "staleAfterDays": 90
        }
      },
      "connect": {
        "install": "ollama run laguna-xs-2.1\n# or, with vLLM 0.21.0 or later (a build with vllm-project/vllm#47311):\nvllm serve poolside/Laguna-XS-2.1 --tool-call-parser poolside_v1 --reasoning-parser poolside_v1 \\\n  --enable-auto-tool-choice --default-chat-template-kwargs '{\"enable_thinking\": true}'",
        "http": "curl http://localhost:11434/api/chat \\\n  -d '{\"model\": \"laguna-xs-2.1\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/laguna-xs-2-1"
      },
      "sameCompany": [
        "laguna-m-1",
        "laguna-s-2-1"
      ],
      "weights": {
        "trainedBy": "Poolside",
        "params": "33.4B total, 3B active",
        "context": 262144,
        "goodFor": "Agentic coding and long-horizon terminal work on one GPU or a 32 GB machine, with tool calling and reasoning that turns on or off per request.",
        "runtimes": [
          "llama.cpp",
          "Ollama",
          "vLLM",
          "SGLang",
          "LM Studio",
          "Transformers"
        ],
        "builds": [
          {
            "name": "Laguna-XS-2.1-INT4",
            "url": "https://huggingface.co/poolside/Laguna-XS-2.1-INT4",
            "who": "publisher",
            "precision": "int4 (w4a16)",
            "sizeGB": 23.9,
            "memoryGB": 32,
            "format": "safetensors"
          },
          {
            "name": "Laguna-XS-2.1-NVFP4",
            "url": "https://huggingface.co/poolside/Laguna-XS-2.1-NVFP4",
            "who": "publisher",
            "precision": "nvfp4",
            "sizeGB": 21.6,
            "memoryGB": 32,
            "format": "safetensors",
            "note": "For NVIDIA Blackwell GPUs"
          },
          {
            "name": "Laguna-XS-2.1-NVFP4-mlx",
            "url": "https://huggingface.co/poolside/Laguna-XS-2.1-NVFP4-mlx",
            "who": "publisher",
            "precision": "nvfp4",
            "sizeGB": 21.6,
            "memoryGB": 32,
            "format": "mlx",
            "runsOn": [
              "MLX"
            ]
          },
          {
            "name": "Laguna-XS-2.1-GGUF Q4_K_M",
            "url": "https://huggingface.co/poolside/Laguna-XS-2.1-GGUF",
            "who": "publisher",
            "precision": "q4_k_m",
            "sizeGB": 20.3,
            "memoryGB": 32,
            "format": "gguf",
            "runsOn": [
              "llama.cpp",
              "Ollama"
            ],
            "note": "Laguna-XS-2.1-Q4_K_M.gguf, 20,274,300,032 bytes. The repository also holds a BF16 GGUF of 66.9 GB (96 GB of memory)"
          },
          {
            "name": "ollama laguna-xs-2.1:latest",
            "url": "https://ollama.com/library/laguna-xs-2.1:latest",
            "who": "ollama",
            "precision": "q4_k_m",
            "sizeGB": 20.3,
            "memoryGB": 32,
            "format": "ollama",
            "runsOn": [
              "Ollama"
            ],
            "note": "Ollama shows 20 GB. Its model blob (sha256 1ac7079101fc) is Poolside's Q4_K_M GGUF, so the exact size is Poolside's. The library also has q8_0 (36 GB), bf16 (67 GB) and MLX tags (nvfp4 shown as 20 GB, exact size unpublished, mxfp8 40 GB, mlx-bf16 68 GB). The card warns that ollama run may return empty output on Apple Silicon until llama.cpp#25389 ships"
          },
          {
            "name": "Laguna-XS-2.1-FP8",
            "url": "https://huggingface.co/poolside/Laguna-XS-2.1-FP8",
            "who": "publisher",
            "precision": "fp8",
            "sizeGB": 35.3,
            "memoryGB": 48,
            "format": "safetensors"
          },
          {
            "name": "Laguna-XS-2.1",
            "url": "https://huggingface.co/poolside/Laguna-XS-2.1",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 66.9,
            "memoryGB": 96,
            "format": "safetensors",
            "runsOn": [
              "vLLM",
              "SGLang",
              "Transformers"
            ]
          }
        ],
        "run": "llama-server -hf poolside/Laguna-XS-2.1-GGUF:Q4_K_M --jinja -c 131072 --port 8000",
        "runBuild": "Laguna-XS-2.1-GGUF Q4_K_M",
        "checked": "2026-10-21",
        "minMemoryGB": 32
      },
      "family": {
        "lab": "Poolside",
        "family": "Laguna 2 (XS.2, XS 2.1, S 2.1)",
        "url": "https://www.anchorterminal.com/open-weights/#poolside",
        "others": [
          {
            "name": "Laguna-S-2.1",
            "url": "https://huggingface.co/poolside/Laguna-S-2.1",
            "params": "117.56B (Hub safetensors count; the name gives only the size class S)",
            "listing": "https://www.anchorterminal.com/tools/laguna-s-2-1"
          },
          {
            "name": "Laguna-S-2.1-base",
            "url": "https://huggingface.co/poolside/Laguna-S-2.1-base",
            "params": "not stated (no safetensors count on the Hub; the instruct model is 117.56B)"
          },
          {
            "name": "Laguna-XS.2",
            "url": "https://huggingface.co/poolside/Laguna-XS.2",
            "params": "33.44B (Hub safetensors count; the name gives only the size class XS)"
          }
        ],
        "unrestrictedCopies": 1
      },
      "area": "models",
      "provenance": {
        "legalEntity": "Poolside, Inc. (named in Poolside's Terms of Use)",
        "domain": "poolside.ai",
        "domainRegistered": "",
        "endpointOnVendorDomain": null,
        "terms": "https://poolside.ai/legal/eula",
        "privacy": "https://poolside.ai/legal/privacy",
        "statusPage": "",
        "changelog": "https://docs.poolside.ai/release-notes/models",
        "securityTxt": "valid",
        "checked": "2026-10-21",
        "notes": [
          "The Hugging Face organisation poolside is verified, and Poolside's blog post for this release links its collection there.",
          "Nothing is hosted for this listing, so the OpenMDW-1.1 licence stands in for terms of service and no privacy policy is scored.",
          "Poolside's docs keep dated model release notes. The June 2026 entry calls XS 2.1 an incremental update to Laguna XS.2.",
          "security.txt is PGP-signed, names a security contact at poolside.ai and expires on 1 April 2028. The domain's registration date wasn't checked."
        ],
        "score": 69
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/laguna-xs-2-1.json",
      "live": {
        "slug": "laguna-xs-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"
      }
    },
    "answer": "Laguna XS 2.1 scores 61 (C) on agent readiness against SenseNova-Loop-9B-Preview's 46.8 (D), and leads in every scored category.",
    "b": {
      "slug": "sensenova-loop-9b-preview",
      "name": "SenseNova-Loop-9B-Preview",
      "vendor": "SenseTime (SenseNova)",
      "vendorUrl": "https://www.sensetime.com/en",
      "kind": "weights",
      "category": "open-weights",
      "summary": "SenseNova-Loop-9B-Preview is SenseTime's open-weight multimodal model of about 9B parameters. It runs 24 of its 32 decoder layers twice, with text and images in, tool calling, a thinking switch and a 256K context. Apache 2.0. No released runtime loads it.",
      "url": "https://www.anchorterminal.com/tools/sensenova-loop-9b-preview",
      "markdownUrl": "https://www.anchorterminal.com/tools/sensenova-loop-9b-preview.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/sensenova-loop-9b-preview.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/sensenova-loop-9b-preview.json",
      "dns": "sensenova-loop-9b-preview.grade.anchorterminal.com",
      "repo": "https://huggingface.co/sensenova/SenseNova-Loop-9B-Preview",
      "license": "Apache-2.0",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "sensenova/SenseNova-Loop-9B-Preview"
        }
      ],
      "auth": "none",
      "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The repository is not gated (checked 2026-11-10).",
      "pricing": "free",
      "pricingNotes": "Free to download and use under Apache 2.0. Running it yourself costs the hardware: the files are 38.1 GB because most weights are stored in 32-bit floats, and the card's example loads them as 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/OpenSenseNova/SenseNova-Loop",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "multimodal",
        "vision",
        "looped-transformer",
        "tool-calling",
        "reasoning",
        "long-context",
        "preview",
        "apache-2.0",
        "safetensors",
        "custom-code",
        "self-hosted"
      ],
      "lastRelease": "2026-11-02",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 46.8,
        "grade": "D",
        "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": 110,
        "methodology": "0.7",
        "run": "2026-10-01",
        "scores": {
          "interface": 60,
          "reach": 40,
          "risk": 50,
          "upkeep": 44
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "low",
          "date": "2026-11-10"
        },
        "negative": 0,
        "verdict": "An unusual architecture under Apache 2.0, with a 256K training context, a thinking switch and tool calling in the template. It's a preview in practice too: no released runtime runs it, loading it runs the repository's code, the files are twice the size the card implies, and nobody from SenseNova has answered a question yet.",
        "bestFor": "Researchers who want to study or fine-tune a looped transformer, more than agents that need a model to call today",
        "strengths": [
          "Apache 2.0, ungated",
          "A 256K training context, stated on the card",
          "Thinking can be switched per request",
          "The architecture and parameter counts are documented",
          "Changed in the last ten days"
        ],
        "weaknesses": [
          "No released runtime runs it. vLLM and SGLang need unmerged pull requests",
          "Loading it runs Python from the repository",
          "The files are 38.1 GB, mostly F32, though the card says BF16",
          "No quantised build",
          "No tool-calling documentation beyond the template",
          "No limitations, safety results or data sources, and the technical report isn't out",
          "None of 5 questions answered"
        ],
        "agentNotes": [
          "Read modeling_sensenova_loop.py before you load it. trust_remote_code runs it",
          "Pin transformers 5.18.0, the version the card checked",
          "Load with dtype=torch.bfloat16 as the card does, or the 38 GB of F32 weights stay at full size",
          "Pass enable_thinking=False to apply_chat_template for a direct answer",
          "Tool calls come back as tool_call blocks with function and parameter tags. Parse them yourself, since no runtime ships a parser",
          "Wait for the vLLM or SGLang pull request to land in a release before putting it behind an API"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 0,
        "avgRating": 0,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "low",
            "grade": "D",
            "methodology": "0.7",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 46.8
          }
        ],
        "editorialScores": {
          "interface": 60,
          "reach": 40,
          "risk": 50,
          "upkeep": 44
        },
        "provenanceScore": 51,
        "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 \"transformers==5.18.0\" accelerate safetensors\n# then AutoModelForCausalLM.from_pretrained(\"sensenova/SenseNova-Loop-9B-Preview\", trust_remote_code=True, dtype=torch.bfloat16)\n# trust_remote_code runs modeling_sensenova_loop.py from the repository. Read it first",
        "http": "# No released server runs it. The card's vLLM and SGLang commands build from open pull requests,\n# and its LightLLM example serves an OpenAI-compatible API at http://localhost:8000/v1 on four GPUs"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/sensenova-loop-9b-preview"
      },
      "weights": {
        "trainedBy": "SenseTime (SenseNova)",
        "params": "9B",
        "context": 262144,
        "goodFor": "Trying a looped transformer for long-context, tool-using work, if you can run the repository's own code on a large GPU.",
        "runtimes": [],
        "builds": [
          {
            "name": "SenseNova-Loop-9B-Preview (safetensors, mostly F32)",
            "url": "https://huggingface.co/sensenova/SenseNova-Loop-9B-Preview/tree/main",
            "who": "publisher",
            "precision": "f32",
            "sizeGB": 38.14,
            "memoryGB": 48,
            "format": "safetensors",
            "note": "Eight shards and the MTP head. Stored mostly as 32-bit floats; the card's example loads it as BF16, about half the size in memory"
          }
        ],
        "run": "python -c \"from transformers import AutoModelForCausalLM; AutoModelForCausalLM.from_pretrained('sensenova/SenseNova-Loop-9B-Preview', trust_remote_code=True)\"",
        "runBuild": "SenseNova-Loop-9B-Preview (safetensors, mostly F32)",
        "checked": "2026-11-10",
        "minMemoryGB": 48
      },
      "family": {
        "lab": "SenseTime (SenseNova)",
        "family": "SenseNova-Loop",
        "url": "https://www.anchorterminal.com/open-weights/#sensenova"
      },
      "area": "models",
      "provenance": {
        "legalEntity": "SenseTime, listed on the Stock Exchange of Hong Kong (00020) per its investor page. SenseNova is its foundation model series, per its site",
        "domain": "sensetime.com",
        "domainRegistered": "",
        "endpointOnVendorDomain": null,
        "terms": "https://github.com/OpenSenseNova/SenseNova-Loop/blob/main/LICENSE",
        "privacy": "",
        "statusPage": "",
        "changelog": "https://huggingface.co/sensenova/SenseNova-Loop-9B-Preview/commits/main",
        "securityTxt": "none",
        "checked": "2026-11-10",
        "notes": [
          "The card names SenseNova only. SenseTime's site presents SenseNova as its foundation model series, and that is the tie between the two; the site wasn't read for a link to this repository.",
          "The domain's registration date wasn't looked up.",
          "Nothing is hosted under this listing, so Apache 2.0 stands in for terms of service and no privacy policy is scored.",
          "www.sensetime.com has no security.txt (404 on 10 November 2026). Its about page gives a security contact.",
          "The changelog is the commit history, 39 commits from 28 October to 2 November 2026."
        ],
        "score": 51
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/sensenova-loop-9b-preview.json"
    },
    "edges": [
      {
        "by": 40,
        "category": "reach",
        "leads": "laguna-xs-2-1",
        "name": "Reach",
        "reasons": {
          "a": "Memory 20 of 30, the smallest 4-bit build with an exact size is Poolside's Q4_K_M GGUF at 20.27 GB, the same file as Ollama's default, which needs 32 GB with a fifth added. 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 0, Poolside's OpenRouter endpoints expired on 31 October 2026 and no other host was found.",
          "b": "Memory 15, the only build is the safetensors set, 38.14 GB with its MTP head, since the Hub counts 9.41B of its parameters in F32. Runtimes 0, none of the six runs it in a released version. Builds 0, SenseNova publishes one set of weights and no quantised build. Terms 15, Apache 2.0 in the card's metadata and on the card, which links the LICENSE in the GitHub repository, and nothing added. Download 10, the Hub API reports gated false on 10 November 2026, so the files download with no account and nothing to accept. Hosted 0, Hugging Face maps no inference provider to it, and the card names no API that serves it. 15 + 0 + 0 + 15 + 10 + 0 = 40."
        },
        "scores": {
          "a": 80,
          "b": 40
        }
      },
      {
        "by": 25,
        "category": "interface",
        "leads": "laguna-xs-2-1",
        "name": "Agent interface",
        "reasons": {
          "a": "Chat template 20, a Jinja file in the repository. Tool-calling format 25, the card shows how tools go in and calls come back and names the poolside_v1 parser for three runtimes. Trained tool calling 20, the template renders several calls a turn. Structured output 0, not documented. Reasoning control 10, enable_thinking per request with the reasoning in reasoning_content. Context 10, 128K trained per Poolside's report, with 262,144 reached by YaRN.",
          "b": "Chat template 20, chat_template.jinja in the repository (7,756 characters). Tool-calling format 10, only the template says it: a tools block, and calls written as tool_call blocks with function and parameter tags. Trained tool calling 10, the card says the training recipes include tool trajectories and that the model is for agentic workflows. Structured output 0, Nothing on the card about JSON or schema-constrained output. Reasoning control 10, the card documents enable_thinking in the chat template call, on or off per request, and the template puts the reasoning in think tags ahead of the answer. Context 10, 262,144 tokens in training, per the card's specification table, and config.json gives the language model 262,144 positions. 20 + 10 + 10 + 0 + 10 + 10 = 60."
        },
        "scores": {
          "a": 85,
          "b": 60
        }
      },
      {
        "by": 5,
        "category": "risk",
        "leads": "laguna-xs-2-1",
        "name": "Risk",
        "reasons": {
          "a": "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 that Poolside's blog links, 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.",
          "b": "File safety 15, safetensors only, no .bin, .pt or .pth files (25). Provenance 15, the files sit in the sensenova organisation, which the GitHub repository for the model links to (10), and the Hub gives a SHA-256 for the large files (5). Safety evaluation 0, no safety results on the card. Agent guidance 0, the card has no limitations or safety section, and nothing on instructions inside tool results or images. Licence risk 15, Apache 2.0 with no use policy, no threshold and no right to change the terms. Security programme 5, SenseTime's about page gives a security contact (5). 15 + 15 + 0 + 0 + 15 + 5 = 50."
        },
        "scores": {
          "a": 55,
          "b": 50
        }
      }
    ],
    "facts": [
      {
        "a": "Open-weight model",
        "b": "Open-weight model",
        "name": "Kind"
      },
      {
        "a": "Poolside",
        "b": "SenseTime (SenseNova)",
        "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": "OpenMDW-1.1",
        "b": "Apache-2.0",
        "name": "Licence"
      },
      {
        "a": "no",
        "b": "no",
        "name": "Read-only variant documented"
      },
      {
        "a": "no",
        "b": "no",
        "name": "llms.txt"
      },
      {
        "a": "2026-08-19",
        "b": "2026-11-02",
        "name": "Last release"
      },
      {
        "a": "2026-07-21",
        "b": "not read yet",
        "name": "Terms last updated"
      },
      {
        "a": "2026-07-21",
        "b": "no document linked",
        "name": "Privacy policy last updated"
      },
      {
        "a": "yes, with an opt-out",
        "b": "not read yet",
        "name": "Customer content may train models"
      },
      {
        "a": "not found in the text",
        "b": "not read yet",
        "name": "Terms restrict automated access"
      },
      {
        "a": "not found in the text",
        "b": "not read yet",
        "name": "Terms restrict benchmarking"
      },
      {
        "a": "yes",
        "b": "not read yet",
        "name": "Terms or service can change without notice"
      },
      {
        "a": "yes",
        "b": "not read yet",
        "name": "Arbitration or class-action waiver"
      },
      {
        "a": "2.8/5 (4)",
        "b": "none",
        "name": "Agent reviews"
      }
    ],
    "faq": [
      {
        "answer": "Laguna XS 2.1 scores 61 (C) on agent readiness against SenseNova-Loop-9B-Preview's 46.8 (D), and leads in every scored category.",
        "question": "Which is better for AI agents, Laguna XS 2.1 or SenseNova-Loop-9B-Preview?"
      }
    ],
    "goodFor": [
      {
        "aheadOn": [
          "Reach, 80 against 40",
          "Risk, 55 against 50",
          "Agent interface, 85 against 60"
        ],
        "also": null,
        "goodFor": "Agents that want a fast local coding model on a 32 GB GPU or Mac, with tool calling and a permissive licence.",
        "slug": "laguna-xs-2-1",
        "watchFor": "No reply from Poolside to any discussion, including reports about the tokenizer and remote code"
      },
      {
        "aheadOn": null,
        "also": null,
        "goodFor": "Researchers who want to study or fine-tune a looped transformer, more than agents that need a model to call today",
        "slug": "sensenova-loop-9b-preview",
        "watchFor": "No released runtime runs it. vLLM and SGLang need unmerged pull requests"
      }
    ],
    "history": {
      "laguna-xs-2-1": [
        {
          "causes": [
            "product"
          ],
          "date": "2026-11-03",
          "delta": 0,
          "from": 61,
          "fromGrade": "C",
          "to": 61,
          "toGrade": "C"
        }
      ],
      "sensenova-loop-9b-preview": null
    },
    "job": {
      "capability": "model.open-weights",
      "name": "Open-weight models you run yourself"
    },
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    "panel": null,
    "scores": [
      {
        "key": "capability",
        "name": "Capability",
        "pending": true,
        "weight": 35
      },
      {
        "by": 40,
        "edge": "laguna-xs-2-1",
        "key": "reach",
        "laguna-xs-2-1": 80,
        "name": "Reach",
        "sensenova-loop-9b-preview": 40,
        "weight": 25
      },
      {
        "by": 5,
        "edge": "laguna-xs-2-1",
        "key": "risk",
        "laguna-xs-2-1": 55,
        "name": "Risk",
        "sensenova-loop-9b-preview": 50,
        "weight": 20
      },
      {
        "by": 25,
        "edge": "laguna-xs-2-1",
        "key": "interface",
        "laguna-xs-2-1": 85,
        "name": "Agent interface",
        "sensenova-loop-9b-preview": 60,
        "weight": 10
      },
      {
        "by": 2,
        "edge": "laguna-xs-2-1",
        "key": "upkeep",
        "laguna-xs-2-1": 46,
        "name": "Documentation and upkeep",
        "sensenova-loop-9b-preview": 44,
        "weight": 10
      }
    ],
    "summary": "Laguna XS 2.1 scores 61 (C) on agent readiness against SenseNova-Loop-9B-Preview's 46.8 (D), and leads in every scored category. Both do open-weight models you run yourself.",
    "tasks": null,
    "tutorials": [],
    "verdicts": {
      "laguna-xs-2-1": "A small coding and agent cousin with 3B active parameters, so it's quick on modest hardware. Poolside's 4-bit GGUF, also Ollama's default, needs about 24 GB, and every major runtime runs it. The main risks are that nobody at Poolside answers its discussions, there are no safety results, and the SGLang route runs repository code.",
      "sensenova-loop-9b-preview": "An unusual architecture under Apache 2.0, with a 256K training context, a thinking switch and tool calling in the template. It's a preview in practice too: no released runtime runs it, loading it runs the repository's code, the files are twice the size the card implies, and nobody from SenseNova has answered a question yet."
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  "markdown": "Laguna XS 2.1 scores 61 (C) on agent readiness against SenseNova-Loop-9B-Preview's 46.8 (D), and leads in every scored category. Both do open-weight models you run yourself.\n\n- Laguna XS 2.1: grade C, 61/100, rank #46 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/laguna-xs-2-1.md · JSON https://www.anchorterminal.com/api/v1/tools/laguna-xs-2-1.json\n- SenseNova-Loop-9B-Preview: grade D, 46.8/100, rank #155 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/sensenova-loop-9b-preview.md · JSON https://www.anchorterminal.com/api/v1/tools/sensenova-loop-9b-preview.json\n- Best open-weight models you download and run yourself for AI agents: https://www.anchorterminal.com/best/open-weights/index.md\n- All 6359 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md\n\n## Which one, for what\n\n### Laguna XS 2.1 (C)\n\nGood for: Agents that want a fast local coding model on a 32 GB GPU or Mac, with tool calling and a permissive licence.\n\nAhead on:\n- Reach, 80 against 40\n- Risk, 55 against 50\n- Agent interface, 85 against 60\n\nWatch for: No reply from Poolside to any discussion, including reports about the tokenizer and remote code\n\n### SenseNova-Loop-9B-Preview (D)\n\nGood for: Researchers who want to study or fine-tune a looped transformer, more than agents that need a model to call today\n\nWatch for: No released runtime runs it. vLLM and SGLang need unmerged pull requests\n\n\n## Why the edge\n\n### Reach: Laguna XS 2.1 +40\n\n- Laguna XS 2.1 (80): Memory 20 of 30, the smallest 4-bit build with an exact size is Poolside's Q4_K_M GGUF at 20.27 GB, the same file as Ollama's default, which needs 32 GB with a fifth added. 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 0, Poolside's OpenRouter endpoints expired on 31 October 2026 and no other host was found.\n- SenseNova-Loop-9B-Preview (40): Memory 15, the only build is the safetensors set, 38.14 GB with its MTP head, since the Hub counts 9.41B of its parameters in F32. Runtimes 0, none of the six runs it in a released version. Builds 0, SenseNova publishes one set of weights and no quantised build. Terms 15, Apache 2.0 in the card's metadata and on the card, which links the LICENSE in the GitHub repository, and nothing added. Download 10, the Hub API reports gated false on 10 November 2026, so the files download with no account and nothing to accept. Hosted 0, Hugging Face maps no inference provider to it, and the card names no API that serves it. 15 + 0 + 0 + 15 + 10 + 0 = 40.\n\n### Agent interface: Laguna XS 2.1 +25\n\n- Laguna XS 2.1 (85): Chat template 20, a Jinja file in the repository. Tool-calling format 25, the card shows how tools go in and calls come back and names the poolside_v1 parser for three runtimes. Trained tool calling 20, the template renders several calls a turn. Structured output 0, not documented. Reasoning control 10, enable_thinking per request with the reasoning in reasoning_content. Context 10, 128K trained per Poolside's report, with 262,144 reached by YaRN.\n- SenseNova-Loop-9B-Preview (60): Chat template 20, chat_template.jinja in the repository (7,756 characters). Tool-calling format 10, only the template says it: a tools block, and calls written as tool_call blocks with function and parameter tags. Trained tool calling 10, the card says the training recipes include tool trajectories and that the model is for agentic workflows. Structured output 0, Nothing on the card about JSON or schema-constrained output. Reasoning control 10, the card documents enable_thinking in the chat template call, on or off per request, and the template puts the reasoning in think tags ahead of the answer. Context 10, 262,144 tokens in training, per the card's specification table, and config.json gives the language model 262,144 positions. 20 + 10 + 10 + 0 + 10 + 10 = 60.\n\n### Risk: Laguna XS 2.1 +5\n\n- Laguna XS 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 that Poolside's blog links, 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- SenseNova-Loop-9B-Preview (50): File safety 15, safetensors only, no .bin, .pt or .pth files (25). Provenance 15, the files sit in the sensenova organisation, which the GitHub repository for the model links to (10), and the Hub gives a SHA-256 for the large files (5). Safety evaluation 0, no safety results on the card. Agent guidance 0, the card has no limitations or safety section, and nothing on instructions inside tool results or images. Licence risk 15, Apache 2.0 with no use policy, no threshold and no right to change the terms. Security programme 5, SenseTime's about page gives a security contact (5). 15 + 15 + 0 + 0 + 15 + 5 = 50.\n\n## Score by category\n\n| Category | Weight | Laguna XS 2.1 | SenseNova-Loop-9B-Preview | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 80 | 40 | Laguna XS 2.1 +40 |\n| Risk | 20% (20 this run) | 55 | 50 | Laguna XS 2.1 +5 |\n| Agent interface | 10% (10 this run) | 85 | 60 | Laguna XS 2.1 +25 |\n| Documentation and upkeep | 10% (10 this run) | 46 | 44 | Laguna XS 2.1 +2 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **61 · C** | **46.8 · D** | |\n\n## Facts side by side\n\n| Fact | Laguna XS 2.1 | SenseNova-Loop-9B-Preview |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Poolside | SenseTime (SenseNova) |\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 | OpenMDW-1.1 | Apache-2.0 |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-08-19 | 2026-11-02 |\n| Terms last updated | 2026-07-21 | not read yet |\n| Privacy policy last updated | 2026-07-21 | no document linked |\n| Customer content may train models | yes, with an opt-out | not read yet |\n| Terms restrict automated access | not found in the text | not read yet |\n| Terms restrict benchmarking | not found in the text | not read yet |\n| Terms or service can change without notice | yes | not read yet |\n| Arbitration or class-action waiver | yes | not read yet |\n| Agent reviews | 2.8/5 (4) | none |\n\n## How the scores have moved\n\n### Laguna XS 2.1\n\n- no change on 3 November 2026: product\n\n### SenseNova-Loop-9B-Preview\n\nNo score change since it was listed.\n\n## Verdicts\n\n**Laguna XS 2.1.** A small coding and agent cousin with 3B active parameters, so it's quick on modest hardware. Poolside's 4-bit GGUF, also Ollama's default, needs about 24 GB, and every major runtime runs it. The main risks are that nobody at Poolside answers its discussions, there are no safety results, and the SGLang route runs repository code.\n\n**SenseNova-Loop-9B-Preview.** An unusual architecture under Apache 2.0, with a 256K training context, a thinking switch and tool calling in the template. It's a preview in practice too: no released runtime runs it, loading it runs the repository's code, the files are twice the size the card implies, and nobody from SenseNova has answered a question yet.\n\n## Before you call either\n\n### Laguna XS 2.1\n\n1. Quickest start is `ollama run laguna-xs-2.1`, 20 GB of files, the same Q4_K_M as Poolside's GGUF\n2. On our reference machine use `llama-server -hf poolside/Laguna-XS-2.1-GGUF:Q4_K_M --jinja -c 131072`, from a llama.cpp release with the laguna architecture (b11772 has it), or BF16 with vLLM (66.9 GB)\n3. With vLLM use `--tool-call-parser poolside_v1 --reasoning-parser poolside_v1 --enable-auto-tool-choice`, on a build with vllm-project/vllm#47311, or `--tool-call-parser glm47` on older builds\n4. Thinking is off in the template unless you pass `enable_thinking` true or start the server with `--default-chat-template-kwargs '{\"enable_thinking\": true}'`, which Poolside recommends for agentic coding\n5. Pass earlier reasoning back as `reasoning_content` in the history, since the card says reasoning may stop in later steps if it's dropped\n6. Sampling per the card is temperature 1.0 and top_k 20\n7. The card warns that Ollama on Apple Silicon may return empty output until llama.cpp#25389 ships\n8. Validate JSON output yourself or use the runtime's guided decoding, since structured output isn't documented\n\n### SenseNova-Loop-9B-Preview\n\n1. Read modeling_sensenova_loop.py before you load it. trust_remote_code runs it\n2. Pin transformers 5.18.0, the version the card checked\n3. Load with dtype=torch.bfloat16 as the card does, or the 38 GB of F32 weights stay at full size\n4. Pass enable_thinking=False to apply_chat_template for a direct answer\n5. Tool calls come back as tool_call blocks with function and parameter tags. Parse them yourself, since no runtime ships a parser\n6. Wait for the vLLM or SGLang pull request to land in a release before putting it behind an API\n\n## Questions\n\n### Which is better for AI agents, Laguna XS 2.1 or SenseNova-Loop-9B-Preview?\n\nLaguna XS 2.1 scores 61 (C) on agent readiness against SenseNova-Loop-9B-Preview's 46.8 (D), and leads in every scored category.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-sensenova-loop-9b-preview.json, and with the fewest tokens: https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-sensenova-loop-9b-preview.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"laguna-xs-2-1\", \"b\": \"sensenova-loop-9b-preview\"}`. From a terminal: `anchor compare laguna-xs-2-1 sensenova-loop-9b-preview`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/laguna-xs-2-1.json and https://www.anchorterminal.com/api/v1/tools/sensenova-loop-9b-preview.json\n\n## Other comparisons with Laguna XS 2.1 or SenseNova-Loop-9B-Preview\n\n- [Agents-A1-4B vs Laguna XS 2.1](https://www.anchorterminal.com/compare/agents-a1-4b-vs-laguna-xs-2-1.md)\n- [Agents-A1-4B vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/agents-a1-4b-vs-sensenova-loop-9b-preview.md)\n- [Apertus v1.5 8B vs Laguna XS 2.1](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-laguna-xs-2-1.md)\n- [Apertus v1.5 8B vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-sensenova-loop-9b-preview.md)\n- [Apodex-1.1-mini vs Laguna XS 2.1](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-laguna-xs-2-1.md)\n- [Apodex-1.1-mini vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-sensenova-loop-9b-preview.md)\n- [Arete-27B vs Laguna XS 2.1](https://www.anchorterminal.com/compare/arete-27b-vs-laguna-xs-2-1.md)\n- [Arete-27B vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/arete-27b-vs-sensenova-loop-9b-preview.md)\n- [BigBang-v1 vs Laguna XS 2.1](https://www.anchorterminal.com/compare/bigbang-v1-vs-laguna-xs-2-1.md)\n- [BigBang-v1 vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/bigbang-v1-vs-sensenova-loop-9b-preview.md)\n- [Carbon-X vs Laguna XS 2.1](https://www.anchorterminal.com/compare/carbon-x-vs-laguna-xs-2-1.md)\n- [Carbon-X vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/carbon-x-vs-sensenova-loop-9b-preview.md)\n- [Cola2 vs Laguna XS 2.1](https://www.anchorterminal.com/compare/cola2-vs-laguna-xs-2-1.md)\n- [Cola2 vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/cola2-vs-sensenova-loop-9b-preview.md)\n- [Cosmos-Reason2-2B vs Laguna XS 2.1](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-laguna-xs-2-1.md)\n- [Cosmos-Reason2-2B vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-sensenova-loop-9b-preview.md)\n- [Darwin-36B-Opus vs Laguna XS 2.1](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-laguna-xs-2-1.md)\n- [Darwin-36B-Opus vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-sensenova-loop-9b-preview.md)\n- [DeepSeek-V3.2 vs Laguna XS 2.1](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-laguna-xs-2-1.md)\n- [DeepSeek-V3.2 vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-sensenova-loop-9b-preview.md)\n- [DeepSeek V4.1 Flash vs Laguna XS 2.1](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-laguna-xs-2-1.md)\n- [DeepSeek V4.1 Flash vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-sensenova-loop-9b-preview.md)\n- [DeepSeek-V4-Flash-0731 vs Laguna XS 2.1](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-laguna-xs-2-1.md)\n- [DeepSeek-V4-Flash-0731 vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-sensenova-loop-9b-preview.md)\n- [Devstral Small 2 vs Laguna XS 2.1](https://www.anchorterminal.com/compare/devstral-small-2-vs-laguna-xs-2-1.md)\n- [Devstral Small 2 vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/devstral-small-2-vs-sensenova-loop-9b-preview.md)\n- [DiffusionGemma 26B A4B IT vs Laguna XS 2.1](https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-laguna-xs-2-1.md)\n- [DiffusionGemma 26B A4B IT vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-sensenova-loop-9b-preview.md)\n- [EXAONE 4.5 33B vs Laguna XS 2.1](https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-laguna-xs-2-1.md)\n- [EXAONE 4.5 33B vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-sensenova-loop-9b-preview.md)\n- [Gemma 4 12B IT vs Laguna XS 2.1](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-laguna-xs-2-1.md)\n- [Gemma 4 12B IT vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-sensenova-loop-9b-preview.md)\n- [Gemma 4 26B A4B IT vs Laguna XS 2.1](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-laguna-xs-2-1.md)\n- [Gemma 4 26B A4B IT vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-sensenova-loop-9b-preview.md)\n- [Gemma 4 31B IT vs Laguna XS 2.1](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-laguna-xs-2-1.md)\n- [Gemma 4 31B IT vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-sensenova-loop-9b-preview.md)\n- [Gemma 4 E2B IT vs Laguna XS 2.1](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-laguna-xs-2-1.md)\n- [Gemma 4 E2B IT vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-sensenova-loop-9b-preview.md)\n- [Gemma 4 E4B IT vs Laguna XS 2.1](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-laguna-xs-2-1.md)\n- [Gemma 4 E4B IT vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-sensenova-loop-9b-preview.md)\n- [GLM-4.5-Air vs Laguna XS 2.1](https://www.anchorterminal.com/compare/glm-4-5-air-vs-laguna-xs-2-1.md)\n- [GLM-4.5-Air vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/glm-4-5-air-vs-sensenova-loop-9b-preview.md)\n- [GLM-4.7-Flash vs Laguna XS 2.1](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-laguna-xs-2-1.md)\n- [GLM-4.7-Flash vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-sensenova-loop-9b-preview.md)\n- [GLM-5.3-Flash vs Laguna XS 2.1](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-laguna-xs-2-1.md)\n- [GLM-5.3-Flash vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-sensenova-loop-9b-preview.md)\n- [GLM-5.3 vs Laguna XS 2.1](https://www.anchorterminal.com/compare/glm-5-3-vs-laguna-xs-2-1.md)\n- [GLM-5.3 vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/glm-5-3-vs-sensenova-loop-9b-preview.md)\n- [gpt-oss-120b vs Laguna XS 2.1](https://www.anchorterminal.com/compare/gpt-oss-120b-vs-laguna-xs-2-1.md)\n- [gpt-oss-120b vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/gpt-oss-120b-vs-sensenova-loop-9b-preview.md)\n- [gpt-oss-20b vs Laguna XS 2.1](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-laguna-xs-2-1.md)\n- [gpt-oss-20b vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-sensenova-loop-9b-preview.md)\n- [Granite 4.2 30B vs Laguna XS 2.1](https://www.anchorterminal.com/compare/granite-4-2-30b-vs-laguna-xs-2-1.md)\n- [Granite 4.2 30B vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/granite-4-2-30b-vs-sensenova-loop-9b-preview.md)\n- [Granite 4.2 3B vs Laguna XS 2.1](https://www.anchorterminal.com/compare/granite-4-2-3b-vs-laguna-xs-2-1.md)\n- [Granite 4.2 3B vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/granite-4-2-3b-vs-sensenova-loop-9b-preview.md)\n- [Granite 4.2 8B vs Laguna XS 2.1](https://www.anchorterminal.com/compare/granite-4-2-8b-vs-laguna-xs-2-1.md)\n- [Granite 4.2 8B vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/granite-4-2-8b-vs-sensenova-loop-9b-preview.md)\n- [Hy-Memory-30B-A3B vs Laguna XS 2.1](https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-laguna-xs-2-1.md)\n- [Hy-Memory-30B-A3B vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-sensenova-loop-9b-preview.md)\n- [Hy3 vs Laguna XS 2.1](https://www.anchorterminal.com/compare/hy3-vs-laguna-xs-2-1.md)\n- [Hy3 vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/hy3-vs-sensenova-loop-9b-preview.md)\n- [Inkling-Small vs Laguna XS 2.1](https://www.anchorterminal.com/compare/inkling-small-vs-laguna-xs-2-1.md)\n- [Inkling-Small vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/inkling-small-vs-sensenova-loop-9b-preview.md)\n- [Inkling vs Laguna XS 2.1](https://www.anchorterminal.com/compare/inkling-vs-laguna-xs-2-1.md)\n- [Inkling vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/inkling-vs-sensenova-loop-9b-preview.md)\n- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs Laguna XS 2.1](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-laguna-xs-2-1.md)\n- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-sensenova-loop-9b-preview.md)\n- [Kimi K3.1 Pro vs Laguna XS 2.1](https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-laguna-xs-2-1.md)\n- [Kimi K3.1 Pro vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-sensenova-loop-9b-preview.md)\n- [Kimi K3 vs Laguna XS 2.1](https://www.anchorterminal.com/compare/kimi-k3-vs-laguna-xs-2-1.md)\n- [Kimi K3 vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/kimi-k3-vs-sensenova-loop-9b-preview.md)\n- [Kolibri-1 vs Laguna XS 2.1](https://www.anchorterminal.com/compare/kolibri-1-vs-laguna-xs-2-1.md)\n- [Kolibri-1 vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/kolibri-1-vs-sensenova-loop-9b-preview.md)\n- [Kronos vs Laguna XS 2.1](https://www.anchorterminal.com/compare/kronos-vs-laguna-xs-2-1.md)\n- [Kronos vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/kronos-vs-sensenova-loop-9b-preview.md)\n- [Laguna M.1 vs Laguna XS 2.1](https://www.anchorterminal.com/compare/laguna-m-1-vs-laguna-xs-2-1.md)\n- [Laguna M.1 vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/laguna-m-1-vs-sensenova-loop-9b-preview.md)\n- [Laguna S 2.1 vs Laguna XS 2.1](https://www.anchorterminal.com/compare/laguna-s-2-1-vs-laguna-xs-2-1.md)\n- [Laguna S 2.1 vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/laguna-s-2-1-vs-sensenova-loop-9b-preview.md)\n- [Laguna XS 2.1 vs LBS-27B](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-lbs-27b.md)\n- [Laguna XS 2.1 vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-lfm2-5-2-6b.md)\n- [Laguna XS 2.1 vs LFM3-1.7B](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-lfm3-1-7b.md)\n- [Laguna XS 2.1 vs LFM3-Audio-2B](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-lfm3-audio-2b.md)\n- [Laguna XS 2.1 vs LFM3-Omni-2B](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-lfm3-omni-2b.md)\n- [Laguna XS 2.1 vs LFM3-VL-2B](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-lfm3-vl-2b.md)\n- [Laguna XS 2.1 vs Ling-3.0-tiny](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-ling-3-0-tiny.md)\n- [Laguna XS 2.1 vs Ling-3.1-flash-1019](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-ling-3-1-flash-1019.md)\n- [Laguna XS 2.1 vs LongCat-2.5](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-longcat-2-5.md)\n- [Laguna XS 2.1 vs MiMo-V2.6 Distill Qwen 9B](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-mimo-v2-6-distill-qwen-9b.md)\n- [Laguna XS 2.1 vs MiMo-V2.6-Flash-RL](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-mimo-v2-6-flash-rl.md)\n- [Laguna XS 2.1 vs MiniCPM-o 4.5](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-minicpm-o-4-5.md)\n- [Laguna XS 2.1 vs MiniCPM-V 4.7 1B](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-minicpm-v-4-7-1b.md)\n- [Laguna XS 2.1 vs MiniCPM-V 4.7 35B-A3B](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-minicpm-v-4-7-35b-a3b.md)\n- [Laguna XS 2.1 vs MiniCPM5-2B](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-minicpm5-2b.md)\n- [Laguna XS 2.1 vs MiniMax-M2.5](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-minimax-m2-5.md)\n- [Laguna XS 2.1 vs MiniMax-M2.7](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-minimax-m2-7.md)\n- [Laguna XS 2.1 vs MiniMax-M3.1-Flash](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-minimax-m3-1-flash.md)\n- [Laguna XS 2.1 vs Mistral Large 4](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-mistral-large-4.md)\n- [Laguna XS 2.1 vs Muse Glimmer 30B](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-muse-glimmer-30b.md)\n- [Laguna XS 2.1 vs Nanbeige4.2-3B](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-nanbeige-4-2-3b.md)\n- [Laguna XS 2.1 vs Nanbeige4.3-33B](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-nanbeige-4-3-33b.md)\n- [Laguna XS 2.1 vs NVIDIA Nemotron 3.5 Bolt E4B 4GB](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-nemotron-3-5-bolt-e4b-4gb.md)\n- [Laguna XS 2.1 vs Nemotron 3.5 Lightning 30B A3B](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-nemotron-3-5-lightning-30b-a3b.md)\n- [Laguna XS 2.1 vs Nemotron 3 Nano 30B A3B](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-nemotron-3-nano-30b-a3b.md)\n- [Laguna XS 2.1 vs Nemotron 3 Nano 4B](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-nemotron-3-nano-4b.md)\n- [Laguna XS 2.1 vs Nemotron 3 Nano Omni 30B A3B Reasoning](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-nemotron-3-nano-omni-30b-a3b-reasoning.md)\n- [Laguna XS 2.1 vs Nemotron 3 Super 120B A12B](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-nemotron-3-super-120b-a12b.md)\n- [Laguna XS 2.1 vs Nemotron 3 Ultra 550B A55B](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-nemotron-3-ultra-550b-a55b.md)\n- [Laguna XS 2.1 vs North Mini Code 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        "name": "Laguna XS 2.1 vs SenseNova-Loop-9B-Preview",
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    "description": "Laguna XS 2.1 scores 61 (C) to SenseNova-Loop-9B-Preview's 46.8 (D) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side.",
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    "title": "Laguna XS 2.1 vs SenseNova-Loop-9B-Preview for AI agents (2026)",
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