{
  "data": {
    "a": {
      "slug": "laguna-m-1",
      "name": "Laguna M.1",
      "vendor": "Poolside",
      "vendorUrl": "https://poolside.ai",
      "kind": "weights",
      "category": "open-weights",
      "summary": "Laguna M.1 is Poolside's first-generation open-weight Mixture-of-Experts model for agentic coding, 225B total and 23B active parameters, with switchable reasoning and a 262,144-token context, published on Hugging Face under Apache 2.0.",
      "url": "https://www.anchorterminal.com/tools/laguna-m-1",
      "markdownUrl": "https://www.anchorterminal.com/tools/laguna-m-1.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/laguna-m-1.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/laguna-m-1.json",
      "repo": "https://huggingface.co/poolside/Laguna-M.1",
      "license": "Apache-2.0",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "poolside/Laguna-M.1"
        },
        {
          "registry": "huggingface",
          "name": "poolside/Laguna-M.1-FP8"
        },
        {
          "registry": "huggingface",
          "name": "poolside/Laguna-M.1-NVFP4"
        }
      ],
      "auth": "none",
      "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The Hub reports gated false for the main repository and both builds, although the card's metadata carries an unused gate description pointing to Poolside's privacy policy (checked 2026-10-21).",
      "pricing": "free",
      "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0. Running it yourself costs the hardware, 451.6 GB for the BF16 files and 139.3 GB for the NVFP4 build. The card links Poolside's API for hosted use, but no inference provider or OpenRouter endpoint serves it today (checked 2026-10-21).",
      "priceSummary": "Free",
      "where": "local",
      "x402": {
        "level": "no",
        "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-10-21).",
        "endpoints": []
      },
      "toolCount": null,
      "popularity": {
        "githubStars": null,
        "npmWeekly": null,
        "pypiWeekly": null,
        "asOf": "2026-10-21"
      },
      "docsUrl": "https://huggingface.co/poolside/Laguna-M.1",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "moe",
        "coding",
        "reasoning",
        "tool-calling",
        "apache-2.0",
        "safetensors",
        "fp8",
        "nvfp4",
        "self-hosted"
      ],
      "lastRelease": "2026-07-14",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 55.5,
        "grade": "C",
        "agentReady": false,
        "rank": 0,
        "ranked": false,
        "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools",
        "rankOf": 1197,
        "categoryRank": 64,
        "methodology": "0.6",
        "run": "2026-10-01",
        "scores": {
          "capability": 46,
          "interface": 85,
          "reach": 65,
          "risk": 55,
          "upkeep": 36
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "high",
          "date": "2026-10-21"
        },
        "negative": 0,
        "verdict": "A large coding and agent cousin under plain Apache 2.0, for agents with a multi-GPU server. Its smallest build needs about 167 GB, more than a 128 GB machine, and no local runtime library packages it. The main risks are no upkeep since July, no hosted fallback shown today, no safety results, and repository code on the SGLang route.",
        "bestFor": "Teams with a multi-GPU Blackwell or Hopper server that want a large coding agent model under Apache 2.0.",
        "strengths": [
          "Plain Apache 2.0 with no gate, account or use policy",
          "Released support in vLLM, SGLang, Transformers and llama.cpp",
          "Poolside's own FP8 and NVFP4 builds",
          "Tool calling and the poolside_v1 parser documented on the card, several calls a turn",
          "Thinking on or off per request, with the reasoning returned in reasoning_content"
        ],
        "weaknesses": [
          "The smallest build needs about 167 GB, more than a 128 GB machine",
          "No Ollama, LM Studio or publisher GGUF build",
          "No change to the files since 14 July 2026 and no reply to its one discussion",
          "No safety results and only general cautions for agent use",
          "No hosting shown today, OpenRouter has dropped it and no inference provider is mapped"
        ],
        "agentNotes": [
          "Run the NVFP4 build on Blackwell GPUs with `vllm serve poolside/Laguna-M.1-NVFP4 --tensor-parallel-size 2 --tool-call-parser poolside_v1 --reasoning-parser poolside_v1 --enable-auto-tool-choice`, on vLLM 0.21.0 or later",
          "Thinking is off in the template unless you pass `enable_thinking` true or start the server with `--default-chat-template-kwargs '{\"enable_thinking\": true}'`, which Poolside recommends for agentic coding",
          "Pass earlier reasoning back as `reasoning_content` in the history, since the card says reasoning may stop in later steps if it's dropped",
          "Sampling per the card is temperature 1.0 and top_k 20",
          "The FP8 build's Docker image and serve.sh predate upstream support and set a 4,096-token context, so prefer a released vLLM",
          "It doesn't fit our 128 GB reference machine at any published precision",
          "Validate JSON output yourself or use the runtime's guided decoding, since structured output isn't documented"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 4,
        "avgRating": 2.5,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "high",
            "grade": "C",
            "methodology": "0.6",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 55.5
          }
        ],
        "editorialScores": {
          "capability": 46,
          "interface": 85,
          "reach": 65,
          "risk": 55,
          "upkeep": 36
        },
        "provenanceScore": 71,
        "checked": {
          "categories": {
            "capability": "2026-10-21",
            "interface": "2026-10-21",
            "reach": "2026-10-21",
            "risk": "2026-10-21",
            "upkeep": "2026-10-21"
          },
          "last": "2026-10-21",
          "oldest": "2026-10-21",
          "stale": false,
          "staleAfterDays": 90
        }
      },
      "connect": {
        "install": "pip install 'vllm\u003e=0.21.0'\nvllm serve poolside/Laguna-M.1-NVFP4 --tensor-parallel-size 2 --tool-call-parser poolside_v1 \\\n  --reasoning-parser poolside_v1 --enable-auto-tool-choice --default-chat-template-kwargs '{\"enable_thinking\": true}'",
        "http": "curl http://localhost:8000/v1/chat/completions \\\n  -H 'Content-Type: application/json' \\\n  -d '{\"model\": \"poolside/Laguna-M.1-NVFP4\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/laguna-m-1"
      },
      "sameCompany": [
        "laguna-s-2-1",
        "laguna-xs-2-1"
      ],
      "weights": {
        "trainedBy": "Poolside",
        "params": "225.8B total, 23B active",
        "context": 262144,
        "goodFor": "Agentic coding on a multi-GPU server, with tool calling and reasoning that turns on or off per request, under Apache 2.0.",
        "runtimes": [
          "llama.cpp",
          "vLLM",
          "SGLang",
          "Transformers"
        ],
        "builds": [
          {
            "name": "Laguna-M.1-NVFP4",
            "url": "https://huggingface.co/poolside/Laguna-M.1-NVFP4",
            "who": "publisher",
            "precision": "nvfp4",
            "sizeGB": 139.3,
            "memoryGB": 192,
            "format": "safetensors",
            "note": "For NVIDIA Blackwell GPUs"
          },
          {
            "name": "Laguna-M.1-FP8",
            "url": "https://huggingface.co/poolside/Laguna-M.1-FP8",
            "who": "publisher",
            "precision": "fp8",
            "sizeGB": 238.6,
            "memoryGB": 512,
            "format": "safetensors",
            "note": "238.6 GB summed from the file list, of which 9.5 GB is a Docker image (image/laguna-vllm-v0.19.0-overlay.tar.gz) and 229.1 GB the weights. Either way 512 GB"
          },
          {
            "name": "Laguna-M.1",
            "url": "https://huggingface.co/poolside/Laguna-M.1",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 451.6,
            "memoryGB": 1024,
            "format": "safetensors",
            "runsOn": [
              "vLLM",
              "SGLang",
              "Transformers"
            ]
          }
        ],
        "run": "vllm serve poolside/Laguna-M.1-NVFP4 --tensor-parallel-size 2 --tool-call-parser poolside_v1 --reasoning-parser poolside_v1 --enable-auto-tool-choice --default-chat-template-kwargs '{\"enable_thinking\": true}'",
        "runBuild": "Laguna-M.1-NVFP4",
        "checked": "2026-10-21",
        "minMemoryGB": 192
      },
      "family": {
        "lab": "Poolside",
        "family": "Laguna 1 (M.1)",
        "url": "https://www.anchorterminal.com/open-weights/#poolside",
        "others": [
          {
            "name": "Laguna-M.1-base",
            "url": "https://huggingface.co/poolside/Laguna-M.1-base",
            "params": "225.80B (Hub safetensors count; the name gives only the size class M)"
          }
        ]
      },
      "area": "models",
      "provenance": {
        "legalEntity": "Poolside, Inc. (named in Poolside's Terms of Use; the LICENSE.md copyright line reads Poolside)",
        "domain": "poolside.ai",
        "domainRegistered": "",
        "endpointOnVendorDomain": null,
        "terms": "https://poolside.ai/legal/eula",
        "privacy": "https://poolside.ai/legal/privacy",
        "statusPage": "",
        "changelog": "https://docs.poolside.ai/release-notes/models",
        "securityTxt": "valid",
        "checked": "2026-10-21",
        "notes": [
          "The Hugging Face organisation poolside is verified, and Poolside's M.1 blog post links the repository.",
          "Nothing is hosted for this listing, so the Apache 2.0 licence stands in for terms of service and no privacy policy is scored.",
          "Poolside's docs keep dated model release notes, with an April 2026 entry for M.1.",
          "security.txt is PGP-signed, names a security contact at poolside.ai and expires on 1 April 2028. The domain's registration date wasn't checked."
        ],
        "score": 71
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/laguna-m-1.json"
    },
    "answer": "Swift 1.5 Qwen3.8-27B scores 61 (C) on agent readiness against Laguna M.1's 55.5 (C), and leads in 2 of 4 scored categories. Laguna M.1 leads on risk and agent interface.",
    "b": {
      "slug": "swift-1-5-qwen3-8-27b",
      "name": "Swift 1.5 Qwen3.8-27B",
      "vendor": "UkisAI",
      "vendorUrl": "https://ukisai.com",
      "kind": "weights",
      "category": "open-weights",
      "summary": "Swift 1.5 Qwen3.8-27B is UkisAI's post-trained Qwen3.8-27B that aims to think in fewer tokens, a 27B dense vision-language model with the base's 262,144-token context, published in BF16 with GGUF, AWQ, NVFP4, FP8 and MLX builds under UkisAI's own licence.",
      "url": "https://www.anchorterminal.com/tools/swift-1-5-qwen3-8-27b",
      "markdownUrl": "https://www.anchorterminal.com/tools/swift-1-5-qwen3-8-27b.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/swift-1-5-qwen3-8-27b.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/swift-1-5-qwen3-8-27b.json",
      "repo": "https://huggingface.co/ukisai/Swift-1.5-Qwen3.8-27b",
      "license": "Swift Open License v1.0 (custom, not an OSI licence) on UkisAI's changes, free for commercial use below US$1M gross annual revenue with a separate Swift Enterprise License above it. Built on Qwen3.8-27B, whose Apache 2.0 still governs the base",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "ukisai/Swift-1.5-Qwen3.8-27b"
        },
        {
          "registry": "huggingface",
          "name": "ukisai/Swift-1.5-Qwen3.8-27B-GGUF"
        }
      ],
      "auth": "none",
      "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The Hub reports the repository as not gated, though the card's metadata says gated true (checked 2026-10-21).",
      "pricing": "free",
      "pricingNotes": "Free to download and use. Commercial use is free for individuals and organisations whose gross annual revenue, affiliates included, is below US$1,000,000, and needs a separate Swift Enterprise License above it. The base Qwen3.8-27B stays under Apache 2.0. UkisAI's card also lists a free API for research purposes at ukisai.com/api/swift/v1. Running it yourself costs the hardware, 55.6 GB in BF16 and 15.9 to 18.4 GB for the 4-bit builds by the Hub's file lists (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/ukisai/Swift-1.5-Qwen3.8-27b",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "dense",
        "multimodal",
        "reasoning",
        "tool-calling",
        "fine-tune",
        "custom-licence",
        "safetensors",
        "gguf",
        "self-hosted"
      ],
      "lastRelease": "2026-09-24",
      "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": 42,
        "methodology": "0.6",
        "run": "2026-10-01",
        "scores": {
          "interface": 60,
          "reach": 80,
          "risk": 50,
          "upkeep": 43
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "low",
          "date": "2026-10-21"
        },
        "negative": 0,
        "verdict": "A Qwen3.8-27B cousin post-trained to think in fewer tokens, for coding and agent work with images in, that fits a 24 GB machine with UkisAI's own 4-bit builds. Commercial use is free only below US$1M revenue. The main risks are that nobody at UkisAI answers its discussions, and that the shorter thinking rests on UkisAI's own benchmarks.",
        "bestFor": "Agents on a 24 to 32 GB GPU or Mac that want Qwen3.8-27B's skills with shorter thinking, for teams under US$1M revenue or research.",
        "strengths": [
          "UkisAI's own 4-bit builds fit in about 20 GB, with GGUF, INT4, NVFP4, FP8 and MLX choices",
          "Runs in released vLLM, SGLang, Transformers and llama.cpp as Qwen3.8-27B does, with the same template and 262,144-token context",
          "Safetensors only, in the account UkisAI's site links to, with a checksum on every file",
          "Several tool calls a turn and post-training aimed at agent and coding tasks",
          "Ungated, with no account or terms to accept"
        ],
        "weaknesses": [
          "Commercial use above US$1M gross annual revenue needs a separate licence",
          "No reply from UkisAI in any discussion, including reports that the model is broken",
          "No safety results, agent guidance or security contact",
          "The card doesn't say how to set reasoning effort, turn thinking off or ask for JSON",
          "Training data described only in general terms, and the efficiency claims are UkisAI's own runs"
        ],
        "agentNotes": [
          "Quickest start is `llama-server -hf ukisai/Swift-1.5-Qwen3.8-27B-GGUF:Q4_K_M --jinja`, about 18.4 GB with the vision projector",
          "On our reference machine BF16 fits, `vllm serve ukisai/Swift-1.5-Qwen3.8-27b --max-model-len 262144 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder`",
          "The template is Qwen3.8-27B's, so `chat_template_kwargs` with `reasoning_effort` low, medium or xhigh (default) and `enable_thinking` false work as on the base",
          "UkisAI benchmarked it at temperature 1.0, top_p 0.95, top_k 20 and presence_penalty 0, which is a reasonable starting point",
          "The MLX 4-bit build needs UkisAI's patched MLX-LM and has no image or video chat",
          "Check the licence before commercial use, it stops at US$1M gross annual revenue including affiliates",
          "MTP self-speculative decoding is available with the base's head, flags on the card"
        ],
        "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": 60,
          "reach": 80,
          "risk": 50,
          "upkeep": 43
        },
        "provenanceScore": 13,
        "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": "vllm serve ukisai/Swift-1.5-Qwen3.8-27b --max-model-len 262144 --reasoning-parser qwen3 \\\n  --enable-auto-tool-choice --tool-call-parser qwen3_coder\n# or the 4-bit GGUF with its vision projector:\nllama-server -hf ukisai/Swift-1.5-Qwen3.8-27B-GGUF:Q4_K_M --jinja",
        "http": "curl http://localhost:8000/v1/chat/completions \\\n  -H 'Content-Type: application/json' \\\n  -d '{\"model\": \"ukisai/Swift-1.5-Qwen3.8-27b\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello, Swift.\"}], \"chat_template_kwargs\": {\"reasoning_effort\": \"low\"}}'"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/swift-1-5-qwen3-8-27b"
      },
      "sameCompany": [
        "swift-1-5-qwen3-8-flash-next"
      ],
      "weights": {
        "trainedBy": "UkisAI, post-training Alibaba's Qwen3.8-27B",
        "builtOn": "Qwen/Qwen3.8-27B (fine-tune by way of Swift 1.0, Apache 2.0)",
        "params": "27B",
        "context": 262144,
        "goodFor": "A Qwen3.8-27B that thinks in fewer tokens, for coding and agent work with images in, tool calling and adjustable reasoning effort.",
        "runtimes": [
          "llama.cpp",
          "vLLM",
          "SGLang",
          "Transformers"
        ],
        "builds": [
          {
            "name": "Swift-1.5-Qwen3.8-27B-GGUF Q4_K_M",
            "url": "https://huggingface.co/ukisai/Swift-1.5-Qwen3.8-27B-GGUF",
            "who": "publisher",
            "precision": "q4_k_m",
            "sizeGB": 18.4,
            "memoryGB": 24,
            "format": "gguf",
            "runsOn": [
              "llama.cpp"
            ],
            "note": "Swift-1.5-Qwen3.8-27B-Q4_K_M.gguf (17.44 GB) plus mmproj-Swift-1.5-Qwen3.8-27B-F16.gguf (0.93 GB), summed from the Hub's file list"
          },
          {
            "name": "Swift-1.5-Qwen3.8-27B-GGUF IQ4_XS",
            "url": "https://huggingface.co/ukisai/Swift-1.5-Qwen3.8-27B-GGUF",
            "who": "publisher",
            "precision": "iq4_xs",
            "sizeGB": 16.4,
            "memoryGB": 24,
            "format": "gguf",
            "runsOn": [
              "llama.cpp"
            ],
            "note": "The smallest 4-bit GGUF, 15.48 GB plus the 0.93 GB vision projector. The repository also has 2- and 3-bit files down to 8.9 GB, and a GSQ-RCO GGUF repository has 2- and 3-bit files from 8.4 GB"
          },
          {
            "name": "Swift-1.5-4bit-MLX",
            "url": "https://huggingface.co/ukisai/Swift-1.5-4bit-MLX",
            "who": "publisher",
            "precision": "4bit",
            "sizeGB": 15.9,
            "memoryGB": 24,
            "format": "mlx",
            "runsOn": [
              "MLX"
            ],
            "note": "Weights with the vision tensors, configs and tokenizer, 15.85 GB. Its card says stock MLX-LM 0.32.0 can't load it and ships a patch, and image and video chat aren't implemented"
          },
          {
            "name": "Swift-1.5-Qwen3.8-27b-W4A16-AWQ",
            "url": "https://huggingface.co/ukisai/Swift-1.5-Qwen3.8-27b-W4A16-AWQ",
            "who": "publisher",
            "precision": "int4",
            "sizeGB": 19.6,
            "memoryGB": 24,
            "format": "safetensors",
            "runsOn": [
              "vLLM"
            ],
            "note": "compressed-tensors W4A16. UkisAI also publishes AutoRound INT4 (19.6 GB) and AWQ plus GPTQ INT4 (19.5 GB)"
          },
          {
            "name": "Swift-1.5-Qwen3.8-27b-NVFP4",
            "url": "https://huggingface.co/ukisai/Swift-1.5-Qwen3.8-27b-NVFP4",
            "who": "publisher",
            "precision": "nvfp4",
            "sizeGB": 21.9,
            "memoryGB": 32,
            "format": "safetensors",
            "note": "For NVIDIA Blackwell GPUs"
          },
          {
            "name": "Swift-1.5-Qwen3.8-27b-Quark-FP8-dynamic-AMD",
            "url": "https://huggingface.co/ukisai/Swift-1.5-Qwen3.8-27b-Quark-FP8-dynamic-AMD",
            "who": "publisher",
            "precision": "fp8",
            "sizeGB": 31.3,
            "memoryGB": 48,
            "format": "safetensors",
            "note": "AMD Quark W8A8, with Python loading helpers in the repository"
          },
          {
            "name": "Swift-1.5-Qwen3.8-27b",
            "url": "https://huggingface.co/ukisai/Swift-1.5-Qwen3.8-27b",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 55.6,
            "memoryGB": 96,
            "format": "safetensors",
            "runsOn": [
              "vLLM (ROCm)",
              "SGLang (ROCm)"
            ]
          }
        ],
        "run": "vllm serve ukisai/Swift-1.5-Qwen3.8-27b --max-model-len 262144 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder",
        "runBuild": "Swift-1.5-Qwen3.8-27b",
        "checked": "2026-10-21",
        "minMemoryGB": 24
      },
      "family": {
        "lab": "UkisAI (ukisai)",
        "family": "Swift 1",
        "url": "https://www.anchorterminal.com/open-weights/#ukisai",
        "others": [
          {
            "name": "Swift-Qwen3.8-27b",
            "url": "https://huggingface.co/ukisai/Swift-Qwen3.8-27b",
            "params": "27B (27,781,427,952 by the Hub's safetensors count)"
          },
          {
            "name": "Swift1.5-Qwen3.8-Flash-Next",
            "url": "https://huggingface.co/ukisai/Swift1.5-Qwen3.8-Flash-Next",
            "params": "125B (Qwen3.8-Flash-Next's stated size; MoE; 179,999,981,459 by the Hub's safetensors count)",
            "listing": "https://www.anchorterminal.com/tools/swift-1-5-qwen3-8-flash-next"
          },
          {
            "name": "Swift-1.5-Ornith-9B",
            "url": "https://huggingface.co/ukisai/Swift-1.5-Ornith-9B",
            "params": "9B (9,653,104,368 by the Hub's safetensors count)"
          },
          {
            "name": "Swift-Bonsai-2-GGUF",
            "url": "https://huggingface.co/ukisai/Swift-Bonsai-2-GGUF",
            "params": "27B (26,895,998,464 by the Hub's GGUF count)"
          }
        ],
        "unrestrictedCopies": 5
      },
      "area": "models",
      "provenance": {
        "legalEntity": "",
        "domain": "ukisai.com",
        "domainRegistered": "",
        "endpointOnVendorDomain": null,
        "terms": "https://huggingface.co/ukisai/Swift-1.5-Qwen3.8-27b/blob/main/LICENSE",
        "privacy": "",
        "statusPage": "",
        "changelog": "",
        "securityTxt": "none",
        "checked": "2026-10-21",
        "notes": [
          "The licence names UkisAI as licensor with no legal entity. ukisai.com describes a Europe-based applied AI lab in Belgrade, Eindhoven and Wilmington. RDAP wasn't queried.",
          "The weights are published from the Hugging Face user account ukisai (not an organisation), which ukisai.com's Swift page links to.",
          "The Swift Open License stands in for terms of service for the weights. The research API isn't graded, so no privacy policy is scored.",
          "No changelog. The repository's history was squashed to two commits.",
          "ukisai.com/.well-known/security.txt is 404, and ukisai/.github has no SECURITY.md."
        ],
        "score": 13
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/swift-1-5-qwen3-8-27b.json"
    },
    "facts": [
      {
        "a": "Open-weight model",
        "b": "Open-weight model",
        "name": "Kind"
      },
      {
        "a": "Poolside",
        "b": "UkisAI",
        "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": "Swift Open License v1.0 (custom, not an OSI licence) on UkisAI's changes, free for commercial use below US$1M gross annual revenue with a separate Swift Enterprise License above it. Built on Qwen3.8-27B, whose Apache 2.0 still governs the base",
        "name": "Licence"
      },
      {
        "a": "no",
        "b": "no",
        "name": "Read-only variant documented"
      },
      {
        "a": "no",
        "b": "no",
        "name": "llms.txt"
      },
      {
        "a": "2026-07-14",
        "b": "2026-09-24",
        "name": "Last release"
      },
      {
        "a": "",
        "b": "",
        "name": "Terms last updated"
      },
      {
        "a": "",
        "b": "no document linked",
        "name": "Privacy policy last updated"
      },
      {
        "a": "",
        "b": "",
        "name": "Customer content may train models"
      },
      {
        "a": "",
        "b": "",
        "name": "Terms restrict automated access"
      },
      {
        "a": "",
        "b": "",
        "name": "Terms restrict benchmarking"
      },
      {
        "a": "",
        "b": "",
        "name": "Terms or service can change without notice"
      },
      {
        "a": "",
        "b": "",
        "name": "Arbitration or class-action waiver"
      },
      {
        "a": "2.5/5 (4)",
        "b": "2.3/5 (4)",
        "name": "Agent reviews"
      }
    ],
    "faq": [
      {
        "answer": "Swift 1.5 Qwen3.8-27B scores 61 (C) on agent readiness against Laguna M.1's 55.5 (C), and leads in 2 of 4 scored categories. Laguna M.1 leads on risk and agent interface.",
        "question": "Which is better for AI agents, Laguna M.1 or Swift 1.5 Qwen3.8-27B?"
      }
    ],
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      {
        "aheadOn": [
          "Risk, 55 against 50",
          "Agent interface, 85 against 60"
        ],
        "also": null,
        "goodFor": "Teams with a multi-GPU Blackwell or Hopper server that want a large coding agent model under Apache 2.0.",
        "slug": "laguna-m-1",
        "watchFor": "The smallest build needs about 167 GB, more than a 128 GB machine"
      },
      {
        "aheadOn": [
          "Reach, 80 against 65",
          "Documentation and upkeep, 43 against 36"
        ],
        "also": null,
        "goodFor": "Agents on a 24 to 32 GB GPU or Mac that want Qwen3.8-27B's skills with shorter thinking, for teams under US$1M revenue or research.",
        "slug": "swift-1-5-qwen3-8-27b",
        "watchFor": "Commercial use above US$1M gross annual revenue needs a separate licence"
      }
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      "name": "Open-weight models you run yourself"
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        "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-laguna-m-1.json",
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        "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-laguna-m-1"
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        "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-swift-1-5-qwen3-8-27b.json",
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        "json": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-laguna-m-1.json",
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        "json": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-laguna-m-1.json",
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        "url": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-swift-1-5-qwen3-8-27b"
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        "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-laguna-m-1"
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        "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-laguna-m-1.json",
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        "url": "https://www.anchorterminal.com/compare/kolibri-1-vs-laguna-m-1"
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        "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-swift-1-5-qwen3-8-27b.json",
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        "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-laguna-xs-2-1.json",
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        "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-laguna-xs-2-1"
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        "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-lfm2-5-2-6b.json",
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        "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-ling-3-0-tiny.json",
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        "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-ling-3-0-tiny"
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        "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-mimo-v2-6-distill-qwen-9b.json",
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        "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-minicpm5-2b.json",
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        "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-minicpm5-2b"
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        "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-minimax-m2-5.json",
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        "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-minimax-m2-5"
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        "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-minimax-m2-7.json",
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        "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-minimax-m2-7"
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        "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-muse-glimmer-30b.json",
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        "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-nemotron-3-5-lightning-30b-a3b.json",
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        "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-5-9b.json",
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        "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-6-27b.json",
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        "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-6-35b-a3b.json",
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        "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-8-27b"
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        "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-8-flash-next.json",
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      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-swift-1-5-qwen3-8-27b.json",
        "title": "Ornith-1.5-397B vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-swift-1-5-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-swift-1-5-qwen3-8-27b.json",
        "title": "Ornith-1.5-9B vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-swift-1-5-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-30b-a3b-instruct-2507-vs-swift-1-5-qwen3-8-27b.json",
        "title": "Qwen3-30B-A3B-Instruct-2507 vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/qwen3-30b-a3b-instruct-2507-vs-swift-1-5-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-4b-instruct-2507-vs-swift-1-5-qwen3-8-27b.json",
        "title": "Qwen3-4B-Instruct-2507 vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/qwen3-4b-instruct-2507-vs-swift-1-5-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-swift-1-5-qwen3-8-27b.json",
        "title": "Qwen3.5-0.8B vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-swift-1-5-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-swift-1-5-qwen3-8-27b.json",
        "title": "Qwen3.5-122B-A10B vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-swift-1-5-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-5-2b-vs-swift-1-5-qwen3-8-27b.json",
        "title": "Qwen3.5-2B vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-2b-vs-swift-1-5-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-swift-1-5-qwen3-8-27b.json",
        "title": "Qwen3.5-397B-A17B vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-swift-1-5-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-5-4b-vs-swift-1-5-qwen3-8-27b.json",
        "title": "Qwen3.5-4B vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-4b-vs-swift-1-5-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-swift-1-5-qwen3-8-27b.json",
        "title": "Qwen3.5-9B vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-swift-1-5-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-6-27b-vs-swift-1-5-qwen3-8-27b.json",
        "title": "Qwen3.6-27B vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/qwen3-6-27b-vs-swift-1-5-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-swift-1-5-qwen3-8-27b.json",
        "title": "Qwen3.6-35B-A3B vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-swift-1-5-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-8-27b-vs-swift-1-5-qwen3-8-27b.json",
        "title": "Qwen3.8-27B vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/qwen3-8-27b-vs-swift-1-5-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-swift-1-5-qwen3-8-27b.json",
        "title": "Qwen3.8-Flash-Next vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-swift-1-5-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-coder-30b-a3b-instruct-vs-swift-1-5-qwen3-8-27b.json",
        "title": "Qwen3-Coder-30B-A3B-Instruct vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/qwen3-coder-30b-a3b-instruct-vs-swift-1-5-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-coder-next-vs-swift-1-5-qwen3-8-27b.json",
        "title": "Qwen3-Coder-Next vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/qwen3-coder-next-vs-swift-1-5-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-omni-30b-a3b-instruct-vs-swift-1-5-qwen3-8-27b.json",
        "title": "Qwen3-Omni-30B-A3B-Instruct vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/qwen3-omni-30b-a3b-instruct-vs-swift-1-5-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-vl-2b-instruct-vs-swift-1-5-qwen3-8-27b.json",
        "title": "Qwen3-VL-2B-Instruct vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/qwen3-vl-2b-instruct-vs-swift-1-5-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-vl-30b-a3b-instruct-vs-swift-1-5-qwen3-8-27b.json",
        "title": "Qwen3-VL-30B-A3B-Instruct vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/qwen3-vl-30b-a3b-instruct-vs-swift-1-5-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-vl-32b-instruct-vs-swift-1-5-qwen3-8-27b.json",
        "title": "Qwen3-VL-32B-Instruct vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/qwen3-vl-32b-instruct-vs-swift-1-5-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-swift-1-5-qwen3-8-27b.json",
        "title": "Qwen3-VL-4B-Instruct vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-swift-1-5-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-vl-8b-instruct-vs-swift-1-5-qwen3-8-27b.json",
        "title": "Qwen3-VL-8B-Instruct vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/qwen3-vl-8b-instruct-vs-swift-1-5-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-swift-1-5-qwen3-8-27b.json",
        "title": "Qwopus3.8-27B-Flash-V2 vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-swift-1-5-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/smollm3-3b-vs-swift-1-5-qwen3-8-27b.json",
        "title": "SmolLM3-3B vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/smollm3-3b-vs-swift-1-5-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/spark-x2-6-4b-vs-swift-1-5-qwen3-8-27b.json",
        "title": "Spark-X2.6-4B vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/spark-x2-6-4b-vs-swift-1-5-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/swift-1-5-qwen3-8-27b-vs-swift-1-5-qwen3-8-flash-next.json",
        "title": "Swift 1.5 Qwen3.8-27B vs Swift 1.5 Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/swift-1-5-qwen3-8-27b-vs-swift-1-5-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/swift-1-5-qwen3-8-27b-vs-thinkingcap-qwen3-8-27b.json",
        "title": "Swift 1.5 Qwen3.8-27B vs ThinkingCap (Qwen3.8 27B)",
        "url": "https://www.anchorterminal.com/compare/swift-1-5-qwen3-8-27b-vs-thinkingcap-qwen3-8-27b"
      }
    ],
    "scores": [
      {
        "key": "capability",
        "name": "Capability",
        "pending": true,
        "weight": 35
      },
      {
        "by": 15,
        "edge": "swift-1-5-qwen3-8-27b",
        "key": "reach",
        "laguna-m-1": 65,
        "name": "Reach",
        "swift-1-5-qwen3-8-27b": 80,
        "weight": 25
      },
      {
        "by": 5,
        "edge": "laguna-m-1",
        "key": "risk",
        "laguna-m-1": 55,
        "name": "Risk",
        "swift-1-5-qwen3-8-27b": 50,
        "weight": 20
      },
      {
        "by": 25,
        "edge": "laguna-m-1",
        "key": "interface",
        "laguna-m-1": 85,
        "name": "Agent interface",
        "swift-1-5-qwen3-8-27b": 60,
        "weight": 10
      },
      {
        "by": 7,
        "edge": "swift-1-5-qwen3-8-27b",
        "key": "upkeep",
        "laguna-m-1": 36,
        "name": "Documentation and upkeep",
        "swift-1-5-qwen3-8-27b": 43,
        "weight": 10
      }
    ],
    "summary": "Swift 1.5 Qwen3.8-27B scores 61 (C) on agent readiness against Laguna M.1's 55.5 (C), and leads in 2 of 4 scored categories. Laguna M.1 leads on risk and agent interface. Both do open-weight models you run yourself.",
    "tutorials": [],
    "verdicts": {
      "laguna-m-1": "A large coding and agent cousin under plain Apache 2.0, for agents with a multi-GPU server. Its smallest build needs about 167 GB, more than a 128 GB machine, and no local runtime library packages it. The main risks are no upkeep since July, no hosted fallback shown today, no safety results, and repository code on the SGLang route.",
      "swift-1-5-qwen3-8-27b": "A Qwen3.8-27B cousin post-trained to think in fewer tokens, for coding and agent work with images in, that fits a 24 GB machine with UkisAI's own 4-bit builds. Commercial use is free only below US$1M revenue. The main risks are that nobody at UkisAI answers its discussions, and that the shorter thinking rests on UkisAI's own benchmarks."
    }
  },
  "kind": "anchor.page",
  "links": {
    "api": "https://www.anchorterminal.com/api/v1/index.json",
    "html": "https://www.anchorterminal.com/compare/laguna-m-1-vs-swift-1-5-qwen3-8-27b",
    "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-swift-1-5-qwen3-8-27b.json",
    "llms": "https://www.anchorterminal.com/llms.txt",
    "markdown": "https://www.anchorterminal.com/compare/laguna-m-1-vs-swift-1-5-qwen3-8-27b.md",
    "mcp": "https://www.anchorterminal.com/mcp",
    "slim": "https://www.anchorterminal.com/compare/laguna-m-1-vs-swift-1-5-qwen3-8-27b.min.md"
  },
  "markdown": "Swift 1.5 Qwen3.8-27B scores 61 (C) on agent readiness against Laguna M.1's 55.5 (C), and leads in 2 of 4 scored categories. Laguna M.1 leads on risk and agent interface. Both do open-weight models you run yourself.\n\n- Laguna M.1: grade C, 55.5/100, rank #79 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/laguna-m-1.md · JSON https://www.anchorterminal.com/api/v1/tools/laguna-m-1.json\n- Swift 1.5 Qwen3.8-27B: grade C, 61/100, rank #49 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/swift-1-5-qwen3-8-27b.md · JSON https://www.anchorterminal.com/api/v1/tools/swift-1-5-qwen3-8-27b.json\n- Best open-weight models you download and run yourself for AI agents: https://www.anchorterminal.com/best/open-weights/index.md\n- All 2870 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md\n\n## Which one, for what\n\n### Laguna M.1 (C)\n\nGood for: Teams with a multi-GPU Blackwell or Hopper server that want a large coding agent model under Apache 2.0.\n\nAhead on:\n- Risk, 55 against 50\n- Agent interface, 85 against 60\n\nWatch for: The smallest build needs about 167 GB, more than a 128 GB machine\n\n### Swift 1.5 Qwen3.8-27B (C)\n\nGood for: Agents on a 24 to 32 GB GPU or Mac that want Qwen3.8-27B's skills with shorter thinking, for teams under US$1M revenue or research.\n\nAhead on:\n- Reach, 80 against 65\n- Documentation and upkeep, 43 against 36\n\nWatch for: Commercial use above US$1M gross annual revenue needs a separate licence\n\n\n## Score by category\n\n| Category | Weight | Laguna M.1 | Swift 1.5 Qwen3.8-27B | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 65 | 80 | Swift 1.5 Qwen3.8-27B +15 |\n| Risk | 20% (20 this run) | 55 | 50 | Laguna M.1 +5 |\n| Agent interface | 10% (10 this run) | 85 | 60 | Laguna M.1 +25 |\n| Documentation and upkeep | 10% (10 this run) | 36 | 43 | Swift 1.5 Qwen3.8-27B +7 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **55.5 · C** | **61 · C** | |\n\n## Facts side by side\n\n| Fact | Laguna M.1 | Swift 1.5 Qwen3.8-27B |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Poolside | UkisAI |\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 | Swift Open License v1.0 (custom, not an OSI licence) on UkisAI's changes, free for commercial use below US$1M gross annual revenue with a separate Swift Enterprise License above it. Built on Qwen3.8-27B, whose Apache 2.0 still governs the base |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-07-14 | 2026-09-24 |\n| Terms last updated |  |  |\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 | 2.5/5 (4) | 2.3/5 (4) |\n\n## Verdicts\n\n**Laguna M.1.** A large coding and agent cousin under plain Apache 2.0, for agents with a multi-GPU server. Its smallest build needs about 167 GB, more than a 128 GB machine, and no local runtime library packages it. The main risks are no upkeep since July, no hosted fallback shown today, no safety results, and repository code on the SGLang route.\n\n**Swift 1.5 Qwen3.8-27B.** A Qwen3.8-27B cousin post-trained to think in fewer tokens, for coding and agent work with images in, that fits a 24 GB machine with UkisAI's own 4-bit builds. Commercial use is free only below US$1M revenue. The main risks are that nobody at UkisAI answers its discussions, and that the shorter thinking rests on UkisAI's own benchmarks.\n\n## Before you call either\n\n### Laguna M.1\n\n1. Run the NVFP4 build on Blackwell GPUs with `vllm serve poolside/Laguna-M.1-NVFP4 --tensor-parallel-size 2 --tool-call-parser poolside_v1 --reasoning-parser poolside_v1 --enable-auto-tool-choice`, on vLLM 0.21.0 or later\n2. Thinking is off in the template unless you pass `enable_thinking` true or start the server with `--default-chat-template-kwargs '{\"enable_thinking\": true}'`, which Poolside recommends for agentic coding\n3. Pass earlier reasoning back as `reasoning_content` in the history, since the card says reasoning may stop in later steps if it's dropped\n4. Sampling per the card is temperature 1.0 and top_k 20\n5. The FP8 build's Docker image and serve.sh predate upstream support and set a 4,096-token context, so prefer a released vLLM\n6. It doesn't fit our 128 GB reference machine at any published precision\n7. Validate JSON output yourself or use the runtime's guided decoding, since structured output isn't documented\n\n### Swift 1.5 Qwen3.8-27B\n\n1. Quickest start is `llama-server -hf ukisai/Swift-1.5-Qwen3.8-27B-GGUF:Q4_K_M --jinja`, about 18.4 GB with the vision projector\n2. On our reference machine BF16 fits, `vllm serve ukisai/Swift-1.5-Qwen3.8-27b --max-model-len 262144 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder`\n3. The template is Qwen3.8-27B's, so `chat_template_kwargs` with `reasoning_effort` low, medium or xhigh (default) and `enable_thinking` false work as on the base\n4. UkisAI benchmarked it at temperature 1.0, top_p 0.95, top_k 20 and presence_penalty 0, which is a reasonable starting point\n5. The MLX 4-bit build needs UkisAI's patched MLX-LM and has no image or video chat\n6. Check the licence before commercial use, it stops at US$1M gross annual revenue including affiliates\n7. MTP self-speculative decoding is available with the base's head, flags on the card\n\n## Questions\n\n### Which is better for AI agents, Laguna M.1 or Swift 1.5 Qwen3.8-27B?\n\nSwift 1.5 Qwen3.8-27B scores 61 (C) on agent readiness against Laguna M.1's 55.5 (C), and leads in 2 of 4 scored categories. Laguna M.1 leads on risk and agent interface.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/laguna-m-1-vs-swift-1-5-qwen3-8-27b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/laguna-m-1-vs-swift-1-5-qwen3-8-27b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"laguna-m-1\", \"b\": \"swift-1-5-qwen3-8-27b\"}`. From a terminal: `anchor compare laguna-m-1 swift-1-5-qwen3-8-27b`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/laguna-m-1.json and https://www.anchorterminal.com/api/v1/tools/swift-1-5-qwen3-8-27b.json\n\n## Other comparisons with Laguna M.1 or Swift 1.5 Qwen3.8-27B\n\n- [Agents-A1-4B vs Laguna M.1](https://www.anchorterminal.com/compare/agents-a1-4b-vs-laguna-m-1.md)\n- [Agents-A1-4B vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-swift-1-5-qwen3-8-27b.md)\n- [Apertus v1.5 8B vs Laguna M.1](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-laguna-m-1.md)\n- [Apertus v1.5 8B vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-swift-1-5-qwen3-8-27b.md)\n- [Apodex-1.1-mini vs Laguna M.1](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-laguna-m-1.md)\n- [Apodex-1.1-mini vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-swift-1-5-qwen3-8-27b.md)\n- [BigBang-v1 vs Laguna M.1](https://www.anchorterminal.com/compare/bigbang-v1-vs-laguna-m-1.md)\n- [BigBang-v1 vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/bigbang-v1-vs-swift-1-5-qwen3-8-27b.md)\n- [Cosmos-Reason2-2B vs Laguna M.1](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-laguna-m-1.md)\n- [Cosmos-Reason2-2B vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-swift-1-5-qwen3-8-27b.md)\n- [Darwin-36B-Opus vs Laguna M.1](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-laguna-m-1.md)\n- [Darwin-36B-Opus vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-swift-1-5-qwen3-8-27b.md)\n- [DeepSeek-V3.2 vs Laguna M.1](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-laguna-m-1.md)\n- [DeepSeek-V3.2 vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-swift-1-5-qwen3-8-27b.md)\n- [DeepSeek V4.1 Flash vs Laguna M.1](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-laguna-m-1.md)\n- [DeepSeek V4.1 Flash vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-swift-1-5-qwen3-8-27b.md)\n- [DeepSeek-V4-Flash-0731 vs Laguna M.1](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-laguna-m-1.md)\n- [DeepSeek-V4-Flash-0731 vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-swift-1-5-qwen3-8-27b.md)\n- [DiffusionGemma 26B A4B IT vs Laguna M.1](https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-laguna-m-1.md)\n- [DiffusionGemma 26B A4B IT vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-swift-1-5-qwen3-8-27b.md)\n- [Gemma 4 12B IT vs Laguna M.1](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-laguna-m-1.md)\n- [Gemma 4 12B IT vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-swift-1-5-qwen3-8-27b.md)\n- [Gemma 4 26B A4B IT vs Laguna M.1](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-laguna-m-1.md)\n- [Gemma 4 26B A4B IT vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-swift-1-5-qwen3-8-27b.md)\n- [Gemma 4 31B IT vs Laguna M.1](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-laguna-m-1.md)\n- [Gemma 4 31B IT vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-swift-1-5-qwen3-8-27b.md)\n- [Gemma 4 E2B IT vs Laguna M.1](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-laguna-m-1.md)\n- [Gemma 4 E2B IT vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-swift-1-5-qwen3-8-27b.md)\n- [Gemma 4 E4B IT vs Laguna M.1](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-laguna-m-1.md)\n- [Gemma 4 E4B IT vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-swift-1-5-qwen3-8-27b.md)\n- [GLM-4.5-Air vs Laguna M.1](https://www.anchorterminal.com/compare/glm-4-5-air-vs-laguna-m-1.md)\n- [GLM-4.5-Air vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/glm-4-5-air-vs-swift-1-5-qwen3-8-27b.md)\n- [GLM-4.7-Flash vs Laguna M.1](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-laguna-m-1.md)\n- [GLM-4.7-Flash vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-swift-1-5-qwen3-8-27b.md)\n- [GLM-5.3-Flash vs Laguna M.1](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-laguna-m-1.md)\n- [GLM-5.3-Flash vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-swift-1-5-qwen3-8-27b.md)\n- [GLM-5.3 vs Laguna M.1](https://www.anchorterminal.com/compare/glm-5-3-vs-laguna-m-1.md)\n- [GLM-5.3 vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/glm-5-3-vs-swift-1-5-qwen3-8-27b.md)\n- [gpt-oss-120b vs Laguna M.1](https://www.anchorterminal.com/compare/gpt-oss-120b-vs-laguna-m-1.md)\n- [gpt-oss-120b vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/gpt-oss-120b-vs-swift-1-5-qwen3-8-27b.md)\n- [gpt-oss-20b vs Laguna M.1](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-laguna-m-1.md)\n- [gpt-oss-20b vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-swift-1-5-qwen3-8-27b.md)\n- [Granite 4.2 3B vs Laguna M.1](https://www.anchorterminal.com/compare/granite-4-2-3b-vs-laguna-m-1.md)\n- [Granite 4.2 3B vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/granite-4-2-3b-vs-swift-1-5-qwen3-8-27b.md)\n- [Hy3 vs Laguna M.1](https://www.anchorterminal.com/compare/hy3-vs-laguna-m-1.md)\n- [Hy3 vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/hy3-vs-swift-1-5-qwen3-8-27b.md)\n- [Inkling-Small vs Laguna M.1](https://www.anchorterminal.com/compare/inkling-small-vs-laguna-m-1.md)\n- [Inkling-Small vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/inkling-small-vs-swift-1-5-qwen3-8-27b.md)\n- [Inkling vs Laguna M.1](https://www.anchorterminal.com/compare/inkling-vs-laguna-m-1.md)\n- [Inkling vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/inkling-vs-swift-1-5-qwen3-8-27b.md)\n- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs Laguna M.1](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-laguna-m-1.md)\n- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-swift-1-5-qwen3-8-27b.md)\n- [Kimi K3 vs Laguna M.1](https://www.anchorterminal.com/compare/kimi-k3-vs-laguna-m-1.md)\n- [Kimi K3 vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/kimi-k3-vs-swift-1-5-qwen3-8-27b.md)\n- [Kolibri-1 vs Laguna M.1](https://www.anchorterminal.com/compare/kolibri-1-vs-laguna-m-1.md)\n- [Kolibri-1 vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/kolibri-1-vs-swift-1-5-qwen3-8-27b.md)\n- [Laguna M.1 vs Laguna S 2.1](https://www.anchorterminal.com/compare/laguna-m-1-vs-laguna-s-2-1.md)\n- [Laguna 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 LFM2.5-2.6B](https://www.anchorterminal.com/compare/laguna-m-1-vs-lfm2-5-2-6b.md)\n- [Laguna M.1 vs Ling-3.0-tiny](https://www.anchorterminal.com/compare/laguna-m-1-vs-ling-3-0-tiny.md)\n- [Laguna M.1 vs MiMo-V2.6 Distill Qwen 9B](https://www.anchorterminal.com/compare/laguna-m-1-vs-mimo-v2-6-distill-qwen-9b.md)\n- [Laguna M.1 vs MiniCPM-o 4.5](https://www.anchorterminal.com/compare/laguna-m-1-vs-minicpm-o-4-5.md)\n- [Laguna M.1 vs MiniCPM5-2B](https://www.anchorterminal.com/compare/laguna-m-1-vs-minicpm5-2b.md)\n- [Laguna M.1 vs MiniMax-M2.5](https://www.anchorterminal.com/compare/laguna-m-1-vs-minimax-m2-5.md)\n- [Laguna M.1 vs MiniMax-M2.7](https://www.anchorterminal.com/compare/laguna-m-1-vs-minimax-m2-7.md)\n- [Laguna M.1 vs Muse Glimmer 30B](https://www.anchorterminal.com/compare/laguna-m-1-vs-muse-glimmer-30b.md)\n- [Laguna M.1 vs Nemotron 3.5 Lightning 30B A3B](https://www.anchorterminal.com/compare/laguna-m-1-vs-nemotron-3-5-lightning-30b-a3b.md)\n- [Laguna M.1 vs Nemotron 3 Nano 30B A3B](https://www.anchorterminal.com/compare/laguna-m-1-vs-nemotron-3-nano-30b-a3b.md)\n- [Laguna M.1 vs Nemotron 3 Nano 4B](https://www.anchorterminal.com/compare/laguna-m-1-vs-nemotron-3-nano-4b.md)\n- [Laguna M.1 vs Nemotron 3 Nano Omni 30B A3B Reasoning](https://www.anchorterminal.com/compare/laguna-m-1-vs-nemotron-3-nano-omni-30b-a3b-reasoning.md)\n- [Laguna M.1 vs Nemotron 3 Super 120B A12B](https://www.anchorterminal.com/compare/laguna-m-1-vs-nemotron-3-super-120b-a12b.md)\n- [Laguna M.1 vs Nemotron 3 Ultra 550B A55B](https://www.anchorterminal.com/compare/laguna-m-1-vs-nemotron-3-ultra-550b-a55b.md)\n- [Laguna M.1 vs occamy-1.0](https://www.anchorterminal.com/compare/laguna-m-1-vs-occamy-1-0.md)\n- [Laguna M.1 vs Olmo 3 7B Think](https://www.anchorterminal.com/compare/laguna-m-1-vs-olmo-3-7b-think.md)\n- [Laguna M.1 vs Ornith-1.5-35B-A3B](https://www.anchorterminal.com/compare/laguna-m-1-vs-ornith-1-5-35b-a3b.md)\n- [Laguna M.1 vs Ornith-1.5-397B](https://www.anchorterminal.com/compare/laguna-m-1-vs-ornith-1-5-397b.md)\n- [Laguna M.1 vs Ornith-1.5-9B](https://www.anchorterminal.com/compare/laguna-m-1-vs-ornith-1-5-9b.md)\n- [Laguna M.1 vs Qwen3-30B-A3B-Instruct-2507](https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-30b-a3b-instruct-2507.md)\n- [Laguna M.1 vs Qwen3-4B-Instruct-2507](https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-4b-instruct-2507.md)\n- [Laguna M.1 vs Qwen3.5-0.8B](https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-5-0-8b.md)\n- [Laguna M.1 vs Qwen3.5-122B-A10B](https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-5-122b-a10b.md)\n- [Laguna M.1 vs Qwen3.5-2B](https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-5-2b.md)\n- [Laguna M.1 vs Qwen3.5-397B-A17B](https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-5-397b-a17b.md)\n- [Laguna M.1 vs Qwen3.5-4B](https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-5-4b.md)\n- [Laguna M.1 vs Qwen3.5-9B](https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-5-9b.md)\n- [Laguna M.1 vs Qwen3.6-27B](https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-6-27b.md)\n- [Laguna M.1 vs Qwen3.6-35B-A3B](https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-6-35b-a3b.md)\n- [Laguna M.1 vs Qwen3.8-27B](https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-8-27b.md)\n- [Laguna M.1 vs Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-8-flash-next.md)\n- [Laguna M.1 vs Qwen3-Coder-30B-A3B-Instruct](https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-coder-30b-a3b-instruct.md)\n- [Laguna M.1 vs Qwen3-Coder-Next](https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-coder-next.md)\n- [Laguna M.1 vs Qwen3-Omni-30B-A3B-Instruct](https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-omni-30b-a3b-instruct.md)\n- [Laguna M.1 vs Qwen3-VL-2B-Instruct](https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-vl-2b-instruct.md)\n- [Laguna M.1 vs Qwen3-VL-30B-A3B-Instruct](https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-vl-30b-a3b-instruct.md)\n- [Laguna M.1 vs Qwen3-VL-32B-Instruct](https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-vl-32b-instruct.md)\n- [Laguna M.1 vs Qwen3-VL-4B-Instruct](https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-vl-4b-instruct.md)\n- [Laguna M.1 vs Qwen3-VL-8B-Instruct](https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-vl-8b-instruct.md)\n- [Laguna M.1 vs Qwopus3.8-27B-Flash-V2](https://www.anchorterminal.com/compare/laguna-m-1-vs-qwopus3-8-27b-flash-v2.md)\n- [Laguna M.1 vs SmolLM3-3B](https://www.anchorterminal.com/compare/laguna-m-1-vs-smollm3-3b.md)\n- [Laguna M.1 vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/laguna-m-1-vs-spark-x2-6-4b.md)\n- [Laguna M.1 vs Swift 1.5 Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/laguna-m-1-vs-swift-1-5-qwen3-8-flash-next.md)\n- [Laguna M.1 vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/laguna-m-1-vs-thinkingcap-qwen3-8-27b.md)\n- [Laguna S 2.1 vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/laguna-s-2-1-vs-swift-1-5-qwen3-8-27b.md)\n- [Laguna XS 2.1 vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-swift-1-5-qwen3-8-27b.md)\n- [LFM2.5-2.6B vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-swift-1-5-qwen3-8-27b.md)\n- [Ling-3.0-tiny vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-swift-1-5-qwen3-8-27b.md)\n- [MiMo-V2.6 Distill Qwen 9B vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-swift-1-5-qwen3-8-27b.md)\n- [MiniCPM-o 4.5 vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-swift-1-5-qwen3-8-27b.md)\n- [MiniCPM5-2B vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/minicpm5-2b-vs-swift-1-5-qwen3-8-27b.md)\n- [MiniMax-M2.5 vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/minimax-m2-5-vs-swift-1-5-qwen3-8-27b.md)\n- [MiniMax-M2.7 vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/minimax-m2-7-vs-swift-1-5-qwen3-8-27b.md)\n- [Muse Glimmer 30B vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-swift-1-5-qwen3-8-27b.md)\n- [Nemotron 3.5 Lightning 30B A3B vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-swift-1-5-qwen3-8-27b.md)\n- [Nemotron 3 Nano 30B A3B vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-swift-1-5-qwen3-8-27b.md)\n- [Nemotron 3 Nano 4B vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-swift-1-5-qwen3-8-27b.md)\n- [Nemotron 3 Nano Omni 30B A3B Reasoning vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-swift-1-5-qwen3-8-27b.md)\n- [Nemotron 3 Super 120B A12B vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-swift-1-5-qwen3-8-27b.md)\n- [Nemotron 3 Ultra 550B A55B vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-swift-1-5-qwen3-8-27b.md)\n- [occamy-1.0 vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/occamy-1-0-vs-swift-1-5-qwen3-8-27b.md)\n- [Olmo 3 7B Think vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-swift-1-5-qwen3-8-27b.md)\n- [Ornith-1.5-35B-A3B vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-swift-1-5-qwen3-8-27b.md)\n- [Ornith-1.5-397B vs Swift 1.5 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Qwen3.8-27B](https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-swift-1-5-qwen3-8-27b.md)\n- [Qwen3.5-4B vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/qwen3-5-4b-vs-swift-1-5-qwen3-8-27b.md)\n- [Qwen3.5-9B vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/qwen3-5-9b-vs-swift-1-5-qwen3-8-27b.md)\n- [Qwen3.6-27B vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/qwen3-6-27b-vs-swift-1-5-qwen3-8-27b.md)\n- [Qwen3.6-35B-A3B vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-swift-1-5-qwen3-8-27b.md)\n- [Qwen3.8-27B vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/qwen3-8-27b-vs-swift-1-5-qwen3-8-27b.md)\n- [Qwen3.8-Flash-Next vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-swift-1-5-qwen3-8-27b.md)\n- [Qwen3-Coder-30B-A3B-Instruct vs Swift 1.5 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