{
  "data": {
    "a": {
      "slug": "exaone-4-5-33b",
      "name": "EXAONE 4.5 33B",
      "vendor": "LG AI Research",
      "vendorUrl": "https://www.lgresearch.ai",
      "kind": "weights",
      "category": "open-weights",
      "summary": "EXAONE 4.5 33B is LG AI Research's open-weight vision-language model, 34B parameters, with tool calling, switchable reasoning and a 256K context, strong on documents and Korean. Its licence allows research and education only, with LG's own GGUF from 18 GB.",
      "url": "https://www.anchorterminal.com/tools/exaone-4-5-33b",
      "markdownUrl": "https://www.anchorterminal.com/tools/exaone-4-5-33b.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/exaone-4-5-33b.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/exaone-4-5-33b.json",
      "dns": "exaone-4-5-33b.grade.anchorterminal.com",
      "repo": "https://huggingface.co/LGAI-EXAONE/EXAONE-4.5-33B",
      "license": "EXAONE AI Model License Agreement 1.2 - NC (research and education only)",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "LGAI-EXAONE/EXAONE-4.5-33B"
        },
        {
          "registry": "huggingface",
          "name": "LGAI-EXAONE/EXAONE-4.5-33B-GGUF"
        },
        {
          "registry": "huggingface",
          "name": "LGAI-EXAONE/EXAONE-4.5-33B-AWQ"
        },
        {
          "registry": "huggingface",
          "name": "LGAI-EXAONE/EXAONE-4.5-33B-FP8"
        }
      ],
      "auth": "none",
      "authNotes": "The weights download from Hugging Face with no account or token. None of the four repositories is gated, though the licence says downloading is agreeing to it (checked 2026-11-10).",
      "pricing": "free",
      "pricingNotes": "Free to download for research and education. The EXAONE AI Model License Agreement 1.2 - NC prohibits commercial use of the model, its derivatives and its output without a separate agreement with LG. Running it yourself takes 20.6 GB at IQ4_XS with its projector, up to 68.7 GB in BF16 (checked 2026-11-10).",
      "priceSummary": "Free",
      "where": "local",
      "x402": {
        "level": "no",
        "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-11-10).",
        "endpoints": []
      },
      "toolCount": null,
      "popularity": {
        "githubStars": null,
        "npmWeekly": null,
        "pypiWeekly": null,
        "asOf": "2026-11-10"
      },
      "docsUrl": "https://github.com/LG-AI-EXAONE/EXAONE-4.5",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "multimodal",
        "vision",
        "documents",
        "korean",
        "reasoning",
        "tool-calling",
        "long-context",
        "non-commercial",
        "safetensors",
        "gguf",
        "awq",
        "fp8",
        "self-hosted"
      ],
      "lastRelease": "2026-07-06",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 61,
        "grade": "C",
        "agentReady": false,
        "rank": 0,
        "ranked": false,
        "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools",
        "rankOf": 1518,
        "categoryRank": 51,
        "methodology": "0.7",
        "run": "2026-10-01",
        "scores": {
          "interface": 85,
          "reach": 65,
          "risk": 50,
          "upkeep": 50
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "low",
          "date": "2026-11-10"
        },
        "negative": 0,
        "verdict": "A well-documented vision-language model with a full set of builds, tool calling, switchable reasoning, a 256K context and a stated knowledge cut-off. The licence decides whether you can use it: research and education only, output included, and LG can change the terms. Nothing answered on the repository and no safety results.",
        "bestFor": "Researchers and students working on documents, Korean or multimodal agents who don't need commercial rights",
        "strengths": [
          "A complete card, down to a knowledge cut-off",
          "GGUF, AWQ and FP8 builds from LG",
          "Released vLLM, Transformers and llama.cpp run it with no repository code",
          "Tool calling and reasoning control documented with parsers",
          "A 256K context, served on one large GPU"
        ],
        "weaknesses": [
          "Research and education only. No commercial use of the model or its output",
          "LG can amend the licence at any time, and it's revocable",
          "No building models that compete with LG's",
          "No reply from LG on the repository's discussions",
          "Not changed since the summer",
          "No safety results or training data sources on the card"
        ],
        "agentNotes": [
          "Don't use it, or anything it writes, commercially. The licence covers the output too",
          "Serve it with vLLM and `--reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser hermes`",
          "Set `enable_thinking` false for a direct answer. Thinking is on by default",
          "Use temperature 1.0, top_p 0.95 and presence_penalty 1.5 in general, and temperature 0.6 with top_k 20 for documents and Korean",
          "For llama.cpp, load a projector file from LG's GGUF repository with the weights for image input",
          "Its knowledge stops at December 2024"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 0,
        "avgRating": 0,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "low",
            "grade": "C",
            "methodology": "0.7",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 61
          }
        ],
        "editorialScores": {
          "interface": 85,
          "reach": 65,
          "risk": 50,
          "upkeep": 50
        },
        "provenanceScore": 49,
        "checked": {
          "categories": {
            "interface": "2026-11-10",
            "reach": "2026-11-10",
            "risk": "2026-11-10",
            "upkeep": "2026-11-10"
          },
          "last": "2026-11-10",
          "oldest": "2026-11-10",
          "stale": false,
          "staleAfterDays": 90
        }
      },
      "connect": {
        "install": "pip install \"vllm\u003e=0.33.0\" \"transformers\u003e=5.8.0\"\nvllm serve LGAI-EXAONE/EXAONE-4.5-33B --max-model-len 262144 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser hermes",
        "http": "# vLLM serves an OpenAI-compatible API at http://localhost:8000/v1. The card recommends temperature 1.0,\n# top_p 0.95 and presence_penalty 1.5, and enable_thinking false for a direct answer"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/exaone-4-5-33b"
      },
      "weights": {
        "trainedBy": "LG AI Research",
        "params": "34B",
        "context": 262144,
        "goodFor": "Research on document understanding, Korean and agent work with images, where non-commercial terms are acceptable.",
        "runtimes": [
          "llama.cpp",
          "vLLM",
          "Transformers"
        ],
        "builds": [
          {
            "name": "EXAONE-4.5-33B-GGUF IQ4_XS",
            "url": "https://huggingface.co/LGAI-EXAONE/EXAONE-4.5-33B-GGUF/blob/main/EXAONE-4.5-33B-IQ4_XS.gguf",
            "who": "publisher",
            "precision": "iq4_xs",
            "sizeGB": 20.6,
            "memoryGB": 32,
            "format": "gguf",
            "runsOn": [
              "llama.cpp"
            ],
            "note": "18.03 GB with the 2.57 GB F16 projector"
          },
          {
            "name": "EXAONE-4.5-33B-GGUF Q4_K_M",
            "url": "https://huggingface.co/LGAI-EXAONE/EXAONE-4.5-33B-GGUF/blob/main/EXAONE-4.5-33B-Q4_K_M.gguf",
            "who": "publisher",
            "precision": "q4_k_m",
            "sizeGB": 22.62,
            "memoryGB": 32,
            "format": "gguf",
            "runsOn": [
              "llama.cpp"
            ],
            "note": "20.05 GB with the 2.57 GB F16 projector"
          },
          {
            "name": "EXAONE-4.5-33B-AWQ",
            "url": "https://huggingface.co/LGAI-EXAONE/EXAONE-4.5-33B-AWQ",
            "who": "publisher",
            "precision": "awq",
            "sizeGB": 22.75,
            "memoryGB": 32,
            "format": "safetensors",
            "runsOn": [
              "vLLM"
            ],
            "note": "LG's 4-bit AWQ build"
          },
          {
            "name": "EXAONE-4.5-33B-GGUF Q8_0",
            "url": "https://huggingface.co/LGAI-EXAONE/EXAONE-4.5-33B-GGUF/blob/main/EXAONE-4.5-33B-Q8_0.gguf",
            "who": "publisher",
            "precision": "q8_0",
            "sizeGB": 37.71,
            "memoryGB": 48,
            "format": "gguf",
            "runsOn": [
              "llama.cpp"
            ],
            "note": "35.14 GB with the 2.57 GB F16 projector"
          },
          {
            "name": "EXAONE-4.5-33B-FP8",
            "url": "https://huggingface.co/LGAI-EXAONE/EXAONE-4.5-33B-FP8",
            "who": "publisher",
            "precision": "fp8",
            "sizeGB": 41.31,
            "memoryGB": 64,
            "format": "safetensors",
            "runsOn": [
              "vLLM"
            ],
            "note": "LG's FP8 build"
          },
          {
            "name": "EXAONE-4.5-33B (BF16 safetensors)",
            "url": "https://huggingface.co/LGAI-EXAONE/EXAONE-4.5-33B",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 68.7,
            "memoryGB": 96,
            "format": "safetensors",
            "runsOn": [
              "vLLM",
              "Transformers"
            ],
            "note": "Two shards"
          }
        ],
        "run": "llama-server -hf LGAI-EXAONE/EXAONE-4.5-33B-GGUF:Q4_K_M --jinja",
        "runBuild": "EXAONE-4.5-33B-GGUF Q4_K_M",
        "checked": "2026-11-10",
        "minMemoryGB": 32
      },
      "family": {
        "lab": "LG AI Research",
        "family": "EXAONE 4",
        "url": "https://www.anchorterminal.com/open-weights/#lg-ai-research",
        "others": [
          {
            "name": "EXAONE-4.0.1-32B",
            "url": "https://huggingface.co/LGAI-EXAONE/EXAONE-4.0.1-32B"
          },
          {
            "name": "EXAONE-4.0-1.2B",
            "url": "https://huggingface.co/LGAI-EXAONE/EXAONE-4.0-1.2B"
          }
        ]
      },
      "area": "models",
      "provenance": {
        "legalEntity": "LG Management Development Institute Co., Ltd., the licensor named in the model's licence. The card names LG AI Research",
        "domain": "lgresearch.ai",
        "domainRegistered": "",
        "endpointOnVendorDomain": null,
        "terms": "https://huggingface.co/LGAI-EXAONE/EXAONE-4.5-33B/blob/main/LICENSE",
        "privacy": "",
        "statusPage": "",
        "changelog": "https://huggingface.co/LGAI-EXAONE/EXAONE-4.5-33B/commits/main",
        "securityTxt": "none",
        "checked": "2026-11-10",
        "notes": [
          "The card and the Hub organisation name LG AI Research, and the licence names LG Management Development Institute Co., Ltd. as licensor. How the two relate wasn't read first-hand.",
          "www.lgresearch.ai's robots.txt allows reading. Its home page gave little text and its about page a 404, so no people or ownership facts were taken from it.",
          "The domain's registration date wasn't looked up. www.lgresearch.ai has no security.txt (404 on 10 November 2026).",
          "Nothing is hosted under this listing, so the model licence stands in for terms of service and no privacy policy is scored.",
          "The changelog is the commit history, 8 commits from 9 April to 6 July 2026."
        ],
        "score": 49
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/exaone-4-5-33b.json"
    },
    "answer": "EXAONE 4.5 33B and Swift 1.5 Qwen3.8-27B score within a point of each other on agent readiness, 61 (C) and 61 (C). Swift 1.5 Qwen3.8-27B leads on reach.",
    "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",
      "dns": "swift-1-5-qwen3-8-27b.grade.anchorterminal.com",
      "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": "2026-11-03"
      },
      "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": 1518,
        "categoryRank": 47,
        "methodology": "0.7",
        "run": "2026-10-01",
        "scores": {
          "interface": 60,
          "reach": 80,
          "risk": 50,
          "upkeep": 43
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "low",
          "date": "2026-11-03"
        },
        "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": "0.7",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 61
          }
        ],
        "editorialScores": {
          "interface": 60,
          "reach": 80,
          "risk": 50,
          "upkeep": 43
        },
        "provenanceScore": 23,
        "checked": {
          "categories": {
            "interface": "2026-11-03",
            "reach": "2026-11-03",
            "risk": "2026-11-03",
            "upkeep": "2026-11-03"
          },
          "last": "2026-11-03",
          "oldest": "2026-11-03",
          "stale": false,
          "staleAfterDays": 90
        }
      },
      "connect": {
        "install": "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": "2026-11-03",
        "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 main repository's history was squashed to two commits; the ONNX builds carry dated commits up to 8 October 2026.",
          "ukisai.com/.well-known/security.txt is 404 (re-read 3 November 2026), and ukisai/.github has no SECURITY.md."
        ],
        "score": 23
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/swift-1-5-qwen3-8-27b.json",
      "live": {
        "slug": "swift-1-5-qwen3-8-27b",
        "securityTxt": {
          "url": "https://ukisai.com/.well-known/security.txt",
          "state": "none",
          "checkedAt": "2026-11-10T16:09:27.002533317Z"
        },
        "domain": {
          "domain": "ukisai.com",
          "registered": "2024-06-01",
          "source": "https://rdap.verisign.com/com/v1/domain/ukisai.com",
          "checkedAt": "2026-11-08T14:50:37.598812458Z"
        },
        "pages": [
          {
            "url": "https://huggingface.co/ukisai/Swift-1.5-Qwen3.8-27b/blob/main/LICENSE",
            "kind": "terms",
            "status": 200,
            "checkedAt": "2026-11-10T16:23:52.674908486Z",
            "changedAt": "2026-11-10T16:23:52.674908486Z",
            "fingerprint": "1e6d67d5be90"
          }
        ],
        "updatedAt": "2026-11-10T16:23:52.674908486Z"
      }
    },
    "edges": [
      {
        "by": 25,
        "category": "interface",
        "leads": "exaone-4-5-33b",
        "name": "Agent interface",
        "reasons": {
          "a": "Chat template 20, chat_template.jinja in the repository (5,930 characters). Tool-calling format 25, the card's vLLM command names the parser, hermes with auto tool choice, and its agentic example calls tools from MCP servers through the OpenAI-compatible API, so declaration and return follow that API. Trained tool calling 20, the card reports agentic tool use among its language results and shows an agent run with several tool servers, and the template renders every call in a turn. Structured output 0, Nothing on the card about JSON or schema-constrained output. Reasoning control 10, enable_thinking is on by default and the card says to set it false for the non-reasoning mode, with an example of each. Context 10, 262,144 tokens per the card's configuration table and config.json, and the card's serve commands run at that length. 20 + 25 + 20 + 0 + 10 + 10 = 85.",
          "b": "Chat template 20, the base's Jinja file. Tool-calling format 10 of 25, the format is only in the template and the parser only in serving commands. Trained tool calling 20, several calls a turn and agentic post-training. Structured output 0, not documented. Reasoning control 0, the card mentions reasoning_effort but not how to set it, turn thinking off or read the reasoning. Context 10, 262,144 tokens native."
        },
        "scores": {
          "a": 85,
          "b": 60
        }
      },
      {
        "by": 15,
        "category": "reach",
        "leads": "swift-1-5-qwen3-8-27b",
        "name": "Reach",
        "reasons": {
          "a": "Memory 20, the smallest build at 4 bits is LG's IQ4_XS GGUF, 18.03 GB with the 2.57 GB projector, and 20.60 GB × 1.2 = 24.7 GB, within 32 GB. Without the projector, for text only, it would fit 24 GB. Runtimes 15, Released vLLM 0.33.0 has exaone4_5.py, Transformers 5.22.2 has the exaone4_5 model, and llama.cpp b12242 has the architecture, with LG's own GGUF. Three at 5 each. Builds 15, LG publishes GGUF at five quantised precisions, an AWQ build and an FP8 build. Terms 5, Research and educational use only. Download 10, the Hub API reports gated false for all four repositories on 10 November 2026, so the files download with no account and no click-through, though the licence says downloading is acceptance. Hosted 0, Hugging Face maps no inference provider to it, and the card names no API that serves it. 20 + 15 + 15 + 5 + 10 + 0 = 65.",
          "b": "Memory 25 of 30, UkisAI's 4-bit MLX build is 15.9 GB and its 4-bit GGUF with the vision projector 16.4 GB, both under 20 GB with a fifth added, which fits 24 GB. Runtimes 20, the architecture is Qwen3.8-27B's, which released llama.cpp, vLLM, SGLang and Transformers run, while Ollama's entry is the base's. Builds 15, GGUF, INT4, NVFP4, FP8 and MLX builds from UkisAI. Terms 10, a custom licence that is free for commercial use below US$1M revenue. Download 10, not gated. Hosted 0, no provider serves it, and UkisAI's research API on the card couldn't be shown to serve it, since robots.txt disallows calling it."
        },
        "scores": {
          "a": 65,
          "b": 80
        }
      },
      {
        "by": 7,
        "category": "upkeep",
        "leads": "exaone-4-5-33b",
        "name": "Documentation and upkeep",
        "reasons": {
          "a": "Model card 20, Uses (the quickstart's question answering, document and agent examples), limitations, the architecture, the parameters, the context, the languages (in the metadata) and a knowledge cut-off of December 2024 are all on the card. Training data 5, A knowledge cut-off and one line on excluding personal, harmful and biased content, with no sources or sizes. Last change 10, the main repository last changed on 6 July 2026 (dependencies moved to official releases), 127 days before this check, and the FP8 repository on 9 August, 93 days. Responsiveness 0, none of the 5 most recent discussions that aren't pull requests (13 April to 4 May 2026) has a reply from an LGAI-EXAONE member, counted on 10 November by author flags and dates with a script, text not read. Version policy 5, EXAONE 4.0 and 4.0.1 stay in their own repositories and 4.5 came out in new ones, with no dated note on the older cards that we read. File health 10, Config, template and card agree on the architecture and the 262,144 tokens, and a July commit moved the dependencies to released versions. 20 + 5 + 10 + 0 + 5 + 10 = 50.",
          "b": "Model card 8 of 20, intended use, parameters and context only. Training data 5, a general description with no cut-off. Last change 20, UkisAI's own ONNX builds of the model took a new card and licence on 8 October, 26 days ago (the main repository's last commit is the 3 October README update). Responsiveness 0, UkisAI answered none of 8 discussions. Version policy 5, Swift 1.0 stays in its own repository without a note. File health 5, reports of a broken model wait without a reply and the card's metadata claims a gate that doesn't exist."
        },
        "scores": {
          "a": 50,
          "b": 43
        }
      }
    ],
    "facts": [
      {
        "a": "Open-weight model",
        "b": "Open-weight model",
        "name": "Kind"
      },
      {
        "a": "LG AI Research",
        "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": "EXAONE AI Model License Agreement 1.2 - NC (research and education only)",
        "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": "2026-07-06",
        "b": "2026-09-24",
        "name": "Last release"
      },
      {
        "a": "not read yet",
        "b": "no date given",
        "name": "Terms last updated"
      },
      {
        "a": "no document linked",
        "b": "no document linked",
        "name": "Privacy policy last updated"
      },
      {
        "a": "not read yet",
        "b": "not found in the text",
        "name": "Customer content may train models"
      },
      {
        "a": "not read yet",
        "b": "not found in the text",
        "name": "Terms restrict automated access"
      },
      {
        "a": "not read yet",
        "b": "not found in the text",
        "name": "Terms restrict benchmarking"
      },
      {
        "a": "not read yet",
        "b": "not found in the text",
        "name": "Terms or service can change without notice"
      },
      {
        "a": "not read yet",
        "b": "not found in the text",
        "name": "Arbitration or class-action waiver"
      },
      {
        "a": "none",
        "b": "2.3/5 (4)",
        "name": "Agent reviews"
      }
    ],
    "faq": [
      {
        "answer": "EXAONE 4.5 33B and Swift 1.5 Qwen3.8-27B score within a point of each other on agent readiness, 61 (C) and 61 (C). Swift 1.5 Qwen3.8-27B leads on reach.",
        "question": "Which is better for AI agents, EXAONE 4.5 33B or Swift 1.5 Qwen3.8-27B?"
      }
    ],
    "goodFor": [
      {
        "aheadOn": [
          "Agent interface, 85 against 60",
          "Documentation and upkeep, 50 against 43"
        ],
        "also": null,
        "goodFor": "Researchers and students working on documents, Korean or multimodal agents who don't need commercial rights",
        "slug": "exaone-4-5-33b",
        "watchFor": "Research and education only. No commercial use of the model or its output"
      },
      {
        "aheadOn": [
          "Reach, 80 against 65"
        ],
        "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"
      }
    ],
    "history": null,
    "job": {
      "capability": "model.open-weights",
      "name": "Open-weight models you run yourself"
    },
    "others": [
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-exaone-4-5-33b.json",
        "title": "Agents-A1-4B vs EXAONE 4.5 33B",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-exaone-4-5-33b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-swift-1-5-qwen3-8-27b.json",
        "title": "Agents-A1-4B vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-swift-1-5-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-exaone-4-5-33b.json",
        "title": "Apertus v1.5 8B vs EXAONE 4.5 33B",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-exaone-4-5-33b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-swift-1-5-qwen3-8-27b.json",
        "title": "Apertus v1.5 8B vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-swift-1-5-qwen3-8-27b"
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      {
        "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-exaone-4-5-33b.json",
        "title": "Apodex-1.1-mini vs EXAONE 4.5 33B",
        "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-exaone-4-5-33b"
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      {
        "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-swift-1-5-qwen3-8-27b.json",
        "title": "Apodex-1.1-mini vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-swift-1-5-qwen3-8-27b"
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        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-exaone-4-5-33b.json",
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        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-exaone-4-5-33b"
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        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-swift-1-5-qwen3-8-27b.json",
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        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-swift-1-5-qwen3-8-27b"
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        "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-exaone-4-5-33b"
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        "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-swift-1-5-qwen3-8-27b.json",
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        "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-swift-1-5-qwen3-8-27b"
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        "json": "https://www.anchorterminal.com/compare/carbon-x-vs-exaone-4-5-33b.json",
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        "url": "https://www.anchorterminal.com/compare/carbon-x-vs-exaone-4-5-33b"
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        "json": "https://www.anchorterminal.com/compare/carbon-x-vs-swift-1-5-qwen3-8-27b.json",
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        "url": "https://www.anchorterminal.com/compare/carbon-x-vs-swift-1-5-qwen3-8-27b"
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        "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-exaone-4-5-33b"
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        "json": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-ripple-65b-preview.json",
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        "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-swift-1-5-qwen3-8-27b"
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        "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-swift-1-5-qwen3-8-27b"
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        "json": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-swift-1-5-qwen3-8-27b.json",
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        "title": "Kimi K3.1 Pro vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-swift-1-5-qwen3-8-27b"
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        "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-swift-1-5-qwen3-8-27b"
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        "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-swift-1-5-qwen3-8-27b.json",
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        "url": "https://www.anchorterminal.com/compare/kolibri-1-vs-swift-1-5-qwen3-8-27b"
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        "url": "https://www.anchorterminal.com/compare/kronos-vs-swift-1-5-qwen3-8-27b"
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        "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-swift-1-5-qwen3-8-27b.json",
        "title": "LFM2.5-2.6B vs Swift 1.5 Qwen3.8-27B",
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        "title": "LFM3-1.7B vs Swift 1.5 Qwen3.8-27B",
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        "json": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-swift-1-5-qwen3-8-27b.json",
        "title": "LFM3-Audio-2B vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-swift-1-5-qwen3-8-27b"
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        "json": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-swift-1-5-qwen3-8-27b.json",
        "title": "LFM3-Omni-2B vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-swift-1-5-qwen3-8-27b"
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        "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-swift-1-5-qwen3-8-27b.json",
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        "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-swift-1-5-qwen3-8-27b"
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        "json": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-swift-1-5-qwen3-8-27b.json",
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        "url": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-swift-1-5-qwen3-8-27b"
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        "json": "https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-swift-1-5-qwen3-8-27b.json",
        "title": "Ling-3.1-flash-1019 vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-swift-1-5-qwen3-8-27b"
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      {
        "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-swift-1-5-qwen3-8-27b.json",
        "title": "LongCat-2.5 vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-swift-1-5-qwen3-8-27b"
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        "json": "https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-swift-1-5-qwen3-8-27b.json",
        "title": "MiMo-V2.6 Distill Qwen 9B vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-swift-1-5-qwen3-8-27b"
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        "title": "MiMo-V2.6-Flash-RL vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-swift-1-5-qwen3-8-27b"
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        "exaone-4-5-33b": 65,
        "key": "reach",
        "name": "Reach",
        "swift-1-5-qwen3-8-27b": 80,
        "weight": 25
      },
      {
        "by": 0,
        "edge": "",
        "exaone-4-5-33b": 50,
        "key": "risk",
        "name": "Risk",
        "swift-1-5-qwen3-8-27b": 50,
        "weight": 20
      },
      {
        "by": 25,
        "edge": "exaone-4-5-33b",
        "exaone-4-5-33b": 85,
        "key": "interface",
        "name": "Agent interface",
        "swift-1-5-qwen3-8-27b": 60,
        "weight": 10
      },
      {
        "by": 7,
        "edge": "exaone-4-5-33b",
        "exaone-4-5-33b": 50,
        "key": "upkeep",
        "name": "Documentation and upkeep",
        "swift-1-5-qwen3-8-27b": 43,
        "weight": 10
      }
    ],
    "summary": "EXAONE 4.5 33B and Swift 1.5 Qwen3.8-27B score within a point of each other on agent readiness, 61 (C) and 61 (C). Swift 1.5 Qwen3.8-27B leads on reach. Both do open-weight models you run yourself.",
    "tasks": null,
    "tutorials": [],
    "verdicts": {
      "exaone-4-5-33b": "A well-documented vision-language model with a full set of builds, tool calling, switchable reasoning, a 256K context and a stated knowledge cut-off. The licence decides whether you can use it: research and education only, output included, and LG can change the terms. Nothing answered on the repository and no safety results.",
      "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": {
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    "html": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-swift-1-5-qwen3-8-27b",
    "json": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-swift-1-5-qwen3-8-27b.json",
    "llms": "https://www.anchorterminal.com/llms.txt",
    "markdown": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-swift-1-5-qwen3-8-27b.md",
    "mcp": "https://www.anchorterminal.com/mcp",
    "slim": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-swift-1-5-qwen3-8-27b.min.md"
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  "markdown": "EXAONE 4.5 33B and Swift 1.5 Qwen3.8-27B score within a point of each other on agent readiness, 61 (C) and 61 (C). Swift 1.5 Qwen3.8-27B leads on reach. Both do open-weight models you run yourself.\n\n- EXAONE 4.5 33B: grade C, 61/100, rank #61 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/exaone-4-5-33b.md · JSON https://www.anchorterminal.com/api/v1/tools/exaone-4-5-33b.json\n- Swift 1.5 Qwen3.8-27B: grade C, 61/100, rank #57 of 160 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 6359 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md\n\n## Which one, for what\n\n### EXAONE 4.5 33B (C)\n\nGood for: Researchers and students working on documents, Korean or multimodal agents who don't need commercial rights\n\nAhead on:\n- Agent interface, 85 against 60\n- Documentation and upkeep, 50 against 43\n\nWatch for: Research and education only. No commercial use of the model or its output\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\nWatch for: Commercial use above US$1M gross annual revenue needs a separate licence\n\n\n## Why the edge\n\n### Agent interface: EXAONE 4.5 33B +25\n\n- EXAONE 4.5 33B (85): Chat template 20, chat_template.jinja in the repository (5,930 characters). Tool-calling format 25, the card's vLLM command names the parser, hermes with auto tool choice, and its agentic example calls tools from MCP servers through the OpenAI-compatible API, so declaration and return follow that API. Trained tool calling 20, the card reports agentic tool use among its language results and shows an agent run with several tool servers, and the template renders every call in a turn. Structured output 0, Nothing on the card about JSON or schema-constrained output. Reasoning control 10, enable_thinking is on by default and the card says to set it false for the non-reasoning mode, with an example of each. Context 10, 262,144 tokens per the card's configuration table and config.json, and the card's serve commands run at that length. 20 + 25 + 20 + 0 + 10 + 10 = 85.\n- Swift 1.5 Qwen3.8-27B (60): Chat template 20, the base's Jinja file. Tool-calling format 10 of 25, the format is only in the template and the parser only in serving commands. Trained tool calling 20, several calls a turn and agentic post-training. Structured output 0, not documented. Reasoning control 0, the card mentions reasoning_effort but not how to set it, turn thinking off or read the reasoning. Context 10, 262,144 tokens native.\n\n### Reach: Swift 1.5 Qwen3.8-27B +15\n\n- EXAONE 4.5 33B (65): Memory 20, the smallest build at 4 bits is LG's IQ4_XS GGUF, 18.03 GB with the 2.57 GB projector, and 20.60 GB × 1.2 = 24.7 GB, within 32 GB. Without the projector, for text only, it would fit 24 GB. Runtimes 15, Released vLLM 0.33.0 has exaone4_5.py, Transformers 5.22.2 has the exaone4_5 model, and llama.cpp b12242 has the architecture, with LG's own GGUF. Three at 5 each. Builds 15, LG publishes GGUF at five quantised precisions, an AWQ build and an FP8 build. Terms 5, Research and educational use only. Download 10, the Hub API reports gated false for all four repositories on 10 November 2026, so the files download with no account and no click-through, though the licence says downloading is acceptance. Hosted 0, Hugging Face maps no inference provider to it, and the card names no API that serves it. 20 + 15 + 15 + 5 + 10 + 0 = 65.\n- Swift 1.5 Qwen3.8-27B (80): Memory 25 of 30, UkisAI's 4-bit MLX build is 15.9 GB and its 4-bit GGUF with the vision projector 16.4 GB, both under 20 GB with a fifth added, which fits 24 GB. Runtimes 20, the architecture is Qwen3.8-27B's, which released llama.cpp, vLLM, SGLang and Transformers run, while Ollama's entry is the base's. Builds 15, GGUF, INT4, NVFP4, FP8 and MLX builds from UkisAI. Terms 10, a custom licence that is free for commercial use below US$1M revenue. Download 10, not gated. Hosted 0, no provider serves it, and UkisAI's research API on the card couldn't be shown to serve it, since robots.txt disallows calling it.\n\n### Documentation and upkeep: EXAONE 4.5 33B +7\n\n- EXAONE 4.5 33B (50): Model card 20, Uses (the quickstart's question answering, document and agent examples), limitations, the architecture, the parameters, the context, the languages (in the metadata) and a knowledge cut-off of December 2024 are all on the card. Training data 5, A knowledge cut-off and one line on excluding personal, harmful and biased content, with no sources or sizes. Last change 10, the main repository last changed on 6 July 2026 (dependencies moved to official releases), 127 days before this check, and the FP8 repository on 9 August, 93 days. Responsiveness 0, none of the 5 most recent discussions that aren't pull requests (13 April to 4 May 2026) has a reply from an LGAI-EXAONE member, counted on 10 November by author flags and dates with a script, text not read. Version policy 5, EXAONE 4.0 and 4.0.1 stay in their own repositories and 4.5 came out in new ones, with no dated note on the older cards that we read. File health 10, Config, template and card agree on the architecture and the 262,144 tokens, and a July commit moved the dependencies to released versions. 20 + 5 + 10 + 0 + 5 + 10 = 50.\n- Swift 1.5 Qwen3.8-27B (43): Model card 8 of 20, intended use, parameters and context only. Training data 5, a general description with no cut-off. Last change 20, UkisAI's own ONNX builds of the model took a new card and licence on 8 October, 26 days ago (the main repository's last commit is the 3 October README update). Responsiveness 0, UkisAI answered none of 8 discussions. Version policy 5, Swift 1.0 stays in its own repository without a note. File health 5, reports of a broken model wait without a reply and the card's metadata claims a gate that doesn't exist.\n\n## Score by category\n\n| Category | Weight | EXAONE 4.5 33B | 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) | 50 | 50 | even |\n| Agent interface | 10% (10 this run) | 85 | 60 | EXAONE 4.5 33B +25 |\n| Documentation and upkeep | 10% (10 this run) | 50 | 43 | EXAONE 4.5 33B +7 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **61 · C** | **61 · C** | |\n\n## Facts side by side\n\n| Fact | EXAONE 4.5 33B | Swift 1.5 Qwen3.8-27B |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | LG AI Research | 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 | EXAONE AI Model License Agreement 1.2 - NC (research and education only) | 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 | 2026-07-06 | 2026-09-24 |\n| Terms last updated | not read yet | no date given |\n| Privacy policy last updated | no document linked | no document linked |\n| Customer content may train models | not read yet | not found in the text |\n| Terms restrict automated access | not read yet | not found in the text |\n| Terms restrict benchmarking | not read yet | not found in the text |\n| Terms or service can change without notice | not read yet | not found in the text |\n| Arbitration or class-action waiver | not read yet | not found in the text |\n| Agent reviews | none | 2.3/5 (4) |\n\n## Verdicts\n\n**EXAONE 4.5 33B.** A well-documented vision-language model with a full set of builds, tool calling, switchable reasoning, a 256K context and a stated knowledge cut-off. The licence decides whether you can use it: research and education only, output included, and LG can change the terms. Nothing answered on the repository and no safety results.\n\n**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### EXAONE 4.5 33B\n\n1. Don't use it, or anything it writes, commercially. The licence covers the output too\n2. Serve it with vLLM and `--reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser hermes`\n3. Set `enable_thinking` false for a direct answer. Thinking is on by default\n4. Use temperature 1.0, top_p 0.95 and presence_penalty 1.5 in general, and temperature 0.6 with top_k 20 for documents and Korean\n5. For llama.cpp, load a projector file from LG's GGUF repository with the weights for image input\n6. Its knowledge stops at December 2024\n\n### 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, EXAONE 4.5 33B or Swift 1.5 Qwen3.8-27B?\n\nEXAONE 4.5 33B and Swift 1.5 Qwen3.8-27B score within a point of each other on agent readiness, 61 (C) and 61 (C). Swift 1.5 Qwen3.8-27B leads on reach.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-swift-1-5-qwen3-8-27b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-swift-1-5-qwen3-8-27b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"exaone-4-5-33b\", \"b\": \"swift-1-5-qwen3-8-27b\"}`. From a terminal: `anchor compare exaone-4-5-33b swift-1-5-qwen3-8-27b`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/exaone-4-5-33b.json and https://www.anchorterminal.com/api/v1/tools/swift-1-5-qwen3-8-27b.json\n\n## Other comparisons with EXAONE 4.5 33B or Swift 1.5 Qwen3.8-27B\n\n- [Agents-A1-4B vs EXAONE 4.5 33B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-exaone-4-5-33b.md)\n- [Agents-A1-4B vs 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 EXAONE 4.5 33B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-exaone-4-5-33b.md)\n- [Apertus v1.5 8B vs 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 EXAONE 4.5 33B](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-exaone-4-5-33b.md)\n- [Apodex-1.1-mini vs Swift 1.5 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