{
  "data": {
    "a": {
      "slug": "mistral-large-4",
      "name": "Mistral Large 4",
      "vendor": "Mistral AI",
      "vendorUrl": "https://mistral.ai",
      "kind": "weights",
      "category": "open-weights",
      "summary": "Mistral Large 4 (\"Le Chonk\") is Mistral AI's open-weight mixture-of-experts model of about 1T parameters with 52B active, taking text and images, with three reasoning efforts and a 1M-token context, published in FP8 and NVFP4.",
      "url": "https://www.anchorterminal.com/tools/mistral-large-4",
      "markdownUrl": "https://www.anchorterminal.com/tools/mistral-large-4.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/mistral-large-4.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/mistral-large-4.json",
      "repo": "https://huggingface.co/mistralai/Mistral-Large-4-1T-A52B",
      "license": "Mistral Ecosystem Open-Weight License (modified MIT)",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "mistralai/Mistral-Large-4-1T-A52B"
        },
        {
          "registry": "huggingface",
          "name": "mistralai/Mistral-Large-4-1T-A52B-NVFP4"
        }
      ],
      "auth": "none",
      "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. Both repositories report gated false (checked 2026-10-30).",
      "pricing": "free",
      "pricingNotes": "Free to download. The Mistral Ecosystem Open-Weight License allows commercial use, except that a licensee or affiliate that operates a Model-as-a-Service business needs a separate commercial licence from Mistral before any commercial use; internal use and use through Mistral's own products are exempt. Mistral's API serves it as mistral-large-4 at $0.70 input, $0.07 cached input and $2.10 output per million tokens (checked 2026-10-30).",
      "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 2026-10-30).",
        "endpoints": []
      },
      "toolCount": null,
      "popularity": {
        "githubStars": null,
        "npmWeekly": null,
        "pypiWeekly": null,
        "asOf": "2026-10-30"
      },
      "docsUrl": "https://huggingface.co/mistralai/Mistral-Large-4-1T-A52B",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "moe",
        "multimodal",
        "frontier",
        "reasoning",
        "tool-calling",
        "long-context",
        "multilingual",
        "safetensors",
        "fp8",
        "nvfp4",
        "self-hosted"
      ],
      "lastRelease": "2026-10-29",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 54.6,
        "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": 1239,
        "categoryRank": 72,
        "methodology": "0.7",
        "run": "2026-10-01",
        "scores": {
          "capability": 32,
          "interface": 95,
          "reach": 50,
          "risk": 75,
          "upkeep": 64
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "medium",
          "date": "2026-10-30"
        },
        "negative": 0,
        "verdict": "A frontier-sized open cousin for coding agents and defensive security, with strict JSON Schema output, parallel tool calls and three reasoning efforts. It needs a multi-GPU server, about 732 GB for the smallest 4-bit build, and vLLM and SGLang run it only from special images. The main risk is a licence that bars Model-as-a-Service businesses without a separate deal.",
        "bestFor": "Operators with an 8-GPU Blackwell or H200 node who want a frontier-sized open model for coding agents and defensive security, with a hosted twin on Mistral's API.",
        "strengths": [
          "Safetensors only, loaded by Transformers' own code with a Jinja template, in Mistral's verified organisation with a hash on every file",
          "Tool calling documented end to end with a named parser, several calls a turn and strict JSON Schema output",
          "Reasoning effort budgeted per request, low, medium or high, with the reasoning returned apart from the answer",
          "FP8 and NVFP4 builds from Mistral, and Mistral's own API serves the same model for agents without the hardware",
          "First among open-weight models on Cognition's FrontierCode 2.0 leaderboard, run in Mistral's own harness"
        ],
        "weaknesses": [
          "The smallest 4-bit build needs about 732 GB, so nothing fits a single workstation or a 128 GB machine",
          "No vLLM or SGLang release supports it yet, only Docker images tagged mistral-large-4",
          "A Model-as-a-Service business needs a separate commercial licence from Mistral before any commercial use",
          "Safety results are Mistral's own with no settings to rerun them, and no limitations, training data sources or knowledge cut-off are published",
          "The 1M context comes from YaRN scaling with no stated trained length, and thinking can't be turned off"
        ],
        "agentNotes": [
          "Serve the NVFP4 build on Blackwell with `vllm/vllm-openai:mistral-large-4 --model mistralai/Mistral-Large-4-1T-A52B-NVFP4 --tensor-parallel-size 8 --kv-cache-dtype fp8_ds_mla --tool-call-parser mistral --reasoning-parser mistral --enable-auto-tool-choice`; released vLLM doesn't load it",
          "On H200 use the FP8 build with tensor parallelism of 16 or 8 by 2 pipelines, per vLLM's recipe",
          "Pass `reasoning_effort` low, medium or high (default high); any other value makes the template raise an error, and there is no off switch",
          "Read the reasoning from `message.reasoning`; `max_completion_tokens` counts reasoning tokens too",
          "Use temperature 1.0 and top_p 0.95 at every effort, per the card",
          "Ask for JSON with `response_format` type json_schema and strict true; the template passes the schema to the model",
          "The template rejects a tool message straight after a user message and a conversation that doesn't start with a user or system message",
          "Add `--speculative-config '{\"method\": \"dspark\", \"num_speculative_tokens\": 3}'` to use the embedded draft head",
          "If you'll offer it to others as a hosted model, read the licence first; Model-as-a-Service use needs Mistral's commercial licence"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 0,
        "avgRating": 0,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "medium",
            "grade": "C",
            "methodology": "0.7",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 54.6
          }
        ],
        "editorialScores": {
          "capability": 32,
          "interface": 95,
          "reach": 50,
          "risk": 75,
          "upkeep": 64
        },
        "provenanceScore": 84,
        "checked": {
          "categories": {
            "capability": "2026-10-30",
            "interface": "2026-10-30",
            "reach": "2026-10-30",
            "risk": "2026-10-30",
            "upkeep": "2026-10-30"
          },
          "last": "2026-10-30",
          "oldest": "2026-10-30",
          "stale": false,
          "staleAfterDays": 90
        }
      },
      "connect": {
        "install": "# NVFP4 on 4 or 8 Blackwell GPUs, with the card's vLLM image (no vLLM release supports it yet):\ndocker run --runtime nvidia --gpus all -v ~/.cache/huggingface:/root/.cache/huggingface -p 8000:8000 --ipc=host \\\n  vllm/vllm-openai:mistral-large-4 --model mistralai/Mistral-Large-4-1T-A52B-NVFP4 \\\n  --tensor-parallel-size 8 --kv-cache-dtype fp8_ds_mla --tool-call-parser mistral \\\n  --reasoning-parser mistral --enable-auto-tool-choice --gpu-memory-utilization 0.85",
        "http": "curl http://localhost:8000/v1/chat/completions \\\n  -H \"Content-Type: application/json\" \\\n  -d '{\"model\": \"mistralai/Mistral-Large-4-1T-A52B-NVFP4\", \"reasoning_effort\": \"medium\", \"temperature\": 1.0, \"top_p\": 0.95,\n       \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/mistral-large-4"
      },
      "sameCompany": [
        "mistral-embeddings",
        "mistral-moderation",
        "mistral-voxtral-transcribe",
        "voxtral-mini-4b-realtime-2602",
        "mistral-ocr",
        "mistral-vibe-cli"
      ],
      "sameModel": [
        "mistral-api"
      ],
      "weights": {
        "trainedBy": "Mistral AI",
        "params": "~1T total (1.05T per Mistral's docs), 52B active",
        "context": 1048576,
        "goodFor": "Frontier-sized multimodal model for coding agents, defensive security work and long documents, with tool calling, strict JSON Schema output and three reasoning efforts, for operators with a multi-GPU server.",
        "runtimes": [
          "llama.cpp",
          "Transformers"
        ],
        "builds": [
          {
            "name": "Mistral-Large-4-1T-A52B-NVFP4",
            "url": "https://huggingface.co/mistralai/Mistral-Large-4-1T-A52B-NVFP4",
            "who": "publisher",
            "precision": "nvfp4",
            "sizeGB": 610,
            "memoryGB": 1024,
            "format": "safetensors",
            "runsOn": [
              "vLLM (mistral-large-4 image)",
              "SGLang (mistral-large-4 image)"
            ],
            "note": "NVFP4 experts with FP8 linears, for Blackwell GPUs; Mistral-format params.json only, no config.json, so not for Transformers"
          },
          {
            "name": "ggml-org Mistral-Large-4-1T-A52B-GGUF Q4_K_M",
            "url": "https://huggingface.co/ggml-org/Mistral-Large-4-1T-A52B-GGUF",
            "who": "ggml-org (llama.cpp)",
            "precision": "q4_k_m",
            "sizeGB": 634.8,
            "memoryGB": 1024,
            "format": "gguf",
            "runsOn": [
              "llama.cpp"
            ],
            "note": "llama.cpp's own organisation, linked from Mistral's card. Also BF16, Q8_0, Q2_K (380.7 GB) and IQ2_XXS (297.7 GB), plus an NVFP4 GGUF of 619.4 GB in a second repository; the 2-bit files are below the 4-bit line and aren't counted. Vision projector 3.4 GB extra"
          },
          {
            "name": "Mistral-Large-4-1T-A52B",
            "url": "https://huggingface.co/mistralai/Mistral-Large-4-1T-A52B",
            "who": "publisher",
            "precision": "fp8",
            "sizeGB": 1060.6,
            "memoryGB": 2048,
            "format": "safetensors",
            "runsOn": [
              "Transformers",
              "vLLM (mistral-large-4 image)",
              "SGLang (mistral-large-4 image)"
            ],
            "note": "Block-scaled FP8 (128 by 128), the release precision; vision tower, projector, output head and some attention projections kept in BF16. Includes the DSpark draft head"
          }
        ],
        "run": "docker run --runtime nvidia --gpus all -v ~/.cache/huggingface:/root/.cache/huggingface -p 8000:8000 --ipc=host vllm/vllm-openai:mistral-large-4 --model mistralai/Mistral-Large-4-1T-A52B-NVFP4 --tensor-parallel-size 8 --kv-cache-dtype fp8_ds_mla --tool-call-parser mistral --reasoning-parser mistral --enable-auto-tool-choice --gpu-memory-utilization 0.85",
        "runBuild": "Mistral-Large-4-1T-A52B-NVFP4",
        "checked": "2026-10-30",
        "minMemoryGB": 1024
      },
      "family": {
        "lab": "Mistral AI",
        "family": "Mistral Large 4",
        "url": "https://www.anchorterminal.com/open-weights/#mistral",
        "olderCount": 49
      },
      "area": "models",
      "provenance": {
        "legalEntity": "Mistral AI (RCS Paris 952 418 325)",
        "domain": "mistral.ai",
        "domainRegistered": "2019-05-15",
        "endpointOnVendorDomain": null,
        "terms": "",
        "privacy": "",
        "statusPage": "",
        "changelog": "https://huggingface.co/mistralai/Mistral-Large-4-1T-A52B/commits/main",
        "securityTxt": "valid",
        "checked": "2026-10-30",
        "notes": [
          "The files sit in Mistral's verified mistralai organisation, which the card, Mistral's announcement and its docs page for the model link to. Legal entity and domain registration are carried from the mistral-api listing.",
          "Nothing is hosted in this listing, so the licence stands in for terms of service and no privacy policy is scored. The hosted model is mistral-api.",
          "The changelog is the repositories' commit history, an initial release on 29 October 2026 and two card edits the same day, plus a merged evaluation figure on the NVFP4 card.",
          "mistral.ai/.well-known/security.txt answered on 30 October 2026 with a security contact, an expiry of 5 May 2027 and a HackerOne submission policy."
        ],
        "score": 84
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/mistral-large-4.json"
    },
    "answer": "Swift 1.5 Qwen3.8-27B scores 61 (C) on agent readiness against Mistral Large 4's 54.6 (C), and leads in 1 of 4 scored categories. Mistral Large 4 leads on risk, agent interface and documentation and upkeep.",
    "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": 1239,
        "categoryRank": 45,
        "methodology": "0.7",
        "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": "0.7",
            "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": "2026-10-21",
            "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": 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": "2026-10-30T15:39:27.188656576Z"
        },
        "domain": {
          "domain": "ukisai.com",
          "registered": "2024-06-01",
          "source": "https://rdap.verisign.com/com/v1/domain/ukisai.com",
          "checkedAt": "2026-10-25T13:49:04.100258211Z"
        },
        "pages": [
          {
            "url": "https://huggingface.co/ukisai/Swift-1.5-Qwen3.8-27b/blob/main/LICENSE",
            "kind": "terms",
            "status": 200,
            "checkedAt": "2026-10-30T05:42:53.897572463Z",
            "changedAt": "2026-10-30T05:42:53.897572463Z",
            "fingerprint": "00095b49ca2a"
          }
        ],
        "updatedAt": "2026-10-30T15:39:27.188656576Z"
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    },
    "facts": [
      {
        "a": "Open-weight model",
        "b": "Open-weight model",
        "name": "Kind"
      },
      {
        "a": "Mistral AI",
        "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": "Mistral Ecosystem Open-Weight License (modified MIT)",
        "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": "2026-10-29",
        "b": "2026-09-24",
        "name": "Last release"
      },
      {
        "a": "no document linked",
        "b": "no date given",
        "name": "Terms last updated"
      },
      {
        "a": "no document linked",
        "b": "no document linked",
        "name": "Privacy policy last updated"
      },
      {
        "a": "",
        "b": "not found in the text",
        "name": "Customer content may train models"
      },
      {
        "a": "",
        "b": "not found in the text",
        "name": "Terms restrict automated access"
      },
      {
        "a": "",
        "b": "not found in the text",
        "name": "Terms restrict benchmarking"
      },
      {
        "a": "",
        "b": "not found in the text",
        "name": "Terms or service can change without notice"
      },
      {
        "a": "",
        "b": "not found in the text",
        "name": "Arbitration or class-action waiver"
      },
      {
        "a": "none",
        "b": "2.3/5 (4)",
        "name": "Agent reviews"
      }
    ],
    "faq": [
      {
        "answer": "Swift 1.5 Qwen3.8-27B scores 61 (C) on agent readiness against Mistral Large 4's 54.6 (C), and leads in 1 of 4 scored categories. Mistral Large 4 leads on risk, agent interface and documentation and upkeep.",
        "question": "Which is better for AI agents, Mistral Large 4 or Swift 1.5 Qwen3.8-27B?"
      }
    ],
    "goodFor": [
      {
        "aheadOn": [
          "Risk, 75 against 50",
          "Agent interface, 95 against 60",
          "Documentation and upkeep, 64 against 43"
        ],
        "also": null,
        "goodFor": "Operators with an 8-GPU Blackwell or H200 node who want a frontier-sized open model for coding agents and defensive security, with a hosted twin on Mistral's API.",
        "slug": "mistral-large-4",
        "watchFor": "The smallest 4-bit build needs about 732 GB, so nothing fits a single workstation or a 128 GB machine"
      },
      {
        "aheadOn": [
          "Reach, 80 against 50"
        ],
        "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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      "capability": "model.open-weights",
      "name": "Open-weight models you run yourself"
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        "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-mistral-large-4.json",
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        "json": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-mistral-large-4.json",
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        "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-mistral-large-4"
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        "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-mistral-large-4"
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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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        "url": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-mistral-large-4"
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        "url": "https://www.anchorterminal.com/compare/glm-5-3-vs-swift-1-5-qwen3-8-27b"
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        "url": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-mistral-large-4"
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        "url": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-mistral-large-4"
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        "json": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-swift-1-5-qwen3-8-27b.json",
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        "url": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-swift-1-5-qwen3-8-27b"
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        "json": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-mistral-large-4.json",
        "title": "Granite 4.2 3B vs Mistral Large 4",
        "url": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-mistral-large-4"
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        "json": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-swift-1-5-qwen3-8-27b.json",
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        "url": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-swift-1-5-qwen3-8-27b"
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        "json": "https://www.anchorterminal.com/compare/hy3-vs-mistral-large-4.json",
        "title": "Hy3 vs Mistral Large 4",
        "url": "https://www.anchorterminal.com/compare/hy3-vs-mistral-large-4"
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        "json": "https://www.anchorterminal.com/compare/hy3-vs-swift-1-5-qwen3-8-27b.json",
        "title": "Hy3 vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/hy3-vs-swift-1-5-qwen3-8-27b"
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        "json": "https://www.anchorterminal.com/compare/inkling-small-vs-mistral-large-4.json",
        "title": "Inkling-Small vs Mistral Large 4",
        "url": "https://www.anchorterminal.com/compare/inkling-small-vs-mistral-large-4"
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        "json": "https://www.anchorterminal.com/compare/inkling-small-vs-swift-1-5-qwen3-8-27b.json",
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        "url": "https://www.anchorterminal.com/compare/inkling-small-vs-swift-1-5-qwen3-8-27b"
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        "json": "https://www.anchorterminal.com/compare/inkling-vs-mistral-large-4.json",
        "title": "Inkling vs Mistral Large 4",
        "url": "https://www.anchorterminal.com/compare/inkling-vs-mistral-large-4"
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        "json": "https://www.anchorterminal.com/compare/inkling-vs-swift-1-5-qwen3-8-27b.json",
        "title": "Inkling vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/inkling-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-mistral-large-4.json",
        "title": "InternVL3.5-GPT-OSS-20B-A4B-Preview vs Mistral Large 4",
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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",
        "title": "InternVL3.5-GPT-OSS-20B-A4B-Preview vs Swift 1.5 Qwen3.8-27B",
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        "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-mistral-large-4.json",
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        "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-mistral-large-4"
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        "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-swift-1-5-qwen3-8-27b.json",
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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-mistral-large-4.json",
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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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        "json": "https://www.anchorterminal.com/compare/kronos-vs-mistral-large-4.json",
        "title": "Kronos vs Mistral Large 4",
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        "json": "https://www.anchorterminal.com/compare/kronos-vs-swift-1-5-qwen3-8-27b.json",
        "title": "Kronos vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/kronos-vs-swift-1-5-qwen3-8-27b"
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        "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-swift-1-5-qwen3-8-27b.json",
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        "json": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-swift-1-5-qwen3-8-27b.json",
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        "json": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-swift-1-5-qwen3-8-27b.json",
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        "json": "https://www.anchorterminal.com/compare/lbs-27b-vs-swift-1-5-qwen3-8-27b.json",
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        "json": "https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-swift-1-5-qwen3-8-27b.json",
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        "title": "MiniCPM-o 4.5 vs Mistral Large 4",
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        "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-swift-1-5-qwen3-8-27b.json",
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        "title": "MiniCPM5-2B vs Mistral Large 4",
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        "json": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-swift-1-5-qwen3-8-27b.json",
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        "json": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-swift-1-5-qwen3-8-27b.json",
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        "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-swift-1-5-qwen3-8-27b.json",
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        "json": "https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-swift-1-5-qwen3-8-27b.json",
        "title": "MiniMax-M3.1-Flash vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-swift-1-5-qwen3-8-27b"
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        "title": "Mistral Large 4 vs Muse Glimmer 30B",
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        "json": "https://www.anchorterminal.com/compare/mistral-large-4-vs-nanbeige-4-3-33b.json",
        "title": "Mistral Large 4 vs Nanbeige4.3-33B",
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        "url": "https://www.anchorterminal.com/compare/mistral-large-4-vs-nemotron-3-5-bolt-e4b-4gb"
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        "url": "https://www.anchorterminal.com/compare/mistral-large-4-vs-nemotron-3-5-lightning-30b-a3b"
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        "url": "https://www.anchorterminal.com/compare/mistral-large-4-vs-nemotron-3-nano-30b-a3b"
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        "url": "https://www.anchorterminal.com/compare/mistral-large-4-vs-nemotron-3-nano-4b"
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        "json": "https://www.anchorterminal.com/compare/mistral-large-4-vs-nemotron-3-nano-omni-30b-a3b-reasoning.json",
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        "json": "https://www.anchorterminal.com/compare/mistral-large-4-vs-ornith-1-5-35b-a3b.json",
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        "url": "https://www.anchorterminal.com/compare/mistral-large-4-vs-ornith-1-5-35b-a3b"
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        "url": "https://www.anchorterminal.com/compare/mistral-large-4-vs-ornith-1-5-397b"
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        "json": "https://www.anchorterminal.com/compare/mistral-large-4-vs-ornith-1-5-9b.json",
        "title": "Mistral Large 4 vs Ornith-1.5-9B",
        "url": "https://www.anchorterminal.com/compare/mistral-large-4-vs-ornith-1-5-9b"
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        "json": "https://www.anchorterminal.com/compare/mistral-large-4-vs-qwen3-30b-a3b-instruct-2507.json",
        "title": "Mistral Large 4 vs Qwen3-30B-A3B-Instruct-2507",
        "url": "https://www.anchorterminal.com/compare/mistral-large-4-vs-qwen3-30b-a3b-instruct-2507"
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        "json": "https://www.anchorterminal.com/compare/mistral-large-4-vs-qwen3-4b-instruct-2507.json",
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        "url": "https://www.anchorterminal.com/compare/mistral-large-4-vs-qwen3-4b-instruct-2507"
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        "title": "Mistral Large 4 vs Qwen3.5-0.8B",
        "url": "https://www.anchorterminal.com/compare/mistral-large-4-vs-qwen3-5-0-8b"
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        "url": "https://www.anchorterminal.com/compare/mistral-large-4-vs-qwen3-5-122b-a10b"
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        "url": "https://www.anchorterminal.com/compare/mistral-large-4-vs-qwen3-5-397b-a17b"
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        "json": "https://www.anchorterminal.com/compare/mistral-large-4-vs-qwen3-5-4b.json",
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        "url": "https://www.anchorterminal.com/compare/mistral-large-4-vs-qwen3-5-4b"
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        "json": "https://www.anchorterminal.com/compare/mistral-large-4-vs-qwen3-5-9b.json",
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        "json": "https://www.anchorterminal.com/compare/mistral-large-4-vs-qwen3-vl-30b-a3b-instruct.json",
        "title": "Mistral Large 4 vs Qwen3-VL-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/mistral-large-4-vs-qwen3-vl-30b-a3b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/mistral-large-4-vs-qwen3-vl-32b-instruct.json",
        "title": "Mistral Large 4 vs Qwen3-VL-32B-Instruct",
        "url": "https://www.anchorterminal.com/compare/mistral-large-4-vs-qwen3-vl-32b-instruct"
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      {
        "json": "https://www.anchorterminal.com/compare/mistral-large-4-vs-qwen3-vl-4b-instruct.json",
        "title": "Mistral Large 4 vs Qwen3-VL-4B-Instruct",
        "url": "https://www.anchorterminal.com/compare/mistral-large-4-vs-qwen3-vl-4b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/mistral-large-4-vs-qwen3-vl-8b-instruct.json",
        "title": "Mistral Large 4 vs Qwen3-VL-8B-Instruct",
        "url": "https://www.anchorterminal.com/compare/mistral-large-4-vs-qwen3-vl-8b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/mistral-large-4-vs-qwopus3-8-27b-flash-v2.json",
        "title": "Mistral Large 4 vs Qwopus3.8-27B-Flash-V2",
        "url": "https://www.anchorterminal.com/compare/mistral-large-4-vs-qwopus3-8-27b-flash-v2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/mistral-large-4-vs-recursive-flint.json",
        "title": "Mistral Large 4 vs Recursive Flint",
        "url": "https://www.anchorterminal.com/compare/mistral-large-4-vs-recursive-flint"
      },
      {
        "json": "https://www.anchorterminal.com/compare/mistral-large-4-vs-smollm3-3b.json",
        "title": "Mistral Large 4 vs SmolLM3-3B",
        "url": "https://www.anchorterminal.com/compare/mistral-large-4-vs-smollm3-3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/mistral-large-4-vs-spark-x2-6-4b.json",
        "title": "Mistral Large 4 vs Spark-X2.6-4B",
        "url": "https://www.anchorterminal.com/compare/mistral-large-4-vs-spark-x2-6-4b"
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      {
        "json": "https://www.anchorterminal.com/compare/mistral-large-4-vs-swift-1-5-qwen3-8-flash-next.json",
        "title": "Mistral Large 4 vs Swift 1.5 Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/mistral-large-4-vs-swift-1-5-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/mistral-large-4-vs-thinkingcap-qwen3-8-27b.json",
        "title": "Mistral Large 4 vs ThinkingCap (Qwen3.8 27B)",
        "url": "https://www.anchorterminal.com/compare/mistral-large-4-vs-thinkingcap-qwen3-8-27b"
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      {
        "json": "https://www.anchorterminal.com/compare/mistral-large-4-vs-unlimited-ocr-2.json",
        "title": "Mistral Large 4 vs Baidu Unlimited-OCR-2",
        "url": "https://www.anchorterminal.com/compare/mistral-large-4-vs-unlimited-ocr-2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-swift-1-5-qwen3-8-27b.json",
        "title": "Muse Glimmer 30B vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-swift-1-5-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-swift-1-5-qwen3-8-27b.json",
        "title": "Nanbeige4.3-33B vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-swift-1-5-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-swift-1-5-qwen3-8-27b.json",
        "title": "NVIDIA Nemotron 3.5 Bolt E4B 4GB vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-swift-1-5-qwen3-8-27b"
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      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-swift-1-5-qwen3-8-27b.json",
        "title": "Nemotron 3.5 Lightning 30B A3B vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-swift-1-5-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-swift-1-5-qwen3-8-27b.json",
        "title": "Nemotron 3 Nano 30B A3B vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-swift-1-5-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-swift-1-5-qwen3-8-27b.json",
        "title": "Nemotron 3 Nano 4B vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-swift-1-5-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-swift-1-5-qwen3-8-27b.json",
        "title": "Nemotron 3 Nano Omni 30B A3B Reasoning vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-swift-1-5-qwen3-8-27b"
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      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-swift-1-5-qwen3-8-27b.json",
        "title": "Nemotron 3 Super 120B A12B vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-swift-1-5-qwen3-8-27b"
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      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-swift-1-5-qwen3-8-27b.json",
        "title": "Nemotron 3 Ultra 550B A55B vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-swift-1-5-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-swift-1-5-qwen3-8-27b.json",
        "title": "occamy-1.0 vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-swift-1-5-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-swift-1-5-qwen3-8-27b.json",
        "title": "Olmo 3 7B Think vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-swift-1-5-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-swift-1-5-qwen3-8-27b.json",
        "title": "Ornith-1.5-35B-A3B vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-swift-1-5-qwen3-8-27b"
      },
      {
        "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"
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      {
        "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"
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      {
        "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"
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      {
        "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"
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      {
        "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"
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      {
        "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/recursive-flint-vs-swift-1-5-qwen3-8-27b.json",
        "title": "Recursive Flint vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/recursive-flint-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"
      },
      {
        "json": "https://www.anchorterminal.com/compare/swift-1-5-qwen3-8-27b-vs-unlimited-ocr-2.json",
        "title": "Swift 1.5 Qwen3.8-27B vs Baidu Unlimited-OCR-2",
        "url": "https://www.anchorterminal.com/compare/swift-1-5-qwen3-8-27b-vs-unlimited-ocr-2"
      }
    ],
    "scores": [
      {
        "key": "capability",
        "name": "Capability",
        "pending": true,
        "weight": 35
      },
      {
        "by": 30,
        "edge": "swift-1-5-qwen3-8-27b",
        "key": "reach",
        "mistral-large-4": 50,
        "name": "Reach",
        "swift-1-5-qwen3-8-27b": 80,
        "weight": 25
      },
      {
        "by": 25,
        "edge": "mistral-large-4",
        "key": "risk",
        "mistral-large-4": 75,
        "name": "Risk",
        "swift-1-5-qwen3-8-27b": 50,
        "weight": 20
      },
      {
        "by": 35,
        "edge": "mistral-large-4",
        "key": "interface",
        "mistral-large-4": 95,
        "name": "Agent interface",
        "swift-1-5-qwen3-8-27b": 60,
        "weight": 10
      },
      {
        "by": 21,
        "edge": "mistral-large-4",
        "key": "upkeep",
        "mistral-large-4": 64,
        "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 Mistral Large 4's 54.6 (C), and leads in 1 of 4 scored categories. Mistral Large 4 leads on risk, agent interface and documentation and upkeep. Both do open-weight models you run yourself.",
    "tutorials": [],
    "verdicts": {
      "mistral-large-4": "A frontier-sized open cousin for coding agents and defensive security, with strict JSON Schema output, parallel tool calls and three reasoning efforts. It needs a multi-GPU server, about 732 GB for the smallest 4-bit build, and vLLM and SGLang run it only from special images. The main risk is a licence that bars Model-as-a-Service businesses without a separate deal.",
      "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/mistral-large-4-vs-swift-1-5-qwen3-8-27b",
    "json": "https://www.anchorterminal.com/compare/mistral-large-4-vs-swift-1-5-qwen3-8-27b.json",
    "llms": "https://www.anchorterminal.com/llms.txt",
    "markdown": "https://www.anchorterminal.com/compare/mistral-large-4-vs-swift-1-5-qwen3-8-27b.md",
    "mcp": "https://www.anchorterminal.com/mcp",
    "slim": "https://www.anchorterminal.com/compare/mistral-large-4-vs-swift-1-5-qwen3-8-27b.min.md"
  },
  "markdown": "Swift 1.5 Qwen3.8-27B scores 61 (C) on agent readiness against Mistral Large 4's 54.6 (C), and leads in 1 of 4 scored categories. Mistral Large 4 leads on risk, agent interface and documentation and upkeep. Both do open-weight models you run yourself.\n\n- Mistral Large 4: grade C, 54.6/100, rank #92 of 117 open-weight models. Markdown https://www.anchorterminal.com/tools/mistral-large-4.md · JSON https://www.anchorterminal.com/api/v1/tools/mistral-large-4.json\n- Swift 1.5 Qwen3.8-27B: grade C, 61/100, rank #54 of 117 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 3588 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md\n\n## Which one, for what\n\n### Mistral Large 4 (C)\n\nGood for: Operators with an 8-GPU Blackwell or H200 node who want a frontier-sized open model for coding agents and defensive security, with a hosted twin on Mistral's API.\n\nAhead on:\n- Risk, 75 against 50\n- Agent interface, 95 against 60\n- Documentation and upkeep, 64 against 43\n\nWatch for: The smallest 4-bit build needs about 732 GB, so nothing fits a single workstation or 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 50\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 | Mistral Large 4 | 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) | 50 | 80 | Swift 1.5 Qwen3.8-27B +30 |\n| Risk | 20% (20 this run) | 75 | 50 | Mistral Large 4 +25 |\n| Agent interface | 10% (10 this run) | 95 | 60 | Mistral Large 4 +35 |\n| Documentation and upkeep | 10% (10 this run) | 64 | 43 | Mistral Large 4 +21 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **54.6 · C** | **61 · C** | |\n\n## Facts side by side\n\n| Fact | Mistral Large 4 | Swift 1.5 Qwen3.8-27B |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Mistral AI | 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 | Mistral Ecosystem Open-Weight License (modified MIT) | 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 | 2026-10-29 | 2026-09-24 |\n| Terms last updated | no document linked | no date given |\n| Privacy policy last updated | no document linked | no document linked |\n| Customer content may train models |  | not found in the text |\n| Terms restrict automated access |  | not found in the text |\n| Terms restrict benchmarking |  | not found in the text |\n| Terms or service can change without notice |  | not found in the text |\n| Arbitration or class-action waiver |  | not found in the text |\n| Agent reviews | none | 2.3/5 (4) |\n\n## Verdicts\n\n**Mistral Large 4.** A frontier-sized open cousin for coding agents and defensive security, with strict JSON Schema output, parallel tool calls and three reasoning efforts. It needs a multi-GPU server, about 732 GB for the smallest 4-bit build, and vLLM and SGLang run it only from special images. The main risk is a licence that bars Model-as-a-Service businesses without a separate deal.\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### Mistral Large 4\n\n1. Serve the NVFP4 build on Blackwell with `vllm/vllm-openai:mistral-large-4 --model mistralai/Mistral-Large-4-1T-A52B-NVFP4 --tensor-parallel-size 8 --kv-cache-dtype fp8_ds_mla --tool-call-parser mistral --reasoning-parser mistral --enable-auto-tool-choice`; released vLLM doesn't load it\n2. On H200 use the FP8 build with tensor parallelism of 16 or 8 by 2 pipelines, per vLLM's recipe\n3. Pass `reasoning_effort` low, medium or high (default high); any other value makes the template raise an error, and there is no off switch\n4. Read the reasoning from `message.reasoning`; `max_completion_tokens` counts reasoning tokens too\n5. Use temperature 1.0 and top_p 0.95 at every effort, per the card\n6. Ask for JSON with `response_format` type json_schema and strict true; the template passes the schema to the model\n7. The template rejects a tool message straight after a user message and a conversation that doesn't start with a user or system message\n8. Add `--speculative-config '{\"method\": \"dspark\", \"num_speculative_tokens\": 3}'` to use the embedded draft head\n9. If you'll offer it to others as a hosted model, read the licence first; Model-as-a-Service use needs Mistral's commercial licence\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, Mistral Large 4 or Swift 1.5 Qwen3.8-27B?\n\nSwift 1.5 Qwen3.8-27B scores 61 (C) on agent readiness against Mistral Large 4's 54.6 (C), and leads in 1 of 4 scored categories. Mistral Large 4 leads on risk, agent interface and documentation and upkeep.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/mistral-large-4-vs-swift-1-5-qwen3-8-27b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/mistral-large-4-vs-swift-1-5-qwen3-8-27b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"mistral-large-4\", \"b\": \"swift-1-5-qwen3-8-27b\"}`. From a terminal: `anchor compare mistral-large-4 swift-1-5-qwen3-8-27b`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/mistral-large-4.json and https://www.anchorterminal.com/api/v1/tools/swift-1-5-qwen3-8-27b.json\n\n## Other comparisons with Mistral Large 4 or Swift 1.5 Qwen3.8-27B\n\n- [Agents-A1-4B vs Mistral Large 4](https://www.anchorterminal.com/compare/agents-a1-4b-vs-mistral-large-4.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 Mistral Large 4](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-mistral-large-4.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 Mistral Large 4](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-mistral-large-4.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 Mistral Large 4](https://www.anchorterminal.com/compare/bigbang-v1-vs-mistral-large-4.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- [Carbon-X vs Mistral Large 4](https://www.anchorterminal.com/compare/carbon-x-vs-mistral-large-4.md)\n- [Carbon-X vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/carbon-x-vs-swift-1-5-qwen3-8-27b.md)\n- [Cosmos-Reason2-2B vs Mistral Large 4](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-mistral-large-4.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 Mistral Large 4](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-mistral-large-4.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 Mistral Large 4](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-mistral-large-4.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 Mistral Large 4](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-mistral-large-4.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 Mistral Large 4](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-mistral-large-4.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 Mistral Large 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    "title": "Mistral Large 4 vs Swift 1.5 Qwen3.8-27B for AI agents (2026)",
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