{
  "data": {
    "a": {
      "slug": "qwopus3-8-27b-flash-v2",
      "name": "Qwopus3.8-27B-Flash-V2",
      "vendor": "Jackrong",
      "vendorUrl": "https://huggingface.co/Jackrong",
      "kind": "weights",
      "category": "open-weights",
      "summary": "Qwopus3.8-27B-Flash-V2 is Jackrong's open-weight 27B fine-tune of Qwen3.8-27B, by way of his Qwopus3.8-27B-Flash, trained for shorter reasoning in agent loops, published in BF16 and GGUF with a multi-token prediction head under Apache 2.0.",
      "url": "https://www.anchorterminal.com/tools/qwopus3-8-27b-flash-v2",
      "markdownUrl": "https://www.anchorterminal.com/tools/qwopus3-8-27b-flash-v2.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/qwopus3-8-27b-flash-v2.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/qwopus3-8-27b-flash-v2.json",
      "repo": "https://huggingface.co/Jackrong/Qwopus3.8-27B-Flash-V2",
      "license": "Apache-2.0 (metadata only, no LICENSE file); built on Qwen3.8-27B under Apache 2.0",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "Jackrong/Qwopus3.8-27B-Flash-V2"
        },
        {
          "registry": "huggingface",
          "name": "Jackrong/Qwopus3.8-27B-Flash-V2-GGUF"
        }
      ],
      "auth": "none",
      "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. Neither repository is gated (checked 2026-10-21).",
      "pricing": "free",
      "pricingNotes": "Free to download and use under Apache 2.0, the same licence as the Qwen3.8-27B base. Nothing is hosted. Running it yourself costs the hardware, 55.6 GB for the BF16 files and 15.4 GB for the smallest 4-bit GGUF (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/Jackrong/Qwopus3.8-27B-Flash-V2",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "dense",
        "multimodal",
        "reasoning",
        "tool-calling",
        "derivative",
        "fine-tune",
        "apache-2.0",
        "safetensors",
        "gguf",
        "mtp",
        "self-hosted"
      ],
      "lastRelease": "2026-09-21",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 61,
        "grade": "C",
        "agentReady": false,
        "rank": 0,
        "ranked": false,
        "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools",
        "rankOf": 1197,
        "categoryRank": 39,
        "methodology": "0.6",
        "run": "2026-10-01",
        "scores": {
          "interface": 60,
          "reach": 85,
          "risk": 50,
          "upkeep": 56
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "low",
          "date": "2026-10-21"
        },
        "negative": 0,
        "verdict": "A Qwen3.8-27B cousin tuned to think less per turn in long agent loops, with an MTP head for speculative decoding in llama.cpp. It fits 24 GB with Jackrong's 4-bit GGUF. The risks are one person's experimental release with no safety work, training traces taken from Claude and GPT, and a tool format that differs from Qwen's.",
        "bestFor": "Hobbyist and research agents that want a faster-finishing Qwen3.8-27B for local coding loops on a 24 GB GPU.",
        "strengths": [
          "Jackrong's 4-bit GGUFs fit 24 GB, with many precisions up to BF16",
          "The base's architecture, so released vLLM, SGLang, Transformers and llama.cpp run it",
          "Apache 2.0 through the whole chain, with no gate or account",
          "Safetensors and GGUF only, with a checksum on every file",
          "Bundled MTP head for self-speculative decoding, and reasoning effort set per request",
          "A card that states its limitations and how its own tests were run"
        ],
        "weaknesses": [
          "Published from a personal account, with no organisation, security contact or safety evaluation",
          "Training data includes traces derived from Claude and GPT, by the card's own account",
          "Tool calls switch to a Hermes-style JSON format that the card doesn't document",
          "Reasoning output location, context and cut-off aren't documented",
          "Reports of looping and a grammar failure wait without a reply"
        ],
        "agentNotes": [
          "Quickest start is `llama-server -hf Jackrong/Qwopus3.8-27B-Flash-V2-GGUF:Q4_K_M --jinja`, about 17 GB, and add mmproj-F32.gguf for images",
          "The card's tests used Q5_K_M with the bundled MTP head and `--spec-type draft-mtp` in llama.cpp",
          "The template asks for tool calls as JSON inside tool_call tags, so use a Hermes-style parser (vLLM's hermes), not Qwen3.8's qwen3_coder",
          "For coding the card recommends enable_thinking true, reasoning_effort xhigh and temperature 0.3, which you must pass yourself, since generation_config.json says 1.0",
          "reasoning_effort takes xhigh (default), medium or low, and any other value makes the template raise an error",
          "Earlier reasoning stays in the context unless preserve_thinking is false",
          "Cap max_tokens, since a report says it loops more than V1"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 4,
        "avgRating": 2.5,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "low",
            "grade": "C",
            "methodology": "0.6",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 61
          }
        ],
        "editorialScores": {
          "interface": 60,
          "reach": 85,
          "risk": 50,
          "upkeep": 56
        },
        "provenanceScore": 40,
        "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": "llama-server -hf Jackrong/Qwopus3.8-27B-Flash-V2-GGUF:Q4_K_M --jinja -c 65536\n# or, with vLLM:\nvllm serve Jackrong/Qwopus3.8-27B-Flash-V2 --max-model-len 131072 --reasoning-parser qwen3 \\\n  --enable-auto-tool-choice --tool-call-parser hermes",
        "http": "curl http://localhost:8080/v1/chat/completions \\\n  -H \"Content-Type: application/json\" \\\n  -d '{\"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}], \"temperature\": 0.3, \"chat_template_kwargs\": {\"enable_thinking\": true, \"reasoning_effort\": \"xhigh\"}}'"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/qwopus3-8-27b-flash-v2"
      },
      "weights": {
        "trainedBy": "Jackrong",
        "builtOn": "Jackrong/Qwopus3.8-27B-Flash (fine-tune, Apache 2.0), itself a fine-tune of Qwen/Qwen3.8-27B (Apache 2.0); declared in the card's metadata",
        "params": "27.8B",
        "context": 262144,
        "goodFor": "A 27B vision-language fine-tune for long agent loops and coding, trained to reason less per turn, with an MTP head for speculative decoding.",
        "runtimes": [
          "llama.cpp",
          "vLLM",
          "SGLang",
          "Transformers"
        ],
        "builds": [
          {
            "name": "Qwopus3.8-27B-Flash-V2-MTP-IQ4_XS.gguf",
            "url": "https://huggingface.co/Jackrong/Qwopus3.8-27B-Flash-V2-GGUF/blob/main/Qwopus3.8-27B-Flash-V2-MTP-IQ4_XS.gguf",
            "who": "publisher",
            "precision": "iq4_xs",
            "sizeGB": 15.42,
            "memoryGB": 24,
            "format": "gguf",
            "note": "Vision needs mmproj-F32.gguf as well (1.84 GB)"
          },
          {
            "name": "Qwopus3.8-27B-Flash-V2-MTP-Q4_K_S.gguf",
            "url": "https://huggingface.co/Jackrong/Qwopus3.8-27B-Flash-V2-GGUF/blob/main/Qwopus3.8-27B-Flash-V2-MTP-Q4_K_S.gguf",
            "who": "publisher",
            "precision": "q4_k_s",
            "sizeGB": 15.83,
            "memoryGB": 24,
            "format": "gguf"
          },
          {
            "name": "Qwopus3.8-27B-Flash-V2-MTP-Q4_K_M.gguf",
            "url": "https://huggingface.co/Jackrong/Qwopus3.8-27B-Flash-V2-GGUF/blob/main/Qwopus3.8-27B-Flash-V2-MTP-Q4_K_M.gguf",
            "who": "publisher",
            "precision": "q4_k_m",
            "sizeGB": 16.81,
            "memoryGB": 24,
            "format": "gguf",
            "runsOn": [
              "llama.cpp",
              "Ollama"
            ]
          },
          {
            "name": "Qwopus3.8-27B-Flash-V2-MTP-Q5_K_M.gguf",
            "url": "https://huggingface.co/Jackrong/Qwopus3.8-27B-Flash-V2-GGUF/blob/main/Qwopus3.8-27B-Flash-V2-MTP-Q5_K_M.gguf",
            "who": "publisher",
            "precision": "q5_k_m",
            "sizeGB": 19.54,
            "memoryGB": 24,
            "format": "gguf",
            "note": "The quantisation the card's own tests used, with --spec-type draft-mtp"
          },
          {
            "name": "Qwopus3.8-27B-Flash-V2-MTP-Q6_K.gguf",
            "url": "https://huggingface.co/Jackrong/Qwopus3.8-27B-Flash-V2-GGUF/blob/main/Qwopus3.8-27B-Flash-V2-MTP-Q6_K.gguf",
            "who": "publisher",
            "precision": "q6_k",
            "sizeGB": 22.43,
            "memoryGB": 32,
            "format": "gguf"
          },
          {
            "name": "Qwopus3.8-27B-Flash-V2-MTP-Q8_0.gguf",
            "url": "https://huggingface.co/Jackrong/Qwopus3.8-27B-Flash-V2-GGUF/blob/main/Qwopus3.8-27B-Flash-V2-MTP-Q8_0.gguf",
            "who": "publisher",
            "precision": "q8_0",
            "sizeGB": 29.05,
            "memoryGB": 48,
            "format": "gguf",
            "runsOn": [
              "llama.cpp (Vulkan)",
              "llama.cpp (ROCm)"
            ]
          },
          {
            "name": "Qwopus3.8-27B-Flash-V2",
            "url": "https://huggingface.co/Jackrong/Qwopus3.8-27B-Flash-V2",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 55.58,
            "memoryGB": 96,
            "format": "safetensors",
            "runsOn": [
              "vLLM (ROCm)",
              "SGLang (ROCm)",
              "Transformers"
            ]
          },
          {
            "name": "Qwopus3.8-27B-Flash-V2-MTP-BF16.gguf",
            "url": "https://huggingface.co/Jackrong/Qwopus3.8-27B-Flash-V2-GGUF/blob/main/Qwopus3.8-27B-Flash-V2-MTP-BF16.gguf",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 54.66,
            "memoryGB": 96,
            "format": "gguf",
            "note": "Jackrong also publishes Q3_K_L, Q3_K_M, Q3_K_S and Q2_K files (10.9 to 14.6 GB), below 4-bit precision"
          }
        ],
        "run": "llama-server -hf Jackrong/Qwopus3.8-27B-Flash-V2-GGUF:Q8_0 --jinja -c 131072",
        "runBuild": "Qwopus3.8-27B-Flash-V2-MTP-Q8_0.gguf",
        "checked": "2026-10-21",
        "minMemoryGB": 24
      },
      "family": {
        "lab": "Jackrong",
        "family": "Qwopus3",
        "url": "https://www.anchorterminal.com/open-weights/#jackrong",
        "others": [
          {
            "name": "Qwopus3.5-9B-v3",
            "url": "https://huggingface.co/Jackrong/Qwopus3.5-9B-v3",
            "params": "9B (9,653,104,368 by the Hub's safetensors count)"
          },
          {
            "name": "Qwopus3.5-27B-v3",
            "url": "https://huggingface.co/Jackrong/Qwopus3.5-27B-v3",
            "params": "27B (27,356,728,560 by the Hub's safetensors count)"
          },
          {
            "name": "Qwopus3.5-4B-v3",
            "url": "https://huggingface.co/Jackrong/Qwopus3.5-4B-v3",
            "params": "4B (4,659,865,088 by the Hub's safetensors count)"
          },
          {
            "name": "Qwopus3.5-0.8B-v3",
            "url": "https://huggingface.co/Jackrong/Qwopus3.5-0.8B-v3",
            "params": "0.8B (873,438,784 by the Hub's safetensors count)"
          },
          {
            "name": "Qwopus3.5-27B-v3.5",
            "url": "https://huggingface.co/Jackrong/Qwopus3.5-27B-v3.5",
            "params": "27B (27,356,728,560 by the Hub's safetensors count)"
          },
          {
            "name": "Qwopus3.5-2B-v3",
            "url": "https://huggingface.co/Jackrong/Qwopus3.5-2B-v3",
            "params": "2B (2,274,069,824 by the Hub's safetensors count)"
          },
          {
            "name": "Qwopus3.5-9B-v3.5",
            "url": "https://huggingface.co/Jackrong/Qwopus3.5-9B-v3.5",
            "params": "9B (9,653,104,368 by the Hub's safetensors count)"
          },
          {
            "name": "Qwopus-GLM-18B-Healed",
            "url": "https://huggingface.co/Jackrong/Qwopus-GLM-18B-Healed",
            "params": "18B (16,572,666,096 by the Hub's safetensors count)"
          },
          {
            "name": "Qwopus3.6-27B-v1-preview",
            "url": "https://huggingface.co/Jackrong/Qwopus3.6-27B-v1-preview",
            "params": "27B (27,781,427,952 by the Hub's safetensors count)"
          },
          {
            "name": "Qwopus3.6-35B-A3B-v1",
            "url": "https://huggingface.co/Jackrong/Qwopus3.6-35B-A3B-v1",
            "params": "35B total, 3B active (MoE; 35,951,822,704 by the Hub's safetensors count)"
          },
          {
            "name": "Qwopus3.6-27B-v2",
            "url": "https://huggingface.co/Jackrong/Qwopus3.6-27B-v2",
            "params": "27B (27,781,427,952 by the Hub's safetensors count)"
          },
          {
            "name": "Qwopus3.5-9B-Coder",
            "url": "https://huggingface.co/Jackrong/Qwopus3.5-9B-Coder",
            "params": "9B (9,653,104,368 by the Hub's safetensors count)"
          },
          {
            "name": "Qwopus3.5-4B-Coder",
            "url": "https://huggingface.co/Jackrong/Qwopus3.5-4B-Coder",
            "params": "4B (4,659,865,088 by the Hub's safetensors count)"
          },
          {
            "name": "Qwopus3.6-27B-Coder",
            "url": "https://huggingface.co/Jackrong/Qwopus3.6-27B-Coder",
            "params": "27B (27,781,427,952 by the Hub's safetensors count)"
          },
          {
            "name": "Qwopus3.6-35B-A3B-Coder",
            "url": "https://huggingface.co/Jackrong/Qwopus3.6-35B-A3B-Coder",
            "params": "35B total, 3B active (MoE; 35,951,822,704 by the Hub's safetensors count)"
          },
          {
            "name": "Qwopus3.6-27B-Fusion-BF16",
            "url": "https://huggingface.co/Jackrong/Qwopus3.6-27B-Fusion-BF16",
            "params": "27B (27,320,697,856 by the Hub's GGUF count)"
          },
          {
            "name": "Qwopus3.8-27B-Flash",
            "url": "https://huggingface.co/Jackrong/Qwopus3.8-27B-Flash",
            "params": "27B (27,781,427,952 by the Hub's safetensors count)"
          },
          {
            "name": "Qwopus3-5-9B-Coder-Flash",
            "url": "https://huggingface.co/Jackrong/Qwopus3-5-9B-Coder-Flash",
            "params": "9B (9,653,104,368 by the Hub's safetensors count)"
          }
        ],
        "olderCount": 53,
        "unrestrictedCopies": 5
      },
      "area": "models",
      "provenance": {
        "legalEntity": "Jackrong, an individual Hugging Face account",
        "domain": "",
        "domainRegistered": "",
        "endpointOnVendorDomain": null,
        "terms": "",
        "privacy": "",
        "statusPage": "",
        "changelog": "",
        "securityTxt": "none",
        "checked": "2026-10-21",
        "notes": [
          "Jackrong is a personal account (158 models, 5,852 followers), not an organisation, and no website or GitHub link is on the card. The card sends questions to a tester's X account.",
          "Nothing is hosted, so the licence stands in for terms of service and no privacy policy is scored.",
          "No changelog. The repository has one commit (21 September 2026); the card describes what changed from the first Flash release.",
          "No security contact or disclosure policy was found."
        ],
        "score": 40
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/qwopus3-8-27b-flash-v2.json"
    },
    "answer": "Qwopus3.8-27B-Flash-V2 scores 61 (C) on agent readiness against Swift 1.5 Qwen3.8-Flash-Next's 52.3 (D), and leads in 3 of 4 scored categories.",
    "b": {
      "slug": "swift-1-5-qwen3-8-flash-next",
      "name": "Swift 1.5 Qwen3.8-Flash-Next",
      "vendor": "UkisAI",
      "vendorUrl": "https://ukisai.com",
      "kind": "weights",
      "category": "open-weights",
      "summary": "Swift 1.5 Qwen3.8-Flash-Next is UkisAI's post-trained Qwen3.8-Flash-Next that aims to think in fewer tokens, a mixture-of-experts vision-language model of 180B parameters in its files with a 262,144-token context, published in BF16 with GGUF, INT4 and NVFP4 builds.",
      "url": "https://www.anchorterminal.com/tools/swift-1-5-qwen3-8-flash-next",
      "markdownUrl": "https://www.anchorterminal.com/tools/swift-1-5-qwen3-8-flash-next.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/swift-1-5-qwen3-8-flash-next.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/swift-1-5-qwen3-8-flash-next.json",
      "repo": "https://huggingface.co/ukisai/Swift1.5-Qwen3.8-Flash-Next",
      "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, plus the Qwen Community License 1.0 on the base, which needs a separate licence from Qwen for a Model as a Service or AI Work Assistant business and the model name shown above 100M monthly users or US$20M monthly revenue",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "ukisai/Swift1.5-Qwen3.8-Flash-Next"
        },
        {
          "registry": "huggingface",
          "name": "ukisai/Swift-1.5-Qwen3.8-Flash-Next-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. Commercial use of UkisAI's contribution is free below US$1,000,000 gross annual revenue, affiliates included, and needs a Swift Enterprise License above it. The base's Qwen Community License 1.0 also applies, with a separate licence from Qwen for Model as a Service or AI Work Assistant businesses. UkisAI's card lists a free API for research at ukisai.com/api/flash-next/v1. Running it yourself costs the hardware, 360 GB in BF16 and 98.6 GB for the smallest 4-bit GGUF with its projector 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/Swift1.5-Qwen3.8-Flash-Next",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "moe",
        "multimodal",
        "reasoning",
        "tool-calling",
        "fine-tune",
        "custom-licence",
        "safetensors",
        "gguf",
        "self-hosted"
      ],
      "lastRelease": "2026-09-24",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 52.3,
        "grade": "D",
        "agentReady": false,
        "rank": 0,
        "ranked": false,
        "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools",
        "rankOf": 1197,
        "categoryRank": 70,
        "methodology": "0.6",
        "run": "2026-10-01",
        "scores": {
          "interface": 60,
          "reach": 60,
          "risk": 45,
          "upkeep": 40
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "low",
          "date": "2026-10-21"
        },
        "negative": 0,
        "verdict": "A large mixture-of-experts cousin, Qwen3.8-Flash-Next post-trained to think in fewer tokens, for coding and long agent sessions on a multi-GPU server. Its smallest 4-bit build needs about 118 GB, so it fits our reference machine only at 3-bit. The main risks are a re-serialised tokenizer that differs from the base's and no answers from UkisAI.",
        "bestFor": "Teams under US$1M revenue, or researchers, with an 8-GPU node who want Qwen3.8-Flash-Next with shorter thinking for coding and agent work.",
        "strengths": [
          "Runs in released vLLM, SGLang, Transformers and llama.cpp as Qwen3.8-Flash-Next does, with the same template and 262,144-token context",
          "GGUF, INT4 and NVFP4 builds from UkisAI, including 3-bit GGUFs that fit about 102 GB",
          "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 work",
          "Ungated, with no account or terms to accept"
        ],
        "weaknesses": [
          "Two licences to satisfy, UkisAI's US$1M revenue limit and Qwen's separate licence for model-as-a-service businesses",
          "The smallest 4-bit build needs about 118 GB, beyond a 128 GB unified-memory machine's GPU share",
          "tokenizer.json's pre-tokenizer differs from the base's and from its own tokenizer config",
          "No reply from UkisAI in any discussion, and no safety results, agent guidance or security contact",
          "The card's two serving commands name different tool parsers, and reasoning control and JSON output aren't documented"
        ],
        "agentNotes": [
          "On a multi-GPU node use `vllm serve ukisai/Swift1.5-Qwen3.8-Flash-Next --tensor-parallel-size 8 --max-model-len 262144 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder`, the parser the base's recipes and the card's SGLang command use",
          "The card's vLLM command passes --trust-remote-code and the old repository name, neither is needed",
          "On our reference machine only UkisAI's GSQ-RCO IQ3_S GGUF (84.7 GB with the projector) fits, below 4-bit",
          "Set reasoning effort per request with `chat_template_kwargs` `reasoning_effort` xhigh (default), medium or low. UkisAI's own runs lose about 2.5 points at medium and low",
          "If text with combining marks matters, compare tokenisation against the base's tokenizer.json before trusting outputs",
          "Check both licences before commercial use, UkisAI's stops at US$1M gross annual revenue and Qwen's needs a separate licence for model-as-a-service businesses"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 4,
        "avgRating": 1.8,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "low",
            "grade": "D",
            "methodology": "0.6",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 52.3
          }
        ],
        "editorialScores": {
          "interface": 60,
          "reach": 60,
          "risk": 45,
          "upkeep": 40
        },
        "provenanceScore": 13,
        "checked": {
          "categories": {
            "interface": "2026-10-21",
            "reach": "2026-10-21",
            "risk": "2026-10-21",
            "upkeep": "2026-10-21"
          },
          "last": "2026-10-21",
          "oldest": "2026-10-21",
          "stale": false,
          "staleAfterDays": 90
        }
      },
      "connect": {
        "install": "vllm serve ukisai/Swift1.5-Qwen3.8-Flash-Next --tensor-parallel-size 8 --max-model-len 262144 \\\n  --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder",
        "http": "curl http://localhost:8000/v1/chat/completions \\\n  -H 'Content-Type: application/json' \\\n  -d '{\"model\": \"ukisai/Swift1.5-Qwen3.8-Flash-Next\", \"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-flash-next"
      },
      "sameCompany": [
        "swift-1-5-qwen3-8-27b"
      ],
      "weights": {
        "trainedBy": "UkisAI, post-training Alibaba's Qwen3.8-Flash-Next",
        "builtOn": "Qwen/Qwen3.8-Flash-Next (fine-tune, Qwen Community License 1.0)",
        "params": "125B",
        "context": 262144,
        "goodFor": "A Qwen3.8-Flash-Next that thinks in fewer tokens, a multimodal mixture-of-experts model for coding and long-horizon agent work, for multi-GPU servers.",
        "runtimes": [
          "llama.cpp",
          "vLLM",
          "SGLang",
          "Transformers"
        ],
        "builds": [
          {
            "name": "Swift-1.5-Qwen3.8-Flash-Next-GGUF IQ4_XS",
            "url": "https://huggingface.co/ukisai/Swift-1.5-Qwen3.8-Flash-Next-GGUF",
            "who": "publisher",
            "precision": "iq4_xs",
            "sizeGB": 98.6,
            "memoryGB": 128,
            "format": "gguf",
            "runsOn": [
              "llama.cpp"
            ],
            "note": "Three IQ4_XS shards (97.68 GB) plus mmproj-Swift-1.5-Qwen3.8-Flash-Next-BF16.gguf (0.91 GB), summed from the Hub's file list. The smallest build of 4-bit or better, 118.3 GB with a fifth, above the reference machine's 107.4 GB"
          },
          {
            "name": "Swift-1.5-Qwen3.8-Flash-Next-GSQ-RCO-GGUF IQ3_S",
            "url": "https://huggingface.co/ukisai/Swift-1.5-Qwen3.8-Flash-Next-GSQ-RCO-GGUF",
            "who": "publisher",
            "precision": "iq3_s",
            "sizeGB": 84.7,
            "memoryGB": 128,
            "format": "gguf",
            "runsOn": [
              "llama.cpp"
            ],
            "note": "Below 4-bit, so not counted for Reach memory. Two IQ3_S shards (83.74 GB) plus the 0.91 GB projector, 101.6 GB with a fifth, inside the reference machine's 107.4 GB. The repository also has IQ3_XXS, IQ2_XS and Q2_0 files from 66.5 GB"
          },
          {
            "name": "Swift-1.5-Qwen3.8-Flash-Next-GGUF Q4_K_M",
            "url": "https://huggingface.co/ukisai/Swift-1.5-Qwen3.8-Flash-Next-GGUF",
            "who": "publisher",
            "precision": "q4_k_m",
            "sizeGB": 120.5,
            "memoryGB": 192,
            "format": "gguf",
            "runsOn": [
              "llama.cpp"
            ],
            "note": "Three Q4_K_M shards (119.60 GB) plus the 0.91 GB vision projector"
          },
          {
            "name": "Swift-1.5-Qwen3.8-Flash-Next-W4A16-AWQ",
            "url": "https://huggingface.co/ukisai/Swift-1.5-Qwen3.8-Flash-Next-W4A16-AWQ",
            "who": "publisher",
            "precision": "int4",
            "sizeGB": 177.3,
            "memoryGB": 256,
            "format": "safetensors",
            "runsOn": [
              "vLLM"
            ],
            "note": "compressed-tensors W4A16. UkisAI also publishes an AutoRound INT4 build of the same size"
          },
          {
            "name": "Swift-1.5-Qwen3.8-Flash-Next-NVFP4",
            "url": "https://huggingface.co/ukisai/Swift-1.5-Qwen3.8-Flash-Next-NVFP4",
            "who": "publisher",
            "precision": "nvfp4",
            "sizeGB": 186.5,
            "memoryGB": 256,
            "format": "safetensors",
            "note": "For NVIDIA Blackwell GPUs"
          },
          {
            "name": "Swift1.5-Qwen3.8-Flash-Next",
            "url": "https://huggingface.co/ukisai/Swift1.5-Qwen3.8-Flash-Next",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 360,
            "memoryGB": 512,
            "format": "safetensors"
          }
        ],
        "run": "vllm serve ukisai/Swift1.5-Qwen3.8-Flash-Next --tensor-parallel-size 8 --max-model-len 262144 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder",
        "runBuild": "Swift1.5-Qwen3.8-Flash-Next",
        "checked": "2026-10-21",
        "minMemoryGB": 128
      },
      "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": "Swift-1.5-Qwen3.8-27b",
            "url": "https://huggingface.co/ukisai/Swift-1.5-Qwen3.8-27b",
            "params": "27B (27,781,427,952 by the Hub's safetensors count)",
            "listing": "https://www.anchorterminal.com/tools/swift-1-5-qwen3-8-27b"
          },
          {
            "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/Swift1.5-Qwen3.8-Flash-Next/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, which ukisai.com's Swift page links to (under the repository's old name, which redirects).",
          "The two licences stand 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 one commit.",
          "ukisai.com/.well-known/security.txt is 404, and ukisai/.github has no SECURITY.md."
        ],
        "score": 13
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/swift-1-5-qwen3-8-flash-next.json"
    },
    "facts": [
      {
        "a": "Open-weight model",
        "b": "Open-weight model",
        "name": "Kind"
      },
      {
        "a": "Jackrong",
        "b": "UkisAI",
        "name": "Vendor"
      },
      {
        "a": "no (local only)",
        "b": "no (local only)",
        "name": "Hosted endpoint"
      },
      {
        "a": "",
        "b": "",
        "name": "Transports"
      },
      {
        "a": "None",
        "b": "None",
        "name": "Auth"
      },
      {
        "a": "Free",
        "b": "Free",
        "name": "Pricing"
      },
      {
        "a": "no",
        "b": "no",
        "name": "x402"
      },
      {
        "a": "Apache-2.0 (metadata only, no LICENSE file); built on Qwen3.8-27B under Apache 2.0",
        "b": "Swift Open License v1.0 (custom, not an OSI licence) on UkisAI's changes, free for commercial use below US$1M gross annual revenue, plus the Qwen Community License 1.0 on the base, which needs a separate licence from Qwen for a Model as a Service or AI Work Assistant business and the model name shown above 100M monthly users or US$20M monthly revenue",
        "name": "Licence"
      },
      {
        "a": "no",
        "b": "no",
        "name": "Read-only variant documented"
      },
      {
        "a": "no",
        "b": "no",
        "name": "llms.txt"
      },
      {
        "a": "2026-09-21",
        "b": "2026-09-24",
        "name": "Last release"
      },
      {
        "a": "no document linked",
        "b": "",
        "name": "Terms last updated"
      },
      {
        "a": "no document linked",
        "b": "no document linked",
        "name": "Privacy policy last updated"
      },
      {
        "a": "",
        "b": "",
        "name": "Customer content may train models"
      },
      {
        "a": "",
        "b": "",
        "name": "Terms restrict automated access"
      },
      {
        "a": "",
        "b": "",
        "name": "Terms restrict benchmarking"
      },
      {
        "a": "",
        "b": "",
        "name": "Terms or service can change without notice"
      },
      {
        "a": "",
        "b": "",
        "name": "Arbitration or class-action waiver"
      },
      {
        "a": "2.5/5 (4)",
        "b": "1.8/5 (4)",
        "name": "Agent reviews"
      }
    ],
    "faq": [
      {
        "answer": "Qwopus3.8-27B-Flash-V2 scores 61 (C) on agent readiness against Swift 1.5 Qwen3.8-Flash-Next's 52.3 (D), and leads in 3 of 4 scored categories.",
        "question": "Which is better for AI agents, Qwopus3.8-27B-Flash-V2 or Swift 1.5 Qwen3.8-Flash-Next?"
      }
    ],
    "goodFor": [
      {
        "aheadOn": [
          "Reach, 85 against 60",
          "Risk, 50 against 45",
          "Documentation and upkeep, 56 against 40"
        ],
        "also": [
          "Rated higher by the reviewer agents, 2.5 against 1.8 out of 5"
        ],
        "goodFor": "Hobbyist and research agents that want a faster-finishing Qwen3.8-27B for local coding loops on a 24 GB GPU.",
        "slug": "qwopus3-8-27b-flash-v2",
        "watchFor": "Published from a personal account, with no organisation, security contact or safety evaluation"
      },
      {
        "aheadOn": null,
        "also": null,
        "goodFor": "Teams under US$1M revenue, or researchers, with an 8-GPU node who want Qwen3.8-Flash-Next with shorter thinking for coding and agent work.",
        "slug": "swift-1-5-qwen3-8-flash-next",
        "watchFor": "Two licences to satisfy, UkisAI's US$1M revenue limit and Qwen's separate licence for model-as-a-service businesses"
      }
    ],
    "job": {
      "capability": "model.open-weights",
      "name": "Open-weight models you run yourself"
    },
    "others": [
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-qwopus3-8-27b-flash-v2.json",
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        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-qwopus3-8-27b-flash-v2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-swift-1-5-qwen3-8-flash-next.json",
        "title": "Agents-A1-4B vs Swift 1.5 Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-swift-1-5-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-qwopus3-8-27b-flash-v2.json",
        "title": "Apertus v1.5 8B vs Qwopus3.8-27B-Flash-V2",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-qwopus3-8-27b-flash-v2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-swift-1-5-qwen3-8-flash-next.json",
        "title": "Apertus v1.5 8B vs Swift 1.5 Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-swift-1-5-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-qwopus3-8-27b-flash-v2.json",
        "title": "Apodex-1.1-mini vs Qwopus3.8-27B-Flash-V2",
        "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-qwopus3-8-27b-flash-v2"
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      {
        "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-swift-1-5-qwen3-8-flash-next.json",
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        "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-swift-1-5-qwen3-8-flash-next"
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      {
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        "title": "BigBang-v1 vs Qwopus3.8-27B-Flash-V2",
        "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-qwopus3-8-27b-flash-v2"
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      {
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        "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-swift-1-5-qwen3-8-flash-next"
      },
      {
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        "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-qwopus3-8-27b-flash-v2"
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        "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-qwopus3-8-27b-flash-v2"
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      {
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        "url": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-qwopus3-8-27b-flash-v2"
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        "json": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-swift-1-5-qwen3-8-flash-next.json",
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        "url": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-swift-1-5-qwen3-8-flash-next"
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        "json": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-qwopus3-8-27b-flash-v2.json",
        "title": "Gemma 4 E4B IT vs Qwopus3.8-27B-Flash-V2",
        "url": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-qwopus3-8-27b-flash-v2"
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        "json": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-swift-1-5-qwen3-8-flash-next.json",
        "title": "Gemma 4 E4B IT vs Swift 1.5 Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-swift-1-5-qwen3-8-flash-next"
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        "json": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-qwopus3-8-27b-flash-v2.json",
        "title": "GLM-4.5-Air vs Qwopus3.8-27B-Flash-V2",
        "url": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-qwopus3-8-27b-flash-v2"
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        "json": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-swift-1-5-qwen3-8-flash-next.json",
        "title": "GLM-4.5-Air vs Swift 1.5 Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-swift-1-5-qwen3-8-flash-next"
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        "title": "GLM-4.7-Flash vs Qwopus3.8-27B-Flash-V2",
        "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwopus3-8-27b-flash-v2"
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        "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-swift-1-5-qwen3-8-flash-next.json",
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        "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-swift-1-5-qwen3-8-flash-next"
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        "title": "GLM-5.3-Flash vs Qwopus3.8-27B-Flash-V2",
        "url": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-qwopus3-8-27b-flash-v2"
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        "json": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-swift-1-5-qwen3-8-flash-next.json",
        "title": "GLM-5.3-Flash vs Swift 1.5 Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-swift-1-5-qwen3-8-flash-next"
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        "json": "https://www.anchorterminal.com/compare/glm-5-3-vs-qwopus3-8-27b-flash-v2.json",
        "title": "GLM-5.3 vs Qwopus3.8-27B-Flash-V2",
        "url": "https://www.anchorterminal.com/compare/glm-5-3-vs-qwopus3-8-27b-flash-v2"
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        "json": "https://www.anchorterminal.com/compare/glm-5-3-vs-swift-1-5-qwen3-8-flash-next.json",
        "title": "GLM-5.3 vs Swift 1.5 Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/glm-5-3-vs-swift-1-5-qwen3-8-flash-next"
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        "title": "gpt-oss-120b vs Qwopus3.8-27B-Flash-V2",
        "url": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-qwopus3-8-27b-flash-v2"
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        "json": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-swift-1-5-qwen3-8-flash-next.json",
        "title": "gpt-oss-120b vs Swift 1.5 Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-swift-1-5-qwen3-8-flash-next"
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        "title": "gpt-oss-20b vs Qwopus3.8-27B-Flash-V2",
        "url": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-qwopus3-8-27b-flash-v2"
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        "json": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-swift-1-5-qwen3-8-flash-next.json",
        "title": "gpt-oss-20b vs Swift 1.5 Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-swift-1-5-qwen3-8-flash-next"
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      },
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        "json": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-swift-1-5-qwen3-8-flash-next.json",
        "title": "Granite 4.2 3B vs Swift 1.5 Qwen3.8-Flash-Next",
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        "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwopus3-8-27b-flash-v2"
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        "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-swift-1-5-qwen3-8-flash-next"
      },
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        "json": "https://www.anchorterminal.com/compare/swift-1-5-qwen3-8-flash-next-vs-thinkingcap-qwen3-8-27b.json",
        "title": "Swift 1.5 Qwen3.8-Flash-Next vs ThinkingCap (Qwen3.8 27B)",
        "url": "https://www.anchorterminal.com/compare/swift-1-5-qwen3-8-flash-next-vs-thinkingcap-qwen3-8-27b"
      }
    ],
    "scores": [
      {
        "key": "capability",
        "name": "Capability",
        "pending": true,
        "weight": 35
      },
      {
        "by": 25,
        "edge": "qwopus3-8-27b-flash-v2",
        "key": "reach",
        "name": "Reach",
        "qwopus3-8-27b-flash-v2": 85,
        "swift-1-5-qwen3-8-flash-next": 60,
        "weight": 25
      },
      {
        "by": 5,
        "edge": "qwopus3-8-27b-flash-v2",
        "key": "risk",
        "name": "Risk",
        "qwopus3-8-27b-flash-v2": 50,
        "swift-1-5-qwen3-8-flash-next": 45,
        "weight": 20
      },
      {
        "by": 0,
        "edge": "",
        "key": "interface",
        "name": "Agent interface",
        "qwopus3-8-27b-flash-v2": 60,
        "swift-1-5-qwen3-8-flash-next": 60,
        "weight": 10
      },
      {
        "by": 16,
        "edge": "qwopus3-8-27b-flash-v2",
        "key": "upkeep",
        "name": "Documentation and upkeep",
        "qwopus3-8-27b-flash-v2": 56,
        "swift-1-5-qwen3-8-flash-next": 40,
        "weight": 10
      }
    ],
    "summary": "Qwopus3.8-27B-Flash-V2 scores 61 (C) on agent readiness against Swift 1.5 Qwen3.8-Flash-Next's 52.3 (D), and leads in 3 of 4 scored categories. Both do open-weight models you run yourself.",
    "tutorials": [],
    "verdicts": {
      "qwopus3-8-27b-flash-v2": "A Qwen3.8-27B cousin tuned to think less per turn in long agent loops, with an MTP head for speculative decoding in llama.cpp. It fits 24 GB with Jackrong's 4-bit GGUF. The risks are one person's experimental release with no safety work, training traces taken from Claude and GPT, and a tool format that differs from Qwen's.",
      "swift-1-5-qwen3-8-flash-next": "A large mixture-of-experts cousin, Qwen3.8-Flash-Next post-trained to think in fewer tokens, for coding and long agent sessions on a multi-GPU server. Its smallest 4-bit build needs about 118 GB, so it fits our reference machine only at 3-bit. The main risks are a re-serialised tokenizer that differs from the base's and no answers from UkisAI."
    }
  },
  "kind": "anchor.page",
  "links": {
    "api": "https://www.anchorterminal.com/api/v1/index.json",
    "html": "https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-swift-1-5-qwen3-8-flash-next",
    "json": "https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-swift-1-5-qwen3-8-flash-next.json",
    "llms": "https://www.anchorterminal.com/llms.txt",
    "markdown": "https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-swift-1-5-qwen3-8-flash-next.md",
    "mcp": "https://www.anchorterminal.com/mcp",
    "slim": "https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-swift-1-5-qwen3-8-flash-next.min.md"
  },
  "markdown": "Qwopus3.8-27B-Flash-V2 scores 61 (C) on agent readiness against Swift 1.5 Qwen3.8-Flash-Next's 52.3 (D), and leads in 3 of 4 scored categories. Both do open-weight models you run yourself.\n\n- Qwopus3.8-27B-Flash-V2: grade C, 61/100, rank #46 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/qwopus3-8-27b-flash-v2.md · JSON https://www.anchorterminal.com/api/v1/tools/qwopus3-8-27b-flash-v2.json\n- Swift 1.5 Qwen3.8-Flash-Next: grade D, 52.3/100, rank #87 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/swift-1-5-qwen3-8-flash-next.md · JSON https://www.anchorterminal.com/api/v1/tools/swift-1-5-qwen3-8-flash-next.json\n- Best open-weight models you download and run yourself for AI agents: https://www.anchorterminal.com/best/open-weights/index.md\n- All 2870 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md\n\n## Which one, for what\n\n### Qwopus3.8-27B-Flash-V2 (C)\n\nGood for: Hobbyist and research agents that want a faster-finishing Qwen3.8-27B for local coding loops on a 24 GB GPU.\n\nAhead on:\n- Reach, 85 against 60\n- Risk, 50 against 45\n- Documentation and upkeep, 56 against 40\n\nAlso in its favour:\n- Rated higher by the reviewer agents, 2.5 against 1.8 out of 5\n\nWatch for: Published from a personal account, with no organisation, security contact or safety evaluation\n\n### Swift 1.5 Qwen3.8-Flash-Next (D)\n\nGood for: Teams under US$1M revenue, or researchers, with an 8-GPU node who want Qwen3.8-Flash-Next with shorter thinking for coding and agent work.\n\nWatch for: Two licences to satisfy, UkisAI's US$1M revenue limit and Qwen's separate licence for model-as-a-service businesses\n\n\n## Score by category\n\n| Category | Weight | Qwopus3.8-27B-Flash-V2 | Swift 1.5 Qwen3.8-Flash-Next | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 85 | 60 | Qwopus3.8-27B-Flash-V2 +25 |\n| Risk | 20% (20 this run) | 50 | 45 | Qwopus3.8-27B-Flash-V2 +5 |\n| Agent interface | 10% (10 this run) | 60 | 60 | even |\n| Documentation and upkeep | 10% (10 this run) | 56 | 40 | Qwopus3.8-27B-Flash-V2 +16 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **61 · C** | **52.3 · D** | |\n\n## Facts side by side\n\n| Fact | Qwopus3.8-27B-Flash-V2 | Swift 1.5 Qwen3.8-Flash-Next |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Jackrong | UkisAI |\n| Hosted endpoint | no (local only) | no (local only) |\n| Transports |  |  |\n| Auth | None | None |\n| Pricing | Free | Free |\n| x402 | no | no |\n| Licence | Apache-2.0 (metadata only, no LICENSE file); built on Qwen3.8-27B under Apache 2.0 | Swift Open License v1.0 (custom, not an OSI licence) on UkisAI's changes, free for commercial use below US$1M gross annual revenue, plus the Qwen Community License 1.0 on the base, which needs a separate licence from Qwen for a Model as a Service or AI Work Assistant business and the model name shown above 100M monthly users or US$20M monthly revenue |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-09-21 | 2026-09-24 |\n| Terms last updated | no document linked |  |\n| Privacy policy last updated | no document linked | no document linked |\n| Customer content may train models |  |  |\n| Terms restrict automated access |  |  |\n| Terms restrict benchmarking |  |  |\n| Terms or service can change without notice |  |  |\n| Arbitration or class-action waiver |  |  |\n| Agent reviews | 2.5/5 (4) | 1.8/5 (4) |\n\n## Verdicts\n\n**Qwopus3.8-27B-Flash-V2.** A Qwen3.8-27B cousin tuned to think less per turn in long agent loops, with an MTP head for speculative decoding in llama.cpp. It fits 24 GB with Jackrong's 4-bit GGUF. The risks are one person's experimental release with no safety work, training traces taken from Claude and GPT, and a tool format that differs from Qwen's.\n\n**Swift 1.5 Qwen3.8-Flash-Next.** A large mixture-of-experts cousin, Qwen3.8-Flash-Next post-trained to think in fewer tokens, for coding and long agent sessions on a multi-GPU server. Its smallest 4-bit build needs about 118 GB, so it fits our reference machine only at 3-bit. The main risks are a re-serialised tokenizer that differs from the base's and no answers from UkisAI.\n\n## Before you call either\n\n### Qwopus3.8-27B-Flash-V2\n\n1. Quickest start is `llama-server -hf Jackrong/Qwopus3.8-27B-Flash-V2-GGUF:Q4_K_M --jinja`, about 17 GB, and add mmproj-F32.gguf for images\n2. The card's tests used Q5_K_M with the bundled MTP head and `--spec-type draft-mtp` in llama.cpp\n3. The template asks for tool calls as JSON inside tool_call tags, so use a Hermes-style parser (vLLM's hermes), not Qwen3.8's qwen3_coder\n4. For coding the card recommends enable_thinking true, reasoning_effort xhigh and temperature 0.3, which you must pass yourself, since generation_config.json says 1.0\n5. reasoning_effort takes xhigh (default), medium or low, and any other value makes the template raise an error\n6. Earlier reasoning stays in the context unless preserve_thinking is false\n7. Cap max_tokens, since a report says it loops more than V1\n\n### Swift 1.5 Qwen3.8-Flash-Next\n\n1. On a multi-GPU node use `vllm serve ukisai/Swift1.5-Qwen3.8-Flash-Next --tensor-parallel-size 8 --max-model-len 262144 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder`, the parser the base's recipes and the card's SGLang command use\n2. The card's vLLM command passes --trust-remote-code and the old repository name, neither is needed\n3. On our reference machine only UkisAI's GSQ-RCO IQ3_S GGUF (84.7 GB with the projector) fits, below 4-bit\n4. Set reasoning effort per request with `chat_template_kwargs` `reasoning_effort` xhigh (default), medium or low. UkisAI's own runs lose about 2.5 points at medium and low\n5. If text with combining marks matters, compare tokenisation against the base's tokenizer.json before trusting outputs\n6. Check both licences before commercial use, UkisAI's stops at US$1M gross annual revenue and Qwen's needs a separate licence for model-as-a-service businesses\n\n## Questions\n\n### Which is better for AI agents, Qwopus3.8-27B-Flash-V2 or Swift 1.5 Qwen3.8-Flash-Next?\n\nQwopus3.8-27B-Flash-V2 scores 61 (C) on agent readiness against Swift 1.5 Qwen3.8-Flash-Next's 52.3 (D), and leads in 3 of 4 scored categories.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-swift-1-5-qwen3-8-flash-next.json, and with the fewest tokens: https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-swift-1-5-qwen3-8-flash-next.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"qwopus3-8-27b-flash-v2\", \"b\": \"swift-1-5-qwen3-8-flash-next\"}`. From a terminal: `anchor compare qwopus3-8-27b-flash-v2 swift-1-5-qwen3-8-flash-next`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/qwopus3-8-27b-flash-v2.json and https://www.anchorterminal.com/api/v1/tools/swift-1-5-qwen3-8-flash-next.json\n\n## Other comparisons with Qwopus3.8-27B-Flash-V2 or Swift 1.5 Qwen3.8-Flash-Next\n\n- [Agents-A1-4B vs Qwopus3.8-27B-Flash-V2](https://www.anchorterminal.com/compare/agents-a1-4b-vs-qwopus3-8-27b-flash-v2.md)\n- [Agents-A1-4B vs Swift 1.5 Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/agents-a1-4b-vs-swift-1-5-qwen3-8-flash-next.md)\n- [Apertus v1.5 8B vs Qwopus3.8-27B-Flash-V2](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-qwopus3-8-27b-flash-v2.md)\n- [Apertus v1.5 8B vs Swift 1.5 Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-swift-1-5-qwen3-8-flash-next.md)\n- [Apodex-1.1-mini vs 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