{
  "data": {
    "a": {
      "slug": "lbs-27b",
      "name": "LBS-27B",
      "vendor": "inclusionAI (Ant Group)",
      "vendorUrl": "https://www.inclusion-ai.org",
      "kind": "weights",
      "category": "open-weights",
      "summary": "LBS-27B is an open-weight vision-language model from inclusionAI's UI-Venus line, fine-tuned from Qwen3.8-27B to find what to click on a screenshot by zooming into regions over several turns, and applied to visual questions on large images.",
      "url": "https://www.anchorterminal.com/tools/lbs-27b",
      "markdownUrl": "https://www.anchorterminal.com/tools/lbs-27b.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/lbs-27b.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/lbs-27b.json",
      "repo": "https://huggingface.co/inclusionAI/LBS-27B",
      "license": "Apache-2.0 (the card's metadata and licence section; the LICENSE file sits in the GitHub repository inclusionAI/UI-Venus on its LBS branch, not in the Hugging Face repository)",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "inclusionAI/LBS-27B"
        }
      ],
      "auth": "none",
      "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The Hub reports gated false for inclusionAI/LBS-27B (checked 2026-10-27).",
      "pricing": "free",
      "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0 as the card states for the weights and the code. Running it costs the hardware, 54.73 GB of BF16 files and no quantised build from the publisher. No hosted API or inference provider serves it (checked 2026-10-27).",
      "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 2026-10-27).",
        "endpoints": []
      },
      "toolCount": null,
      "popularity": {
        "githubStars": null,
        "npmWeekly": null,
        "pypiWeekly": null,
        "asOf": "2026-10-27"
      },
      "docsUrl": "https://github.com/inclusionAI/UI-Venus/tree/LBS",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "dense",
        "qwen",
        "vision",
        "gui-grounding",
        "computer-use",
        "reasoning",
        "safetensors",
        "self-hosted",
        "new"
      ],
      "lastRelease": "2026-10-26",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 53.9,
        "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": 1217,
        "categoryRank": 73,
        "methodology": "0.6",
        "run": "2026-10-01",
        "scores": {
          "interface": 70,
          "reach": 55,
          "risk": 50,
          "upkeep": 43
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "low",
          "date": "2026-10-27"
        },
        "negative": 0,
        "verdict": "A 27B Qwen3.8 fine-tune for GUI grounding that zooms into screenshots over a few turns before naming a point, and inclusionAI says it also helps with fine detail in large images. It needs about 96 GB at BF16 and a client that runs its crop actions. Every result so far is inclusionAI's own, with no safety results or injection guidance.",
        "bestFor": "Agents with about 96 GB of GPU memory that need to find click targets on dense professional screenshots, or read fine detail in large images, and can run a small crop loop and wait for independent results.",
        "strengths": [
          "Qwen3.8-27B's stock architecture with no repository code, so released vLLM, SGLang, Transformers and llama.cpp load it",
          "Apache 2.0 for the weights and the base, ungated, safetensors only",
          "A simple, documented plain-text crop protocol with a point answer on a 0 to 1000 scale, and the training recipe and evaluation kit published",
          "Up to two crops per turn, learned in GRPO, and 262,144 tokens of context from the base",
          "inclusionAI publishes per-item scores and selected trajectories for its own benchmark runs"
        ],
        "weaknesses": [
          "No independent benchmark result yet; ScreenSpot-Pro, OS-World-G, PerceptionBench and HR-Bench numbers are inclusionAI's own runs",
          "BF16 only, about 66 GB to load, and no Ollama, LM Studio or quantised build",
          "The crop loop and system prompt come from the publisher's Python, not a runtime parser or a template",
          "No safety evaluation, prompt-injection guidance, training data sources or knowledge cut-off",
          "No hosted API or inference provider serves it"
        ],
        "agentNotes": [
          "Serve with `vllm serve inclusionAI/LBS-27B --tensor-parallel-size 4 --max-model-len 65536`, which is what the card's wrapper script runs; keep the context at 32,768 or more for multi-turn images",
          "Build the system prompt with `build_system_prompt` from the LBS branch's `zoomin_grounding_protocol.py`, send the screenshot after `image_path: \u003cname\u003e`, and don't pass `tools`; the model answers in plain text",
          "Parse `\u003caction\u003ecrop_image(image_path=..., x1=..., y1=..., x2=..., y2=...)\u003c/action\u003e`, cut the crop yourself and return it with its mapping to the original image; allow at most two crops per turn and three per episode",
          "Read the final answer as `[x.xx,y.yy]` on a 0 to 1000 scale of the original image, and use your own executor to click",
          "Pass `reasoning_effort` as `xhigh`, `medium` or `low` only, since the template raises an error for `high`, and don't count on `thinking_budget`, which the template ignores",
          "Don't turn on MTP speculative decoding; the MTP weights aren't in the repository though config.json names one MTP layer"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 0,
        "avgRating": 0,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "low",
            "grade": "D",
            "methodology": "0.6",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 53.9
          }
        ],
        "editorialScores": {
          "interface": 70,
          "reach": 55,
          "risk": 50,
          "upkeep": 43
        },
        "provenanceScore": 57,
        "checked": {
          "categories": {
            "interface": "2026-10-27",
            "reach": "2026-10-27",
            "risk": "2026-10-27",
            "upkeep": "2026-10-27"
          },
          "last": "2026-10-27",
          "oldest": "2026-10-27",
          "stale": false,
          "staleAfterDays": 90
        }
      },
      "connect": {
        "install": "# The card's route clones the LBS branch and wraps vllm serve (four GPUs in its example)\ngit clone --branch LBS --single-branch https://github.com/inclusionAI/UI-Venus.git\ncd UI-Venus/evaluation\nbash scripts/serve_vllm.sh start inclusionAI/LBS-27B --name lbs --tp 4 --dp 1 --daemon\n# the same server without the wrapper (the script's defaults are a 65,536-token context and 64 sequences)\nvllm serve inclusionAI/LBS-27B --served-model-name lbs --tensor-parallel-size 4 --max-model-len 65536",
        "http": "curl http://localhost:8000/v1/chat/completions -H 'Content-Type: application/json' \\\n  -d '{\"model\": \"lbs\", \"max_tokens\": 2560,\n       \"messages\": [{\"role\": \"system\", \"content\": \"\u003ctask, image size, crop_image spec and answer format, as build_system_prompt in the LBS branch writes them\u003e\"},\n                    {\"role\": \"user\", \"content\": [{\"type\": \"text\", \"text\": \"image_path: screenshot.png\"},\n                                                {\"type\": \"image_url\", \"image_url\": {\"url\": \"data:image/png;base64,...\"}}]}],\n       \"chat_template_kwargs\": {\"reasoning_effort\": \"xhigh\"}}'"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/lbs-27b"
      },
      "sameCompany": [
        "ming-3-0-asr-tiny",
        "ming-3-0-audio-flash",
        "ming-3-0-audio-tiny",
        "ming-3-0-tts-tiny",
        "ling-3-0-tiny"
      ],
      "weights": {
        "trainedBy": "inclusionAI (Ant Group), with Wuhan University of Technology, CASIA and Zhejiang University",
        "builtOn": "Qwen/Qwen3.8-27B (fine-tune, named in the card's text, not in its base_model metadata; Apache 2.0, copyright Alibaba Cloud)",
        "params": "27B",
        "context": 262144,
        "goodFor": "A cousin that finds the point to click on a screenshot, and answers questions about fine detail in large images, by zooming in over a few turns, on a machine with about 96 GB of memory and a client that runs the crops, if inclusionAI's own results hold up.",
        "runtimes": [
          "vLLM",
          "SGLang",
          "Transformers",
          "llama.cpp"
        ],
        "builds": [
          {
            "name": "LBS-27B (BF16)",
            "url": "https://huggingface.co/inclusionAI/LBS-27B",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 54.73,
            "memoryGB": 96,
            "format": "safetensors",
            "runsOn": [
              "vLLM",
              "SGLang",
              "Transformers"
            ],
            "note": "Whole repository, 54.73 GB, almost all of it 12 BF16 shards. 54.73 GB × 1.2 = 65.7 GB, just over 64 GB. The only build; no Ollama, LM Studio or ggml-org build and no copy by anyone else on 27 October 2026"
          }
        ],
        "run": "vllm serve inclusionAI/LBS-27B --served-model-name lbs --tensor-parallel-size 4 --max-model-len 65536",
        "runBuild": "LBS-27B (BF16)",
        "checked": "2026-10-27",
        "minMemoryGB": 96
      },
      "family": {
        "lab": "inclusionAI (Ant Group)",
        "family": "UI-Venus",
        "url": "https://www.anchorterminal.com/open-weights/#inclusionai",
        "others": [
          {
            "name": "UI-Venus-2-9B",
            "url": "https://huggingface.co/inclusionAI/UI-Venus-2-9B",
            "params": "9B"
          },
          {
            "name": "UI-Venus-1.5-30B-A3B",
            "url": "https://huggingface.co/inclusionAI/UI-Venus-1.5-30B-A3B",
            "params": "30B total, 3B active"
          },
          {
            "name": "UI-Venus-1.5-8B",
            "url": "https://huggingface.co/inclusionAI/UI-Venus-1.5-8B",
            "params": "8B"
          },
          {
            "name": "UI-Venus-1.5-2B",
            "url": "https://huggingface.co/inclusionAI/UI-Venus-1.5-2B",
            "params": "2B"
          }
        ],
        "olderCount": 54
      },
      "area": "models",
      "provenance": {
        "legalEntity": "inclusionAI, Ant Group (named in the site footer of inclusion-ai.org); the work is credited to authors from Wuhan University of Technology, CASIA, Zhejiang University and Ant Group",
        "domain": "inclusion-ai.org",
        "domainRegistered": "2025-04-03",
        "endpointOnVendorDomain": null,
        "terms": "",
        "privacy": "",
        "statusPage": "",
        "changelog": "https://huggingface.co/inclusionAI/LBS-27B/commits/main",
        "securityTxt": "none",
        "checked": "2026-10-27",
        "notes": [
          "inclusion-ai.org links to huggingface.co/inclusionAI and github.com/inclusionAI (read 2026-10-27). The Hub doesn't verify the organisation, which the calibration of 21 October credits as the publisher's own. The domain registration date is carried from the Ming-3.0 listings' check of 21 October 2026.",
          "The model card links github.com/inclusionAI/UI-Venus on its LBS branch, where the LICENSE, the NOTICE, the training recipe and the evaluation kit sit. The NOTICE reads Copyright 2026 the LBS contributors.",
          "Nothing is hosted, so the Apache 2.0 licence stands in for terms of service and no privacy policy is scored.",
          "The changelog is the Hugging Face commit history, two commits on 26 October 2026 (initial commit and one upload).",
          "inclusion-ai.org/.well-known/security.txt reset the connection on 27 October 2026 and answered 404 on 21 October. The LBS branch has no SECURITY.md. Ant Group runs a company-wide security response centre, AntSRC (https://security.alipay.com/, answering on 27 October 2026); nothing on the card or the branch links it."
        ],
        "score": 57
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/lbs-27b.json",
      "live": {
        "slug": "lbs-27b",
        "securityTxt": {
          "url": "https://inclusion-ai.org/.well-known/security.txt",
          "state": "unknown",
          "checkedAt": "2026-10-27T15:31:25.810013407Z"
        },
        "updatedAt": "2026-10-27T15:31:25.810013407Z"
      }
    },
    "answer": "LBS-27B scores 53.9 (D) on agent readiness against Swift 1.5 Qwen3.8-Flash-Next's 52.3 (D), and leads in 3 of 4 scored categories. Swift 1.5 Qwen3.8-Flash-Next leads on reach.",
    "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": 1217,
        "categoryRank": 77,
        "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",
      "live": {
        "slug": "swift-1-5-qwen3-8-flash-next",
        "securityTxt": {
          "url": "https://ukisai.com/.well-known/security.txt",
          "state": "none",
          "checkedAt": "2026-10-27T15:31:09.264198972Z"
        },
        "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/Swift1.5-Qwen3.8-Flash-Next/blob/main/LICENSE",
            "kind": "terms",
            "status": 200,
            "checkedAt": "2026-10-27T03:49:19.860723218Z",
            "changedAt": "2026-10-27T03:49:19.860723218Z",
            "fingerprint": "968732ffbb38"
          }
        ],
        "updatedAt": "2026-10-27T15:31:09.264198972Z"
      }
    },
    "facts": [
      {
        "a": "Open-weight model",
        "b": "Open-weight model",
        "name": "Kind"
      },
      {
        "a": "inclusionAI (Ant Group)",
        "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 (the card's metadata and licence section; the LICENSE file sits in the GitHub repository inclusionAI/UI-Venus on its LBS branch, not in the Hugging Face repository)",
        "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": "2026-10-26",
        "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": "none",
        "b": "1.8/5 (4)",
        "name": "Agent reviews"
      }
    ],
    "faq": [
      {
        "answer": "LBS-27B scores 53.9 (D) on agent readiness against Swift 1.5 Qwen3.8-Flash-Next's 52.3 (D), and leads in 3 of 4 scored categories. Swift 1.5 Qwen3.8-Flash-Next leads on reach.",
        "question": "Which is better for AI agents, LBS-27B or Swift 1.5 Qwen3.8-Flash-Next?"
      }
    ],
    "goodFor": [
      {
        "aheadOn": [
          "Risk, 50 against 45",
          "Agent interface, 70 against 60"
        ],
        "also": null,
        "goodFor": "Agents with about 96 GB of GPU memory that need to find click targets on dense professional screenshots, or read fine detail in large images, and can run a small crop loop and wait for independent results.",
        "slug": "lbs-27b",
        "watchFor": "No independent benchmark result yet; ScreenSpot-Pro, OS-World-G, PerceptionBench and HR-Bench numbers are inclusionAI's own runs"
      },
      {
        "aheadOn": [
          "Reach, 60 against 55"
        ],
        "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"
    },
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        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-lbs-27b"
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        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-swift-1-5-qwen3-8-flash-next.json",
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        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-lbs-27b.json",
        "title": "Apertus v1.5 8B vs LBS-27B",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-lbs-27b"
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      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-swift-1-5-qwen3-8-flash-next.json",
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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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        "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-lbs-27b.json",
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        "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-lbs-27b"
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        "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-swift-1-5-qwen3-8-flash-next.json",
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        "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-swift-1-5-qwen3-8-flash-next.json",
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        "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-swift-1-5-qwen3-8-flash-next.json",
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        "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-swift-1-5-qwen3-8-flash-next"
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        "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-lbs-27b.json",
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        "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-swift-1-5-qwen3-8-flash-next.json",
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        "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-lbs-27b.json",
        "title": "DeepSeek V4.1 Flash vs LBS-27B",
        "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-lbs-27b"
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        "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-swift-1-5-qwen3-8-flash-next.json",
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        "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-swift-1-5-qwen3-8-flash-next"
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        "url": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-lbs-27b"
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        "url": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-swift-1-5-qwen3-8-flash-next"
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        "url": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-lbs-27b"
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        "json": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-swift-1-5-qwen3-8-flash-next.json",
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        "url": "https://www.anchorterminal.com/compare/hy3-vs-lbs-27b"
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        "json": "https://www.anchorterminal.com/compare/hy3-vs-swift-1-5-qwen3-8-flash-next.json",
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        "json": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-lbs-27b.json",
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        "json": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-lbs-27b.json",
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        "json": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-swift-1-5-qwen3-8-flash-next.json",
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        "json": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-swift-1-5-qwen3-8-flash-next.json",
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        "json": "https://www.anchorterminal.com/compare/lbs-27b-vs-minimax-m2-7.json",
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        "json": "https://www.anchorterminal.com/compare/lbs-27b-vs-qwen3-6-27b.json",
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      {
        "json": "https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-swift-1-5-qwen3-8-flash-next.json",
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        "url": "https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-swift-1-5-qwen3-8-flash-next"
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      {
        "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-swift-1-5-qwen3-8-flash-next.json",
        "title": "MiniCPM-o 4.5 vs Swift 1.5 Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-swift-1-5-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-swift-1-5-qwen3-8-flash-next.json",
        "title": "MiniCPM5-2B vs Swift 1.5 Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-swift-1-5-qwen3-8-flash-next"
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      {
        "json": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-swift-1-5-qwen3-8-flash-next.json",
        "title": "MiniMax-M2.5 vs Swift 1.5 Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-swift-1-5-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-swift-1-5-qwen3-8-flash-next.json",
        "title": "MiniMax-M2.7 vs Swift 1.5 Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-swift-1-5-qwen3-8-flash-next"
      },
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        "json": "https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-swift-1-5-qwen3-8-flash-next.json",
        "title": "MiniMax-M3.1-Flash vs Swift 1.5 Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-swift-1-5-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-swift-1-5-qwen3-8-flash-next.json",
        "title": "Muse Glimmer 30B vs Swift 1.5 Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-swift-1-5-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-swift-1-5-qwen3-8-flash-next.json",
        "title": "Nanbeige4.3-33B vs Swift 1.5 Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-swift-1-5-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-swift-1-5-qwen3-8-flash-next.json",
        "title": "NVIDIA Nemotron 3.5 Bolt E4B 4GB vs Swift 1.5 Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-swift-1-5-qwen3-8-flash-next"
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      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-swift-1-5-qwen3-8-flash-next.json",
        "title": "Nemotron 3.5 Lightning 30B A3B vs Swift 1.5 Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-swift-1-5-qwen3-8-flash-next"
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      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-swift-1-5-qwen3-8-flash-next.json",
        "title": "Nemotron 3 Nano 30B A3B vs Swift 1.5 Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-swift-1-5-qwen3-8-flash-next"
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      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-swift-1-5-qwen3-8-flash-next.json",
        "title": "Nemotron 3 Nano 4B vs Swift 1.5 Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-swift-1-5-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-swift-1-5-qwen3-8-flash-next.json",
        "title": "Nemotron 3 Nano Omni 30B A3B Reasoning vs Swift 1.5 Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-swift-1-5-qwen3-8-flash-next"
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      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-swift-1-5-qwen3-8-flash-next.json",
        "title": "Nemotron 3 Super 120B A12B vs Swift 1.5 Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-swift-1-5-qwen3-8-flash-next"
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      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-swift-1-5-qwen3-8-flash-next.json",
        "title": "Nemotron 3 Ultra 550B A55B vs Swift 1.5 Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-swift-1-5-qwen3-8-flash-next"
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      {
        "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-swift-1-5-qwen3-8-flash-next.json",
        "title": "occamy-1.0 vs Swift 1.5 Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-swift-1-5-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-swift-1-5-qwen3-8-flash-next.json",
        "title": "Olmo 3 7B Think vs Swift 1.5 Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-swift-1-5-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-swift-1-5-qwen3-8-flash-next.json",
        "title": "Ornith-1.5-35B-A3B vs Swift 1.5 Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-swift-1-5-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-swift-1-5-qwen3-8-flash-next.json",
        "title": "Ornith-1.5-397B vs Swift 1.5 Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-swift-1-5-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-swift-1-5-qwen3-8-flash-next.json",
        "title": "Ornith-1.5-9B vs Swift 1.5 Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-swift-1-5-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-30b-a3b-instruct-2507-vs-swift-1-5-qwen3-8-flash-next.json",
        "title": "Qwen3-30B-A3B-Instruct-2507 vs Swift 1.5 Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/qwen3-30b-a3b-instruct-2507-vs-swift-1-5-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-4b-instruct-2507-vs-swift-1-5-qwen3-8-flash-next.json",
        "title": "Qwen3-4B-Instruct-2507 vs Swift 1.5 Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/qwen3-4b-instruct-2507-vs-swift-1-5-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-swift-1-5-qwen3-8-flash-next.json",
        "title": "Qwen3.5-0.8B vs Swift 1.5 Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-swift-1-5-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-swift-1-5-qwen3-8-flash-next.json",
        "title": "Qwen3.5-122B-A10B vs Swift 1.5 Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-swift-1-5-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-5-2b-vs-swift-1-5-qwen3-8-flash-next.json",
        "title": "Qwen3.5-2B vs Swift 1.5 Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-2b-vs-swift-1-5-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-swift-1-5-qwen3-8-flash-next.json",
        "title": "Qwen3.5-397B-A17B vs Swift 1.5 Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-swift-1-5-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-5-4b-vs-swift-1-5-qwen3-8-flash-next.json",
        "title": "Qwen3.5-4B vs Swift 1.5 Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-4b-vs-swift-1-5-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-swift-1-5-qwen3-8-flash-next.json",
        "title": "Qwen3.5-9B vs Swift 1.5 Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-swift-1-5-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-6-27b-vs-swift-1-5-qwen3-8-flash-next.json",
        "title": "Qwen3.6-27B vs Swift 1.5 Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/qwen3-6-27b-vs-swift-1-5-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-swift-1-5-qwen3-8-flash-next.json",
        "title": "Qwen3.6-35B-A3B vs Swift 1.5 Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-swift-1-5-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-8-27b-vs-swift-1-5-qwen3-8-flash-next.json",
        "title": "Qwen3.8-27B vs Swift 1.5 Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/qwen3-8-27b-vs-swift-1-5-qwen3-8-flash-next"
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      {
        "json": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-swift-1-5-qwen3-8-flash-next.json",
        "title": "Qwen3.8-Flash-Next vs Swift 1.5 Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-swift-1-5-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-coder-30b-a3b-instruct-vs-swift-1-5-qwen3-8-flash-next.json",
        "title": "Qwen3-Coder-30B-A3B-Instruct vs Swift 1.5 Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/qwen3-coder-30b-a3b-instruct-vs-swift-1-5-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-coder-next-vs-swift-1-5-qwen3-8-flash-next.json",
        "title": "Qwen3-Coder-Next vs Swift 1.5 Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/qwen3-coder-next-vs-swift-1-5-qwen3-8-flash-next"
      },
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        "json": "https://www.anchorterminal.com/compare/qwen3-omni-30b-a3b-instruct-vs-swift-1-5-qwen3-8-flash-next.json",
        "title": "Qwen3-Omni-30B-A3B-Instruct vs Swift 1.5 Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/qwen3-omni-30b-a3b-instruct-vs-swift-1-5-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-vl-2b-instruct-vs-swift-1-5-qwen3-8-flash-next.json",
        "title": "Qwen3-VL-2B-Instruct vs Swift 1.5 Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/qwen3-vl-2b-instruct-vs-swift-1-5-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-vl-30b-a3b-instruct-vs-swift-1-5-qwen3-8-flash-next.json",
        "title": "Qwen3-VL-30B-A3B-Instruct vs Swift 1.5 Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/qwen3-vl-30b-a3b-instruct-vs-swift-1-5-qwen3-8-flash-next"
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      {
        "json": "https://www.anchorterminal.com/compare/qwen3-vl-32b-instruct-vs-swift-1-5-qwen3-8-flash-next.json",
        "title": "Qwen3-VL-32B-Instruct vs Swift 1.5 Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/qwen3-vl-32b-instruct-vs-swift-1-5-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-swift-1-5-qwen3-8-flash-next.json",
        "title": "Qwen3-VL-4B-Instruct vs Swift 1.5 Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-swift-1-5-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-vl-8b-instruct-vs-swift-1-5-qwen3-8-flash-next.json",
        "title": "Qwen3-VL-8B-Instruct vs Swift 1.5 Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/qwen3-vl-8b-instruct-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",
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        "url": "https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-swift-1-5-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/recursive-flint-vs-swift-1-5-qwen3-8-flash-next.json",
        "title": "Recursive Flint vs Swift 1.5 Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/recursive-flint-vs-swift-1-5-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/smollm3-3b-vs-swift-1-5-qwen3-8-flash-next.json",
        "title": "SmolLM3-3B vs Swift 1.5 Qwen3.8-Flash-Next",
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      },
      {
        "json": "https://www.anchorterminal.com/compare/spark-x2-6-4b-vs-swift-1-5-qwen3-8-flash-next.json",
        "title": "Spark-X2.6-4B vs Swift 1.5 Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/spark-x2-6-4b-vs-swift-1-5-qwen3-8-flash-next"
      },
      {
        "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-flash-next-vs-thinkingcap-qwen3-8-27b.json",
        "title": "Swift 1.5 Qwen3.8-Flash-Next vs ThinkingCap (Qwen3.8 27B)",
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      },
      {
        "json": "https://www.anchorterminal.com/compare/swift-1-5-qwen3-8-flash-next-vs-unlimited-ocr-2.json",
        "title": "Swift 1.5 Qwen3.8-Flash-Next vs Baidu Unlimited-OCR-2",
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      }
    ],
    "scores": [
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        "name": "Capability",
        "pending": true,
        "weight": 35
      },
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        "edge": "swift-1-5-qwen3-8-flash-next",
        "key": "reach",
        "lbs-27b": 55,
        "name": "Reach",
        "swift-1-5-qwen3-8-flash-next": 60,
        "weight": 25
      },
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        "key": "risk",
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        "name": "Risk",
        "swift-1-5-qwen3-8-flash-next": 45,
        "weight": 20
      },
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        "edge": "lbs-27b",
        "key": "interface",
        "lbs-27b": 70,
        "name": "Agent interface",
        "swift-1-5-qwen3-8-flash-next": 60,
        "weight": 10
      },
      {
        "by": 3,
        "edge": "lbs-27b",
        "key": "upkeep",
        "lbs-27b": 43,
        "name": "Documentation and upkeep",
        "swift-1-5-qwen3-8-flash-next": 40,
        "weight": 10
      }
    ],
    "summary": "LBS-27B scores 53.9 (D) on agent readiness against Swift 1.5 Qwen3.8-Flash-Next's 52.3 (D), and leads in 3 of 4 scored categories. Swift 1.5 Qwen3.8-Flash-Next leads on reach. Both do open-weight models you run yourself.",
    "tutorials": [],
    "verdicts": {
      "lbs-27b": "A 27B Qwen3.8 fine-tune for GUI grounding that zooms into screenshots over a few turns before naming a point, and inclusionAI says it also helps with fine detail in large images. It needs about 96 GB at BF16 and a client that runs its crop actions. Every result so far is inclusionAI's own, with no safety results or injection guidance.",
      "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."
    }
  },
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  "links": {
    "api": "https://www.anchorterminal.com/api/v1/index.json",
    "html": "https://www.anchorterminal.com/compare/lbs-27b-vs-swift-1-5-qwen3-8-flash-next",
    "json": "https://www.anchorterminal.com/compare/lbs-27b-vs-swift-1-5-qwen3-8-flash-next.json",
    "llms": "https://www.anchorterminal.com/llms.txt",
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    "slim": "https://www.anchorterminal.com/compare/lbs-27b-vs-swift-1-5-qwen3-8-flash-next.min.md"
  },
  "markdown": "LBS-27B scores 53.9 (D) on agent readiness against Swift 1.5 Qwen3.8-Flash-Next's 52.3 (D), and leads in 3 of 4 scored categories. Swift 1.5 Qwen3.8-Flash-Next leads on reach. Both do open-weight models you run yourself.\n\n- LBS-27B: grade D, 53.9/100, rank #91 of 113 open-weight models. Markdown https://www.anchorterminal.com/tools/lbs-27b.md · JSON https://www.anchorterminal.com/api/v1/tools/lbs-27b.json\n- Swift 1.5 Qwen3.8-Flash-Next: grade D, 52.3/100, rank #97 of 113 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 3504 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md\n\n## Which one, for what\n\n### LBS-27B (D)\n\nGood for: Agents with about 96 GB of GPU memory that need to find click targets on dense professional screenshots, or read fine detail in large images, and can run a small crop loop and wait for independent results.\n\nAhead on:\n- Risk, 50 against 45\n- Agent interface, 70 against 60\n\nWatch for: No independent benchmark result yet; ScreenSpot-Pro, OS-World-G, PerceptionBench and HR-Bench numbers are inclusionAI's own runs\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\nAhead on:\n- Reach, 60 against 55\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 | LBS-27B | 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) | 55 | 60 | Swift 1.5 Qwen3.8-Flash-Next +5 |\n| Risk | 20% (20 this run) | 50 | 45 | LBS-27B +5 |\n| Agent interface | 10% (10 this run) | 70 | 60 | LBS-27B +10 |\n| Documentation and upkeep | 10% (10 this run) | 43 | 40 | LBS-27B +3 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **53.9 · D** | **52.3 · D** | |\n\n## Facts side by side\n\n| Fact | LBS-27B | Swift 1.5 Qwen3.8-Flash-Next |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | inclusionAI (Ant Group) | 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 (the card's metadata and licence section; the LICENSE file sits in the GitHub repository inclusionAI/UI-Venus on its LBS branch, not in the Hugging Face repository) | 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 | 2026-10-26 | 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 | none | 1.8/5 (4) |\n\n## Verdicts\n\n**LBS-27B.** A 27B Qwen3.8 fine-tune for GUI grounding that zooms into screenshots over a few turns before naming a point, and inclusionAI says it also helps with fine detail in large images. It needs about 96 GB at BF16 and a client that runs its crop actions. Every result so far is inclusionAI's own, with no safety results or injection guidance.\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### LBS-27B\n\n1. Serve with `vllm serve inclusionAI/LBS-27B --tensor-parallel-size 4 --max-model-len 65536`, which is what the card's wrapper script runs; keep the context at 32,768 or more for multi-turn images\n2. Build the system prompt with `build_system_prompt` from the LBS branch's `zoomin_grounding_protocol.py`, send the screenshot after `image_path: \u003cname\u003e`, and don't pass `tools`; the model answers in plain text\n3. Parse `\u003caction\u003ecrop_image(image_path=..., x1=..., y1=..., x2=..., y2=...)\u003c/action\u003e`, cut the crop yourself and return it with its mapping to the original image; allow at most two crops per turn and three per episode\n4. Read the final answer as `[x.xx,y.yy]` on a 0 to 1000 scale of the original image, and use your own executor to click\n5. Pass `reasoning_effort` as `xhigh`, `medium` or `low` only, since the template raises an error for `high`, and don't count on `thinking_budget`, which the template ignores\n6. Don't turn on MTP speculative decoding; the MTP weights aren't in the repository though config.json names one MTP layer\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, LBS-27B or Swift 1.5 Qwen3.8-Flash-Next?\n\nLBS-27B scores 53.9 (D) on agent readiness against Swift 1.5 Qwen3.8-Flash-Next's 52.3 (D), and leads in 3 of 4 scored categories. Swift 1.5 Qwen3.8-Flash-Next leads on reach.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/lbs-27b-vs-swift-1-5-qwen3-8-flash-next.json, and with the fewest tokens: https://www.anchorterminal.com/compare/lbs-27b-vs-swift-1-5-qwen3-8-flash-next.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"lbs-27b\", \"b\": \"swift-1-5-qwen3-8-flash-next\"}`. From a terminal: `anchor compare lbs-27b swift-1-5-qwen3-8-flash-next`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/lbs-27b.json and https://www.anchorterminal.com/api/v1/tools/swift-1-5-qwen3-8-flash-next.json\n\n## Other comparisons with LBS-27B or Swift 1.5 Qwen3.8-Flash-Next\n\n- [Agents-A1-4B vs LBS-27B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-lbs-27b.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 LBS-27B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-lbs-27b.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 LBS-27B](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-lbs-27b.md)\n- [Apodex-1.1-mini vs Swift 1.5 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