{
  "data": {
    "a": {
      "slug": "ling-3-1-flash-1019",
      "name": "Ling-3.1-flash-1019",
      "vendor": "inclusionAI (Ant Group)",
      "vendorUrl": "https://www.inclusion-ai.org",
      "kind": "weights",
      "category": "open-weights",
      "summary": "Ling-3.1-flash-1019 is inclusionAI's open-weight hybrid reasoning mixture of experts with 560B parameters and 25B active, a post-trained update of Ling-3.1-flash, with switchable thinking, tool calling and a 262,144-token native context, published in FP8 under MIT.",
      "url": "https://www.anchorterminal.com/tools/ling-3-1-flash-1019",
      "markdownUrl": "https://www.anchorterminal.com/tools/ling-3-1-flash-1019.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/ling-3-1-flash-1019.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/ling-3-1-flash-1019.json",
      "dns": "ling-3-1-flash-1019.grade.anchorterminal.com",
      "repo": "https://huggingface.co/inclusionAI/Ling-3.1-flash-1019",
      "license": "MIT",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "inclusionAI/Ling-3.1-flash-1019"
        }
      ],
      "auth": "none",
      "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The repository is not gated (checked 2026-11-10).",
      "pricing": "free",
      "pricingNotes": "Free to download and use, commercial use included, under MIT (named in the card's metadata; the repository holds no LICENSE file). Running it yourself costs the hardware, 573.8 GB of FP8 files, which the card serves on 8 H200 or 4 GB300 GPUs (checked 2026-11-10).",
      "priceSummary": "Free",
      "where": "local",
      "x402": {
        "level": "no",
        "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-11-10).",
        "endpoints": []
      },
      "toolCount": null,
      "popularity": {
        "githubStars": null,
        "npmWeekly": null,
        "pypiWeekly": null,
        "asOf": "2026-11-10"
      },
      "docsUrl": "https://huggingface.co/inclusionAI/Ling-3.1-flash-1019",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "moe",
        "hybrid-attention",
        "reasoning",
        "tool-calling",
        "long-context",
        "mit",
        "safetensors",
        "fp8",
        "self-hosted"
      ],
      "lastRelease": "2026-11-04",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 54.8,
        "grade": "C",
        "agentReady": false,
        "rank": 0,
        "ranked": false,
        "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools",
        "rankOf": 1518,
        "categoryRank": 94,
        "methodology": "0.7",
        "run": "2026-10-01",
        "scores": {
          "interface": 60,
          "reach": 60,
          "risk": 45,
          "upkeep": 56
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "low",
          "date": "2026-11-10"
        },
        "negative": 0,
        "verdict": "inclusionAI's newest flagship, a 560B hybrid mixture of experts under plain MIT that vLLM, SGLang, Transformers and llama.cpp run in released versions. It ships only as 574 GB of FP8, so it needs a multi-GPU server. The card overstates the context, documents little about tool calls, and has no safety results.",
        "bestFor": "Teams with a multi-GPU server that want an MIT-licensed flagship for long agent runs and coding",
        "strengths": [
          "Plain MIT, ungated, with a hash for every shard",
          "Released vLLM, SGLang, Transformers and llama.cpp all have the architecture",
          "A 262,144-token native context, and 1M with YaRN per SGLang's cookbook",
          "A training-content summary that names source categories, the size band and a collection date",
          "Updated a week before this check"
        ],
        "weaknesses": [
          "574 GB in FP8 with no smaller build from inclusionAI, so no single machine under 690 GB of GPU memory runs it",
          "The card says 1M tokens of context where the files give 262,144",
          "The tool-call format is only in the template",
          "No safety results, no agent guidance and no security contact",
          "No inference provider mapped for this release, only for the earlier one",
          "Neither question on the repository has a reply"
        ],
        "agentNotes": [
          "Serve it with SGLang 0.5.23 or later on 8 H200 or 4 GB300 GPUs, per the card, and use this repository's name as the model path, not the earlier one the card's command shows",
          "With vLLM pass `--trust-remote-code`, `--tool-call-parser ling3` and `--reasoning-parser ling3`",
          "Set `chat_template_kwargs` `enable_thinking` false for direct answers. Reasoning comes back in `reasoning_content` per SGLang's cookbook",
          "Plan on 262,144 tokens of context. The 1M figure needs the YaRN override in SGLang's cookbook",
          "Use temperature 1.0, top_p 0.95 and top_k 20, per the card",
          "Validate JSON output yourself, since structured output isn't documented"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 0,
        "avgRating": 0,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "low",
            "grade": "C",
            "methodology": "0.7",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 54.8
          }
        ],
        "editorialScores": {
          "interface": 60,
          "reach": 60,
          "risk": 45,
          "upkeep": 56
        },
        "provenanceScore": 61,
        "checked": {
          "categories": {
            "interface": "2026-11-10",
            "reach": "2026-11-10",
            "risk": "2026-11-10",
            "upkeep": "2026-11-10"
          },
          "last": "2026-11-10",
          "oldest": "2026-11-10",
          "stale": false,
          "staleAfterDays": 90
        }
      },
      "connect": {
        "install": "# with SGLang 0.5.23 or later, on 8 H200 GPUs:\npython3 -m sglang.launch_server --model-path inclusionAI/Ling-3.1-flash-1019 \\\n  --tp 8 --ep 8 --speculative-algorithm NEXTN --host 0.0.0.0 --port 30000\n# or with vLLM 0.33.0, per the card's serve command:\nvllm serve inclusionAI/Ling-3.1-flash-1019 --trust-remote-code --tensor-parallel-size 8 \\\n  --enable-prefix-caching --enable-expert-parallel --mamba-cache-mode align \\\n  --enable-auto-tool-choice --tool-call-parser ling3 --reasoning-parser ling3\n# or, with llama.cpp and bartowski's GGUF, a third party's conversion:\nllama-server -hf bartowski/Ling-3.1-flash-1019-GGUF:Q4_K_M --jinja",
        "http": "curl http://localhost:30000/v1/chat/completions \\\n  -H \"Content-Type: application/json\" \\\n  -d '{\"model\": \"inclusionAI/Ling-3.1-flash-1019\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}], \"chat_template_kwargs\": {\"enable_thinking\": false}}'"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/ling-3-1-flash-1019"
      },
      "sameCompany": [
        "ming-3-0-asr-tiny",
        "ming-3-0-audio-flash",
        "ming-3-0-audio-tiny",
        "ming-3-0-tts-tiny",
        "lbs-27b",
        "ling-3-0-tiny"
      ],
      "weights": {
        "trainedBy": "inclusionAI (Ant Group)",
        "builtOn": "inclusionAI/Ling-3.1-flash, post-trained further (named on the card; the metadata names no base model)",
        "params": "560B (25B active)",
        "context": 262144,
        "goodFor": "Large hybrid reasoning model for agents, office work and software engineering on a multi-GPU server, with thinking switchable per request. It needs about 690 GB of GPU memory in FP8.",
        "runtimes": [
          "llama.cpp",
          "vLLM",
          "SGLang",
          "Transformers"
        ],
        "builds": [
          {
            "name": "bartowski Ling-3.1-flash-1019-GGUF (Q4_K_M)",
            "url": "https://huggingface.co/bartowski/Ling-3.1-flash-1019-GGUF",
            "who": "bartowski",
            "precision": "q4_k_m",
            "sizeGB": 357.97,
            "memoryGB": 512,
            "format": "gguf",
            "runsOn": [
              "llama.cpp"
            ],
            "note": "From bartowski, a third party's conversion and not inclusionAI's. 357.97 GB of Q4_K_M weights, from the Hub's model information on 10 November 2026. Reach doesn't count it"
          },
          {
            "name": "Ling-3.1-flash-1019",
            "url": "https://huggingface.co/inclusionAI/Ling-3.1-flash-1019",
            "who": "publisher",
            "precision": "fp8",
            "sizeGB": 573.78,
            "memoryGB": 1024,
            "format": "safetensors",
            "runsOn": [
              "vLLM",
              "SGLang",
              "Transformers"
            ],
            "note": "The release itself, FP8 E4M3 weights in 128×128 blocks. inclusionAI publishes no smaller build"
          }
        ],
        "run": "python3 -m sglang.launch_server --model-path inclusionAI/Ling-3.1-flash-1019 --tp 8 --ep 8 --speculative-algorithm NEXTN",
        "runBuild": "Ling-3.1-flash-1019",
        "checked": "2026-11-10",
        "minMemoryGB": 512
      },
      "family": {
        "lab": "inclusionAI (Ant Group)",
        "family": "Ling 3",
        "url": "https://www.anchorterminal.com/open-weights/#inclusionai",
        "others": [
          {
            "name": "Ling-3.0-flash",
            "url": "https://huggingface.co/inclusionAI/Ling-3.0-flash",
            "params": "124B total, 5.1B active"
          },
          {
            "name": "Ling-3.0-flash-base",
            "url": "https://huggingface.co/inclusionAI/Ling-3.0-flash-base",
            "params": "124B total, 5.1B active"
          },
          {
            "name": "Ling-3.0-flash-base-30T",
            "url": "https://huggingface.co/inclusionAI/Ling-3.0-flash-base-30T",
            "params": "124B total, 5.1B active"
          },
          {
            "name": "Ling-3.0-flash-base-midtrain",
            "url": "https://huggingface.co/inclusionAI/Ling-3.0-flash-base-midtrain",
            "params": "124B total, 5.1B active"
          },
          {
            "name": "Ling-3.0-flash-Fin",
            "url": "https://huggingface.co/inclusionAI/Ling-3.0-flash-Fin",
            "params": "124B total, 5.1B active"
          },
          {
            "name": "Ling-3.0-flash-VL",
            "url": "https://huggingface.co/inclusionAI/Ling-3.0-flash-VL",
            "params": "124B total, 5.5B active"
          },
          {
            "name": "Ling-3.0-flash-dspark",
            "url": "https://huggingface.co/inclusionAI/Ling-3.0-flash-dspark",
            "params": "1.36B (draft)"
          },
          {
            "name": "Ling-3.0-flash-singprobe",
            "url": "https://huggingface.co/inclusionAI/Ling-3.0-flash-singprobe",
            "params": "5.18M (probe)"
          },
          {
            "name": "Ling-3.0-tiny",
            "url": "https://huggingface.co/inclusionAI/Ling-3.0-tiny",
            "params": "7.9B total, 1.3B active",
            "listing": "https://www.anchorterminal.com/tools/ling-3-0-tiny"
          },
          {
            "name": "Ling-3.0-tiny-base",
            "url": "https://huggingface.co/inclusionAI/Ling-3.0-tiny-base",
            "params": "7.9B total, 1.3B active"
          },
          {
            "name": "Ling-3.0-tiny-base-30T",
            "url": "https://huggingface.co/inclusionAI/Ling-3.0-tiny-base-30T",
            "params": "7.9B total, 1.3B active"
          },
          {
            "name": "Ling-3.0-tiny-base-midtrain",
            "url": "https://huggingface.co/inclusionAI/Ling-3.0-tiny-base-midtrain",
            "params": "7.9B total, 1.3B active"
          },
          {
            "name": "Ling-3.0-tiny-singprobe",
            "url": "https://huggingface.co/inclusionAI/Ling-3.0-tiny-singprobe",
            "params": "3.22M (probe)"
          },
          {
            "name": "Ling-3.1-flash",
            "url": "https://huggingface.co/inclusionAI/Ling-3.1-flash",
            "params": "560B total, 25B active"
          },
          {
            "name": "Ling-3.1-flash-base",
            "url": "https://huggingface.co/inclusionAI/Ling-3.1-flash-base",
            "params": "560B total, 25B active"
          }
        ],
        "olderCount": 54
      },
      "area": "models",
      "provenance": {
        "legalEntity": "Ant Intelligence (Hang Zhou) Technology Co., Ltd (named as provider in the training-content summary); inclusionAI, Ant Group in the site footer of inclusion-ai.org",
        "domain": "inclusion-ai.org",
        "domainRegistered": "2025-04-03",
        "endpointOnVendorDomain": null,
        "terms": "",
        "privacy": "",
        "statusPage": "",
        "changelog": "https://huggingface.co/inclusionAI/Ling-3.1-flash-1019/commits/main",
        "securityTxt": "none",
        "checked": "2026-11-10",
        "notes": [
          "inclusion-ai.org links to huggingface.co/inclusionAI and github.com/inclusionAI. The domain registration date is carried from the RDAP check made for the Ming 3.0 listings on 21 October 2026.",
          "Nothing is hosted by the publisher, so the MIT licence stands in for terms of service and no privacy policy is scored.",
          "The changelog is the Hugging Face commit history, 8 commits from 27 October to 4 November 2026, the last three card edits.",
          "www.inclusion-ai.org/.well-known/security.txt answered 404 on 10 November 2026."
        ],
        "score": 61
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/ling-3-1-flash-1019.json"
    },
    "answer": "Qwopus3.8-27B-Flash-V2 scores 61 (C) on agent readiness against Ling-3.1-flash-1019's 54.8 (C), and leads in 2 of 4 scored categories. Ling-3.1-flash-1019 leads on documentation and upkeep.",
    "b": {
      "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",
      "dns": "qwopus3-8-27b-flash-v2.grade.anchorterminal.com",
      "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": "2026-11-03"
      },
      "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": 1518,
        "categoryRank": 43,
        "methodology": "0.7",
        "run": "2026-10-01",
        "scores": {
          "interface": 60,
          "reach": 85,
          "risk": 50,
          "upkeep": 51
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "low",
          "date": "2026-11-03"
        },
        "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 Qwen3.8-27B tuned for shorter reasoning, 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": "0.7",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 61
          }
        ],
        "editorialScores": {
          "interface": 60,
          "reach": 85,
          "risk": 50,
          "upkeep": 51
        },
        "provenanceScore": 44,
        "checked": {
          "categories": {
            "interface": "2026-11-03",
            "reach": "2026-11-03",
            "risk": "2026-11-03",
            "upkeep": "2026-11-03"
          },
          "last": "2026-11-03",
          "lastMoved": "2026-11-03",
          "oldest": "2026-11-03",
          "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": 44
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/qwopus3-8-27b-flash-v2.json"
    },
    "edges": [
      {
        "by": 25,
        "category": "reach",
        "leads": "qwopus3-8-27b-flash-v2",
        "name": "Reach",
        "reasons": {
          "a": "Memory 0, inclusionAI's only build is the FP8 release at 573.78 GB, × 1.2 = 688.5 GB, above 256 GB, and no runtime's library has a build. Runtimes 25, Released model code in Transformers 5.22.2 (bailing_hybrid), vLLM 0.33.0 (BailingMoeV3ForCausalLM, with ling3 tool and reasoning parsers), SGLang 0.5.23 (bailing_moe_v3.py, its bailing_hybrid config and MTP module) and llama.cpp b12242 (bailingmoe3), and Ollama, credited through its pinned llama.cpp as on the Ling-3.0-tiny listing: v0.40.16 sets LLAMA_CPP_VERSION to b12177. Builds 10, the release is FP8, the one quantised precision inclusionAI publishes for it. Terms 15, MIT in the card's metadata, with no use policy or other terms added on the card or in the repository. Download 10, the Hub API's model info gives gated false on 10 November 2026, so the files download with no account and no terms to accept. Hosted 0, the Hub API's provider mapping is empty for this repository on 10 November 2026. 0 + 25 + 10 + 15 + 10 + 0 = 60.",
          "b": "Memory 25 of 30, Jackrong's IQ4_XS GGUF is 15.4 GB, about 18.5 GB with a fifth added, which fits 24 GB. Runtimes 20, it keeps Qwen3.8-27B's architecture, which released vLLM, SGLang, Transformers and llama.cpp run, while Ollama's entry is the base's, not this model's. Builds 15, GGUFs at many precisions from 4-bit to 8-bit and BF16. Terms 15, Apache 2.0 through the chain. Download 10, nothing is gated. Hosted 0, no provider serves it."
        },
        "scores": {
          "a": 60,
          "b": 85
        }
      },
      {
        "by": 5,
        "category": "risk",
        "leads": "qwopus3-8-27b-flash-v2",
        "name": "Risk",
        "reasons": {
          "a": "File safety 15, safetensors only, no .bin, .pt or .pth files (25), less 10 because the card's documented vLLM command passes --trust-remote-code, which runs the repository's configuration_bailing_moe_v3.py, since vLLM 0.33.0 registers no bailing_hybrid config of its own. Provenance 15, files in the inclusionAI organisation that inclusion-ai.org links to, per the Ling-3.0-tiny listing (10). Safety evaluation 0, no safety results for the model on its card or in the training-content summary. Agent guidance 0, the card has nothing on prompt injection, refusals or safeguards, and inclusionAI publishes no guardrail probe for this model as it did for Ling 3.0. Licence risk 15, MIT with no use policy, no field-of-use limits, no threshold and no clause letting inclusionAI change or revoke it. Security programme 0, www.inclusion-ai.org answers 404 for security.txt on 10 November 2026, and the Ling-3.0-tiny listing found no SECURITY.md or disclosure policy in the inclusionAI .github repository. 15 + 15 + 0 + 0 + 15 + 0 = 45.",
          "b": "File safety 25, safetensors and GGUF only, nothing from the repository runs. Provenance 5 of 15, the files are on a personal account with no site or organisation behind it, though every file has a checksum. Safety evaluation 0, the card says none was done. Agent guidance 5, general cautions about loops and tool reliability. Licence risk 15, plain Apache 2.0. Security programme 0, no contact or policy."
        },
        "scores": {
          "a": 45,
          "b": 50
        }
      },
      {
        "by": 5,
        "category": "upkeep",
        "leads": "ling-3-1-flash-1019",
        "name": "Documentation and upkeep",
        "reasons": {
          "a": "Model card 11, Intended use (complex reasoning, multi-step tasks and agentic workflows), architecture, parameters (560B total, 25B active) and context (1M claimed) are on the card. Training data 15, the training-content summary the card links names the source categories (Common Crawl and other public datasets, commercially licensed data, the Ant Spider crawler, synthetic data), the size band (over 10 trillion text tokens), data collected up to about July 2026 and the filtering, without listing datasets with sizes. Last change 20, the newest commit, on 4 November 2026, edited the card, six days before this check, and the files went up on 27 October. Responsiveness 0, Neither of the 2 discussions that aren't pull requests (both opened on 27 October 2026) has a reply from an inclusionAI member. Version policy 5, the update went into a repository of its own and Ling-3.1-flash and Ling 3.0 stay in theirs. File health 5, the card states a 1M-token context while config.json gives 262,144 with no rope scaling, and its serve commands name the earlier repository as the model path. 11 + 15 + 20 + 0 + 5 + 5 = 56.",
          "b": "Model card 11 of 20, missing architecture, context and a cut-off. Training data 5, a description with no cut-off. Last change 15, the files went up on 21 September 2026, 43 days ago, with no commit since. Responsiveness 10, Jackrong answered 2 of 8 discussions across the model's two repositories. Version policy 5, the first Flash release stays up without a dated note. File health 5, no LICENSE file, an experiment note in the template and open reports of looping and a grammar failure."
        },
        "scores": {
          "a": 56,
          "b": 51
        }
      }
    ],
    "facts": [
      {
        "a": "Open-weight model",
        "b": "Open-weight model",
        "name": "Kind"
      },
      {
        "a": "inclusionAI (Ant Group)",
        "b": "Jackrong",
        "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": "MIT",
        "b": "Apache-2.0 (metadata only, no LICENSE file); built on Qwen3.8-27B under Apache 2.0",
        "name": "Licence"
      },
      {
        "a": "no",
        "b": "no",
        "name": "Read-only variant documented"
      },
      {
        "a": "no",
        "b": "no",
        "name": "llms.txt"
      },
      {
        "a": "2026-11-04",
        "b": "2026-09-21",
        "name": "Last release"
      },
      {
        "a": "no document linked",
        "b": "no document linked",
        "name": "Terms last updated"
      },
      {
        "a": "no document linked",
        "b": "no document linked",
        "name": "Privacy policy last updated"
      },
      {
        "a": "none",
        "b": "2.5/5 (4)",
        "name": "Agent reviews"
      }
    ],
    "faq": [
      {
        "answer": "Qwopus3.8-27B-Flash-V2 scores 61 (C) on agent readiness against Ling-3.1-flash-1019's 54.8 (C), and leads in 2 of 4 scored categories. Ling-3.1-flash-1019 leads on documentation and upkeep.",
        "question": "Which is better for AI agents, Ling-3.1-flash-1019 or Qwopus3.8-27B-Flash-V2?"
      }
    ],
    "goodFor": [
      {
        "aheadOn": [
          "Documentation and upkeep, 56 against 51"
        ],
        "also": null,
        "goodFor": "Teams with a multi-GPU server that want an MIT-licensed flagship for long agent runs and coding",
        "slug": "ling-3-1-flash-1019",
        "watchFor": "574 GB in FP8 with no smaller build from inclusionAI, so no single machine under 690 GB of GPU memory runs it"
      },
      {
        "aheadOn": [
          "Reach, 85 against 60",
          "Risk, 50 against 45"
        ],
        "also": null,
        "goodFor": "Hobbyist and research agents that want a Qwen3.8-27B tuned for shorter reasoning, 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"
      }
    ],
    "history": {
      "ling-3-1-flash-1019": null,
      "qwopus3-8-27b-flash-v2": [
        {
          "causes": [
            "time"
          ],
          "date": "2026-11-03",
          "delta": 0,
          "from": 61,
          "fromGrade": "C",
          "to": 61,
          "toGrade": "C"
        }
      ]
    },
    "job": {
      "capability": "model.open-weights",
      "name": "Open-weight models you run yourself"
    },
    "others": [
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-ling-3-1-flash-1019.json",
        "title": "Agents-A1-4B vs Ling-3.1-flash-1019",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-ling-3-1-flash-1019"
      },
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-qwopus3-8-27b-flash-v2.json",
        "title": "Agents-A1-4B vs Qwopus3.8-27B-Flash-V2",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-qwopus3-8-27b-flash-v2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-ling-3-1-flash-1019.json",
        "title": "Apertus v1.5 8B vs Ling-3.1-flash-1019",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-ling-3-1-flash-1019"
      },
      {
        "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/apodex-1-1-mini-vs-ling-3-1-flash-1019.json",
        "title": "Apodex-1.1-mini vs Ling-3.1-flash-1019",
        "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-ling-3-1-flash-1019"
      },
      {
        "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"
      },
      {
        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-ling-3-1-flash-1019.json",
        "title": "Arete-27B vs Ling-3.1-flash-1019",
        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-ling-3-1-flash-1019"
      },
      {
        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-qwopus3-8-27b-flash-v2.json",
        "title": "Arete-27B vs Qwopus3.8-27B-Flash-V2",
        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-qwopus3-8-27b-flash-v2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-ling-3-1-flash-1019.json",
        "title": "BigBang-v1 vs Ling-3.1-flash-1019",
        "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-ling-3-1-flash-1019"
      },
      {
        "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-qwopus3-8-27b-flash-v2.json",
        "title": "BigBang-v1 vs Qwopus3.8-27B-Flash-V2",
        "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-qwopus3-8-27b-flash-v2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/carbon-x-vs-ling-3-1-flash-1019.json",
        "title": "Carbon-X vs Ling-3.1-flash-1019",
        "url": "https://www.anchorterminal.com/compare/carbon-x-vs-ling-3-1-flash-1019"
      },
      {
        "json": "https://www.anchorterminal.com/compare/carbon-x-vs-qwopus3-8-27b-flash-v2.json",
        "title": "Carbon-X vs Qwopus3.8-27B-Flash-V2",
        "url": "https://www.anchorterminal.com/compare/carbon-x-vs-qwopus3-8-27b-flash-v2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cola2-vs-ling-3-1-flash-1019.json",
        "title": "Cola2 vs Ling-3.1-flash-1019",
        "url": "https://www.anchorterminal.com/compare/cola2-vs-ling-3-1-flash-1019"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cola2-vs-qwopus3-8-27b-flash-v2.json",
        "title": "Cola2 vs Qwopus3.8-27B-Flash-V2",
        "url": "https://www.anchorterminal.com/compare/cola2-vs-qwopus3-8-27b-flash-v2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-ling-3-1-flash-1019.json",
        "title": "Cosmos-Reason2-2B vs Ling-3.1-flash-1019",
        "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-ling-3-1-flash-1019"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-qwopus3-8-27b-flash-v2.json",
        "title": "Cosmos-Reason2-2B vs Qwopus3.8-27B-Flash-V2",
        "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-qwopus3-8-27b-flash-v2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-ling-3-1-flash-1019.json",
        "title": "Darwin-36B-Opus vs Ling-3.1-flash-1019",
        "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-ling-3-1-flash-1019"
      },
      {
        "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-qwopus3-8-27b-flash-v2.json",
        "title": "Darwin-36B-Opus vs Qwopus3.8-27B-Flash-V2",
        "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-qwopus3-8-27b-flash-v2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-ling-3-1-flash-1019.json",
        "title": "DeepSeek-V3.2 vs Ling-3.1-flash-1019",
        "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-ling-3-1-flash-1019"
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      {
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      {
        "json": "https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-ripple-65b-preview.json",
        "title": "Qwopus3.8-27B-Flash-V2 vs Ripple-65B Preview",
        "url": "https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-ripple-65b-preview"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-sensenova-loop-9b-preview.json",
        "title": "Qwopus3.8-27B-Flash-V2 vs SenseNova-Loop-9B-Preview",
        "url": "https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-sensenova-loop-9b-preview"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-smollm3-3b.json",
        "title": "Qwopus3.8-27B-Flash-V2 vs SmolLM3-3B",
        "url": "https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-smollm3-3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-spark-x2-6-4b.json",
        "title": "Qwopus3.8-27B-Flash-V2 vs Spark-X2.6-4B",
        "url": "https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-spark-x2-6-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-step-3-7-flash.json",
        "title": "Qwopus3.8-27B-Flash-V2 vs Step 3.7 Flash",
        "url": "https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-step-3-7-flash"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-swift-1-5-qwen3-8-27b.json",
        "title": "Qwopus3.8-27B-Flash-V2 vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-swift-1-5-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-swift-1-5-qwen3-8-flash-next.json",
        "title": "Qwopus3.8-27B-Flash-V2 vs Swift 1.5 Qwen3.8-Flash-Next",
        "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/qwopus3-8-27b-flash-v2-vs-thinkingcap-qwen3-8-27b.json",
        "title": "Qwopus3.8-27B-Flash-V2 vs ThinkingCap (Qwen3.8 27B)",
        "url": "https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-thinkingcap-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-unlimited-ocr-2.json",
        "title": "Qwopus3.8-27B-Flash-V2 vs Baidu Unlimited-OCR-2",
        "url": "https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-unlimited-ocr-2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-wevisone-vl-2b.json",
        "title": "Qwopus3.8-27B-Flash-V2 vs WeVisOne-VL-2B",
        "url": "https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-wevisone-vl-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-wevisone-vl-4b.json",
        "title": "Qwopus3.8-27B-Flash-V2 vs WeVisOne-VL-4B",
        "url": "https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-wevisone-vl-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-zaya1-8b.json",
        "title": "Qwopus3.8-27B-Flash-V2 vs ZAYA1-8B",
        "url": "https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-zaya1-8b"
      }
    ],
    "panel": null,
    "scores": [
      {
        "key": "capability",
        "name": "Capability",
        "pending": true,
        "weight": 35
      },
      {
        "by": 25,
        "edge": "qwopus3-8-27b-flash-v2",
        "key": "reach",
        "ling-3-1-flash-1019": 60,
        "name": "Reach",
        "qwopus3-8-27b-flash-v2": 85,
        "weight": 25
      },
      {
        "by": 5,
        "edge": "qwopus3-8-27b-flash-v2",
        "key": "risk",
        "ling-3-1-flash-1019": 45,
        "name": "Risk",
        "qwopus3-8-27b-flash-v2": 50,
        "weight": 20
      },
      {
        "by": 0,
        "edge": "",
        "key": "interface",
        "ling-3-1-flash-1019": 60,
        "name": "Agent interface",
        "qwopus3-8-27b-flash-v2": 60,
        "weight": 10
      },
      {
        "by": 5,
        "edge": "ling-3-1-flash-1019",
        "key": "upkeep",
        "ling-3-1-flash-1019": 56,
        "name": "Documentation and upkeep",
        "qwopus3-8-27b-flash-v2": 51,
        "weight": 10
      }
    ],
    "summary": "Qwopus3.8-27B-Flash-V2 scores 61 (C) on agent readiness against Ling-3.1-flash-1019's 54.8 (C), and leads in 2 of 4 scored categories. Ling-3.1-flash-1019 leads on documentation and upkeep. Both do open-weight models you run yourself.",
    "tasks": null,
    "tutorials": [],
    "verdicts": {
      "ling-3-1-flash-1019": "inclusionAI's newest flagship, a 560B hybrid mixture of experts under plain MIT that vLLM, SGLang, Transformers and llama.cpp run in released versions. It ships only as 574 GB of FP8, so it needs a multi-GPU server. The card overstates the context, documents little about tool calls, and has no safety results.",
      "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."
    }
  },
  "kind": "anchor.page",
  "links": {
    "api": "https://www.anchorterminal.com/api/v1/index.json",
    "html": "https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-qwopus3-8-27b-flash-v2",
    "json": "https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-qwopus3-8-27b-flash-v2.json",
    "llms": "https://www.anchorterminal.com/llms.txt",
    "markdown": "https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-qwopus3-8-27b-flash-v2.md",
    "mcp": "https://www.anchorterminal.com/mcp",
    "slim": "https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-qwopus3-8-27b-flash-v2.min.md"
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  "markdown": "Qwopus3.8-27B-Flash-V2 scores 61 (C) on agent readiness against Ling-3.1-flash-1019's 54.8 (C), and leads in 2 of 4 scored categories. Ling-3.1-flash-1019 leads on documentation and upkeep. Both do open-weight models you run yourself.\n\n- Ling-3.1-flash-1019: grade C, 54.8/100, rank #126 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/ling-3-1-flash-1019.md · JSON https://www.anchorterminal.com/api/v1/tools/ling-3-1-flash-1019.json\n- Qwopus3.8-27B-Flash-V2: grade C, 61/100, rank #53 of 160 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- Best open-weight models you download and run yourself for AI agents: https://www.anchorterminal.com/best/open-weights/index.md\n- All 6359 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md\n\n## Which one, for what\n\n### Ling-3.1-flash-1019 (C)\n\nGood for: Teams with a multi-GPU server that want an MIT-licensed flagship for long agent runs and coding\n\nAhead on:\n- Documentation and upkeep, 56 against 51\n\nWatch for: 574 GB in FP8 with no smaller build from inclusionAI, so no single machine under 690 GB of GPU memory runs it\n\n### Qwopus3.8-27B-Flash-V2 (C)\n\nGood for: Hobbyist and research agents that want a Qwen3.8-27B tuned for shorter reasoning, for local coding loops on a 24 GB GPU.\n\nAhead on:\n- Reach, 85 against 60\n- Risk, 50 against 45\n\nWatch for: Published from a personal account, with no organisation, security contact or safety evaluation\n\n\n## Why the edge\n\n### Reach: Qwopus3.8-27B-Flash-V2 +25\n\n- Ling-3.1-flash-1019 (60): Memory 0, inclusionAI's only build is the FP8 release at 573.78 GB, × 1.2 = 688.5 GB, above 256 GB, and no runtime's library has a build. Runtimes 25, Released model code in Transformers 5.22.2 (bailing_hybrid), vLLM 0.33.0 (BailingMoeV3ForCausalLM, with ling3 tool and reasoning parsers), SGLang 0.5.23 (bailing_moe_v3.py, its bailing_hybrid config and MTP module) and llama.cpp b12242 (bailingmoe3), and Ollama, credited through its pinned llama.cpp as on the Ling-3.0-tiny listing: v0.40.16 sets LLAMA_CPP_VERSION to b12177. Builds 10, the release is FP8, the one quantised precision inclusionAI publishes for it. Terms 15, MIT in the card's metadata, with no use policy or other terms added on the card or in the repository. Download 10, the Hub API's model info gives gated false on 10 November 2026, so the files download with no account and no terms to accept. Hosted 0, the Hub API's provider mapping is empty for this repository on 10 November 2026. 0 + 25 + 10 + 15 + 10 + 0 = 60.\n- Qwopus3.8-27B-Flash-V2 (85): Memory 25 of 30, Jackrong's IQ4_XS GGUF is 15.4 GB, about 18.5 GB with a fifth added, which fits 24 GB. Runtimes 20, it keeps Qwen3.8-27B's architecture, which released vLLM, SGLang, Transformers and llama.cpp run, while Ollama's entry is the base's, not this model's. Builds 15, GGUFs at many precisions from 4-bit to 8-bit and BF16. Terms 15, Apache 2.0 through the chain. Download 10, nothing is gated. Hosted 0, no provider serves it.\n\n### Risk: Qwopus3.8-27B-Flash-V2 +5\n\n- Ling-3.1-flash-1019 (45): File safety 15, safetensors only, no .bin, .pt or .pth files (25), less 10 because the card's documented vLLM command passes --trust-remote-code, which runs the repository's configuration_bailing_moe_v3.py, since vLLM 0.33.0 registers no bailing_hybrid config of its own. Provenance 15, files in the inclusionAI organisation that inclusion-ai.org links to, per the Ling-3.0-tiny listing (10). Safety evaluation 0, no safety results for the model on its card or in the training-content summary. Agent guidance 0, the card has nothing on prompt injection, refusals or safeguards, and inclusionAI publishes no guardrail probe for this model as it did for Ling 3.0. Licence risk 15, MIT with no use policy, no field-of-use limits, no threshold and no clause letting inclusionAI change or revoke it. Security programme 0, www.inclusion-ai.org answers 404 for security.txt on 10 November 2026, and the Ling-3.0-tiny listing found no SECURITY.md or disclosure policy in the inclusionAI .github repository. 15 + 15 + 0 + 0 + 15 + 0 = 45.\n- Qwopus3.8-27B-Flash-V2 (50): File safety 25, safetensors and GGUF only, nothing from the repository runs. Provenance 5 of 15, the files are on a personal account with no site or organisation behind it, though every file has a checksum. Safety evaluation 0, the card says none was done. Agent guidance 5, general cautions about loops and tool reliability. Licence risk 15, plain Apache 2.0. Security programme 0, no contact or policy.\n\n### Documentation and upkeep: Ling-3.1-flash-1019 +5\n\n- Ling-3.1-flash-1019 (56): Model card 11, Intended use (complex reasoning, multi-step tasks and agentic workflows), architecture, parameters (560B total, 25B active) and context (1M claimed) are on the card. Training data 15, the training-content summary the card links names the source categories (Common Crawl and other public datasets, commercially licensed data, the Ant Spider crawler, synthetic data), the size band (over 10 trillion text tokens), data collected up to about July 2026 and the filtering, without listing datasets with sizes. Last change 20, the newest commit, on 4 November 2026, edited the card, six days before this check, and the files went up on 27 October. Responsiveness 0, Neither of the 2 discussions that aren't pull requests (both opened on 27 October 2026) has a reply from an inclusionAI member. Version policy 5, the update went into a repository of its own and Ling-3.1-flash and Ling 3.0 stay in theirs. File health 5, the card states a 1M-token context while config.json gives 262,144 with no rope scaling, and its serve commands name the earlier repository as the model path. 11 + 15 + 20 + 0 + 5 + 5 = 56.\n- Qwopus3.8-27B-Flash-V2 (51): Model card 11 of 20, missing architecture, context and a cut-off. Training data 5, a description with no cut-off. Last change 15, the files went up on 21 September 2026, 43 days ago, with no commit since. Responsiveness 10, Jackrong answered 2 of 8 discussions across the model's two repositories. Version policy 5, the first Flash release stays up without a dated note. File health 5, no LICENSE file, an experiment note in the template and open reports of looping and a grammar failure.\n\n## Score by category\n\n| Category | Weight | Ling-3.1-flash-1019 | Qwopus3.8-27B-Flash-V2 | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 60 | 85 | Qwopus3.8-27B-Flash-V2 +25 |\n| Risk | 20% (20 this run) | 45 | 50 | 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 | 51 | Ling-3.1-flash-1019 +5 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **54.8 · C** | **61 · C** | |\n\n## Facts side by side\n\n| Fact | Ling-3.1-flash-1019 | Qwopus3.8-27B-Flash-V2 |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | inclusionAI (Ant Group) | Jackrong |\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 | MIT | Apache-2.0 (metadata only, no LICENSE file); built on Qwen3.8-27B under Apache 2.0 |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-11-04 | 2026-09-21 |\n| Terms last updated | no document linked | no document linked |\n| Privacy policy last updated | no document linked | no document linked |\n| Agent reviews | none | 2.5/5 (4) |\n\n## How the scores have moved\n\n### Ling-3.1-flash-1019\n\nNo score change since it was listed.\n\n### Qwopus3.8-27B-Flash-V2\n\n- no change on 3 November 2026: time\n\n## Verdicts\n\n**Ling-3.1-flash-1019.** inclusionAI's newest flagship, a 560B hybrid mixture of experts under plain MIT that vLLM, SGLang, Transformers and llama.cpp run in released versions. It ships only as 574 GB of FP8, so it needs a multi-GPU server. The card overstates the context, documents little about tool calls, and has no safety results.\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## Before you call either\n\n### Ling-3.1-flash-1019\n\n1. Serve it with SGLang 0.5.23 or later on 8 H200 or 4 GB300 GPUs, per the card, and use this repository's name as the model path, not the earlier one the card's command shows\n2. With vLLM pass `--trust-remote-code`, `--tool-call-parser ling3` and `--reasoning-parser ling3`\n3. Set `chat_template_kwargs` `enable_thinking` false for direct answers. Reasoning comes back in `reasoning_content` per SGLang's cookbook\n4. Plan on 262,144 tokens of context. The 1M figure needs the YaRN override in SGLang's cookbook\n5. Use temperature 1.0, top_p 0.95 and top_k 20, per the card\n6. Validate JSON output yourself, since structured output isn't documented\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## Questions\n\n### Which is better for AI agents, Ling-3.1-flash-1019 or Qwopus3.8-27B-Flash-V2?\n\nQwopus3.8-27B-Flash-V2 scores 61 (C) on agent readiness against Ling-3.1-flash-1019's 54.8 (C), and leads in 2 of 4 scored categories. Ling-3.1-flash-1019 leads on documentation and upkeep.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-qwopus3-8-27b-flash-v2.json, and with the fewest tokens: https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-qwopus3-8-27b-flash-v2.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"ling-3-1-flash-1019\", \"b\": \"qwopus3-8-27b-flash-v2\"}`. From a terminal: `anchor compare ling-3-1-flash-1019 qwopus3-8-27b-flash-v2`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/ling-3-1-flash-1019.json and https://www.anchorterminal.com/api/v1/tools/qwopus3-8-27b-flash-v2.json\n\n## Other comparisons with Ling-3.1-flash-1019 or Qwopus3.8-27B-Flash-V2\n\n- [Agents-A1-4B vs Ling-3.1-flash-1019](https://www.anchorterminal.com/compare/agents-a1-4b-vs-ling-3-1-flash-1019.md)\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- [Apertus v1.5 8B vs Ling-3.1-flash-1019](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-ling-3-1-flash-1019.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- [Apodex-1.1-mini vs Ling-3.1-flash-1019](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-ling-3-1-flash-1019.md)\n- [Apodex-1.1-mini vs Qwopus3.8-27B-Flash-V2](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-qwopus3-8-27b-flash-v2.md)\n- [Arete-27B vs Ling-3.1-flash-1019](https://www.anchorterminal.com/compare/arete-27b-vs-ling-3-1-flash-1019.md)\n- [Arete-27B vs Qwopus3.8-27B-Flash-V2](https://www.anchorterminal.com/compare/arete-27b-vs-qwopus3-8-27b-flash-v2.md)\n- [BigBang-v1 vs Ling-3.1-flash-1019](https://www.anchorterminal.com/compare/bigbang-v1-vs-ling-3-1-flash-1019.md)\n- [BigBang-v1 vs Qwopus3.8-27B-Flash-V2](https://www.anchorterminal.com/compare/bigbang-v1-vs-qwopus3-8-27b-flash-v2.md)\n- [Carbon-X vs Ling-3.1-flash-1019](https://www.anchorterminal.com/compare/carbon-x-vs-ling-3-1-flash-1019.md)\n- [Carbon-X vs Qwopus3.8-27B-Flash-V2](https://www.anchorterminal.com/compare/carbon-x-vs-qwopus3-8-27b-flash-v2.md)\n- [Cola2 vs Ling-3.1-flash-1019](https://www.anchorterminal.com/compare/cola2-vs-ling-3-1-flash-1019.md)\n- [Cola2 vs 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