{
  "data": {
    "a": {
      "slug": "lfm3-audio-2b",
      "name": "LFM3-Audio-2B",
      "vendor": "Liquid AI",
      "vendorUrl": "https://www.liquid.ai",
      "kind": "weights",
      "category": "open-weights",
      "summary": "LFM3-Audio-2B is Liquid AI's open-weight 2B audio model for on-device use, taking text or speech and answering in text, speech or both, with transcription, translation, tool calling and a 32K-token context, released on 27 October 2026.",
      "url": "https://www.anchorterminal.com/tools/lfm3-audio-2b",
      "markdownUrl": "https://www.anchorterminal.com/tools/lfm3-audio-2b.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/lfm3-audio-2b.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/lfm3-audio-2b.json",
      "dns": "lfm3-audio-2b.grade.anchorterminal.com",
      "repo": "https://huggingface.co/LiquidAI/LFM3-Audio-2B",
      "license": "LFM Open License v1.0 (free commercial use below $10M annual revenue)",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "LiquidAI/LFM3-Audio-2B"
        },
        {
          "registry": "huggingface",
          "name": "LiquidAI/LFM3-Audio-2B-GGUF"
        },
        {
          "registry": "huggingface",
          "name": "LiquidAI/LFM3-Audio-2B-MLX-4bit"
        },
        {
          "registry": "ollama",
          "name": "lfm3-audio"
        }
      ],
      "auth": "none",
      "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The repository and Liquid's GGUF, MLX and ONNX repositories are not gated (checked 2026-11-10).",
      "pricing": "free",
      "pricingNotes": "Free to download and use under the LFM Open License v1.0, commercial use included for organisations with under $10M in annual revenue; above that, commercial use isn't licensed. Running it yourself costs little hardware, 1.10 GB for Liquid's Q4_K_M GGUF with its 0.17 GB projector file and 4.16 GB in BF16. Liquid's API also serves it, at prices shown in its console (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://docs.liquid.ai/lfm/models/lfm3-audio-2b",
      "llmsTxt": "https://docs.liquid.ai/llms.txt",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "audio",
        "speech",
        "speech-to-speech",
        "transcription",
        "small",
        "edge",
        "tool-calling",
        "custom-licence",
        "safetensors",
        "gguf",
        "mlx",
        "onnx",
        "self-hosted"
      ],
      "lastRelease": "2026-11-03",
      "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": 1520,
        "categoryRank": 56,
        "methodology": "0.7",
        "run": "2026-10-01",
        "scores": {
          "interface": 85,
          "reach": 80,
          "risk": 40,
          "upkeep": 52
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "low",
          "date": "2026-11-10"
        },
        "negative": 0,
        "verdict": "A 2B model that listens and speaks on an 8 GB machine, transcribes and translates 19 languages and turns spoken requests into tool calls. Only llama.cpp and Ollama run it in a release. The context is 32K, voice chat is English only, Liquid hasn't answered the repository's questions, and there are no safety results or data sources.",
        "bestFor": "Voice agents on a laptop or phone that need speech in, speech out and tool calls from one small model",
        "strengths": [
          "Speech in and speech out on an 8 GB machine, streamed",
          "Transcribes, speaks and translates 19 languages and locales",
          "Tool calls from spoken requests, in a documented format",
          "Reasoning can be turned on per text reply",
          "Liquid's own API serves it"
        ],
        "weaknesses": [
          "Only llama.cpp and Ollama run it in a release, though the card lists vLLM and SGLang",
          "A 32K-token context",
          "Voice chat is English only",
          "Neither question on the repository has a reply from Liquid",
          "Loading in Transformers runs code from the repository",
          "No safety results, security contact or training data description"
        ],
        "agentNotes": [
          "Start it with `llama-server -hf LiquidAI/LFM3-Audio-2B-GGUF:Q4_K_M --jinja` or `ollama run lfm3-audio`",
          "llama.cpp ships examples/realtime_client.py for live speech with this model",
          "Prefill the assistant turn with an open think tag to let it reason before a text reply",
          "The context is 32K",
          "Voice chat is English only, while transcription, synthesis and translation cover 19 languages and locales",
          "Liquid doesn't recommend it for agentic coding or complex multi-step reasoning"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 0,
        "avgRating": 0,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "low",
            "grade": "C",
            "methodology": "0.7",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 61
          }
        ],
        "editorialScores": {
          "interface": 85,
          "reach": 80,
          "risk": 40,
          "upkeep": 52
        },
        "provenanceScore": 73,
        "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": "llama-server -hf LiquidAI/LFM3-Audio-2B-GGUF:Q4_K_M --jinja\n# or\nollama run lfm3-audio",
        "http": "curl http://localhost:8080/v1/chat/completions \\\n  -H 'Content-Type: application/json' \\\n  -d '{\"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/lfm3-audio-2b"
      },
      "sameCompany": [
        "liquid-d1",
        "lfm2-5-2-6b",
        "lfm3-1-7b",
        "lfm3-omni-2b",
        "lfm3-vl-2b"
      ],
      "weights": {
        "trainedBy": "Liquid AI",
        "builtOn": "The LFM3 backbone (Liquid's own; the metadata names no base model)",
        "params": "2.00B",
        "context": 32000,
        "goodFor": "Small on-device audio model for voice chat, transcription, speech synthesis and translation in 19 languages and locales, questions about recordings and tool calls from spoken requests. Voice chat is English only.",
        "runtimes": [
          "llama.cpp",
          "Ollama"
        ],
        "builds": [
          {
            "name": "LFM3-Audio-2B",
            "url": "https://huggingface.co/LiquidAI/LFM3-Audio-2B",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 4.16,
            "memoryGB": 8,
            "format": "safetensors",
            "runsOn": [
              "Transformers"
            ],
            "note": "Loads in Transformers with the repository's own code. Liquid also publishes an ONNX repository. It has no FP8 or NVFP4 build of this model"
          },
          {
            "name": "LFM3-Audio-2B-GGUF Q8_0",
            "url": "https://huggingface.co/LiquidAI/LFM3-Audio-2B-GGUF",
            "who": "publisher",
            "precision": "q8_0",
            "sizeGB": 2.15,
            "memoryGB": 8,
            "format": "gguf",
            "runsOn": [
              "llama.cpp"
            ],
            "note": "LFM3-Audio-2B-Q8_0.gguf (1.85 GB) plus mmproj-LFM3-Audio-2B-Q8_0.gguf (0.30 GB)"
          },
          {
            "name": "LFM3-Audio-2B-GGUF Q4_K_M",
            "url": "https://huggingface.co/LiquidAI/LFM3-Audio-2B-GGUF",
            "who": "publisher",
            "precision": "q4_k_m",
            "sizeGB": 1.27,
            "memoryGB": 8,
            "format": "gguf",
            "runsOn": [
              "llama.cpp"
            ],
            "note": "LFM3-Audio-2B-Q4_K_M.gguf (1.10 GB) plus Liquid's projector file mmproj-LFM3-Audio-2B-Q4_K_M.gguf (0.17 GB), from the Hub's file list. The repository also holds Q4_0, Q8_0, F16 and BF16 files"
          },
          {
            "name": "LFM3-Audio-2B-MLX-4bit",
            "url": "https://huggingface.co/LiquidAI/LFM3-Audio-2B-MLX-4bit",
            "who": "publisher",
            "precision": "4bit",
            "sizeGB": 1.73,
            "memoryGB": 8,
            "format": "mlx",
            "runsOn": [
              "MLX-VLM"
            ],
            "note": "Liquid also publishes 8-bit and BF16 MLX builds"
          },
          {
            "name": "ollama lfm3-audio",
            "url": "https://ollama.com/library/lfm3-audio",
            "who": "ollama",
            "precision": "q4_k_m",
            "sizeGB": 1.7,
            "memoryGB": 8,
            "format": "ollama",
            "runsOn": [
              "Ollama"
            ],
            "note": "Ollama's library build, size as its tags page shows it on 10 November 2026. The library also has q8_0, bf16, mxfp8 and nvfp4 tags"
          }
        ],
        "run": "llama-server -hf LiquidAI/LFM3-Audio-2B-GGUF:Q4_K_M --jinja",
        "runBuild": "LFM3-Audio-2B-GGUF Q4_K_M",
        "checked": "2026-11-10",
        "minMemoryGB": 8
      },
      "family": {
        "lab": "Liquid AI",
        "family": "LFM3",
        "url": "https://www.anchorterminal.com/open-weights/#liquid",
        "others": [
          {
            "name": "LFM3-1.7B-Base",
            "url": "https://huggingface.co/LiquidAI/LFM3-1.7B-Base",
            "params": "1.73B"
          },
          {
            "name": "LFM3-1.7B",
            "url": "https://huggingface.co/LiquidAI/LFM3-1.7B",
            "params": "1.73B",
            "listing": "https://www.anchorterminal.com/tools/lfm3-1-7b"
          },
          {
            "name": "LFM3-VL-2B",
            "url": "https://huggingface.co/LiquidAI/LFM3-VL-2B",
            "params": "1.92B",
            "listing": "https://www.anchorterminal.com/tools/lfm3-vl-2b"
          },
          {
            "name": "LFM3-Omni-2B",
            "url": "https://huggingface.co/LiquidAI/LFM3-Omni-2B",
            "params": "2.17B",
            "listing": "https://www.anchorterminal.com/tools/lfm3-omni-2b"
          }
        ]
      },
      "area": "models",
      "provenance": {
        "legalEntity": "Liquid AI, Inc. (the licensor named in the LFM Open License)",
        "domain": "liquid.ai",
        "domainRegistered": "2017-12-16",
        "endpointOnVendorDomain": null,
        "terms": "",
        "privacy": "",
        "statusPage": "",
        "changelog": "https://huggingface.co/LiquidAI/LFM3-Audio-2B/commits/main",
        "securityTxt": "none",
        "checked": "2026-11-10",
        "notes": [
          "The licence names Liquid AI, Inc. as licensor. The domain's registration date is as recorded on the liquid-d1 listing on 8 October 2026",
          "www.liquid.ai/.well-known/security.txt answered 404 on 10 November 2026"
        ],
        "score": 73
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/lfm3-audio-2b.json",
      "live": {
        "slug": "lfm3-audio-2b",
        "securityTxt": {
          "url": "https://liquid.ai/.well-known/security.txt",
          "state": "none",
          "checkedAt": "2026-11-11T16:11:58.387968894Z"
        },
        "llmsTxt": {
          "url": "https://docs.liquid.ai/llms.txt",
          "ok": true,
          "status": 200,
          "checkedAt": "2026-11-11T15:47:19.138774945Z"
        },
        "pages": [
          {
            "url": "https://huggingface.co/LiquidAI/LFM3-Audio-2B/commits/main",
            "kind": "changelog",
            "status": 200,
            "checkedAt": "2026-11-11T17:02:30.858889862Z",
            "changedAt": "0001-01-01T00:00:00Z",
            "fingerprint": "573c4f8f5920"
          }
        ],
        "updatedAt": "2026-11-11T17:02:30.858889862Z"
      }
    },
    "answer": "LFM3-Audio-2B and Qwopus3.8-27B-Flash-V2 score within a point of each other on agent readiness, 61 (C) and 61 (C). Qwopus3.8-27B-Flash-V2 leads on reach and risk.",
    "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": 1520,
        "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": "interface",
        "leads": "lfm3-audio-2b",
        "name": "Agent interface",
        "reasons": {
          "a": "Chat template 20, chat_template.jinja in the repository (9,328 characters), with roles, audio, tools, deferred tools and tool results. Tool-calling format 25, the card and Liquid's tool-use page document tools passed through `tools=` in apply_chat_template, calls returned as XML and results sent back as tool messages. Trained tool calling 20, the template loops over every entry in an assistant turn's tool_calls, so several calls fit in one turn, and the card names turning spoken requests into tool calls as one of its four uses. Structured output 5, the card says the model is designed for structured outputs without saying how to ask for JSON. Liquid's API guide documents `response_format` with lfm3-1.7b as its example. Reasoning control 10, the card documents reasoning per request for text replies: the caller prefills the assistant turn with an open think tag to let the model reason, or a closed one for a direct answer, with a use_thinking example. Context 5, 32K tokens of input and output per the card. 20 + 25 + 20 + 5 + 10 + 5 = 85.",
          "b": "Chat template 20, a Jinja file in the repository. Tool-calling format 10 of 25, only the template shows the Hermes-style JSON calls, which differ from the base's format. Trained tool calling 20, several calls a turn. Structured output 0, not documented. Reasoning control 0, the switches are documented but not where the reasoning comes back. Context 10, 262,144 tokens from the base."
        },
        "scores": {
          "a": 85,
          "b": 60
        }
      },
      {
        "by": 10,
        "category": "risk",
        "leads": "qwopus3-8-27b-flash-v2",
        "name": "Risk",
        "reasons": {
          "a": "File safety 15, safetensors only, no .bin, .pt or .pth files (25). Provenance 15, files in the LiquidAI organisation, which Liquid's docs page for the model and its blog post link to (10). Safety evaluation 0, no safety results on the card or in Liquid's blog post for the LFM3 family, neither of which mentions safety testing. Agent guidance 0, Nothing on prompt injection, an instruction hierarchy, what refusals cover or safeguards to add. Licence risk 10, 15 less 5 for the $10M annual revenue threshold on commercial use. Security programme 0, no way to report a flaw found. 15 + 15 + 0 + 0 + 10 + 0 = 40.",
          "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": 40,
          "b": 50
        }
      },
      {
        "by": 5,
        "category": "reach",
        "leads": "qwopus3-8-27b-flash-v2",
        "name": "Reach",
        "reasons": {
          "a": "Memory 30, Liquid's Q4_K_M GGUF is 1.10 GB with its 0.17 GB projector file, and 1.27 GB × 1.2 = 1.5 GB, which fits 8 GB, well inside the 16 GB line. Runtimes 10, Released llama.cpp has the lfm3 architecture with the LFM3 audio projector and speech output (src/models/lfm3.cpp and tools/mtmd at b12242, with a realtime client example that starts this model), and Ollama's library has an lfm3-audio entry. Builds 15, Liquid's GGUF repository holds Q4_0, Q4_K_M, Q8_0, F16 and BF16 files, and Liquid publishes 4-bit and 8-bit MLX builds and an ONNX repository, all in the LiquidAI organisation. Terms 10, the LFM Open License v1.0, a custom licence that lets anyone use it commercially for free below a threshold of $10M annual revenue. Download 10, the Hub API reports gated false for the model and its GGUF, MLX and ONNX repositories on 10 November 2026, so the files download with no account and nothing to accept. Hosted 5, Liquid's own API lists lfm3-audio-2b among its models, so the publisher hosts it. 30 + 10 + 15 + 10 + 10 + 5 = 80.",
          "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": 80,
          "b": 85
        }
      }
    ],
    "facts": [
      {
        "a": "Open-weight model",
        "b": "Open-weight model",
        "name": "Kind"
      },
      {
        "a": "Liquid AI",
        "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": "LFM Open License v1.0 (free commercial use below $10M annual revenue)",
        "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": "yes",
        "b": "no",
        "name": "llms.txt"
      },
      {
        "a": "2026-11-03",
        "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": "LFM3-Audio-2B and Qwopus3.8-27B-Flash-V2 score within a point of each other on agent readiness, 61 (C) and 61 (C). Qwopus3.8-27B-Flash-V2 leads on reach and risk.",
        "question": "Which is better for AI agents, LFM3-Audio-2B or Qwopus3.8-27B-Flash-V2?"
      }
    ],
    "goodFor": [
      {
        "aheadOn": [
          "Agent interface, 85 against 60"
        ],
        "also": null,
        "goodFor": "Voice agents on a laptop or phone that need speech in, speech out and tool calls from one small model",
        "slug": "lfm3-audio-2b",
        "watchFor": "Only llama.cpp and Ollama run it in a release, though the card lists vLLM and SGLang"
      },
      {
        "aheadOn": [
          "Reach, 85 against 80",
          "Risk, 50 against 40"
        ],
        "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": {
      "lfm3-audio-2b": 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-lfm3-audio-2b.json",
        "title": "Agents-A1-4B vs LFM3-Audio-2B",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-lfm3-audio-2b"
      },
      {
        "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-lfm3-audio-2b.json",
        "title": "Apertus v1.5 8B vs LFM3-Audio-2B",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-lfm3-audio-2b"
      },
      {
        "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-lfm3-audio-2b.json",
        "title": "Apodex-1.1-mini vs LFM3-Audio-2B",
        "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-lfm3-audio-2b"
      },
      {
        "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-lfm3-audio-2b.json",
        "title": "Arete-27B vs LFM3-Audio-2B",
        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-lfm3-audio-2b"
      },
      {
        "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-lfm3-audio-2b.json",
        "title": "BigBang-v1 vs LFM3-Audio-2B",
        "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-lfm3-audio-2b"
      },
      {
        "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-lfm3-audio-2b.json",
        "title": "Carbon-X vs LFM3-Audio-2B",
        "url": "https://www.anchorterminal.com/compare/carbon-x-vs-lfm3-audio-2b"
      },
      {
        "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-lfm3-audio-2b.json",
        "title": "Cola2 vs LFM3-Audio-2B",
        "url": "https://www.anchorterminal.com/compare/cola2-vs-lfm3-audio-2b"
      },
      {
        "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-lfm3-audio-2b.json",
        "title": "Cosmos-Reason2-2B vs LFM3-Audio-2B",
        "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-lfm3-audio-2b"
      },
      {
        "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-lfm3-audio-2b.json",
        "title": "Darwin-36B-Opus vs LFM3-Audio-2B",
        "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-lfm3-audio-2b"
      },
      {
        "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-lfm3-audio-2b.json",
        "title": "DeepSeek-V3.2 vs LFM3-Audio-2B",
        "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-lfm3-audio-2b"
      },
      {
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        "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-qwopus3-8-27b-flash-v2"
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        "title": "DeepSeek V4.1 Flash vs LFM3-Audio-2B",
        "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-lfm3-audio-2b"
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        "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwopus3-8-27b-flash-v2"
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        "url": "https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-qwopus3-8-27b-flash-v2"
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        "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwopus3-8-27b-flash-v2"
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      },
      {
        "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": 5,
        "edge": "qwopus3-8-27b-flash-v2",
        "key": "reach",
        "lfm3-audio-2b": 80,
        "name": "Reach",
        "qwopus3-8-27b-flash-v2": 85,
        "weight": 25
      },
      {
        "by": 10,
        "edge": "qwopus3-8-27b-flash-v2",
        "key": "risk",
        "lfm3-audio-2b": 40,
        "name": "Risk",
        "qwopus3-8-27b-flash-v2": 50,
        "weight": 20
      },
      {
        "by": 25,
        "edge": "lfm3-audio-2b",
        "key": "interface",
        "lfm3-audio-2b": 85,
        "name": "Agent interface",
        "qwopus3-8-27b-flash-v2": 60,
        "weight": 10
      },
      {
        "by": 1,
        "edge": "lfm3-audio-2b",
        "key": "upkeep",
        "lfm3-audio-2b": 52,
        "name": "Documentation and upkeep",
        "qwopus3-8-27b-flash-v2": 51,
        "weight": 10
      }
    ],
    "summary": "LFM3-Audio-2B and Qwopus3.8-27B-Flash-V2 score within a point of each other on agent readiness, 61 (C) and 61 (C). Qwopus3.8-27B-Flash-V2 leads on reach and risk. Both do open-weight models you run yourself.",
    "tasks": null,
    "tutorials": [],
    "verdicts": {
      "lfm3-audio-2b": "A 2B model that listens and speaks on an 8 GB machine, transcribes and translates 19 languages and turns spoken requests into tool calls. Only llama.cpp and Ollama run it in a release. The context is 32K, voice chat is English only, Liquid hasn't answered the repository's questions, and there are no safety results or data sources.",
      "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/lfm3-audio-2b-vs-qwopus3-8-27b-flash-v2",
    "json": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-qwopus3-8-27b-flash-v2.json",
    "llms": "https://www.anchorterminal.com/llms.txt",
    "markdown": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-qwopus3-8-27b-flash-v2.md",
    "mcp": "https://www.anchorterminal.com/mcp",
    "slim": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-qwopus3-8-27b-flash-v2.min.md"
  },
  "markdown": "LFM3-Audio-2B and Qwopus3.8-27B-Flash-V2 score within a point of each other on agent readiness, 61 (C) and 61 (C). Qwopus3.8-27B-Flash-V2 leads on reach and risk. Both do open-weight models you run yourself.\n\n- LFM3-Audio-2B: grade C, 61/100, rank #70 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/lfm3-audio-2b.md · JSON https://www.anchorterminal.com/api/v1/tools/lfm3-audio-2b.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### LFM3-Audio-2B (C)\n\nGood for: Voice agents on a laptop or phone that need speech in, speech out and tool calls from one small model\n\nAhead on:\n- Agent interface, 85 against 60\n\nWatch for: Only llama.cpp and Ollama run it in a release, though the card lists vLLM and SGLang\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 80\n- Risk, 50 against 40\n\nWatch for: Published from a personal account, with no organisation, security contact or safety evaluation\n\n\n## Why the edge\n\n### Agent interface: LFM3-Audio-2B +25\n\n- LFM3-Audio-2B (85): Chat template 20, chat_template.jinja in the repository (9,328 characters), with roles, audio, tools, deferred tools and tool results. Tool-calling format 25, the card and Liquid's tool-use page document tools passed through `tools=` in apply_chat_template, calls returned as XML and results sent back as tool messages. Trained tool calling 20, the template loops over every entry in an assistant turn's tool_calls, so several calls fit in one turn, and the card names turning spoken requests into tool calls as one of its four uses. Structured output 5, the card says the model is designed for structured outputs without saying how to ask for JSON. Liquid's API guide documents `response_format` with lfm3-1.7b as its example. Reasoning control 10, the card documents reasoning per request for text replies: the caller prefills the assistant turn with an open think tag to let the model reason, or a closed one for a direct answer, with a use_thinking example. Context 5, 32K tokens of input and output per the card. 20 + 25 + 20 + 5 + 10 + 5 = 85.\n- Qwopus3.8-27B-Flash-V2 (60): Chat template 20, a Jinja file in the repository. Tool-calling format 10 of 25, only the template shows the Hermes-style JSON calls, which differ from the base's format. Trained tool calling 20, several calls a turn. Structured output 0, not documented. Reasoning control 0, the switches are documented but not where the reasoning comes back. Context 10, 262,144 tokens from the base.\n\n### Risk: Qwopus3.8-27B-Flash-V2 +10\n\n- LFM3-Audio-2B (40): File safety 15, safetensors only, no .bin, .pt or .pth files (25). Provenance 15, files in the LiquidAI organisation, which Liquid's docs page for the model and its blog post link to (10). Safety evaluation 0, no safety results on the card or in Liquid's blog post for the LFM3 family, neither of which mentions safety testing. Agent guidance 0, Nothing on prompt injection, an instruction hierarchy, what refusals cover or safeguards to add. Licence risk 10, 15 less 5 for the $10M annual revenue threshold on commercial use. Security programme 0, no way to report a flaw found. 15 + 15 + 0 + 0 + 10 + 0 = 40.\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### Reach: Qwopus3.8-27B-Flash-V2 +5\n\n- LFM3-Audio-2B (80): Memory 30, Liquid's Q4_K_M GGUF is 1.10 GB with its 0.17 GB projector file, and 1.27 GB × 1.2 = 1.5 GB, which fits 8 GB, well inside the 16 GB line. Runtimes 10, Released llama.cpp has the lfm3 architecture with the LFM3 audio projector and speech output (src/models/lfm3.cpp and tools/mtmd at b12242, with a realtime client example that starts this model), and Ollama's library has an lfm3-audio entry. Builds 15, Liquid's GGUF repository holds Q4_0, Q4_K_M, Q8_0, F16 and BF16 files, and Liquid publishes 4-bit and 8-bit MLX builds and an ONNX repository, all in the LiquidAI organisation. Terms 10, the LFM Open License v1.0, a custom licence that lets anyone use it commercially for free below a threshold of $10M annual revenue. Download 10, the Hub API reports gated false for the model and its GGUF, MLX and ONNX repositories on 10 November 2026, so the files download with no account and nothing to accept. Hosted 5, Liquid's own API lists lfm3-audio-2b among its models, so the publisher hosts it. 30 + 10 + 15 + 10 + 10 + 5 = 80.\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## Score by category\n\n| Category | Weight | LFM3-Audio-2B | Qwopus3.8-27B-Flash-V2 | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 80 | 85 | Qwopus3.8-27B-Flash-V2 +5 |\n| Risk | 20% (20 this run) | 40 | 50 | Qwopus3.8-27B-Flash-V2 +10 |\n| Agent interface | 10% (10 this run) | 85 | 60 | LFM3-Audio-2B +25 |\n| Documentation and upkeep | 10% (10 this run) | 52 | 51 | LFM3-Audio-2B +1 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **61 · C** | **61 · C** | |\n\n## Facts side by side\n\n| Fact | LFM3-Audio-2B | Qwopus3.8-27B-Flash-V2 |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Liquid AI | 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 | LFM Open License v1.0 (free commercial use below $10M annual revenue) | 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 | yes | no |\n| Last release | 2026-11-03 | 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### LFM3-Audio-2B\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**LFM3-Audio-2B.** A 2B model that listens and speaks on an 8 GB machine, transcribes and translates 19 languages and turns spoken requests into tool calls. Only llama.cpp and Ollama run it in a release. The context is 32K, voice chat is English only, Liquid hasn't answered the repository's questions, and there are no safety results or data sources.\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### LFM3-Audio-2B\n\n1. Start it with `llama-server -hf LiquidAI/LFM3-Audio-2B-GGUF:Q4_K_M --jinja` or `ollama run lfm3-audio`\n2. llama.cpp ships examples/realtime_client.py for live speech with this model\n3. Prefill the assistant turn with an open think tag to let it reason before a text reply\n4. The context is 32K\n5. Voice chat is English only, while transcription, synthesis and translation cover 19 languages and locales\n6. Liquid doesn't recommend it for agentic coding or complex multi-step reasoning\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, LFM3-Audio-2B or Qwopus3.8-27B-Flash-V2?\n\nLFM3-Audio-2B and Qwopus3.8-27B-Flash-V2 score within a point of each other on agent readiness, 61 (C) and 61 (C). Qwopus3.8-27B-Flash-V2 leads on reach and risk.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-qwopus3-8-27b-flash-v2.json, and with the fewest tokens: https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-qwopus3-8-27b-flash-v2.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"lfm3-audio-2b\", \"b\": \"qwopus3-8-27b-flash-v2\"}`. From a terminal: `anchor compare lfm3-audio-2b qwopus3-8-27b-flash-v2`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/lfm3-audio-2b.json and https://www.anchorterminal.com/api/v1/tools/qwopus3-8-27b-flash-v2.json\n\n## Other comparisons with LFM3-Audio-2B or Qwopus3.8-27B-Flash-V2\n\n- [Agents-A1-4B vs LFM3-Audio-2B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-lfm3-audio-2b.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 LFM3-Audio-2B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-lfm3-audio-2b.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 LFM3-Audio-2B](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-lfm3-audio-2b.md)\n- [Apodex-1.1-mini vs 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