{
  "data": {
    "a": {
      "slug": "gemma-4-e2b-it",
      "name": "Gemma 4 E2B IT",
      "vendor": "Google DeepMind",
      "vendorUrl": "https://gemma.ai",
      "kind": "weights",
      "category": "open-weights",
      "summary": "Gemma 4 E2B IT is Google DeepMind's small open-weight instruction-tuned model for phones and laptops, 2.3B effective parameters (5.1B with per-layer embeddings), taking text, images, video frames and audio with a 131,072-token context, published on Hugging Face under Apache 2.0.",
      "url": "https://www.anchorterminal.com/tools/gemma-4-e2b-it",
      "markdownUrl": "https://www.anchorterminal.com/tools/gemma-4-e2b-it.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/gemma-4-e2b-it.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/gemma-4-e2b-it.json",
      "dns": "gemma-4-e2b-it.grade.anchorterminal.com",
      "repo": "https://huggingface.co/google/gemma-4-E2B-it",
      "license": "Apache-2.0",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "google/gemma-4-E2B-it"
        },
        {
          "registry": "huggingface",
          "name": "google/gemma-4-E2B-it-qat-q4_0-gguf"
        },
        {
          "registry": "huggingface",
          "name": "google/gemma-4-E2B-it-qat-w4a16-ct"
        },
        {
          "registry": "ollama",
          "name": "gemma4:e2b"
        }
      ],
      "auth": "none",
      "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The repository and Google's QAT repositories are not gated (checked 2026-10-21).",
      "pricing": "free",
      "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0. No hosted version, since the Gemini API serves only the 26B A4B and 31B sizes and Hugging Face lists no inference provider for it. Running it yourself costs the hardware, 11.4 GB at BF16, 5.7 GB at SFP8, 2.9 GB at Q4_0, 1.1 GB for the mobile build and 0.84 GB for the mobile build text only to load the weights per Google (checked 2026-10-21).",
      "priceSummary": "Free",
      "where": "local",
      "x402": {
        "level": "no",
        "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-10-21).",
        "endpoints": []
      },
      "toolCount": null,
      "popularity": {
        "githubStars": null,
        "npmWeekly": null,
        "pypiWeekly": null,
        "asOf": "2026-10-21"
      },
      "docsUrl": "https://gemma.ai/models/gemma-4/",
      "llmsTxt": "https://gemma.ai/llms.txt",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "dense",
        "on-device",
        "multimodal",
        "audio",
        "reasoning",
        "tool-calling",
        "apache-2.0",
        "safetensors",
        "gguf",
        "qat",
        "self-hosted"
      ],
      "lastRelease": "2026-11-03",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 60.4,
        "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": 70,
        "methodology": "0.7",
        "run": "2026-10-01",
        "scores": {
          "capability": 19,
          "interface": 85,
          "reach": 95,
          "risk": 70,
          "upkeep": 75
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "high",
          "date": "2026-11-04"
        },
        "negative": 0,
        "verdict": "A small open-weight model for light agent work, tool calls, short audio and images on a phone or laptop, under Apache 2.0. Google's 4-bit build needs about 5 GB with working space and runs in all the common runtimes. Google's card puts it far below larger Gemma 4 sizes on agent and coding tasks, and no safety results are published.",
        "bestFor": "Agents that need a cheap, permissively licensed helper on 8 GB of memory, for tool calls, short audio or images, rather than hard reasoning",
        "strengths": [
          "Plain Apache 2.0, no account or gate, safetensors and GGUF only with a checksum on every file",
          "Google's own 4-bit QAT GGUF needs about 5.2 GB with working space",
          "Released support in vLLM, SGLang, llama.cpp, Ollama, LM Studio and Transformers",
          "Tool declaration and call format documented by Google, with parallel calls and a shipped response parser",
          "131,072 tokens of native context and thinking that turns on or off per request",
          "Takes audio as well as images and video frames"
        ],
        "weaknesses": [
          "No safety evaluation figures published",
          "No documented way to ask for JSON or schema output",
          "No hosted version, since the Gemini API serves only the two largest sizes",
          "Training data described only by type and cut-off",
          "Google's own card numbers put it far below the 12B, 26B A4B and 31B on agent and coding tasks",
          "Chat-template reports since June wait without a reply"
        ],
        "agentNotes": [
          "On the reference machine run `llama-server -hf google/gemma-4-E2B-it-qat-q4_0-gguf --hf-file gemma-4-E2B_q4_0-it.gguf --jinja -c 131072`, and add `--mmproj` with gemma-4-E2B-it-mmproj.gguf for images and audio",
          "With Ollama run `ollama run gemma4:e2b` and raise num_ctx or OLLAMA_CONTEXT_LENGTH, since the default context is small and agents want at least 64K",
          "In vLLM or SGLang pass `--tool-call-parser gemma4 --reasoning-parser gemma4` (vLLM also `--enable-auto-tool-choice`), or tool calls come back as text; Google's w4a16 build is the 4-bit route there",
          "Thinking is off by default; pass enable_thinking=True or start the system prompt with \u003c|think|\u003e, and drop earlier turns' thinking from history except on tool-call turns",
          "Put images before the text and audio after it, keep audio to 30 seconds and video to 60 frames, and validate tool names and arguments before running them"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 4,
        "avgRating": 3.5,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "high",
            "grade": "C",
            "methodology": "0.7",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 60.4
          }
        ],
        "editorialScores": {
          "capability": 19,
          "interface": 85,
          "reach": 95,
          "risk": 70,
          "upkeep": 75
        },
        "provenanceScore": 72,
        "checked": {
          "categories": {
            "capability": "2026-11-04",
            "interface": "2026-11-04",
            "reach": "2026-11-04",
            "risk": "2026-11-04",
            "upkeep": "2026-11-04"
          },
          "last": "2026-11-04",
          "oldest": "2026-11-04",
          "stale": false,
          "staleAfterDays": 90
        }
      },
      "connect": {
        "install": "ollama run gemma4:e2b\n# or, with llama.cpp and Google's QAT build:\nllama-server -hf google/gemma-4-E2B-it-qat-q4_0-gguf --hf-file gemma-4-E2B_q4_0-it.gguf --jinja -c 131072",
        "http": "curl http://localhost:11434/api/chat \\\n  -d '{\"model\": \"gemma4:e2b\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}], \"options\": {\"num_ctx\": 65536}}'"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/gemma-4-e2b-it"
      },
      "sameCompany": [
        "embeddinggemma-2",
        "embeddinggemma-300m",
        "shieldgemma",
        "diffusiongemma-26b-a4b-it",
        "gemma-4-12b-it",
        "gemma-4-26b-a4b-it",
        "gemma-4-31b-it",
        "gemma-4-e4b-it"
      ],
      "weights": {
        "trainedBy": "Google DeepMind",
        "builtOn": "google/gemma-4-E2B",
        "params": "5.1B",
        "context": 131072,
        "goodFor": "Small on-device assistant and agent model, 2.3B effective, with tool calling and optional thinking, taking text, images, video frames and short audio clips for transcription and translation.",
        "runtimes": [
          "llama.cpp",
          "Ollama",
          "vLLM",
          "SGLang",
          "LM Studio",
          "Transformers"
        ],
        "builds": [
          {
            "name": "gemma-4-E2B_q4_0-it",
            "url": "https://huggingface.co/google/gemma-4-E2B-it-qat-q4_0-gguf/blob/main/gemma-4-E2B_q4_0-it.gguf",
            "who": "publisher",
            "precision": "q4_0",
            "sizeGB": 4.34,
            "memoryGB": 8,
            "format": "gguf",
            "runsOn": [
              "llama.cpp (Vulkan)",
              "llama.cpp (ROCm)",
              "LM Studio"
            ],
            "note": "Quantisation-aware trained by Google. Size is the repository's two files summed, the 3.35 GB model and the 0.99 GB projector (gemma-4-E2B-it-mmproj.gguf) for image and audio input"
          },
          {
            "name": "gemma-4-E2B-it-qat-mobile-ct",
            "url": "https://huggingface.co/google/gemma-4-E2B-it-qat-mobile-ct",
            "who": "publisher",
            "precision": "wNa8o8",
            "sizeGB": 2.7,
            "memoryGB": 8,
            "format": "safetensors",
            "note": "Google's mobile scheme with 2-bit decoding layers, so below 4-bit in places and not the build Reach's memory line reads. Compressed-tensors format"
          },
          {
            "name": "gemma-4-E2B-it-qat-mobile-transformers",
            "url": "https://huggingface.co/google/gemma-4-E2B-it-qat-mobile-transformers",
            "who": "publisher",
            "precision": "wNa8o8",
            "sizeGB": 2.49,
            "memoryGB": 8,
            "format": "safetensors",
            "note": "The same mobile scheme as a Transformers reference, which Google says other formats are built from. Google's mobile memory figures are for LiteRT-LM"
          },
          {
            "name": "ollama gemma4:e2b",
            "url": "https://ollama.com/library/gemma4:e2b",
            "who": "ollama",
            "precision": "q4_k_m",
            "sizeGB": 4.6,
            "memoryGB": 8,
            "format": "ollama",
            "runsOn": [
              "Ollama"
            ],
            "note": "Q4_K_M weights (3.5 GB) with a BF16 vision projector (987 MB) and a Q8_0 draft model (98 MB) (digest b37049369adf), the llama.cpp variant. gemma4:e2b-it-qat (4.3 GB) is Ollama's copy of Google's Q4_0 QAT build"
          },
          {
            "name": "lmstudio google/gemma-4-e2b",
            "url": "https://lmstudio.ai/models/google/gemma-4-e2b",
            "who": "lmstudio",
            "precision": "q4_k_m",
            "sizeGB": 4.2,
            "memoryGB": 8,
            "format": "gguf",
            "runsOn": [
              "LM Studio"
            ],
            "note": "LM Studio's catalogue build, from lmstudio-community/gemma-4-E2B-it-GGUF, with MLX builds at 4 to 8 bits beside it. Size as the catalogue shows it, and Q4_K_M is inferred from that repository's files rather than stated by LM Studio"
          },
          {
            "name": "gemma-4-E2B-it-qat-w4a16-ct",
            "url": "https://huggingface.co/google/gemma-4-E2B-it-qat-w4a16-ct",
            "who": "publisher",
            "precision": "w4a16",
            "sizeGB": 8.35,
            "memoryGB": 12,
            "format": "safetensors",
            "note": "Quantisation-aware trained, 4-bit weights with 16-bit activations in compressed-tensors format, which Google routes to vLLM and SGLang. The per-layer embedding tables stay large, so it's bigger than the GGUF"
          },
          {
            "name": "gemma-4-E2B-it",
            "url": "https://huggingface.co/google/gemma-4-E2B-it",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 10.28,
            "memoryGB": 16,
            "format": "safetensors",
            "runsOn": [
              "vLLM (ROCm)",
              "SGLang (ROCm)"
            ]
          }
        ],
        "run": "llama-server -hf google/gemma-4-E2B-it-qat-q4_0-gguf --hf-file gemma-4-E2B_q4_0-it.gguf --jinja -c 131072",
        "runBuild": "gemma-4-E2B_q4_0-it",
        "checked": "2026-10-21",
        "minMemoryGB": 8
      },
      "family": {
        "lab": "Google (Gemma)",
        "family": "Gemma 4",
        "url": "https://www.anchorterminal.com/open-weights/#google",
        "others": [
          {
            "name": "gemma-4-26B-A4B",
            "url": "https://huggingface.co/google/gemma-4-26B-A4B",
            "params": "25.2B total, 3.8B active"
          },
          {
            "name": "gemma-4-26B-A4B-it",
            "url": "https://huggingface.co/google/gemma-4-26B-A4B-it",
            "params": "25.2B total, 3.8B active",
            "listing": "https://www.anchorterminal.com/tools/gemma-4-26b-a4b-it"
          },
          {
            "name": "gemma-4-31B",
            "url": "https://huggingface.co/google/gemma-4-31B",
            "params": "30.7B"
          },
          {
            "name": "gemma-4-31B-it",
            "url": "https://huggingface.co/google/gemma-4-31B-it",
            "params": "30.7B",
            "listing": "https://www.anchorterminal.com/tools/gemma-4-31b-it"
          },
          {
            "name": "gemma-4-E2B",
            "url": "https://huggingface.co/google/gemma-4-E2B",
            "params": "2.3B effective (5.1B with embeddings)"
          },
          {
            "name": "gemma-4-E4B",
            "url": "https://huggingface.co/google/gemma-4-E4B",
            "params": "4.5B effective (8B with embeddings)"
          },
          {
            "name": "gemma-4-E4B-it",
            "url": "https://huggingface.co/google/gemma-4-E4B-it",
            "params": "4.5B effective (8B with embeddings)",
            "listing": "https://www.anchorterminal.com/tools/gemma-4-e4b-it"
          },
          {
            "name": "gemma-4-26B-A4B-it-assistant",
            "url": "https://huggingface.co/google/gemma-4-26B-A4B-it-assistant",
            "params": "420M (file metadata on Hugging Face; drafter)"
          },
          {
            "name": "gemma-4-31B-it-assistant",
            "url": "https://huggingface.co/google/gemma-4-31B-it-assistant",
            "params": "470M (file metadata on Hugging Face; drafter)"
          },
          {
            "name": "gemma-4-E2B-it-assistant",
            "url": "https://huggingface.co/google/gemma-4-E2B-it-assistant",
            "params": "78M (file metadata on Hugging Face; drafter)"
          },
          {
            "name": "gemma-4-E4B-it-assistant",
            "url": "https://huggingface.co/google/gemma-4-E4B-it-assistant",
            "params": "79M (file metadata on Hugging Face; drafter)"
          },
          {
            "name": "gemma-4-12B",
            "url": "https://huggingface.co/google/gemma-4-12B",
            "params": "11.95B"
          },
          {
            "name": "gemma-4-12B-it",
            "url": "https://huggingface.co/google/gemma-4-12B-it",
            "params": "11.95B",
            "listing": "https://www.anchorterminal.com/tools/gemma-4-12b-it"
          },
          {
            "name": "gemma-4-12B-it-assistant",
            "url": "https://huggingface.co/google/gemma-4-12B-it-assistant",
            "params": "423M (file metadata on Hugging Face; drafter)"
          },
          {
            "name": "diffusiongemma-26B-A4B-it",
            "url": "https://huggingface.co/google/diffusiongemma-26B-A4B-it",
            "params": "25.2B total, 3.8B active",
            "listing": "https://www.anchorterminal.com/tools/diffusiongemma-26b-a4b-it"
          },
          {
            "name": "embeddinggemma-2",
            "url": "https://huggingface.co/google/embeddinggemma-2",
            "params": "740M",
            "listing": "https://www.anchorterminal.com/tools/embeddinggemma-2"
          }
        ],
        "olderCount": 171,
        "unrestrictedCopies": 5
      },
      "area": "models",
      "provenance": {
        "legalEntity": "Google LLC (RDAP registrant of gemma.ai as recorded for the first batch's Gemma 4 listings; the card names Google DeepMind as author)",
        "domain": "gemma.ai",
        "domainRegistered": "2017-12-16",
        "endpointOnVendorDomain": null,
        "terms": "",
        "privacy": "",
        "statusPage": "",
        "changelog": "https://gemma.ai/models/releases/",
        "securityTxt": "none",
        "checked": "2026-10-21",
        "notes": [
          "The registrant and registration date are carried from the gemma-4-26b-a4b-it and gemma-4-31b-it facts read earlier on 21 October 2026, since rdap.org rate-limited our request this time and wasn't retried.",
          "Nothing is hosted under this listing, so the Apache 2.0 licence stands in for terms of service and no privacy policy is scored.",
          "gemma.ai keeps a dated releases page for the Gemma family (this size on 31 March 2026). The Hugging Face commit history says what each change fixed.",
          "gemma.ai/.well-known/security.txt answers 404. Google's company-wide security.txt at google.com points to its vulnerability reward programme."
        ],
        "score": 72
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/gemma-4-e2b-it.json",
      "live": {
        "slug": "gemma-4-e2b-it",
        "securityTxt": {
          "url": "https://gemma.ai/.well-known/security.txt",
          "state": "none",
          "checkedAt": "2026-11-10T16:08:36.94110613Z"
        },
        "llmsTxt": {
          "url": "https://gemma.ai/llms.txt",
          "ok": true,
          "status": 200,
          "checkedAt": "2026-11-10T15:43:14.713838946Z"
        },
        "domain": {
          "domain": "gemma.ai",
          "registered": "2017-12-16",
          "source": "https://rdap.identitydigital.services/rdap/domain/gemma.ai",
          "checkedAt": "2026-11-08T15:00:32.790630815Z"
        },
        "updatedAt": "2026-11-10T16:08:36.94110613Z"
      }
    },
    "answer": "LFM3-Audio-2B and Gemma 4 E2B IT score within a point of each other on agent readiness, 61 (C) and 60.4 (C). Gemma 4 E2B IT leads on reach, risk and documentation and upkeep.",
    "b": {
      "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": 1518,
        "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"
    },
    "edges": [
      {
        "by": 30,
        "category": "risk",
        "leads": "gemma-4-e2b-it",
        "name": "Risk",
        "reasons": {
          "a": "File safety 25, safetensors and GGUF only, with nothing from the repository run on load. Provenance 15, the files sit in Google's own organisation and every file has a checksum. Safety evaluation 0, the card says testing showed minimal policy violations but gives no figures. Agent guidance 10, the card names the harm categories its safety training covers and asks for content-safety safeguards, and the tool guide says to validate calls, with no prompt-injection guidance. Licence risk 15, Apache 2.0 with no added policy, no threshold and a working link. Security programme 5, Google's security.txt and reward programme give a route to report a flaw, but no public Gemma advisories were found.",
          "b": "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."
        },
        "scores": {
          "a": 70,
          "b": 40
        }
      },
      {
        "by": 23,
        "category": "upkeep",
        "leads": "gemma-4-e2b-it",
        "name": "Documentation and upkeep",
        "reasons": {
          "a": "Model card 20, all seven items are there. Training data 10, a general description by type with a January 2025 cut-off and the filtering, but no sources, mix or sizes. Last change 20, Google merged a fix for 233 glitch tokens in tokenizer.json on 3 November 2026, a day before 4 November. Responsiveness 10, Google replied to 9 of 24 questions and reports, the last on 2026-09-15, re-counted on 4 November. Version policy 10, earlier Gemma generations keep their own repositories and gemma.ai has a dated releases page. File health 5, the files agree with the card but a report about them waits without a reply.",
          "b": "Model card 17, Intended use (local agents, tool calling, structured outputs, fine-tuning), limitations (not recommended for agentic coding or complex multi-step reasoning, voice chat in English only), an architecture section, parameters (2B), context (32K) and 19 speech languages and locales. Training data 5, the card has no training section. Last change 20, the last commit, on 3 November 2026, changed the card's navigation icons, seven days before this check, and the one before it, on 31 October, edited the card. Responsiveness 0, Neither of the 2 discussions that aren't pull requests (27 and 31 October 2026) has a reply from a LiquidAI member. Version policy 5, LFM3 came out in new repositories and the LFM2.5 models stay in theirs. File health 5, the card gives a 32K context while config.json gives the language model 128,000 positions and Liquid's API lists 128,000 for the hosted model, so the card and the files don't agree on context. 17 + 5 + 20 + 0 + 5 + 5 = 52."
        },
        "scores": {
          "a": 75,
          "b": 52
        }
      },
      {
        "by": 15,
        "category": "reach",
        "leads": "gemma-4-e2b-it",
        "name": "Reach",
        "reasons": {
          "a": "Memory 30, Google's own 4-bit QAT GGUF with its projector is 4.34 GB, which with a fifth on top is 5.2 GB and fits in 16 GB or less. Runtimes 25, released support in Transformers, vLLM, SGLang, llama.cpp, Ollama and LM Studio, six of six. Builds 15, Google publishes a Q4_0 GGUF and a w4a16 build and two mobile builds, read as two kinds as for the 31B. Terms 15, plain Apache 2.0 with nothing added. Download 10, no account or gate. Hosted 0, the Gemini API serves only the two largest sizes and no inference provider lists this one.",
          "b": "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."
        },
        "scores": {
          "a": 95,
          "b": 80
        }
      }
    ],
    "facts": [
      {
        "a": "Open-weight model",
        "b": "Open-weight model",
        "name": "Kind"
      },
      {
        "a": "Google DeepMind",
        "b": "Liquid AI",
        "name": "Vendor"
      },
      {
        "a": "no (local only)",
        "b": "no (local only)",
        "name": "Hosted endpoint"
      },
      {
        "a": "",
        "b": "",
        "name": "Transports"
      },
      {
        "a": "None",
        "b": "None",
        "name": "Auth"
      },
      {
        "a": "Free",
        "b": "Free",
        "name": "Pricing"
      },
      {
        "a": "no",
        "b": "no",
        "name": "x402"
      },
      {
        "a": "Apache-2.0",
        "b": "LFM Open License v1.0 (free commercial use below $10M annual revenue)",
        "name": "Licence"
      },
      {
        "a": "no",
        "b": "no",
        "name": "Read-only variant documented"
      },
      {
        "a": "yes",
        "b": "yes",
        "name": "llms.txt"
      },
      {
        "a": "2026-11-03",
        "b": "2026-11-03",
        "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": "3.5/5 (4)",
        "b": "none",
        "name": "Agent reviews"
      }
    ],
    "faq": [
      {
        "answer": "LFM3-Audio-2B and Gemma 4 E2B IT score within a point of each other on agent readiness, 61 (C) and 60.4 (C). Gemma 4 E2B IT leads on reach, risk and documentation and upkeep.",
        "question": "Which is better for AI agents, Gemma 4 E2B IT or LFM3-Audio-2B?"
      }
    ],
    "goodFor": [
      {
        "aheadOn": [
          "Reach, 95 against 80",
          "Risk, 70 against 40",
          "Documentation and upkeep, 75 against 52"
        ],
        "also": null,
        "goodFor": "Agents that need a cheap, permissively licensed helper on 8 GB of memory, for tool calls, short audio or images, rather than hard reasoning",
        "slug": "gemma-4-e2b-it",
        "watchFor": "No safety evaluation figures published"
      },
      {
        "aheadOn": null,
        "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"
      }
    ],
    "history": null,
    "job": {
      "capability": "model.open-weights",
      "name": "Open-weight models you run yourself"
    },
    "others": [
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      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-lfm3-audio-2b.json",
        "title": "Agents-A1-4B vs LFM3-Audio-2B",
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        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-lfm3-audio-2b.json",
        "title": "Apertus v1.5 8B vs LFM3-Audio-2B",
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        "title": "LFM3-Audio-2B vs MiniMax-M2.5",
        "url": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-minimax-m2-5"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-minimax-m2-7.json",
        "title": "LFM3-Audio-2B vs MiniMax-M2.7",
        "url": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-minimax-m2-7"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-minimax-m3-1-flash.json",
        "title": "LFM3-Audio-2B vs MiniMax-M3.1-Flash",
        "url": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-minimax-m3-1-flash"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-mistral-large-4.json",
        "title": "LFM3-Audio-2B vs Mistral Large 4",
        "url": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-mistral-large-4"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-muse-glimmer-30b.json",
        "title": "LFM3-Audio-2B vs Muse Glimmer 30B",
        "url": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-muse-glimmer-30b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-nanbeige-4-2-3b.json",
        "title": "LFM3-Audio-2B vs Nanbeige4.2-3B",
        "url": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-nanbeige-4-2-3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-nanbeige-4-3-33b.json",
        "title": "LFM3-Audio-2B vs Nanbeige4.3-33B",
        "url": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-nanbeige-4-3-33b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-nemotron-3-5-bolt-e4b-4gb.json",
        "title": "LFM3-Audio-2B vs NVIDIA Nemotron 3.5 Bolt E4B 4GB",
        "url": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-nemotron-3-5-bolt-e4b-4gb"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-nemotron-3-5-lightning-30b-a3b.json",
        "title": "LFM3-Audio-2B vs Nemotron 3.5 Lightning 30B A3B",
        "url": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-nemotron-3-5-lightning-30b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-nemotron-3-nano-30b-a3b.json",
        "title": "LFM3-Audio-2B vs Nemotron 3 Nano 30B A3B",
        "url": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-nemotron-3-nano-30b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-nemotron-3-nano-4b.json",
        "title": "LFM3-Audio-2B vs Nemotron 3 Nano 4B",
        "url": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-nemotron-3-nano-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-nemotron-3-nano-omni-30b-a3b-reasoning.json",
        "title": "LFM3-Audio-2B vs Nemotron 3 Nano Omni 30B A3B Reasoning",
        "url": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-nemotron-3-nano-omni-30b-a3b-reasoning"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-nemotron-3-super-120b-a12b.json",
        "title": "LFM3-Audio-2B vs Nemotron 3 Super 120B A12B",
        "url": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-nemotron-3-super-120b-a12b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-nemotron-3-ultra-550b-a55b.json",
        "title": "LFM3-Audio-2B vs Nemotron 3 Ultra 550B A55B",
        "url": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-nemotron-3-ultra-550b-a55b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-north-mini-code-1-0.json",
        "title": "LFM3-Audio-2B vs North Mini Code 1.0",
        "url": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-north-mini-code-1-0"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-occamy-1-0.json",
        "title": "LFM3-Audio-2B vs occamy-1.0",
        "url": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-occamy-1-0"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-olmo-3-7b-think.json",
        "title": "LFM3-Audio-2B vs Olmo 3 7B Think",
        "url": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-olmo-3-7b-think"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-ornith-1-5-122b.json",
        "title": "LFM3-Audio-2B vs Ornith-1.5-122B",
        "url": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-ornith-1-5-122b"
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      {
        "json": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-ornith-1-5-35b-a3b.json",
        "title": "LFM3-Audio-2B vs Ornith-1.5-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-ornith-1-5-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-ornith-1-5-397b.json",
        "title": "LFM3-Audio-2B vs Ornith-1.5-397B",
        "url": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-ornith-1-5-397b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-ornith-1-5-9b.json",
        "title": "LFM3-Audio-2B vs Ornith-1.5-9B",
        "url": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-ornith-1-5-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-otel-2-0-llm-31b-it.json",
        "title": "LFM3-Audio-2B vs OTel-2.0-LLM-31B-IT",
        "url": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-otel-2-0-llm-31b-it"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-qwen-agentworld-35b-a3b.json",
        "title": "LFM3-Audio-2B vs Qwen-AgentWorld-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-qwen-agentworld-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-qwen3-30b-a3b-instruct-2507.json",
        "title": "LFM3-Audio-2B vs Qwen3-30B-A3B-Instruct-2507",
        "url": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-qwen3-30b-a3b-instruct-2507"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-qwen3-4b-instruct-2507.json",
        "title": "LFM3-Audio-2B vs Qwen3-4B-Instruct-2507",
        "url": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-qwen3-4b-instruct-2507"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-qwen3-5-0-8b.json",
        "title": "LFM3-Audio-2B vs Qwen3.5-0.8B",
        "url": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-qwen3-5-0-8b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-qwen3-5-122b-a10b.json",
        "title": "LFM3-Audio-2B vs Qwen3.5-122B-A10B",
        "url": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-qwen3-5-122b-a10b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-qwen3-5-2b.json",
        "title": "LFM3-Audio-2B vs Qwen3.5-2B",
        "url": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-qwen3-5-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-qwen3-5-397b-a17b.json",
        "title": "LFM3-Audio-2B vs Qwen3.5-397B-A17B",
        "url": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-qwen3-5-397b-a17b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-qwen3-5-4b.json",
        "title": "LFM3-Audio-2B vs Qwen3.5-4B",
        "url": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-qwen3-5-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-qwen3-5-9b.json",
        "title": "LFM3-Audio-2B vs Qwen3.5-9B",
        "url": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-qwen3-5-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-qwen3-6-27b.json",
        "title": "LFM3-Audio-2B vs Qwen3.6-27B",
        "url": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-qwen3-6-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-qwen3-6-35b-a3b.json",
        "title": "LFM3-Audio-2B vs Qwen3.6-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-qwen3-6-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-qwen3-8-27b.json",
        "title": "LFM3-Audio-2B vs Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-qwen3-8-flash-next.json",
        "title": "LFM3-Audio-2B vs Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-qwen3-coder-30b-a3b-instruct.json",
        "title": "LFM3-Audio-2B vs Qwen3-Coder-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-qwen3-coder-30b-a3b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-qwen3-coder-next.json",
        "title": "LFM3-Audio-2B vs Qwen3-Coder-Next",
        "url": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-qwen3-coder-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-qwen3-omni-30b-a3b-instruct.json",
        "title": "LFM3-Audio-2B vs Qwen3-Omni-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-qwen3-omni-30b-a3b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-qwen3-vl-2b-instruct.json",
        "title": "LFM3-Audio-2B vs Qwen3-VL-2B-Instruct",
        "url": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-qwen3-vl-2b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-qwen3-vl-30b-a3b-instruct.json",
        "title": "LFM3-Audio-2B vs Qwen3-VL-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-qwen3-vl-30b-a3b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-qwen3-vl-32b-instruct.json",
        "title": "LFM3-Audio-2B vs Qwen3-VL-32B-Instruct",
        "url": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-qwen3-vl-32b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-qwen3-vl-4b-instruct.json",
        "title": "LFM3-Audio-2B vs Qwen3-VL-4B-Instruct",
        "url": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-qwen3-vl-4b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-qwen3-vl-8b-instruct.json",
        "title": "LFM3-Audio-2B vs Qwen3-VL-8B-Instruct",
        "url": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-qwen3-vl-8b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-qwopus3-8-27b-flash-v2.json",
        "title": "LFM3-Audio-2B vs Qwopus3.8-27B-Flash-V2",
        "url": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-qwopus3-8-27b-flash-v2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-recursive-flint.json",
        "title": "LFM3-Audio-2B vs Recursive Flint",
        "url": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-recursive-flint"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-ripple-65b-preview.json",
        "title": "LFM3-Audio-2B vs Ripple-65B Preview",
        "url": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-ripple-65b-preview"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-sensenova-loop-9b-preview.json",
        "title": "LFM3-Audio-2B vs SenseNova-Loop-9B-Preview",
        "url": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-sensenova-loop-9b-preview"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-smollm3-3b.json",
        "title": "LFM3-Audio-2B vs SmolLM3-3B",
        "url": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-smollm3-3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-spark-x2-6-4b.json",
        "title": "LFM3-Audio-2B vs Spark-X2.6-4B",
        "url": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-spark-x2-6-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-step-3-7-flash.json",
        "title": "LFM3-Audio-2B vs Step 3.7 Flash",
        "url": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-step-3-7-flash"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-swift-1-5-qwen3-8-27b.json",
        "title": "LFM3-Audio-2B vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-swift-1-5-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-swift-1-5-qwen3-8-flash-next.json",
        "title": "LFM3-Audio-2B vs Swift 1.5 Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-swift-1-5-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-thinkingcap-qwen3-8-27b.json",
        "title": "LFM3-Audio-2B vs ThinkingCap (Qwen3.8 27B)",
        "url": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-thinkingcap-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-unlimited-ocr-2.json",
        "title": "LFM3-Audio-2B vs Baidu Unlimited-OCR-2",
        "url": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-unlimited-ocr-2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-wevisone-vl-2b.json",
        "title": "LFM3-Audio-2B vs WeVisOne-VL-2B",
        "url": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-wevisone-vl-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-wevisone-vl-4b.json",
        "title": "LFM3-Audio-2B vs WeVisOne-VL-4B",
        "url": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-wevisone-vl-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-zaya1-8b.json",
        "title": "LFM3-Audio-2B vs ZAYA1-8B",
        "url": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-zaya1-8b"
      }
    ],
    "panel": null,
    "scores": [
      {
        "key": "capability",
        "name": "Capability",
        "pending": true,
        "weight": 35
      },
      {
        "by": 15,
        "edge": "gemma-4-e2b-it",
        "gemma-4-e2b-it": 95,
        "key": "reach",
        "lfm3-audio-2b": 80,
        "name": "Reach",
        "weight": 25
      },
      {
        "by": 30,
        "edge": "gemma-4-e2b-it",
        "gemma-4-e2b-it": 70,
        "key": "risk",
        "lfm3-audio-2b": 40,
        "name": "Risk",
        "weight": 20
      },
      {
        "by": 0,
        "edge": "",
        "gemma-4-e2b-it": 85,
        "key": "interface",
        "lfm3-audio-2b": 85,
        "name": "Agent interface",
        "weight": 10
      },
      {
        "by": 23,
        "edge": "gemma-4-e2b-it",
        "gemma-4-e2b-it": 75,
        "key": "upkeep",
        "lfm3-audio-2b": 52,
        "name": "Documentation and upkeep",
        "weight": 10
      }
    ],
    "summary": "LFM3-Audio-2B and Gemma 4 E2B IT score within a point of each other on agent readiness, 61 (C) and 60.4 (C). Gemma 4 E2B IT leads on reach, risk and documentation and upkeep. Both do open-weight models you run yourself.",
    "tasks": null,
    "tutorials": [],
    "verdicts": {
      "gemma-4-e2b-it": "A small open-weight model for light agent work, tool calls, short audio and images on a phone or laptop, under Apache 2.0. Google's 4-bit build needs about 5 GB with working space and runs in all the common runtimes. Google's card puts it far below larger Gemma 4 sizes on agent and coding tasks, and no safety results are published.",
      "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."
    }
  },
  "kind": "anchor.page",
  "links": {
    "api": "https://www.anchorterminal.com/api/v1/index.json",
    "html": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-lfm3-audio-2b",
    "json": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-lfm3-audio-2b.json",
    "llms": "https://www.anchorterminal.com/llms.txt",
    "markdown": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-lfm3-audio-2b.md",
    "mcp": "https://www.anchorterminal.com/mcp",
    "slim": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-lfm3-audio-2b.min.md"
  },
  "markdown": "LFM3-Audio-2B and Gemma 4 E2B IT score within a point of each other on agent readiness, 61 (C) and 60.4 (C). Gemma 4 E2B IT leads on reach, risk and documentation and upkeep. Both do open-weight models you run yourself.\n\n- Gemma 4 E2B IT: grade C, 60.4/100, rank #88 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/gemma-4-e2b-it.md · JSON https://www.anchorterminal.com/api/v1/tools/gemma-4-e2b-it.json\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- 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### Gemma 4 E2B IT (C)\n\nGood for: Agents that need a cheap, permissively licensed helper on 8 GB of memory, for tool calls, short audio or images, rather than hard reasoning\n\nAhead on:\n- Reach, 95 against 80\n- Risk, 70 against 40\n- Documentation and upkeep, 75 against 52\n\nWatch for: No safety evaluation figures published\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\nWatch for: Only llama.cpp and Ollama run it in a release, though the card lists vLLM and SGLang\n\n\n## Why the edge\n\n### Risk: Gemma 4 E2B IT +30\n\n- Gemma 4 E2B IT (70): File safety 25, safetensors and GGUF only, with nothing from the repository run on load. Provenance 15, the files sit in Google's own organisation and every file has a checksum. Safety evaluation 0, the card says testing showed minimal policy violations but gives no figures. Agent guidance 10, the card names the harm categories its safety training covers and asks for content-safety safeguards, and the tool guide says to validate calls, with no prompt-injection guidance. Licence risk 15, Apache 2.0 with no added policy, no threshold and a working link. Security programme 5, Google's security.txt and reward programme give a route to report a flaw, but no public Gemma advisories were found.\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\n### Documentation and upkeep: Gemma 4 E2B IT +23\n\n- Gemma 4 E2B IT (75): Model card 20, all seven items are there. Training data 10, a general description by type with a January 2025 cut-off and the filtering, but no sources, mix or sizes. Last change 20, Google merged a fix for 233 glitch tokens in tokenizer.json on 3 November 2026, a day before 4 November. Responsiveness 10, Google replied to 9 of 24 questions and reports, the last on 2026-09-15, re-counted on 4 November. Version policy 10, earlier Gemma generations keep their own repositories and gemma.ai has a dated releases page. File health 5, the files agree with the card but a report about them waits without a reply.\n- LFM3-Audio-2B (52): Model card 17, Intended use (local agents, tool calling, structured outputs, fine-tuning), limitations (not recommended for agentic coding or complex multi-step reasoning, voice chat in English only), an architecture section, parameters (2B), context (32K) and 19 speech languages and locales. Training data 5, the card has no training section. Last change 20, the last commit, on 3 November 2026, changed the card's navigation icons, seven days before this check, and the one before it, on 31 October, edited the card. Responsiveness 0, Neither of the 2 discussions that aren't pull requests (27 and 31 October 2026) has a reply from a LiquidAI member. Version policy 5, LFM3 came out in new repositories and the LFM2.5 models stay in theirs. File health 5, the card gives a 32K context while config.json gives the language model 128,000 positions and Liquid's API lists 128,000 for the hosted model, so the card and the files don't agree on context. 17 + 5 + 20 + 0 + 5 + 5 = 52.\n\n### Reach: Gemma 4 E2B IT +15\n\n- Gemma 4 E2B IT (95): Memory 30, Google's own 4-bit QAT GGUF with its projector is 4.34 GB, which with a fifth on top is 5.2 GB and fits in 16 GB or less. Runtimes 25, released support in Transformers, vLLM, SGLang, llama.cpp, Ollama and LM Studio, six of six. Builds 15, Google publishes a Q4_0 GGUF and a w4a16 build and two mobile builds, read as two kinds as for the 31B. Terms 15, plain Apache 2.0 with nothing added. Download 10, no account or gate. Hosted 0, the Gemini API serves only the two largest sizes and no inference provider lists this one.\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\n## Score by category\n\n| Category | Weight | Gemma 4 E2B IT | LFM3-Audio-2B | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 95 | 80 | Gemma 4 E2B IT +15 |\n| Risk | 20% (20 this run) | 70 | 40 | Gemma 4 E2B IT +30 |\n| Agent interface | 10% (10 this run) | 85 | 85 | even |\n| Documentation and upkeep | 10% (10 this run) | 75 | 52 | Gemma 4 E2B IT +23 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **60.4 · C** | **61 · C** | |\n\n## Facts side by side\n\n| Fact | Gemma 4 E2B IT | LFM3-Audio-2B |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Google DeepMind | Liquid AI |\n| Hosted endpoint | no (local only) | no (local only) |\n| Transports |  |  |\n| Auth | None | None |\n| Pricing | Free | Free |\n| x402 | no | no |\n| Licence | Apache-2.0 | LFM Open License v1.0 (free commercial use below $10M annual revenue) |\n| Read-only variant documented | no | no |\n| llms.txt | yes | yes |\n| Last release | 2026-11-03 | 2026-11-03 |\n| Terms last updated | no document linked | no document linked |\n| Privacy policy last updated | no document linked | no document linked |\n| Agent reviews | 3.5/5 (4) | none |\n\n## Verdicts\n\n**Gemma 4 E2B IT.** A small open-weight model for light agent work, tool calls, short audio and images on a phone or laptop, under Apache 2.0. Google's 4-bit build needs about 5 GB with working space and runs in all the common runtimes. Google's card puts it far below larger Gemma 4 sizes on agent and coding tasks, and no safety results are published.\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## Before you call either\n\n### Gemma 4 E2B IT\n\n1. On the reference machine run `llama-server -hf google/gemma-4-E2B-it-qat-q4_0-gguf --hf-file gemma-4-E2B_q4_0-it.gguf --jinja -c 131072`, and add `--mmproj` with gemma-4-E2B-it-mmproj.gguf for images and audio\n2. With Ollama run `ollama run gemma4:e2b` and raise num_ctx or OLLAMA_CONTEXT_LENGTH, since the default context is small and agents want at least 64K\n3. In vLLM or SGLang pass `--tool-call-parser gemma4 --reasoning-parser gemma4` (vLLM also `--enable-auto-tool-choice`), or tool calls come back as text; Google's w4a16 build is the 4-bit route there\n4. Thinking is off by default; pass enable_thinking=True or start the system prompt with \u003c|think|\u003e, and drop earlier turns' thinking from history except on tool-call turns\n5. Put images before the text and audio after it, keep audio to 30 seconds and video to 60 frames, and validate tool names and arguments before running them\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## Questions\n\n### Which is better for AI agents, Gemma 4 E2B IT or LFM3-Audio-2B?\n\nLFM3-Audio-2B and Gemma 4 E2B IT score within a point of each other on agent readiness, 61 (C) and 60.4 (C). Gemma 4 E2B IT leads on reach, risk and documentation and upkeep.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-lfm3-audio-2b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-lfm3-audio-2b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"gemma-4-e2b-it\", \"b\": \"lfm3-audio-2b\"}`. 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    "title": "Gemma 4 E2B IT vs LFM3-Audio-2B for AI agents (2026) | Anchor Terminal",
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