{
  "data": {
    "a": {
      "slug": "gemma-4-26b-a4b-it",
      "name": "Gemma 4 26B A4B IT",
      "vendor": "Google DeepMind",
      "vendorUrl": "https://gemma.ai",
      "kind": "weights",
      "category": "open-weights",
      "summary": "Gemma 4 26B A4B IT is Google DeepMind's open-weight instruction-tuned mixture-of-experts model, 25.2B parameters with 3.8B active, taking text and images with a 262,144-token context, published on Hugging Face under Apache 2.0.",
      "url": "https://www.anchorterminal.com/tools/gemma-4-26b-a4b-it",
      "markdownUrl": "https://www.anchorterminal.com/tools/gemma-4-26b-a4b-it.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/gemma-4-26b-a4b-it.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/gemma-4-26b-a4b-it.json",
      "dns": "gemma-4-26b-a4b-it.grade.anchorterminal.com",
      "repo": "https://huggingface.co/google/gemma-4-26B-A4B-it",
      "license": "Apache-2.0",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "google/gemma-4-26B-A4B-it"
        },
        {
          "registry": "huggingface",
          "name": "google/gemma-4-26B-A4B-it-qat-q4_0-gguf"
        },
        {
          "registry": "ollama",
          "name": "gemma4:26b"
        }
      ],
      "auth": "none",
      "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The repository is not gated (checked 2026-10-21).",
      "pricing": "free",
      "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0. Google also hosts it as gemma-4-26b-a4b-it on the Gemini API, which needs an API key. Running it yourself costs the hardware, 57.7 GB at BF16 and 14.4 GB at Q4_0 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/",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "moe",
        "multimodal",
        "reasoning",
        "tool-calling",
        "apache-2.0",
        "safetensors",
        "gguf",
        "self-hosted"
      ],
      "lastRelease": "2026-07-20",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 72.2,
        "grade": "BB",
        "agentReady": true,
        "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": 3,
        "methodology": "0.7",
        "run": "2026-10-01",
        "scores": {
          "capability": 59,
          "interface": 85,
          "reach": 90,
          "risk": 70,
          "upkeep": 65
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "high",
          "date": "2026-11-01"
        },
        "negative": 0,
        "verdict": "A fast mixture-of-experts cousin for general agent work with tools, optional thinking and image input, under Apache 2.0. Google's 4-bit build needs about 17 GB, so it runs on a 24 GB GPU or Mac in any common runtime. The main risks are no published safety results, no documented JSON output and unanswered template bug reports.",
        "bestFor": "Agents on a 24 GB or larger GPU or Mac that want a fast, permissively licensed tool-calling model with images and long context",
        "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 17 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 named parsers",
          "262,144 tokens of native context and thinking that turns on or off per request",
          "Also served by the Gemini API and four inference providers"
        ],
        "weaknesses": [
          "No safety evaluation figures published",
          "No documented way to ask for JSON or schema output",
          "Only one quantised precision from Google, no FP8",
          "Files unchanged since 20 July 2026, with chat-template bug reports unanswered",
          "Training data described only by type and cut-off"
        ],
        "agentNotes": [
          "On the reference machine run `llama-server -hf google/gemma-4-26B-A4B-it-qat-q4_0-gguf --hf-file gemma-4-26B_q4_0-it.gguf --jinja -c 65536`, and add the mmproj file for images",
          "With Ollama run `ollama run gemma4:26b` and raise num_ctx or OLLAMA_CONTEXT_LENGTH, since Google's guide says it defaults to 4K below 24 GiB of VRAM 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",
          "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",
          "Validate tool names and arguments before running them, and check any JSON yourself"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 4,
        "avgRating": 3.8,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "high",
            "grade": "BB",
            "methodology": "0.7",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 72.2
          }
        ],
        "editorialScores": {
          "capability": 59,
          "interface": 85,
          "reach": 90,
          "risk": 70,
          "upkeep": 65
        },
        "provenanceScore": 72,
        "checked": {
          "categories": {
            "capability": "2026-11-01",
            "interface": "2026-11-01",
            "reach": "2026-11-01",
            "risk": "2026-11-01",
            "upkeep": "2026-11-01"
          },
          "last": "2026-11-01",
          "oldest": "2026-11-01",
          "stale": false,
          "staleAfterDays": 90
        }
      },
      "connect": {
        "install": "ollama run gemma4:26b\n# or, with vLLM:\nvllm serve google/gemma-4-26B-A4B-it --max-model-len 262144 --reasoning-parser gemma4 \\\n  --enable-auto-tool-choice --tool-call-parser gemma4",
        "http": "curl http://localhost:11434/api/chat \\\n  -d '{\"model\": \"gemma4:26b\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}], \"options\": {\"num_ctx\": 65536}}'"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/gemma-4-26b-a4b-it"
      },
      "sameCompany": [
        "embeddinggemma-2",
        "embeddinggemma-300m",
        "shieldgemma",
        "diffusiongemma-26b-a4b-it",
        "gemma-4-12b-it",
        "gemma-4-31b-it",
        "gemma-4-e2b-it",
        "gemma-4-e4b-it"
      ],
      "sameModel": [
        "gemini-api"
      ],
      "weights": {
        "trainedBy": "Google DeepMind",
        "builtOn": "google/gemma-4-26B-A4B",
        "params": "25.2B",
        "active": "3.8B",
        "context": 262144,
        "goodFor": "General assistant and agent model with tool calling and optional thinking, taking text, images and video frames, fast for its size as a mixture of experts.",
        "runtimes": [
          "llama.cpp",
          "Ollama",
          "vLLM",
          "SGLang",
          "LM Studio",
          "Transformers"
        ],
        "builds": [
          {
            "name": "gemma-4-26B_q4_0-it",
            "url": "https://huggingface.co/google/gemma-4-26B-A4B-it-qat-q4_0-gguf/blob/main/gemma-4-26B_q4_0-it.gguf",
            "who": "publisher",
            "precision": "q4_0",
            "sizeGB": 14.4,
            "memoryGB": 24,
            "format": "gguf",
            "runsOn": [
              "llama.cpp (Vulkan)",
              "llama.cpp (ROCm)",
              "LM Studio"
            ],
            "note": "Quantisation-aware trained by Google. Add gemma-4-26B-it-mmproj.gguf (1.2 GB) for image input"
          },
          {
            "name": "ollama gemma4:26b",
            "url": "https://ollama.com/library/gemma4:26b",
            "who": "ollama",
            "precision": "q4_k_m",
            "sizeGB": 19,
            "memoryGB": 24,
            "format": "ollama",
            "runsOn": [
              "Ollama"
            ],
            "note": "Q4_K_M weights with a BF16 vision projector and a Q8_0 draft model (digest 001e5dafc3c7). Size as Ollama's tags page shows it, rounded to whole GB above 10 GB; the Linux build is the llama.cpp variant"
          },
          {
            "name": "gemma-4-26B-A4B-it",
            "url": "https://huggingface.co/google/gemma-4-26B-A4B-it",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 51.6,
            "memoryGB": 64,
            "format": "safetensors",
            "runsOn": [
              "vLLM (ROCm)",
              "SGLang (ROCm)"
            ]
          }
        ],
        "run": "llama-server -hf google/gemma-4-26B-A4B-it-qat-q4_0-gguf --hf-file gemma-4-26B_q4_0-it.gguf --jinja -c 65536",
        "runBuild": "gemma-4-26B_q4_0-it",
        "checked": "2026-10-21",
        "minMemoryGB": 24
      },
      "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-4-31B",
            "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-E2B-it",
            "url": "https://huggingface.co/google/gemma-4-E2B-it",
            "params": "2.3B effective (5.1B with embeddings)",
            "listing": "https://www.anchorterminal.com/tools/gemma-4-e2b-it"
          },
          {
            "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; 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": [
          "RDAP names Google LLC as the registrant of gemma.ai, registered 16 December 2017, with MarkMonitor as registrar and Google's name servers. ai.google.dev's Gemma pages say the documentation moved to gemma.ai.",
          "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 (Gemma 4 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-4-26b-a4b-it.json",
      "live": {
        "slug": "gemma-4-26b-a4b-it",
        "securityTxt": {
          "url": "https://gemma.ai/.well-known/security.txt",
          "state": "none",
          "checkedAt": "2026-11-10T16:08:36.94110613Z"
        },
        "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": "Gemma 4 26B A4B IT scores 72.2 (BB) on agent readiness against LFM3-1.7B's 61 (C), and leads in 2 of 4 scored categories. LFM3-1.7B leads on agent interface.",
    "b": {
      "slug": "lfm3-1-7b",
      "name": "LFM3-1.7B",
      "vendor": "Liquid AI",
      "vendorUrl": "https://www.liquid.ai",
      "kind": "weights",
      "category": "open-weights",
      "summary": "LFM3-1.7B is Liquid AI's open-weight 1.73B text model for on-device agents, with tool calling, deferred tools, switchable thinking and a 256K-token context, released on 27 October 2026 with Liquid's own GGUF, FP8, NVFP4, MLX and ONNX builds.",
      "url": "https://www.anchorterminal.com/tools/lfm3-1-7b",
      "markdownUrl": "https://www.anchorterminal.com/tools/lfm3-1-7b.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/lfm3-1-7b.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/lfm3-1-7b.json",
      "dns": "lfm3-1-7b.grade.anchorterminal.com",
      "repo": "https://huggingface.co/LiquidAI/LFM3-1.7B",
      "license": "LFM Open License v1.0 (free commercial use below $10M annual revenue)",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "LiquidAI/LFM3-1.7B"
        },
        {
          "registry": "huggingface",
          "name": "LiquidAI/LFM3-1.7B-GGUF"
        },
        {
          "registry": "huggingface",
          "name": "LiquidAI/LFM3-1.7B-MLX-4bit"
        },
        {
          "registry": "ollama",
          "name": "lfm3"
        }
      ],
      "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 and 3.46 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/lfm3-1.7b",
      "llmsTxt": "https://docs.liquid.ai/llms.txt",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "dense",
        "small",
        "edge",
        "reasoning",
        "tool-calling",
        "long-context",
        "custom-licence",
        "safetensors",
        "gguf",
        "mlx",
        "onnx",
        "self-hosted"
      ],
      "lastRelease": "2026-11-09",
      "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": 55,
        "methodology": "0.7",
        "run": "2026-10-01",
        "scores": {
          "interface": 100,
          "reach": 85,
          "risk": 40,
          "upkeep": 67
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "low",
          "date": "2026-11-10"
        },
        "negative": 0,
        "verdict": "A 1.73B model that fits an 8 GB machine with room to spare, with a documented tool-call format, deferred tools, a thinking switch and a 256K context. It runs today in llama.cpp, Ollama and LM Studio only, loads in Transformers through repository code, and has no safety results, security contact or data sources.",
        "bestFor": "Agents on a laptop, phone or small server that need tool calling and a long context from a model under 2 GB at 4 bits",
        "strengths": [
          "Fits an 8 GB machine from Liquid's own GGUF, at 1.10 GB for Q4_K_M",
          "The card and docs document the tool-call format, deferred tools and tool search",
          "A 256K-token context, trained in mid-training per the card",
          "Thinking can be turned off per request",
          "Liquid's own API serves it, and its guide documents JSON Schema output"
        ],
        "weaknesses": [
          "vLLM and SGLang have no released support, though the card lists both",
          "Loading in Transformers runs code from the repository",
          "No safety results and no security contact",
          "Liquid says turning thinking off can be inconsistent",
          "The licence stops free commercial use at $10M in annual revenue",
          "Training data is described without sources or a cut-off"
        ],
        "agentNotes": [
          "Start it with `llama-server -hf LiquidAI/LFM3-1.7B-GGUF:Q4_K_M --jinja` or `ollama run lfm3`",
          "Use temperature 0.1, top_k 50 and repetition penalty 1.1 for chat, tools and agents, per the card",
          "Pass `enable_thinking=False` for direct answers, and expect it to reason anyway on hard tasks",
          "For a large tool catalogue mark tools `defer_loading` and give it a search tool",
          "Don't plan on vLLM or SGLang until a release carries LFM3",
          "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": 100,
          "reach": 85,
          "risk": 40,
          "upkeep": 67
        },
        "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/LFM3-1.7B-GGUF:Q4_K_M --jinja\n# or\nollama run lfm3",
        "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/lfm3-1-7b"
      },
      "sameCompany": [
        "liquid-d1",
        "lfm2-5-2-6b",
        "lfm3-audio-2b",
        "lfm3-omni-2b",
        "lfm3-vl-2b"
      ],
      "weights": {
        "trainedBy": "Liquid AI",
        "builtOn": "LiquidAI/LFM3-1.7B-Base",
        "params": "1.73B",
        "context": 262144,
        "goodFor": "Small on-device agent model for tool calling, tool search over large catalogues, structured output and long-context work on a laptop or phone, not for agentic coding or complex multi-step reasoning.",
        "runtimes": [
          "llama.cpp",
          "Ollama",
          "LM Studio"
        ],
        "builds": [
          {
            "name": "LFM3-1.7B",
            "url": "https://huggingface.co/LiquidAI/LFM3-1.7B",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 3.46,
            "memoryGB": 8,
            "format": "safetensors",
            "runsOn": [
              "Transformers"
            ],
            "note": "Loads in Transformers with the repository's own code. Liquid also publishes FP8 (2.21 GB) and NVFP4 (1.83 GB) safetensors builds, which the card pairs with SGLang, and an ONNX repository"
          },
          {
            "name": "LFM3-1.7B-GGUF Q8_0",
            "url": "https://huggingface.co/LiquidAI/LFM3-1.7B-GGUF",
            "who": "publisher",
            "precision": "q8_0",
            "sizeGB": 1.85,
            "memoryGB": 8,
            "format": "gguf",
            "runsOn": [
              "llama.cpp",
              "LM Studio"
            ]
          },
          {
            "name": "LFM3-1.7B-GGUF Q4_K_M",
            "url": "https://huggingface.co/LiquidAI/LFM3-1.7B-GGUF",
            "who": "publisher",
            "precision": "q4_k_m",
            "sizeGB": 1.1,
            "memoryGB": 8,
            "format": "gguf",
            "runsOn": [
              "llama.cpp",
              "LM Studio"
            ],
            "note": "LFM3-1.7B-Q4_K_M.gguf, from the Hub's file list. The repository also holds a quantisation-aware LFM3-1.7B-QAD-Q4_0.gguf (1.05 GB), Q8_0, F16 and BF16 files"
          },
          {
            "name": "LFM3-1.7B-MLX-4bit",
            "url": "https://huggingface.co/LiquidAI/LFM3-1.7B-MLX-4bit",
            "who": "publisher",
            "precision": "4bit",
            "sizeGB": 0.97,
            "memoryGB": 8,
            "format": "mlx",
            "runsOn": [
              "MLX"
            ],
            "note": "Liquid also publishes 5, 6 and 8-bit and BF16 MLX builds"
          },
          {
            "name": "ollama lfm3",
            "url": "https://ollama.com/library/lfm3",
            "who": "ollama",
            "precision": "q4_k_m",
            "sizeGB": 1.1,
            "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/LFM3-1.7B-GGUF:Q4_K_M --jinja",
        "runBuild": "LFM3-1.7B-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-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"
          },
          {
            "name": "LFM3-Audio-2B",
            "url": "https://huggingface.co/LiquidAI/LFM3-Audio-2B",
            "params": "2.00B",
            "listing": "https://www.anchorterminal.com/tools/lfm3-audio-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/LFM3-1.7B/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/lfm3-1-7b.json"
    },
    "edges": [
      {
        "by": 30,
        "category": "risk",
        "leads": "gemma-4-26b-a4b-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 policy give a route to report a flaw, but no public Gemma advisories were found and GitHub's advisory tab couldn't be checked.",
          "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": 15,
        "category": "interface",
        "leads": "lfm3-1-7b",
        "name": "Agent interface",
        "reasons": {
          "a": "Chat template 20, a Jinja file in the repository. Tool-calling format 25, Google documents how tools are declared and how calls come back, ships a response schema for Transformers' parser, and names the gemma4 parser in vLLM and SGLang. Trained tool calling 20, several calls in one turn. Structured output 0, nothing documents how to ask the weights for JSON or a schema. Reasoning control 10, thinking is off by default, turned on per request, and returned in a thought channel. Context 10, 262,144 tokens native.",
          "b": "Chat template 20, chat_template.jinja in the repository (12,129 characters), with roles, tools, deferred tools, tool results and the thinking switch. 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 says the model was trained on tool calls and agentic traces and with reinforcement learning inside agent harnesses. Structured output 15, Liquid's API guide documents `response_format` with a JSON Schema, with lfm3-1.7b as the model in its examples, and says the schema is checked before generation starts. Reasoning control 10, the card documents `enable_thinking=False` in the chat template to turn reasoning off per request, with reasoning on by default inside think tags, and `preserve_thinking` for earlier turns. Context 10, 256K tokens per the card, which says the mid-training stage extended the context window to 256K, and max_position_embeddings 262144 in config.json. 20 + 25 + 20 + 15 + 10 + 10 = 100."
        },
        "scores": {
          "a": 85,
          "b": 100
        }
      },
      {
        "by": 5,
        "category": "reach",
        "leads": "gemma-4-26b-a4b-it",
        "name": "Reach",
        "reasons": {
          "a": "Memory 25, Google's own 4-bit QAT GGUF is 14.4 GB, which with a fifth on top is 17.3 GB and lands in the 24 GB band, with the 1.2 GB vision projector extra for images. Runtimes 25, released support in vLLM, Transformers, llama.cpp, Ollama, SGLang and LM Studio, six of six. Builds 10, Google publishes one quantised precision (Q4_0 GGUF), since its unquantised QAT repository is BF16 and there's no FP8 build. Terms 15, plain Apache 2.0 with nothing added. Download 10, no account or gate. Hosted 5, the Gemini API serves it and four inference providers are live on Hugging Face.",
          "b": "Memory 30, Liquid's Q4_K_M GGUF is 1.10 GB, and 1.10 GB × 1.2 = 1.3 GB, which fits 8 GB, well inside the 16 GB line. Runtimes 15, Released llama.cpp has the lfm3 architecture (src/models/lfm3.cpp at b12242), Ollama's library has an lfm3 entry, and LM Studio's catalogue runs the architecture (its liquid/lfm3-vl-2b entry; lmstudio-community publishes a GGUF of this model). 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 lfm3-1.7b among its models, so the publisher hosts it. 30 + 15 + 15 + 10 + 10 + 5 = 85."
        },
        "scores": {
          "a": 90,
          "b": 85
        }
      }
    ],
    "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": "no",
        "b": "yes",
        "name": "llms.txt"
      },
      {
        "a": "2026-07-20",
        "b": "2026-11-09",
        "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": "3.8/5 (4)",
        "b": "none",
        "name": "Agent reviews"
      }
    ],
    "faq": [
      {
        "answer": "Gemma 4 26B A4B IT scores 72.2 (BB) on agent readiness against LFM3-1.7B's 61 (C), and leads in 2 of 4 scored categories. LFM3-1.7B leads on agent interface.",
        "question": "Which is better for AI agents, Gemma 4 26B A4B IT or LFM3-1.7B?"
      }
    ],
    "goodFor": [
      {
        "aheadOn": [
          "Reach, 90 against 85",
          "Risk, 70 against 40"
        ],
        "also": [
          "Agent-ready, a grade of BB or better"
        ],
        "goodFor": "Agents on a 24 GB or larger GPU or Mac that want a fast, permissively licensed tool-calling model with images and long context",
        "slug": "gemma-4-26b-a4b-it",
        "watchFor": "No safety evaluation figures published"
      },
      {
        "aheadOn": [
          "Agent interface, 100 against 85"
        ],
        "also": null,
        "goodFor": "Agents on a laptop, phone or small server that need tool calling and a long context from a model under 2 GB at 4 bits",
        "slug": "lfm3-1-7b",
        "watchFor": "vLLM and SGLang have no released support, though the card lists both"
      }
    ],
    "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-lfm3-1-7b.json",
        "title": "Agents-A1-4B vs LFM3-1.7B",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-lfm3-1-7b"
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      {
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        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-gemma-4-26b-a4b-it"
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        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-lfm3-1-7b.json",
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        "json": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-minimax-m2-5.json",
        "title": "LFM3-1.7B vs MiniMax-M2.5",
        "url": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-minimax-m2-5"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-minimax-m2-7.json",
        "title": "LFM3-1.7B vs MiniMax-M2.7",
        "url": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-minimax-m2-7"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-minimax-m3-1-flash.json",
        "title": "LFM3-1.7B vs MiniMax-M3.1-Flash",
        "url": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-minimax-m3-1-flash"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-mistral-large-4.json",
        "title": "LFM3-1.7B vs Mistral Large 4",
        "url": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-mistral-large-4"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-muse-glimmer-30b.json",
        "title": "LFM3-1.7B vs Muse Glimmer 30B",
        "url": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-muse-glimmer-30b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-nanbeige-4-2-3b.json",
        "title": "LFM3-1.7B vs Nanbeige4.2-3B",
        "url": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-nanbeige-4-2-3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-nanbeige-4-3-33b.json",
        "title": "LFM3-1.7B vs Nanbeige4.3-33B",
        "url": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-nanbeige-4-3-33b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-nemotron-3-5-bolt-e4b-4gb.json",
        "title": "LFM3-1.7B vs NVIDIA Nemotron 3.5 Bolt E4B 4GB",
        "url": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-nemotron-3-5-bolt-e4b-4gb"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-nemotron-3-5-lightning-30b-a3b.json",
        "title": "LFM3-1.7B vs Nemotron 3.5 Lightning 30B A3B",
        "url": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-nemotron-3-5-lightning-30b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-nemotron-3-nano-30b-a3b.json",
        "title": "LFM3-1.7B vs Nemotron 3 Nano 30B A3B",
        "url": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-nemotron-3-nano-30b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-nemotron-3-nano-4b.json",
        "title": "LFM3-1.7B vs Nemotron 3 Nano 4B",
        "url": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-nemotron-3-nano-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-nemotron-3-nano-omni-30b-a3b-reasoning.json",
        "title": "LFM3-1.7B vs Nemotron 3 Nano Omni 30B A3B Reasoning",
        "url": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-nemotron-3-nano-omni-30b-a3b-reasoning"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-nemotron-3-super-120b-a12b.json",
        "title": "LFM3-1.7B vs Nemotron 3 Super 120B A12B",
        "url": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-nemotron-3-super-120b-a12b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-nemotron-3-ultra-550b-a55b.json",
        "title": "LFM3-1.7B vs Nemotron 3 Ultra 550B A55B",
        "url": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-nemotron-3-ultra-550b-a55b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-north-mini-code-1-0.json",
        "title": "LFM3-1.7B vs North Mini Code 1.0",
        "url": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-north-mini-code-1-0"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-occamy-1-0.json",
        "title": "LFM3-1.7B vs occamy-1.0",
        "url": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-occamy-1-0"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-olmo-3-7b-think.json",
        "title": "LFM3-1.7B vs Olmo 3 7B Think",
        "url": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-olmo-3-7b-think"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-ornith-1-5-122b.json",
        "title": "LFM3-1.7B vs Ornith-1.5-122B",
        "url": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-ornith-1-5-122b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-ornith-1-5-35b-a3b.json",
        "title": "LFM3-1.7B vs Ornith-1.5-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-ornith-1-5-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-ornith-1-5-397b.json",
        "title": "LFM3-1.7B vs Ornith-1.5-397B",
        "url": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-ornith-1-5-397b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-ornith-1-5-9b.json",
        "title": "LFM3-1.7B vs Ornith-1.5-9B",
        "url": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-ornith-1-5-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-otel-2-0-llm-31b-it.json",
        "title": "LFM3-1.7B vs OTel-2.0-LLM-31B-IT",
        "url": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-otel-2-0-llm-31b-it"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-qwen-agentworld-35b-a3b.json",
        "title": "LFM3-1.7B vs Qwen-AgentWorld-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-qwen-agentworld-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-qwen3-30b-a3b-instruct-2507.json",
        "title": "LFM3-1.7B vs Qwen3-30B-A3B-Instruct-2507",
        "url": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-qwen3-30b-a3b-instruct-2507"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-qwen3-4b-instruct-2507.json",
        "title": "LFM3-1.7B vs Qwen3-4B-Instruct-2507",
        "url": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-qwen3-4b-instruct-2507"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-qwen3-5-0-8b.json",
        "title": "LFM3-1.7B vs Qwen3.5-0.8B",
        "url": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-qwen3-5-0-8b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-qwen3-5-122b-a10b.json",
        "title": "LFM3-1.7B vs Qwen3.5-122B-A10B",
        "url": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-qwen3-5-122b-a10b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-qwen3-5-2b.json",
        "title": "LFM3-1.7B vs Qwen3.5-2B",
        "url": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-qwen3-5-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-qwen3-5-397b-a17b.json",
        "title": "LFM3-1.7B vs Qwen3.5-397B-A17B",
        "url": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-qwen3-5-397b-a17b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-qwen3-5-4b.json",
        "title": "LFM3-1.7B vs Qwen3.5-4B",
        "url": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-qwen3-5-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-qwen3-5-9b.json",
        "title": "LFM3-1.7B vs Qwen3.5-9B",
        "url": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-qwen3-5-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-qwen3-6-27b.json",
        "title": "LFM3-1.7B vs Qwen3.6-27B",
        "url": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-qwen3-6-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-qwen3-6-35b-a3b.json",
        "title": "LFM3-1.7B vs Qwen3.6-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-qwen3-6-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-qwen3-8-27b.json",
        "title": "LFM3-1.7B vs Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-qwen3-8-flash-next.json",
        "title": "LFM3-1.7B vs Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-qwen3-coder-30b-a3b-instruct.json",
        "title": "LFM3-1.7B vs Qwen3-Coder-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-qwen3-coder-30b-a3b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-qwen3-coder-next.json",
        "title": "LFM3-1.7B vs Qwen3-Coder-Next",
        "url": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-qwen3-coder-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-qwen3-omni-30b-a3b-instruct.json",
        "title": "LFM3-1.7B vs Qwen3-Omni-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-qwen3-omni-30b-a3b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-qwen3-vl-2b-instruct.json",
        "title": "LFM3-1.7B vs Qwen3-VL-2B-Instruct",
        "url": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-qwen3-vl-2b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-qwen3-vl-30b-a3b-instruct.json",
        "title": "LFM3-1.7B vs Qwen3-VL-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-qwen3-vl-30b-a3b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-qwen3-vl-32b-instruct.json",
        "title": "LFM3-1.7B vs Qwen3-VL-32B-Instruct",
        "url": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-qwen3-vl-32b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-qwen3-vl-4b-instruct.json",
        "title": "LFM3-1.7B vs Qwen3-VL-4B-Instruct",
        "url": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-qwen3-vl-4b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-qwen3-vl-8b-instruct.json",
        "title": "LFM3-1.7B vs Qwen3-VL-8B-Instruct",
        "url": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-qwen3-vl-8b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-qwopus3-8-27b-flash-v2.json",
        "title": "LFM3-1.7B vs Qwopus3.8-27B-Flash-V2",
        "url": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-qwopus3-8-27b-flash-v2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-recursive-flint.json",
        "title": "LFM3-1.7B vs Recursive Flint",
        "url": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-recursive-flint"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-ripple-65b-preview.json",
        "title": "LFM3-1.7B vs Ripple-65B Preview",
        "url": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-ripple-65b-preview"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-sensenova-loop-9b-preview.json",
        "title": "LFM3-1.7B vs SenseNova-Loop-9B-Preview",
        "url": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-sensenova-loop-9b-preview"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-smollm3-3b.json",
        "title": "LFM3-1.7B vs SmolLM3-3B",
        "url": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-smollm3-3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-spark-x2-6-4b.json",
        "title": "LFM3-1.7B vs Spark-X2.6-4B",
        "url": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-spark-x2-6-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-step-3-7-flash.json",
        "title": "LFM3-1.7B vs Step 3.7 Flash",
        "url": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-step-3-7-flash"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-swift-1-5-qwen3-8-27b.json",
        "title": "LFM3-1.7B vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-swift-1-5-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-swift-1-5-qwen3-8-flash-next.json",
        "title": "LFM3-1.7B vs Swift 1.5 Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-swift-1-5-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-thinkingcap-qwen3-8-27b.json",
        "title": "LFM3-1.7B vs ThinkingCap (Qwen3.8 27B)",
        "url": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-thinkingcap-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-unlimited-ocr-2.json",
        "title": "LFM3-1.7B vs Baidu Unlimited-OCR-2",
        "url": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-unlimited-ocr-2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-wevisone-vl-2b.json",
        "title": "LFM3-1.7B vs WeVisOne-VL-2B",
        "url": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-wevisone-vl-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-wevisone-vl-4b.json",
        "title": "LFM3-1.7B vs WeVisOne-VL-4B",
        "url": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-wevisone-vl-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-zaya1-8b.json",
        "title": "LFM3-1.7B vs ZAYA1-8B",
        "url": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-zaya1-8b"
      }
    ],
    "panel": null,
    "scores": [
      {
        "key": "capability",
        "name": "Capability",
        "pending": true,
        "weight": 35
      },
      {
        "by": 5,
        "edge": "gemma-4-26b-a4b-it",
        "gemma-4-26b-a4b-it": 90,
        "key": "reach",
        "lfm3-1-7b": 85,
        "name": "Reach",
        "weight": 25
      },
      {
        "by": 30,
        "edge": "gemma-4-26b-a4b-it",
        "gemma-4-26b-a4b-it": 70,
        "key": "risk",
        "lfm3-1-7b": 40,
        "name": "Risk",
        "weight": 20
      },
      {
        "by": 15,
        "edge": "lfm3-1-7b",
        "gemma-4-26b-a4b-it": 85,
        "key": "interface",
        "lfm3-1-7b": 100,
        "name": "Agent interface",
        "weight": 10
      },
      {
        "by": 2,
        "edge": "lfm3-1-7b",
        "gemma-4-26b-a4b-it": 65,
        "key": "upkeep",
        "lfm3-1-7b": 67,
        "name": "Documentation and upkeep",
        "weight": 10
      }
    ],
    "summary": "Gemma 4 26B A4B IT scores 72.2 (BB) on agent readiness against LFM3-1.7B's 61 (C), and leads in 2 of 4 scored categories. LFM3-1.7B leads on agent interface. Both do open-weight models you run yourself.",
    "tasks": null,
    "tutorials": [],
    "verdicts": {
      "gemma-4-26b-a4b-it": "A fast mixture-of-experts cousin for general agent work with tools, optional thinking and image input, under Apache 2.0. Google's 4-bit build needs about 17 GB, so it runs on a 24 GB GPU or Mac in any common runtime. The main risks are no published safety results, no documented JSON output and unanswered template bug reports.",
      "lfm3-1-7b": "A 1.73B model that fits an 8 GB machine with room to spare, with a documented tool-call format, deferred tools, a thinking switch and a 256K context. It runs today in llama.cpp, Ollama and LM Studio only, loads in Transformers through repository code, and has no safety results, security contact or data sources."
    }
  },
  "kind": "anchor.page",
  "links": {
    "api": "https://www.anchorterminal.com/api/v1/index.json",
    "html": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-lfm3-1-7b",
    "json": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-lfm3-1-7b.json",
    "llms": "https://www.anchorterminal.com/llms.txt",
    "markdown": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-lfm3-1-7b.md",
    "mcp": "https://www.anchorterminal.com/mcp",
    "slim": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-lfm3-1-7b.min.md"
  },
  "markdown": "Gemma 4 26B A4B IT scores 72.2 (BB) on agent readiness against LFM3-1.7B's 61 (C), and leads in 2 of 4 scored categories. LFM3-1.7B leads on agent interface. Both do open-weight models you run yourself.\n\n- Gemma 4 26B A4B IT: grade BB, 72.2/100, rank #5 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/gemma-4-26b-a4b-it.md · JSON https://www.anchorterminal.com/api/v1/tools/gemma-4-26b-a4b-it.json\n- LFM3-1.7B: grade C, 61/100, rank #69 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/lfm3-1-7b.md · JSON https://www.anchorterminal.com/api/v1/tools/lfm3-1-7b.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 26B A4B IT (BB)\n\nGood for: Agents on a 24 GB or larger GPU or Mac that want a fast, permissively licensed tool-calling model with images and long context\n\nAhead on:\n- Reach, 90 against 85\n- Risk, 70 against 40\n\nAlso in its favour:\n- Agent-ready, a grade of BB or better\n\nWatch for: No safety evaluation figures published\n\n### LFM3-1.7B (C)\n\nGood for: Agents on a laptop, phone or small server that need tool calling and a long context from a model under 2 GB at 4 bits\n\nAhead on:\n- Agent interface, 100 against 85\n\nWatch for: vLLM and SGLang have no released support, though the card lists both\n\n\n## Why the edge\n\n### Risk: Gemma 4 26B A4B IT +30\n\n- Gemma 4 26B A4B 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 policy give a route to report a flaw, but no public Gemma advisories were found and GitHub's advisory tab couldn't be checked.\n- LFM3-1.7B (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### Agent interface: LFM3-1.7B +15\n\n- Gemma 4 26B A4B IT (85): Chat template 20, a Jinja file in the repository. Tool-calling format 25, Google documents how tools are declared and how calls come back, ships a response schema for Transformers' parser, and names the gemma4 parser in vLLM and SGLang. Trained tool calling 20, several calls in one turn. Structured output 0, nothing documents how to ask the weights for JSON or a schema. Reasoning control 10, thinking is off by default, turned on per request, and returned in a thought channel. Context 10, 262,144 tokens native.\n- LFM3-1.7B (100): Chat template 20, chat_template.jinja in the repository (12,129 characters), with roles, tools, deferred tools, tool results and the thinking switch. 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 says the model was trained on tool calls and agentic traces and with reinforcement learning inside agent harnesses. Structured output 15, Liquid's API guide documents `response_format` with a JSON Schema, with lfm3-1.7b as the model in its examples, and says the schema is checked before generation starts. Reasoning control 10, the card documents `enable_thinking=False` in the chat template to turn reasoning off per request, with reasoning on by default inside think tags, and `preserve_thinking` for earlier turns. Context 10, 256K tokens per the card, which says the mid-training stage extended the context window to 256K, and max_position_embeddings 262144 in config.json. 20 + 25 + 20 + 15 + 10 + 10 = 100.\n\n### Reach: Gemma 4 26B A4B IT +5\n\n- Gemma 4 26B A4B IT (90): Memory 25, Google's own 4-bit QAT GGUF is 14.4 GB, which with a fifth on top is 17.3 GB and lands in the 24 GB band, with the 1.2 GB vision projector extra for images. Runtimes 25, released support in vLLM, Transformers, llama.cpp, Ollama, SGLang and LM Studio, six of six. Builds 10, Google publishes one quantised precision (Q4_0 GGUF), since its unquantised QAT repository is BF16 and there's no FP8 build. Terms 15, plain Apache 2.0 with nothing added. Download 10, no account or gate. Hosted 5, the Gemini API serves it and four inference providers are live on Hugging Face.\n- LFM3-1.7B (85): Memory 30, Liquid's Q4_K_M GGUF is 1.10 GB, and 1.10 GB × 1.2 = 1.3 GB, which fits 8 GB, well inside the 16 GB line. Runtimes 15, Released llama.cpp has the lfm3 architecture (src/models/lfm3.cpp at b12242), Ollama's library has an lfm3 entry, and LM Studio's catalogue runs the architecture (its liquid/lfm3-vl-2b entry; lmstudio-community publishes a GGUF of this model). 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 lfm3-1.7b among its models, so the publisher hosts it. 30 + 15 + 15 + 10 + 10 + 5 = 85.\n\n## Score by category\n\n| Category | Weight | Gemma 4 26B A4B IT | LFM3-1.7B | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 90 | 85 | Gemma 4 26B A4B IT +5 |\n| Risk | 20% (20 this run) | 70 | 40 | Gemma 4 26B A4B IT +30 |\n| Agent interface | 10% (10 this run) | 85 | 100 | LFM3-1.7B +15 |\n| Documentation and upkeep | 10% (10 this run) | 65 | 67 | LFM3-1.7B +2 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **72.2 · BB** | **61 · C** | |\n\n## Facts side by side\n\n| Fact | Gemma 4 26B A4B IT | LFM3-1.7B |\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 | no | yes |\n| Last release | 2026-07-20 | 2026-11-09 |\n| Terms last updated | no document linked | no document linked |\n| Privacy policy last updated | no document linked | no document linked |\n| Agent reviews | 3.8/5 (4) | none |\n\n## Verdicts\n\n**Gemma 4 26B A4B IT.** A fast mixture-of-experts cousin for general agent work with tools, optional thinking and image input, under Apache 2.0. Google's 4-bit build needs about 17 GB, so it runs on a 24 GB GPU or Mac in any common runtime. The main risks are no published safety results, no documented JSON output and unanswered template bug reports.\n\n**LFM3-1.7B.** A 1.73B model that fits an 8 GB machine with room to spare, with a documented tool-call format, deferred tools, a thinking switch and a 256K context. It runs today in llama.cpp, Ollama and LM Studio only, loads in Transformers through repository code, and has no safety results, security contact or data sources.\n\n## Before you call either\n\n### Gemma 4 26B A4B IT\n\n1. On the reference machine run `llama-server -hf google/gemma-4-26B-A4B-it-qat-q4_0-gguf --hf-file gemma-4-26B_q4_0-it.gguf --jinja -c 65536`, and add the mmproj file for images\n2. With Ollama run `ollama run gemma4:26b` and raise num_ctx or OLLAMA_CONTEXT_LENGTH, since Google's guide says it defaults to 4K below 24 GiB of VRAM 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\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. Validate tool names and arguments before running them, and check any JSON yourself\n\n### LFM3-1.7B\n\n1. Start it with `llama-server -hf LiquidAI/LFM3-1.7B-GGUF:Q4_K_M --jinja` or `ollama run lfm3`\n2. Use temperature 0.1, top_k 50 and repetition penalty 1.1 for chat, tools and agents, per the card\n3. Pass `enable_thinking=False` for direct answers, and expect it to reason anyway on hard tasks\n4. For a large tool catalogue mark tools `defer_loading` and give it a search tool\n5. Don't plan on vLLM or SGLang until a release carries LFM3\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 26B A4B IT or LFM3-1.7B?\n\nGemma 4 26B A4B IT scores 72.2 (BB) on agent readiness against LFM3-1.7B's 61 (C), and leads in 2 of 4 scored categories. LFM3-1.7B leads on agent interface.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-lfm3-1-7b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-lfm3-1-7b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"gemma-4-26b-a4b-it\", \"b\": \"lfm3-1-7b\"}`. 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