{
  "site": "Agentic Weekly",
  "dataset": "Agentic Weekly model facts",
  "as_of": "2026-10-31",
  "license": {
    "name": "CC BY 4.0",
    "url": "https://creativecommons.org/licenses/by/4.0/"
  },
  "license_spdx": "CC-BY-4.0",
  "attribution": "Data: Agentic Weekly (https://theagenticweekly.com), CC BY 4.0",
  "usage": "Quoting, summarizing and citing this data is encouraged. Credit Agentic Weekly (https://theagenticweekly.com), link the page you used and keep its Reported or Tested label.",
  "page": "https://theagenticweekly.com/models",
  "evidence_labels": {
    "Reported": "Read from the cited vendor page on the as-of date; not run by us.",
    "Tested": "We ran it ourselves; scope is stated in tested_note. No model is Tested yet."
  },
  "provenance": {
    "note": "Every fact object carries its own provenance: fact, value, source_url, source_type, verified_at, evidence_status and, for tested facts, test_id. Read it per fact, not per page. Full explanation: https://theagenticweekly.com/methodology#provenance",
    "source_type": {
      "vendor_reported": "Read from the vendor's own page (docs, pricing, release notes, post); not run by us.",
      "independently_tested": "We ran it; test_id names the test log. Smoke tests are not benchmarks.",
      "editorial": "Our own judgment or a ranking built on stated criteria; never presented as a vendor fact."
    },
    "evidence_status": {
      "confirmed": "The cited source_url states this value; we read it on verified_at.",
      "not_confirmed_on_source": "The cited page does not itself state this value; the note says what we infer and from where.",
      "promo": "The price is temporary; pricing.list holds the standing price and pricing.promo_label says when the promo ends.",
      "partial": "Part of the value was read on the cited page.",
      "not_applicable": "Editorial judgment or a relationship disclosure, not a checkable fact."
    },
    "verified_at": "The date we last re-read the cited page. Only models re-read on a date carry that date; others keep their earlier date."
  },
  "model": {
    "slug": "lfm3-1-7b",
    "name": "LFM3-1.7B",
    "vendor": "Liquid AI",
    "family": "LFM3",
    "category": "llm",
    "status": "generally-available",
    "groups": [
      "open-weights",
      "local",
      "small-fast"
    ],
    "short_answer": "LFM3-1.7B is Liquid AI's latest language model in our dataset, released Oct 27, 2026. Open weights (LFM Open License v1.0). Liquid AI's 1.7B open-weights text model for on-device agents, the text member of the six-model LFM3 family announced Oct 27, 2026, with a 256K-token context window. Verified against vendor sources on Oct 31, 2026.",
    "summary": "Liquid AI's 1.7B open-weights text model for on-device agents, the text member of the six-model LFM3 family announced Oct 27, 2026, with a 256K-token context window.",
    "release_date": "2026-10-27",
    "latest_update": null,
    "api_id": null,
    "context_tokens": 256000,
    "max_output_tokens": null,
    "input_modalities": [
      "text"
    ],
    "output_modalities": [
      "text"
    ],
    "parameters": {
      "total": "1.7B",
      "architecture": "Hybrid LFM3 architecture; pre-trained on 22T tokens"
    },
    "license": "LFM Open License v1.0",
    "open_weights": true,
    "pricing": null,
    "evidence": "Reported",
    "tested_note": null,
    "last_verified": "2026-10-31",
    "is_stale": false,
    "facts": {
      "release_date": {
        "fact": "release_date",
        "value": "2026-10-27",
        "source_url": "https://www.liquid.ai/blog/lfm3",
        "source_type": "vendor_reported",
        "verified_at": "2026-10-31",
        "evidence_status": "confirmed",
        "as_of": "2026-10-31",
        "evidence": "Reported",
        "note": "Date on Liquid AI's launch post.",
        "anchor_url": "https://theagenticweekly.com/models/lfm3-1-7b#fact-release-date"
      },
      "context_tokens": {
        "fact": "context_tokens",
        "value": 256000,
        "source_url": "https://www.liquid.ai/blog/lfm3",
        "source_type": "vendor_reported",
        "verified_at": "2026-10-31",
        "evidence_status": "confirmed",
        "as_of": "2026-10-31",
        "evidence": "Reported",
        "note": "Stated as 256K tokens.",
        "anchor_url": "https://theagenticweekly.com/models/lfm3-1-7b#fact-context-tokens"
      },
      "input_modalities": {
        "fact": "input_modalities",
        "value": [
          "text"
        ],
        "source_url": "https://huggingface.co/LiquidAI/LFM3-1.7B",
        "source_type": "vendor_reported",
        "verified_at": "2026-10-31",
        "evidence_status": "confirmed",
        "as_of": "2026-10-31",
        "evidence": "Reported",
        "anchor_url": "https://theagenticweekly.com/models/lfm3-1-7b#fact-input-modalities"
      },
      "output_modalities": {
        "fact": "output_modalities",
        "value": [
          "text"
        ],
        "source_url": "https://huggingface.co/LiquidAI/LFM3-1.7B",
        "source_type": "vendor_reported",
        "verified_at": "2026-10-31",
        "evidence_status": "confirmed",
        "as_of": "2026-10-31",
        "evidence": "Reported",
        "anchor_url": "https://theagenticweekly.com/models/lfm3-1-7b#fact-output-modalities"
      },
      "parameters": {
        "fact": "parameters",
        "value": {
          "total": "1.7B",
          "architecture": "Hybrid LFM3 architecture; pre-trained on 22T tokens"
        },
        "source_url": "https://www.liquid.ai/blog/lfm3",
        "source_type": "vendor_reported",
        "verified_at": "2026-10-31",
        "evidence_status": "confirmed",
        "as_of": "2026-10-31",
        "evidence": "Reported",
        "anchor_url": "https://theagenticweekly.com/models/lfm3-1-7b#fact-parameters"
      },
      "license": {
        "fact": "license",
        "value": {
          "type": "open-weights-restricted",
          "name": "LFM Open License v1.0",
          "openWeights": true
        },
        "source_url": "https://huggingface.co/LiquidAI/LFM3-1.7B",
        "source_type": "vendor_reported",
        "verified_at": "2026-10-31",
        "evidence_status": "confirmed",
        "as_of": "2026-10-31",
        "evidence": "Reported",
        "anchor_url": "https://theagenticweekly.com/models/lfm3-1-7b#fact-license"
      }
    },
    "vendor_claims": [
      "Liquid AI says the text model fits in about 1 GB at 4-bit precision and outperforms its LFM2.5-2.6B while using 35% fewer parameters. Vendor claim; we did not run it."
    ],
    "notes": [
      "GGUF, MLX, ONNX, FP8 and NVFP4 builds are on Hugging Face."
    ],
    "not_verified": [
      "The vision, audio, omni and two decision models in the LFM3 family are not tracked as separate entries yet.",
      "Benchmark scores were not captured."
    ],
    "sources": [
      {
        "label": "Liquid AI blog: LFM3",
        "url": "https://www.liquid.ai/blog/lfm3"
      },
      {
        "label": "Hugging Face model card",
        "url": "https://huggingface.co/LiquidAI/LFM3-1.7B"
      },
      {
        "label": "Agentic Weekly brief",
        "url": "https://theagenticweekly.com/news/liquid-ai-lfm3-on-device-model-family"
      }
    ],
    "url": "https://theagenticweekly.com/models/lfm3-1-7b",
    "primary_source_url": "https://www.liquid.ai/blog/lfm3",
    "anchors": {
      "facts": "https://theagenticweekly.com/models/lfm3-1-7b#facts",
      "sources": "https://theagenticweekly.com/models/lfm3-1-7b#sources",
      "tested": "https://theagenticweekly.com/models/lfm3-1-7b#tested",
      "not_verified": "https://theagenticweekly.com/models/lfm3-1-7b#left-out"
    },
    "markdown": "https://theagenticweekly.com/models/lfm3-1-7b.md",
    "api": "https://theagenticweekly.com/api/models/lfm3-1-7b"
  }
}