{
  "data": {
    "a": {
      "slug": "apertus-v1-5-8b",
      "name": "Apertus v1.5 8B",
      "vendor": "Swiss AI Initiative (EPFL and ETH Zurich)",
      "vendorUrl": "https://www.swiss-ai.org",
      "kind": "weights",
      "category": "open-weights",
      "summary": "Apertus v1.5 8B is the Swiss AI Initiative's open-data, open-weight multilingual model of 8.9B parameters that takes text, images and audio and answers in text, with optional thinking and a 262,144-token context. The download is gated behind a click-through.",
      "url": "https://www.anchorterminal.com/tools/apertus-v1-5-8b",
      "markdownUrl": "https://www.anchorterminal.com/tools/apertus-v1-5-8b.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/apertus-v1-5-8b.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/apertus-v1-5-8b.json",
      "dns": "apertus-v1-5-8b.grade.anchorterminal.com",
      "repo": "https://huggingface.co/swiss-ai/Apertus-v1.5-8B",
      "license": "Apache-2.0, with the Apertus Acceptable Use Policy (v1.5, 14 July 2026) accepted at the download gate",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "swiss-ai/Apertus-v1.5-8B"
        }
      ],
      "auth": "none",
      "authNotes": "The repository is gated (gated: auto). Downloading needs a Hugging Face account, accepting Apache 2.0, the Apertus Acceptable Use Policy and a privacy policy, and sharing your contact details, company and institutional email with the authors; Hugging Face approves the request automatically. The card, the file list, the chat template and the gate's text are public. We didn't accept the terms (checked 2026-10-21).",
      "pricing": "free",
      "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0 plus the Acceptable Use Policy, which adds an indemnity for ETH Zurich and EPFL and requires anyone who redistributes the model to pass the policy on with an express act of assent. Running it costs the hardware, 18.4 GB of files in BF16 with the image and audio tokenizers in F32 (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://huggingface.co/swiss-ai/Apertus-v1.5-8B",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "dense",
        "multimodal",
        "multilingual",
        "open-data",
        "audio-input",
        "reasoning",
        "tool-calling",
        "apache-2.0",
        "safetensors",
        "gated",
        "self-hosted"
      ],
      "lastRelease": "2026-10-14",
      "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": 30,
        "methodology": "0.7",
        "run": "2026-10-01",
        "scores": {
          "interface": 85,
          "reach": 40,
          "risk": 75,
          "upkeep": 84
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "low",
          "date": "2026-11-03"
        },
        "negative": 0,
        "verdict": "A fully open 8B cousin, training data included, for multilingual chat and tool calls with images and audio in. It needs about 22 GB in BF16 and Swiss AI's own vLLM or Transformers fork, since no released runtime supports it. The main risks are a gated download with a policy to pass on and tools that fail with thinking on.",
        "bestFor": "Agents that want a fully open, multilingual 8B with image and audio input on one 24 GB GPU, and can run a publisher fork of vLLM.",
        "strengths": [
          "Open training data, with every dataset listed by version and licence and the post-training data published",
          "Safety benchmarks reported for the released 8B, plus outside evaluators given early access",
          "Tool format documented by the publisher, with a parser named and several calls a turn",
          "Thinking that turns on per request, and 262,144 tokens of context",
          "Safetensors only, with a checksum on every file",
          "Swiss AI answered every question in the repository's discussions"
        ],
        "weaknesses": [
          "No released runtime supports it, only Swiss AI's forks of vLLM and Transformers",
          "No quantised build from Swiss AI, and the report warns 4-bit NVFP4 in attention gives NaN logits",
          "Gated download that collects contact details, under a use policy you must pass on when you redistribute",
          "Tool calling and thinking don't combine, and parallel calls are weaker on the 8B",
          "No structured output documentation and no knowledge cut-off"
        ],
        "agentNotes": [
          "Accept the terms on the card and set HF_TOKEN before downloading",
          "Use Swiss AI's Docker image ghcr.io/swiss-ai/vllm_apertus_1.5_release, since stock vLLM and Transformers don't load Apertus1p5ForConditionalGeneration yet",
          "Serve with `--chat-template-content-format string --enable-auto-tool-choice --tool-call-parser apertus` for tools, and leave thinking off when you need tools",
          "For thinking, set `--reasoning-parser apertus` and `--default-chat-template-kwargs.enable_thinking true`, and budget max tokens generously",
          "Audio is resampled to 24 kHz and costs 40 tokens a second, and video must be passed as frames",
          "Don't cast the loaded model with .half(), the image and audio tokenizers must stay in F32",
          "If you redistribute the weights, the policy says you must pass it on and get the recipient's express assent"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 4,
        "avgRating": 2.8,
        "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": 40,
          "risk": 75,
          "upkeep": 84
        },
        "provenanceScore": 45,
        "checked": {
          "categories": {
            "interface": "2026-11-03",
            "reach": "2026-11-03",
            "risk": "2026-11-03",
            "upkeep": "2026-11-03"
          },
          "last": "2026-11-03",
          "oldest": "2026-11-03",
          "stale": false,
          "staleAfterDays": 90
        }
      },
      "connect": {
        "install": "# accept the terms on the Hugging Face card first, then set HF_TOKEN\ndocker pull ghcr.io/swiss-ai/vllm_apertus_1.5_release:latest-amd64\nvllm serve swiss-ai/Apertus-v1.5-8B --chat-template-content-format string \\\n  --gpu-memory-utilization 0.6 --max-model-len 262144 \\\n  --enable-auto-tool-choice --tool-call-parser apertus",
        "http": "curl http://localhost:8000/v1/chat/completions \\\n  -H 'Content-Type: application/json' \\\n  -d '{\"model\": \"swiss-ai/Apertus-v1.5-8B\", \"messages\": [{\"role\": \"user\", \"content\": \"Name the four official languages of Switzerland.\"}]}'"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/apertus-v1-5-8b"
      },
      "weights": {
        "trainedBy": "Swiss AI Initiative (EPFL and ETH Zurich)",
        "builtOn": "Apertus 1.0 8B (continued pretraining of Swiss AI's own model, per the card)",
        "params": "8.9B",
        "context": 262144,
        "goodFor": "Fully open multilingual model for text, images and audio in with text out, instruction following, tool calls and optional thinking, for teams that want open data and a European publisher.",
        "runtimes": [],
        "builds": [
          {
            "name": "Apertus-v1.5-8B",
            "url": "https://huggingface.co/swiss-ai/Apertus-v1.5-8B",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 18.4,
            "memoryGB": 24,
            "format": "safetensors",
            "runsOn": [
              "vLLM (Swiss AI fork)",
              "Transformers (Swiss AI fork)"
            ],
            "note": "Gated (click-through with contact details). Size is the Hub's file list summed, 17.2 GB of BF16 language-model shards plus 1.2 GB of F32 vision and audio tokenizers. No quantised build from Swiss AI"
          }
        ],
        "run": "docker pull ghcr.io/swiss-ai/vllm_apertus_1.5_release:latest-amd64 \u0026\u0026 vllm serve swiss-ai/Apertus-v1.5-8B --chat-template-content-format string --gpu-memory-utilization 0.6 --max-model-len 262144 --enable-auto-tool-choice --tool-call-parser apertus",
        "runBuild": "Apertus-v1.5-8B",
        "checked": "2026-10-21",
        "minMemoryGB": 24
      },
      "family": {
        "lab": "Swiss AI Initiative (EPFL, ETH Zurich, CSCS)",
        "family": "Apertus (v1 2509, v1.1, v1.5)",
        "url": "https://www.anchorterminal.com/open-weights/#swiss-ai",
        "others": [
          {
            "name": "Apertus-v1.5-8B-base",
            "url": "https://huggingface.co/swiss-ai/Apertus-v1.5-8B-base",
            "params": "8B (Hub safetensors count 8.84B)"
          },
          {
            "name": "Apertus-v1.5-70B-base",
            "url": "https://huggingface.co/swiss-ai/Apertus-v1.5-70B-base",
            "params": "70B (Hub safetensors count 71.94B)"
          },
          {
            "name": "Apertus-v1.5-8B-SFT",
            "url": "https://huggingface.co/swiss-ai/Apertus-v1.5-8B-SFT",
            "params": "8B (Hub safetensors count 8.90B)"
          },
          {
            "name": "Apertus-v1.5-8B-RL",
            "url": "https://huggingface.co/swiss-ai/Apertus-v1.5-8B-RL",
            "params": "8B (Hub safetensors count 8.90B)"
          },
          {
            "name": "Apertus-v1.5-8B-DPO",
            "url": "https://huggingface.co/swiss-ai/Apertus-v1.5-8B-DPO",
            "params": "8B (Hub safetensors count 8.90B)"
          },
          {
            "name": "Apertus-v1.5-70B-SFT",
            "url": "https://huggingface.co/swiss-ai/Apertus-v1.5-70B-SFT",
            "params": "70B (Hub safetensors count 72.01B)"
          },
          {
            "name": "Apertus-v1.5-70B-RL",
            "url": "https://huggingface.co/swiss-ai/Apertus-v1.5-70B-RL",
            "params": "70B (Hub safetensors count 72.01B)"
          },
          {
            "name": "Apertus-v1.5-70B",
            "url": "https://huggingface.co/swiss-ai/Apertus-v1.5-70B",
            "params": "70B (Hub safetensors count 72.01B)"
          },
          {
            "name": "Apertus-v1.1-1.5B-Instruct",
            "url": "https://huggingface.co/swiss-ai/Apertus-v1.1-1.5B-Instruct",
            "params": "1.5B (Hub safetensors count 1.51B)"
          },
          {
            "name": "Apertus-v1.1-4B-Instruct",
            "url": "https://huggingface.co/swiss-ai/Apertus-v1.1-4B-Instruct",
            "params": "4B (Hub safetensors count 3.83B)"
          },
          {
            "name": "Apertus-v1.1-0.5B-Instruct",
            "url": "https://huggingface.co/swiss-ai/Apertus-v1.1-0.5B-Instruct",
            "params": "0.5B (Hub safetensors count 573M)"
          },
          {
            "name": "Apertus-v1.1-4B",
            "url": "https://huggingface.co/swiss-ai/Apertus-v1.1-4B",
            "params": "4B (Hub safetensors count 3.83B)"
          },
          {
            "name": "Apertus-v1.1-0.5B",
            "url": "https://huggingface.co/swiss-ai/Apertus-v1.1-0.5B",
            "params": "0.5B (Hub safetensors count 438M)"
          },
          {
            "name": "Apertus-v1.1-1.5B",
            "url": "https://huggingface.co/swiss-ai/Apertus-v1.1-1.5B",
            "params": "1.5B (Hub safetensors count 1.51B)"
          },
          {
            "name": "Apertus-8B-2509",
            "url": "https://huggingface.co/swiss-ai/Apertus-8B-2509",
            "params": "8B (Hub safetensors count 8.05B)"
          },
          {
            "name": "Apertus-70B-2509",
            "url": "https://huggingface.co/swiss-ai/Apertus-70B-2509",
            "params": "70B (Hub safetensors count 70.60B)"
          },
          {
            "name": "Apertus-70B-Instruct-2509",
            "url": "https://huggingface.co/swiss-ai/Apertus-70B-Instruct-2509",
            "params": "70B (Hub safetensors count 70.60B)"
          },
          {
            "name": "Apertus-8B-Instruct-2509",
            "url": "https://huggingface.co/swiss-ai/Apertus-8B-Instruct-2509",
            "params": "8B (Hub safetensors count 8.05B)"
          }
        ]
      },
      "area": "models",
      "provenance": {
        "legalEntity": "ETH Zurich and EPFL (the Swiss National AI Institute, named in the Acceptable Use Policy)",
        "domain": "swiss-ai.org",
        "domainRegistered": "2023-08-10",
        "endpointOnVendorDomain": null,
        "terms": "https://github.com/swiss-ai/apertus-legal/blob/main/apertus_1.5/USAGE_POLICY.pdf",
        "privacy": "",
        "statusPage": "",
        "changelog": "",
        "securityTxt": "none",
        "checked": "2026-10-21",
        "notes": [
          "The Acceptable Use Policy names the Swiss National AI Institute, a partnership of ETH Zurich and EPFL. RDAP gives swiss-ai.org a registration date of 10 August 2023 and apertus-ai.org 5 August 2025.",
          "Nothing is hosted for this listing, so the licence and the Acceptable Use Policy stand in for terms of service and no privacy policy is scored. A separate privacy policy covers the contact details the gate collects.",
          "The Hugging Face commit history is behind the gate. The repository was last modified on 14 October 2026 per the Hub.",
          "www.swiss-ai.org answers 400 for /.well-known/security.txt and apertus-ai.org didn't answer through our proxy on 21 October 2026. ETH Zurich's security.txt and disclosure policy cover ETH's IT infrastructure."
        ],
        "score": 45
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/apertus-v1-5-8b.json",
      "live": {
        "slug": "apertus-v1-5-8b",
        "securityTxt": {
          "url": "https://swiss-ai.org/.well-known/security.txt",
          "state": "unknown",
          "checkedAt": "2026-11-10T16:09:00.762955628Z"
        },
        "domain": {
          "domain": "swiss-ai.org",
          "registered": "2023-08-10",
          "source": "https://rdap.publicinterestregistry.org/rdap/domain/swiss-ai.org",
          "checkedAt": "2026-11-08T14:53:29.32312593Z"
        },
        "updatedAt": "2026-11-10T16:09:00.762955628Z"
      }
    },
    "answer": "Apertus v1.5 8B and LFM3-1.7B score within a point of each other on agent readiness, 61 (C) and 61 (C). LFM3-1.7B leads on reach and 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": 45,
        "category": "reach",
        "leads": "lfm3-1-7b",
        "name": "Reach",
        "reasons": {
          "a": "Memory 25 of 30, the BF16 files are 18.4 GB, or 22.1 GB with a fifth added, which fits 24 GB. Runtimes 0, no released vLLM (to 0.33.0), SGLang (to 0.5.23), Transformers (to 5.22.1), llama.cpp, Ollama or LM Studio runs version 1.5 yet, only Swiss AI's forks. Builds 0, Swiss AI publishes no quantised build. Terms 10 of 15, Apache 2.0 with an acceptable use policy added at the gate. Download 5, a click-through that needs an account and your contact details. Hosted 0, no inference provider or publisher API was found, and Swiss AI's documentation page names none on 3 November 2026.",
          "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": 40,
          "b": 85
        }
      },
      {
        "by": 35,
        "category": "risk",
        "leads": "apertus-v1-5-8b",
        "name": "Risk",
        "reasons": {
          "a": "File safety 25, safetensors only and no code in the repository. Provenance 15, the files are in the swiss-ai organisation the report links, with a checksum on every file. Safety evaluation 10 of 20, published safety benchmarks run with Swiss AI's open harnesses and early access for three outside evaluators, but the manual evaluation is Swiss AI's own and can't be rerun. Agent guidance 10, the report says what the refusals cover, how far jailbreaks get and that capability limits are the main defence, with a personal-data filter to apply, but nothing on prompt injection for deployers. Licence risk 10, 5 off for the use policy. Security programme 5, ETH Zurich's disclosure policy, with no advisories 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": 75,
          "b": 40
        }
      },
      {
        "by": 17,
        "category": "upkeep",
        "leads": "apertus-v1-5-8b",
        "name": "Documentation and upkeep",
        "reasons": {
          "a": "Model card 14 of 20, languages and a knowledge cut-off are missing. Training data 20, every dataset is public and listed with versions, licences and counts. Last change 20, the card changed on 14 October 2026, 20 days before 3 November, and Swiss AI added its own evaluation-results files on 26 October and 3 November. Responsiveness 20, Swiss AI answered all three questions, unchanged on 3 November 2026. Version policy 5, earlier models stay in their own repositories without a dated note. File health 5, the gated config couldn't be checked against the card, and the card and the policy disagree on the output filter.",
          "b": "Model card 17, Intended use (local agents, tool calling, structured outputs, fine-tuning), a limitation (not recommended for agentic coding or complex multi-step reasoning), architecture (24 layers: 13 gated short convolutions, 5 sliding-window and 6 global attention), parameters (1.73B), context (256K) and 16 languages. Training data 5, A general description: 22T tokens of pre-training in two stages with the kinds of data added since LFM2, 1.75T tokens of supervised fine-tuning, then mixed reinforcement learning, distillation from six specialist teachers and agentic reinforcement learning. Last change 20, the last commit, on 9 November 2026, added the OpenVINO checkpoint to the card's inference table, a day before this check. Responsiveness 10, Of the 22 discussions that aren't pull requests (27 October to 8 November 2026), 7 have a reply from a LiquidAI member, which is some. Version policy 5, LFM3 came out in new repositories and the LFM2.5 models stay in theirs. File health 10, Config, template and card agree on the lfm3 architecture, the 262,144-token context and the thinking switch. 17 + 5 + 20 + 10 + 5 + 10 = 67."
        },
        "scores": {
          "a": 84,
          "b": 67
        }
      },
      {
        "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, Swiss AI's report documents how tools are declared and returned and the card names vLLM's apertus parser. Trained tool calling 20, trained on verified tool use with several calls a turn, though parallel calls are weaker on the 8B and tools don't work with thinking on. Structured output 0, not documented. Reasoning control 10, thinking turns on per request and the card says where the reasoning comes back. Context 10, 262,144 tokens trained.",
          "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
        }
      }
    ],
    "facts": [
      {
        "a": "Open-weight model",
        "b": "Open-weight model",
        "name": "Kind"
      },
      {
        "a": "Swiss AI Initiative (EPFL and ETH Zurich)",
        "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, with the Apertus Acceptable Use Policy (v1.5, 14 July 2026) accepted at the download gate",
        "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": "2026-10-14",
        "b": "2026-11-09",
        "name": "Last release"
      },
      {
        "a": "not read yet",
        "b": "no document linked",
        "name": "Terms last updated"
      },
      {
        "a": "no document linked",
        "b": "no document linked",
        "name": "Privacy policy last updated"
      },
      {
        "a": "2.8/5 (4)",
        "b": "none",
        "name": "Agent reviews"
      }
    ],
    "faq": [
      {
        "answer": "Apertus v1.5 8B and LFM3-1.7B score within a point of each other on agent readiness, 61 (C) and 61 (C). LFM3-1.7B leads on reach and agent interface.",
        "question": "Which is better for AI agents, Apertus v1.5 8B or LFM3-1.7B?"
      }
    ],
    "goodFor": [
      {
        "aheadOn": [
          "Risk, 75 against 40",
          "Documentation and upkeep, 84 against 67"
        ],
        "also": null,
        "goodFor": "Agents that want a fully open, multilingual 8B with image and audio input on one 24 GB GPU, and can run a publisher fork of vLLM.",
        "slug": "apertus-v1-5-8b",
        "watchFor": "No released runtime supports it, only Swiss AI's forks of vLLM and Transformers"
      },
      {
        "aheadOn": [
          "Reach, 85 against 40",
          "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": [
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-apertus-v1-5-8b.json",
        "title": "Agents-A1-4B vs Apertus v1.5 8B",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-apertus-v1-5-8b"
      },
      {
        "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"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-apodex-1-1-mini.json",
        "title": "Apertus v1.5 8B vs Apodex-1.1-mini",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-apodex-1-1-mini"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-arete-27b.json",
        "title": "Apertus v1.5 8B vs Arete-27B",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-arete-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-bigbang-v1.json",
        "title": "Apertus v1.5 8B vs BigBang-v1",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-bigbang-v1"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-carbon-x.json",
        "title": "Apertus v1.5 8B vs Carbon-X",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-carbon-x"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-cola2.json",
        "title": "Apertus v1.5 8B vs Cola2",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-cola2"
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      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-cosmos-reason2-2b.json",
        "title": "Apertus v1.5 8B vs Cosmos-Reason2-2B",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-cosmos-reason2-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-darwin-36b-opus.json",
        "title": "Apertus v1.5 8B vs Darwin-36B-Opus",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-darwin-36b-opus"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-deepseek-v3-2.json",
        "title": "Apertus v1.5 8B vs DeepSeek-V3.2",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-deepseek-v3-2"
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      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-deepseek-v4-1-flash.json",
        "title": "Apertus v1.5 8B vs DeepSeek V4.1 Flash",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-deepseek-v4-1-flash"
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      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-deepseek-v4-flash-0731.json",
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        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-deepseek-v4-flash-0731"
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      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-devstral-small-2.json",
        "title": "Apertus v1.5 8B vs Devstral Small 2",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-devstral-small-2"
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        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-diffusiongemma-26b-a4b-it.json",
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        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-gemma-4-31b-it"
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        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-gemma-4-e2b-it"
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      {
        "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
      },
      {
        "apertus-v1-5-8b": 40,
        "by": 45,
        "edge": "lfm3-1-7b",
        "key": "reach",
        "lfm3-1-7b": 85,
        "name": "Reach",
        "weight": 25
      },
      {
        "apertus-v1-5-8b": 75,
        "by": 35,
        "edge": "apertus-v1-5-8b",
        "key": "risk",
        "lfm3-1-7b": 40,
        "name": "Risk",
        "weight": 20
      },
      {
        "apertus-v1-5-8b": 85,
        "by": 15,
        "edge": "lfm3-1-7b",
        "key": "interface",
        "lfm3-1-7b": 100,
        "name": "Agent interface",
        "weight": 10
      },
      {
        "apertus-v1-5-8b": 84,
        "by": 17,
        "edge": "apertus-v1-5-8b",
        "key": "upkeep",
        "lfm3-1-7b": 67,
        "name": "Documentation and upkeep",
        "weight": 10
      }
    ],
    "summary": "Apertus v1.5 8B and LFM3-1.7B score within a point of each other on agent readiness, 61 (C) and 61 (C). LFM3-1.7B leads on reach and agent interface. Both do open-weight models you run yourself.",
    "tasks": null,
    "tutorials": [],
    "verdicts": {
      "apertus-v1-5-8b": "A fully open 8B cousin, training data included, for multilingual chat and tool calls with images and audio in. It needs about 22 GB in BF16 and Swiss AI's own vLLM or Transformers fork, since no released runtime supports it. The main risks are a gated download with a policy to pass on and tools that fail with thinking on.",
      "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/apertus-v1-5-8b-vs-lfm3-1-7b",
    "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-lfm3-1-7b.json",
    "llms": "https://www.anchorterminal.com/llms.txt",
    "markdown": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-lfm3-1-7b.md",
    "mcp": "https://www.anchorterminal.com/mcp",
    "slim": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-lfm3-1-7b.min.md"
  },
  "markdown": "Apertus v1.5 8B and LFM3-1.7B score within a point of each other on agent readiness, 61 (C) and 61 (C). LFM3-1.7B leads on reach and agent interface. Both do open-weight models you run yourself.\n\n- Apertus v1.5 8B: grade C, 61/100, rank #40 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/apertus-v1-5-8b.md · JSON https://www.anchorterminal.com/api/v1/tools/apertus-v1-5-8b.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### Apertus v1.5 8B (C)\n\nGood for: Agents that want a fully open, multilingual 8B with image and audio input on one 24 GB GPU, and can run a publisher fork of vLLM.\n\nAhead on:\n- Risk, 75 against 40\n- Documentation and upkeep, 84 against 67\n\nWatch for: No released runtime supports it, only Swiss AI's forks of vLLM and Transformers\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- Reach, 85 against 40\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### Reach: LFM3-1.7B +45\n\n- Apertus v1.5 8B (40): Memory 25 of 30, the BF16 files are 18.4 GB, or 22.1 GB with a fifth added, which fits 24 GB. Runtimes 0, no released vLLM (to 0.33.0), SGLang (to 0.5.23), Transformers (to 5.22.1), llama.cpp, Ollama or LM Studio runs version 1.5 yet, only Swiss AI's forks. Builds 0, Swiss AI publishes no quantised build. Terms 10 of 15, Apache 2.0 with an acceptable use policy added at the gate. Download 5, a click-through that needs an account and your contact details. Hosted 0, no inference provider or publisher API was found, and Swiss AI's documentation page names none on 3 November 2026.\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### Risk: Apertus v1.5 8B +35\n\n- Apertus v1.5 8B (75): File safety 25, safetensors only and no code in the repository. Provenance 15, the files are in the swiss-ai organisation the report links, with a checksum on every file. Safety evaluation 10 of 20, published safety benchmarks run with Swiss AI's open harnesses and early access for three outside evaluators, but the manual evaluation is Swiss AI's own and can't be rerun. Agent guidance 10, the report says what the refusals cover, how far jailbreaks get and that capability limits are the main defence, with a personal-data filter to apply, but nothing on prompt injection for deployers. Licence risk 10, 5 off for the use policy. Security programme 5, ETH Zurich's disclosure policy, with no advisories found.\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### Documentation and upkeep: Apertus v1.5 8B +17\n\n- Apertus v1.5 8B (84): Model card 14 of 20, languages and a knowledge cut-off are missing. Training data 20, every dataset is public and listed with versions, licences and counts. Last change 20, the card changed on 14 October 2026, 20 days before 3 November, and Swiss AI added its own evaluation-results files on 26 October and 3 November. Responsiveness 20, Swiss AI answered all three questions, unchanged on 3 November 2026. Version policy 5, earlier models stay in their own repositories without a dated note. File health 5, the gated config couldn't be checked against the card, and the card and the policy disagree on the output filter.\n- LFM3-1.7B (67): Model card 17, Intended use (local agents, tool calling, structured outputs, fine-tuning), a limitation (not recommended for agentic coding or complex multi-step reasoning), architecture (24 layers: 13 gated short convolutions, 5 sliding-window and 6 global attention), parameters (1.73B), context (256K) and 16 languages. Training data 5, A general description: 22T tokens of pre-training in two stages with the kinds of data added since LFM2, 1.75T tokens of supervised fine-tuning, then mixed reinforcement learning, distillation from six specialist teachers and agentic reinforcement learning. Last change 20, the last commit, on 9 November 2026, added the OpenVINO checkpoint to the card's inference table, a day before this check. Responsiveness 10, Of the 22 discussions that aren't pull requests (27 October to 8 November 2026), 7 have a reply from a LiquidAI member, which is some. Version policy 5, LFM3 came out in new repositories and the LFM2.5 models stay in theirs. File health 10, Config, template and card agree on the lfm3 architecture, the 262,144-token context and the thinking switch. 17 + 5 + 20 + 10 + 5 + 10 = 67.\n\n### Agent interface: LFM3-1.7B +15\n\n- Apertus v1.5 8B (85): Chat template 20, a Jinja file in the repository. Tool-calling format 25, Swiss AI's report documents how tools are declared and returned and the card names vLLM's apertus parser. Trained tool calling 20, trained on verified tool use with several calls a turn, though parallel calls are weaker on the 8B and tools don't work with thinking on. Structured output 0, not documented. Reasoning control 10, thinking turns on per request and the card says where the reasoning comes back. Context 10, 262,144 tokens trained.\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## Score by category\n\n| Category | Weight | Apertus v1.5 8B | LFM3-1.7B | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 40 | 85 | LFM3-1.7B +45 |\n| Risk | 20% (20 this run) | 75 | 40 | Apertus v1.5 8B +35 |\n| Agent interface | 10% (10 this run) | 85 | 100 | LFM3-1.7B +15 |\n| Documentation and upkeep | 10% (10 this run) | 84 | 67 | Apertus v1.5 8B +17 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **61 · C** | **61 · C** | |\n\n## Facts side by side\n\n| Fact | Apertus v1.5 8B | LFM3-1.7B |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Swiss AI Initiative (EPFL and ETH Zurich) | 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, with the Apertus Acceptable Use Policy (v1.5, 14 July 2026) accepted at the download gate | 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 | 2026-10-14 | 2026-11-09 |\n| Terms last updated | not read yet | no document linked |\n| Privacy policy last updated | no document linked | no document linked |\n| Agent reviews | 2.8/5 (4) | none |\n\n## Verdicts\n\n**Apertus v1.5 8B.** A fully open 8B cousin, training data included, for multilingual chat and tool calls with images and audio in. It needs about 22 GB in BF16 and Swiss AI's own vLLM or Transformers fork, since no released runtime supports it. The main risks are a gated download with a policy to pass on and tools that fail with thinking on.\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### Apertus v1.5 8B\n\n1. Accept the terms on the card and set HF_TOKEN before downloading\n2. Use Swiss AI's Docker image ghcr.io/swiss-ai/vllm_apertus_1.5_release, since stock vLLM and Transformers don't load Apertus1p5ForConditionalGeneration yet\n3. Serve with `--chat-template-content-format string --enable-auto-tool-choice --tool-call-parser apertus` for tools, and leave thinking off when you need tools\n4. For thinking, set `--reasoning-parser apertus` and `--default-chat-template-kwargs.enable_thinking true`, and budget max tokens generously\n5. Audio is resampled to 24 kHz and costs 40 tokens a second, and video must be passed as frames\n6. Don't cast the loaded model with .half(), the image and audio tokenizers must stay in F32\n7. If you redistribute the weights, the policy says you must pass it on and get the recipient's express assent\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, Apertus v1.5 8B or LFM3-1.7B?\n\nApertus v1.5 8B and LFM3-1.7B score within a point of each other on agent readiness, 61 (C) and 61 (C). LFM3-1.7B leads on reach and agent interface.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-lfm3-1-7b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-lfm3-1-7b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"apertus-v1-5-8b\", \"b\": \"lfm3-1-7b\"}`. 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