{ "data": { "a": { "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" }, "answer": "LFM3-1.7B and occamy-1.0 score within a point of each other on agent readiness, 61 (C) and 61 (C). occamy-1.0 leads on reach, risk and documentation and upkeep.", "b": { "slug": "occamy-1-0", "name": "occamy-1.0", "vendor": "Accio (Accio-Lab)", "vendorUrl": "https://accio-lab.github.io/occamy/", "kind": "weights", "category": "open-weights", "summary": "Occamy-1.0 is Accio's open-weight agent model for long multi-step work with tools, files and code, a fine-tune of Qwen3.6-35B-A3B (35B mixture of experts, 3B active) published on Hugging Face under Apache 2.0.", "url": "https://www.anchorterminal.com/tools/occamy-1-0", "markdownUrl": "https://www.anchorterminal.com/tools/occamy-1-0.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/occamy-1-0.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/occamy-1-0.json", "dns": "occamy-1-0.grade.anchorterminal.com", "repo": "https://huggingface.co/Accio-Lab/occamy-1.0", "license": "Apache-2.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "Accio-Lab/occamy-1.0" }, { "registry": "huggingface", "name": "Accio-Lab/occamy-1.0-FP8" }, { "registry": "huggingface", "name": "Accio-Lab/occamy-1.0-NVFP4" }, { "registry": "huggingface", "name": "Accio-Lab/occamy-1.0-GGUF" }, { "registry": "huggingface", "name": "Accio-Lab/occamy-1.0-APEX-GGUF" }, { "registry": "huggingface", "name": "Accio-Lab/occamy-1.0-MLX-4bit" }, { "registry": "huggingface", "name": "Accio-Lab/occamy-1.0-MLX-mxfp4" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The main repository and all of Accio's build repositories are not gated (checked 2026-10-21).", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0, the same licence as the Qwen3.6-35B-A3B base. Running it yourself costs the hardware, 70.2 GB for BF16 and 18.4 GB for the smallest 4-bit build (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/Accio-Lab/occamy-1.0", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "moe", "multimodal", "reasoning", "tool-calling", "agent", "coding", "derivative", "apache-2.0", "safetensors", "gguf", "mlx", "fp8", "nvfp4", "self-hosted" ], "lastRelease": "2026-10-02", "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": 49, "methodology": "0.7", "run": "2026-10-01", "scores": { "interface": 70, "reach": 90, "risk": 55, "upkeep": 74 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-11-03" }, "negative": 0, "verdict": "A Qwen3.6-35B-A3B fine-tuned for long agent work with tools, files and code, under plain Apache 2.0, and Accio's 4-bit builds run in about 23 GB. The main gaps are no safety results, agent guidance or security contact, and every benchmark figure is Accio's own.", "bestFor": "Agents that want an Apache 2.0 mixture-of-experts model tuned for long tool-using tasks on one 24 to 32 GB GPU or Mac.", "strengths": [ "Apache 2.0 like its base, with nothing gated", "Accio publishes FP8, NVFP4, GGUF and MLX builds, the 4-bit ones within 24 GB", "Same architecture and template as Qwen3.6-35B-A3B, so released llama.cpp, Ollama, vLLM, SGLang and Transformers run it", "A technical report, the supervised data mix with token counts and a published data subset", "MD5 and SHA-256 checksums for every file" ], "weaknesses": [ "No safety results, prompt-injection guidance or security contact", "No hosted API or inference provider", "Tool-calling format and structured output aren't documented beyond the template and parser name", "Languages and a knowledge cut-off aren't stated", "The MLX builds await Apple Metal acceptance, per Accio's own cards" ], "agentNotes": [ "Serve with `--reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder` on vLLM 0.19.0 or SGLang 0.5.10 and later", "BF16 is 70.2 GB, so on a 128 GB machine run it without the card's 8-way tensor parallelism", "Keep the whole assistant message, reasoning and tool calls included, in later turns, as the card says", "Sampling per the card is temperature 1.0, top_p 0.95, top_k 20 and presence_penalty 1.5", "With the GGUFs, NFC-normalise input text and read TOKENIZER.md, since the files store qwen2 pre-tokenizer metadata", "The base's MTP layer isn't in this checkpoint, Accio-Lab/occamy-1.0-MTP is an experimental separate head", "Ollama has no Occamy library entry, so load Accio's GGUF in Ollama 0.40.14 or later, which builds a llama.cpp with the qwen35moe architecture" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 3, "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": 70, "reach": 90, "risk": 55, "upkeep": 74 }, "provenanceScore": 24, "checked": { "categories": { "interface": "2026-11-03", "reach": "2026-11-03", "risk": "2026-11-03", "upkeep": "2026-11-03" }, "last": "2026-11-03", "lastMoved": "2026-11-03", "oldest": "2026-11-03", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "vllm serve Accio-Lab/occamy-1.0 --max-model-len 262144 --reasoning-parser qwen3 \\\n --enable-auto-tool-choice --tool-call-parser qwen3_coder\n# or a 4-bit GGUF with llama.cpp:\nllama-server -hf Accio-Lab/occamy-1.0-GGUF:Q4_K_M --jinja -c 131072", "http": "curl http://localhost:8000/v1/chat/completions \\\n -H 'Content-Type: application/json' \\\n -d '{\"model\": \"Accio-Lab/occamy-1.0\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/occamy-1-0" }, "weights": { "trainedBy": "Accio (Accio-Lab)", "builtOn": "Qwen/Qwen3.6-35B-A3B (Apache 2.0), declared in the card's metadata (base_model) and text as the post-trained starting checkpoint. Same qwen3_5_moe architecture and config apart from the removed MTP layer, and the same chat template. Occamy is also Apache 2.0", "params": "35B (3B active)", "active": "3B", "context": 262144, "goodFor": "Mixture-of-experts agent model for long multi-step work with tools, files, code and office tasks, text and images in, thinking on by default.", "runtimes": [ "llama.cpp", "Ollama", "vLLM", "SGLang", "Transformers" ], "builds": [ { "name": "occamy-1.0-MLX-mxfp4", "url": "https://huggingface.co/Accio-Lab/occamy-1.0-MLX-mxfp4", "who": "publisher", "precision": "mxfp4", "sizeGB": 18.4, "memoryGB": 24, "format": "mlx", "runsOn": [ "MLX" ], "note": "Text only. Apple Metal acceptance pending, per the card" }, { "name": "occamy-1.0-MLX-4bit", "url": "https://huggingface.co/Accio-Lab/occamy-1.0-MLX-4bit", "who": "publisher", "precision": "mlx-4bit", "sizeGB": 19.5, "memoryGB": 24, "format": "mlx", "runsOn": [ "MLX" ], "note": "Text only. Apple Metal acceptance pending, per the card" }, { "name": "occamy-1.0-IQ4_XS", "url": "https://huggingface.co/Accio-Lab/occamy-1.0-GGUF/blob/main/occamy-1.0-IQ4_XS.gguf", "who": "publisher", "precision": "iq4_xs", "sizeGB": 18.7, "memoryGB": 24, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)" ], "note": "Image input needs the 0.90 GB mmproj file. Input should be NFC-normalised, per the card" }, { "name": "occamy-1.0-APEX-Compact", "url": "https://huggingface.co/Accio-Lab/occamy-1.0-APEX-GGUF/blob/main/occamy-1.0-APEX-Compact.gguf", "who": "publisher", "precision": "mixed-q3-q4", "sizeGB": 16.5, "memoryGB": 24, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)" ], "note": "Most routed experts at Q3_K, so it doesn't count as a 4-bit build for the memory line" }, { "name": "occamy-1.0-NVFP4", "url": "https://huggingface.co/Accio-Lab/occamy-1.0-NVFP4", "who": "publisher", "precision": "nvfp4", "sizeGB": 23.9, "memoryGB": 32, "format": "safetensors", "note": "Expert-only mixed precision, checked on H200 with Marlin W4A16, per the card" }, { "name": "occamy-1.0-Q4_K_M", "url": "https://huggingface.co/Accio-Lab/occamy-1.0-GGUF/blob/main/occamy-1.0-Q4_K_M.gguf", "who": "publisher", "precision": "q4_k_m", "sizeGB": 21.2, "memoryGB": 32, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)" ] }, { "name": "occamy-1.0-FP8", "url": "https://huggingface.co/Accio-Lab/occamy-1.0-FP8", "who": "publisher", "precision": "fp8", "sizeGB": 36.6, "memoryGB": 48, "format": "safetensors", "runsOn": [ "vLLM", "SGLang" ] }, { "name": "occamy-1.0-Q8_0", "url": "https://huggingface.co/Accio-Lab/occamy-1.0-GGUF/blob/main/occamy-1.0-Q8_0.gguf", "who": "publisher", "precision": "q8_0", "sizeGB": 36.9, "memoryGB": 48, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)" ] }, { "name": "occamy-1.0", "url": "https://huggingface.co/Accio-Lab/occamy-1.0", "who": "publisher", "precision": "bf16", "sizeGB": 70.2, "memoryGB": 96, "format": "safetensors", "runsOn": [ "vLLM (ROCm)", "SGLang (ROCm)", "Transformers" ] } ], "run": "vllm serve Accio-Lab/occamy-1.0 --max-model-len 262144 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder", "runBuild": "occamy-1.0", "checked": "2026-11-03", "minMemoryGB": 24 }, "family": { "lab": "Accio (Accio-Lab)", "family": "occamy", "url": "https://www.anchorterminal.com/open-weights/#accio-lab", "others": [ { "name": "occamy-1.0-MTP", "url": "https://huggingface.co/Accio-Lab/occamy-1.0-MTP", "params": "not stated (draft head only)" } ], "olderCount": 4, "unrestrictedCopies": 5 }, "area": "models", "provenance": { "legalEntity": "", "domain": "accio-lab.github.io", "domainRegistered": "", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "", "securityTxt": "none", "checked": "2026-10-21", "notes": [ "No legal entity is named on the card, the report or the repository. The report's byline is Accio Team, and the Hugging Face organisation Accio-Lab (fullname Accio, 15 members) isn't verified. The GitHub README and the project website link the organisation.", "Nothing is hosted, so the licence stands in for terms of service and no privacy policy is scored.", "The project website is a GitHub Pages site drawn by script, so its text couldn't be read beyond the title.", "No SECURITY.md in Accio-Lab/occamy. The README sends bug reports and feature requests to GitHub issues." ], "score": 24 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/occamy-1-0.json", "live": { "slug": "occamy-1-0", "securityTxt": { "url": "https://accio-lab.github.io/.well-known/security.txt", "state": "none", "checkedAt": "2026-11-10T16:08:29.220890734Z" }, "domain": { "domain": "github.io", "checkedAt": "2026-11-08T15:08:12.013832199Z" }, "updatedAt": "2026-11-10T16:08:29.220890734Z" } }, "edges": [ { "by": 30, "category": "interface", "leads": "lfm3-1-7b", "name": "Agent interface", "reasons": { "a": "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.", "b": "Chat template 20, the base's Jinja template in the repository. Tool-calling format 10 of 25, the card names the parser but the format lives only in the template. Trained tool calling 20, trained on tool use with several calls a turn. Structured output 0, not documented. Reasoning control 10, enable_thinking per request and the card says to keep the returned reasoning content. Context 10, 262,144 tokens." }, "scores": { "a": 100, "b": 70 } }, { "by": 15, "category": "risk", "leads": "occamy-1-0", "name": "Risk", "reasons": { "a": "File safety 15, safetensors only, no .bin, .pt or .pth files (25). Provenance 15, files in the LiquidAI organisation, which Liquid's docs page for the model and its blog post link to (10). Safety evaluation 0, no safety results on the card or in Liquid's blog post for the LFM3 family, neither of which mentions safety testing. Agent guidance 0, Nothing on prompt injection, an instruction hierarchy, what refusals cover or safeguards to add. Licence risk 10, 15 less 5 for the $10M annual revenue threshold on commercial use. Security programme 0, no way to report a flaw found. 15 + 15 + 0 + 0 + 10 + 0 = 40.", "b": "File safety 25, safetensors only and nothing from the repository runs to load it or build prompts. Provenance 15, the files are in the organisation Accio's GitHub links, with MD5 sums and SHA-256 for every file. Safety evaluation 0, none published. Agent guidance 0. Licence risk 15, plain Apache 2.0 that hasn't changed. Security programme 0, bug reports go to public GitHub issues and there's no security contact." }, "scores": { "a": 40, "b": 55 } }, { "by": 7, "category": "upkeep", "leads": "occamy-1-0", "name": "Documentation and upkeep", "reasons": { "a": "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.", "b": "Model card 14 of 20, languages and a knowledge cut-off are missing. Training data 15, the mix by type with token counts, the method and a published subset, without a cut-off. Last change 20, Accio's GGUF build card changed on 19 October 2026 and the main card on 2 October. Responsiveness 10, Accio answered 4 of 9 questions. Version policy 5, a first release with no files replaced. File health 10, the files agree with the card. 14 + 15 + 20 + 10 + 5 + 10 = 74." }, "scores": { "a": 67, "b": 74 } }, { "by": 5, "category": "reach", "leads": "occamy-1-0", "name": "Reach", "reasons": { "a": "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.", "b": "Memory 25 of 30, Accio's MLX mxfp4 build is 18.4 GB, 22.1 GB with a fifth added, and its IQ4_XS GGUF needs 22.5 GB, both within 24 GB. Runtimes 25, the most the line gives, it keeps the base's qwen3_5_moe architecture, which llama.cpp, vLLM, SGLang and Transformers run in released versions, and Ollama v0.40.14 builds a llama.cpp (b11963) that has it. Builds 15, FP8, NVFP4, GGUF and MLX from Accio. Terms 15, Apache 2.0 like its base. Download 10, nothing is gated. Hosted 0, no API or inference provider serves it. 25 + 25 + 15 + 15 + 10 + 0 = 90." }, "scores": { "a": 85, "b": 90 } } ], "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "Liquid AI", "b": "Accio (Accio-Lab)", "name": "Vendor" }, { "a": "no (local only)", "b": "no (local only)", "name": "Hosted endpoint" }, { "a": "", "b": "", "name": "Transports" }, { "a": "None", "b": "None", "name": "Auth" }, { "a": "Free", "b": "Free", "name": "Pricing" }, { "a": "no", "b": "no", "name": "x402" }, { "a": "LFM Open License v1.0 (free commercial use below $10M annual revenue)", "b": "Apache-2.0", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "yes", "b": "no", "name": "llms.txt" }, { "a": "2026-11-09", "b": "2026-10-02", "name": "Last release" }, { "a": "no document linked", "b": "no document linked", "name": "Terms last updated" }, { "a": "no document linked", "b": "no document linked", "name": "Privacy policy last updated" }, { "a": "none", "b": "3/5 (4)", "name": "Agent reviews" } ], "faq": [ { "answer": "LFM3-1.7B and occamy-1.0 score within a point of each other on agent readiness, 61 (C) and 61 (C). occamy-1.0 leads on reach, risk and documentation and upkeep.", "question": "Which is better for AI agents, LFM3-1.7B or occamy-1.0?" } ], "goodFor": [ { "aheadOn": [ "Agent interface, 100 against 70" ], "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" }, { "aheadOn": [ "Reach, 90 against 85", "Risk, 55 against 40", "Documentation and upkeep, 74 against 67" ], "also": null, "goodFor": "Agents that want an Apache 2.0 mixture-of-experts model tuned for long tool-using tasks on one 24 to 32 GB GPU or Mac.", "slug": "occamy-1-0", "watchFor": "No safety results, prompt-injection guidance or security contact" } ], "history": { "lfm3-1-7b": null, "occamy-1-0": [ { "causes": [ "correction" ], "date": "2026-11-03", "delta": 0, "from": 61, "fromGrade": "C", "to": 61, "toGrade": "C" } ] }, "job": { "capability": "model.open-weights", "name": "Open-weight models you run yourself" }, "others": [ { "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-lfm3-1-7b.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/agents-a1-4b-vs-occamy-1-0.json", "title": "Agents-A1-4B vs occamy-1.0", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-occamy-1-0" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-lfm3-1-7b.json", "title": "Apertus v1.5 8B vs LFM3-1.7B", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-lfm3-1-7b" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-occamy-1-0.json", "title": 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"json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-lfm3-1-7b.json", "title": "Laguna M.1 vs LFM3-1.7B", "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-lfm3-1-7b" }, { "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-occamy-1-0.json", "title": "Laguna M.1 vs occamy-1.0", "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-occamy-1-0" }, { "json": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-lfm3-1-7b.json", "title": "Laguna S 2.1 vs LFM3-1.7B", "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-lfm3-1-7b" }, { "json": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-occamy-1-0.json", "title": "Laguna S 2.1 vs occamy-1.0", "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-occamy-1-0" }, { "json": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-lfm3-1-7b.json", "title": "Laguna XS 2.1 vs LFM3-1.7B", "url": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-lfm3-1-7b" }, { "json": 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{ "json": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-longcat-2-5.json", "title": "LFM3-1.7B vs LongCat-2.5", "url": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-longcat-2-5" }, { "json": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-mimo-v2-6-distill-qwen-9b.json", "title": "LFM3-1.7B vs MiMo-V2.6 Distill Qwen 9B", "url": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-mimo-v2-6-distill-qwen-9b" }, { "json": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-mimo-v2-6-flash-rl.json", "title": "LFM3-1.7B vs MiMo-V2.6-Flash-RL", "url": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-mimo-v2-6-flash-rl" }, { "json": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-minicpm-o-4-5.json", "title": "LFM3-1.7B vs MiniCPM-o 4.5", "url": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-minicpm-o-4-5" }, { "json": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-minicpm-v-4-7-1b.json", "title": "LFM3-1.7B vs MiniCPM-V 4.7 1B", "url": 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"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", 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"https://www.anchorterminal.com/compare/occamy-1-0-vs-ornith-1-5-9b" }, { "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-otel-2-0-llm-31b-it.json", "title": "occamy-1.0 vs OTel-2.0-LLM-31B-IT", "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-otel-2-0-llm-31b-it" }, { "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen-agentworld-35b-a3b.json", "title": "occamy-1.0 vs Qwen-AgentWorld-35B-A3B", "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen-agentworld-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-30b-a3b-instruct-2507.json", "title": "occamy-1.0 vs Qwen3-30B-A3B-Instruct-2507", "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-30b-a3b-instruct-2507" }, { "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-4b-instruct-2507.json", "title": "occamy-1.0 vs Qwen3-4B-Instruct-2507", "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-4b-instruct-2507" }, { "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-5-0-8b.json", "title": "occamy-1.0 vs Qwen3.5-0.8B", "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-5-0-8b" }, { "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-5-122b-a10b.json", "title": "occamy-1.0 vs Qwen3.5-122B-A10B", "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-5-122b-a10b" }, { "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-5-2b.json", "title": "occamy-1.0 vs Qwen3.5-2B", "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-5-2b" }, { "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-5-397b-a17b.json", "title": "occamy-1.0 vs Qwen3.5-397B-A17B", "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-5-397b-a17b" }, { "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-5-4b.json", "title": "occamy-1.0 vs Qwen3.5-4B", "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-5-4b" }, { "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-5-9b.json", "title": "occamy-1.0 vs Qwen3.5-9B", "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-5-9b" }, { "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-6-27b.json", "title": "occamy-1.0 vs Qwen3.6-27B", "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-6-27b" }, { "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-6-35b-a3b.json", "title": "occamy-1.0 vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-8-27b.json", "title": "occamy-1.0 vs Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-8-flash-next.json", "title": "occamy-1.0 vs Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-coder-30b-a3b-instruct.json", "title": "occamy-1.0 vs Qwen3-Coder-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-coder-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-coder-next.json", "title": "occamy-1.0 vs Qwen3-Coder-Next", "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-coder-next" }, { "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-omni-30b-a3b-instruct.json", "title": "occamy-1.0 vs Qwen3-Omni-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-omni-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-vl-2b-instruct.json", "title": "occamy-1.0 vs Qwen3-VL-2B-Instruct", "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-vl-2b-instruct" }, { "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-vl-30b-a3b-instruct.json", "title": "occamy-1.0 vs Qwen3-VL-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-vl-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-vl-32b-instruct.json", "title": "occamy-1.0 vs Qwen3-VL-32B-Instruct", "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-vl-32b-instruct" }, { "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-vl-4b-instruct.json", "title": "occamy-1.0 vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-vl-8b-instruct.json", "title": "occamy-1.0 vs Qwen3-VL-8B-Instruct", "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-vl-8b-instruct" }, { "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwopus3-8-27b-flash-v2.json", "title": "occamy-1.0 vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-recursive-flint.json", "title": "occamy-1.0 vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-ripple-65b-preview.json", "title": "occamy-1.0 vs Ripple-65B Preview", "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-ripple-65b-preview" }, { "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-sensenova-loop-9b-preview.json", "title": "occamy-1.0 vs SenseNova-Loop-9B-Preview", "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-sensenova-loop-9b-preview" }, { "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-smollm3-3b.json", "title": "occamy-1.0 vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-spark-x2-6-4b.json", "title": "occamy-1.0 vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-step-3-7-flash.json", "title": "occamy-1.0 vs Step 3.7 Flash", "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-step-3-7-flash" }, { "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-swift-1-5-qwen3-8-27b.json", "title": "occamy-1.0 vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-swift-1-5-qwen3-8-flash-next.json", "title": "occamy-1.0 vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-thinkingcap-qwen3-8-27b.json", "title": "occamy-1.0 vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-unlimited-ocr-2.json", "title": "occamy-1.0 vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-wevisone-vl-2b.json", "title": "occamy-1.0 vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-wevisone-vl-4b.json", "title": "occamy-1.0 vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-zaya1-8b.json", "title": "occamy-1.0 vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-zaya1-8b" } ], "panel": null, "scores": [ { "key": "capability", "name": "Capability", "pending": true, "weight": 35 }, { "by": 5, "edge": "occamy-1-0", "key": "reach", "lfm3-1-7b": 85, "name": "Reach", "occamy-1-0": 90, "weight": 25 }, { "by": 15, "edge": "occamy-1-0", "key": "risk", "lfm3-1-7b": 40, "name": "Risk", "occamy-1-0": 55, "weight": 20 }, { "by": 30, "edge": "lfm3-1-7b", "key": "interface", "lfm3-1-7b": 100, "name": "Agent interface", "occamy-1-0": 70, "weight": 10 }, { "by": 7, "edge": "occamy-1-0", "key": "upkeep", "lfm3-1-7b": 67, "name": "Documentation and upkeep", "occamy-1-0": 74, "weight": 10 } ], "summary": "LFM3-1.7B and occamy-1.0 score within a point of each other on agent readiness, 61 (C) and 61 (C). occamy-1.0 leads on reach, risk and documentation and upkeep. Both do open-weight models you run yourself.", "tasks": null, "tutorials": [], "verdicts": { "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.", "occamy-1-0": "A Qwen3.6-35B-A3B fine-tuned for long agent work with tools, files and code, under plain Apache 2.0, and Accio's 4-bit builds run in about 23 GB. The main gaps are no safety results, agent guidance or security contact, and every benchmark figure is Accio's own." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-occamy-1-0", "json": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-occamy-1-0.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-occamy-1-0.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-occamy-1-0.min.md" }, "markdown": "LFM3-1.7B and occamy-1.0 score within a point of each other on agent readiness, 61 (C) and 61 (C). occamy-1.0 leads on reach, risk and documentation and upkeep. Both do open-weight models you run yourself.\n\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- occamy-1.0: grade C, 61/100, rank #59 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/occamy-1-0.md · JSON https://www.anchorterminal.com/api/v1/tools/occamy-1-0.json\n- Best open-weight models you download and run yourself for AI agents: https://www.anchorterminal.com/best/open-weights/index.md\n- All 6359 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md\n\n## Which one, for what\n\n### LFM3-1.7B (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 70\n\nWatch for: vLLM and SGLang have no released support, though the card lists both\n\n### occamy-1.0 (C)\n\nGood for: Agents that want an Apache 2.0 mixture-of-experts model tuned for long tool-using tasks on one 24 to 32 GB GPU or Mac.\n\nAhead on:\n- Reach, 90 against 85\n- Risk, 55 against 40\n- Documentation and upkeep, 74 against 67\n\nWatch for: No safety results, prompt-injection guidance or security contact\n\n\n## Why the edge\n\n### Agent interface: LFM3-1.7B +30\n\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- occamy-1.0 (70): Chat template 20, the base's Jinja template in the repository. Tool-calling format 10 of 25, the card names the parser but the format lives only in the template. Trained tool calling 20, trained on tool use with several calls a turn. Structured output 0, not documented. Reasoning control 10, enable_thinking per request and the card says to keep the returned reasoning content. Context 10, 262,144 tokens.\n\n### Risk: occamy-1.0 +15\n\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- occamy-1.0 (55): File safety 25, safetensors only and nothing from the repository runs to load it or build prompts. Provenance 15, the files are in the organisation Accio's GitHub links, with MD5 sums and SHA-256 for every file. Safety evaluation 0, none published. Agent guidance 0. Licence risk 15, plain Apache 2.0 that hasn't changed. Security programme 0, bug reports go to public GitHub issues and there's no security contact.\n\n### Documentation and upkeep: occamy-1.0 +7\n\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- occamy-1.0 (74): Model card 14 of 20, languages and a knowledge cut-off are missing. Training data 15, the mix by type with token counts, the method and a published subset, without a cut-off. Last change 20, Accio's GGUF build card changed on 19 October 2026 and the main card on 2 October. Responsiveness 10, Accio answered 4 of 9 questions. Version policy 5, a first release with no files replaced. File health 10, the files agree with the card. 14 + 15 + 20 + 10 + 5 + 10 = 74.\n\n### Reach: occamy-1.0 +5\n\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- occamy-1.0 (90): Memory 25 of 30, Accio's MLX mxfp4 build is 18.4 GB, 22.1 GB with a fifth added, and its IQ4_XS GGUF needs 22.5 GB, both within 24 GB. Runtimes 25, the most the line gives, it keeps the base's qwen3_5_moe architecture, which llama.cpp, vLLM, SGLang and Transformers run in released versions, and Ollama v0.40.14 builds a llama.cpp (b11963) that has it. Builds 15, FP8, NVFP4, GGUF and MLX from Accio. Terms 15, Apache 2.0 like its base. Download 10, nothing is gated. Hosted 0, no API or inference provider serves it. 25 + 25 + 15 + 15 + 10 + 0 = 90.\n\n## Score by category\n\n| Category | Weight | LFM3-1.7B | occamy-1.0 | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 85 | 90 | occamy-1.0 +5 |\n| Risk | 20% (20 this run) | 40 | 55 | occamy-1.0 +15 |\n| Agent interface | 10% (10 this run) | 100 | 70 | LFM3-1.7B +30 |\n| Documentation and upkeep | 10% (10 this run) | 67 | 74 | occamy-1.0 +7 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **61 · C** | **61 · C** | |\n\n## Facts side by side\n\n| Fact | LFM3-1.7B | occamy-1.0 |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Liquid AI | Accio (Accio-Lab) |\n| Hosted endpoint | no (local only) | no (local only) |\n| Transports | | |\n| Auth | None | None |\n| Pricing | Free | Free |\n| x402 | no | no |\n| Licence | LFM Open License v1.0 (free commercial use below $10M annual revenue) | Apache-2.0 |\n| Read-only variant documented | no | no |\n| llms.txt | yes | no |\n| Last release | 2026-11-09 | 2026-10-02 |\n| Terms last updated | no document linked | no document linked |\n| Privacy policy last updated | no document linked | no document linked |\n| Agent reviews | none | 3/5 (4) |\n\n## How the scores have moved\n\n### LFM3-1.7B\n\nNo score change since it was listed.\n\n### occamy-1.0\n\n- no change on 3 November 2026: correction\n\n## Verdicts\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**occamy-1.0.** A Qwen3.6-35B-A3B fine-tuned for long agent work with tools, files and code, under plain Apache 2.0, and Accio's 4-bit builds run in about 23 GB. The main gaps are no safety results, agent guidance or security contact, and every benchmark figure is Accio's own.\n\n## Before you call either\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### occamy-1.0\n\n1. Serve with `--reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder` on vLLM 0.19.0 or SGLang 0.5.10 and later\n2. BF16 is 70.2 GB, so on a 128 GB machine run it without the card's 8-way tensor parallelism\n3. Keep the whole assistant message, reasoning and tool calls included, in later turns, as the card says\n4. Sampling per the card is temperature 1.0, top_p 0.95, top_k 20 and presence_penalty 1.5\n5. With the GGUFs, NFC-normalise input text and read TOKENIZER.md, since the files store qwen2 pre-tokenizer metadata\n6. The base's MTP layer isn't in this checkpoint, Accio-Lab/occamy-1.0-MTP is an experimental separate head\n7. Ollama has no Occamy library entry, so load Accio's GGUF in Ollama 0.40.14 or later, which builds a llama.cpp with the qwen35moe architecture\n\n## Questions\n\n### Which is better for AI agents, LFM3-1.7B or occamy-1.0?\n\nLFM3-1.7B and occamy-1.0 score within a point of each other on agent readiness, 61 (C) and 61 (C). occamy-1.0 leads on reach, risk and documentation and upkeep.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/lfm3-1-7b-vs-occamy-1-0.json, and with the fewest tokens: https://www.anchorterminal.com/compare/lfm3-1-7b-vs-occamy-1-0.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"lfm3-1-7b\", \"b\": \"occamy-1-0\"}`. From a terminal: `anchor compare lfm3-1-7b occamy-1-0`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/lfm3-1-7b.json and https://www.anchorterminal.com/api/v1/tools/occamy-1-0.json\n\n## Other comparisons with LFM3-1.7B or occamy-1.0\n\n- [Agents-A1-4B vs LFM3-1.7B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-lfm3-1-7b.md)\n- [Agents-A1-4B vs occamy-1.0](https://www.anchorterminal.com/compare/agents-a1-4b-vs-occamy-1-0.md)\n- [Apertus v1.5 8B vs LFM3-1.7B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-lfm3-1-7b.md)\n- [Apertus v1.5 8B vs occamy-1.0](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-occamy-1-0.md)\n- [Apodex-1.1-mini vs LFM3-1.7B](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-lfm3-1-7b.md)\n- [Apodex-1.1-mini vs occamy-1.0](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-occamy-1-0.md)\n- [Arete-27B vs LFM3-1.7B](https://www.anchorterminal.com/compare/arete-27b-vs-lfm3-1-7b.md)\n- [Arete-27B vs occamy-1.0](https://www.anchorterminal.com/compare/arete-27b-vs-occamy-1-0.md)\n- [BigBang-v1 vs LFM3-1.7B](https://www.anchorterminal.com/compare/bigbang-v1-vs-lfm3-1-7b.md)\n- [BigBang-v1 vs occamy-1.0](https://www.anchorterminal.com/compare/bigbang-v1-vs-occamy-1-0.md)\n- [Carbon-X vs LFM3-1.7B](https://www.anchorterminal.com/compare/carbon-x-vs-lfm3-1-7b.md)\n- [Carbon-X vs occamy-1.0](https://www.anchorterminal.com/compare/carbon-x-vs-occamy-1-0.md)\n- [Cola2 vs LFM3-1.7B](https://www.anchorterminal.com/compare/cola2-vs-lfm3-1-7b.md)\n- [Cola2 vs occamy-1.0](https://www.anchorterminal.com/compare/cola2-vs-occamy-1-0.md)\n- [Cosmos-Reason2-2B vs LFM3-1.7B](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-lfm3-1-7b.md)\n- [Cosmos-Reason2-2B vs occamy-1.0](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-occamy-1-0.md)\n- [Darwin-36B-Opus vs LFM3-1.7B](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-lfm3-1-7b.md)\n- [Darwin-36B-Opus vs occamy-1.0](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-occamy-1-0.md)\n- [DeepSeek-V3.2 vs LFM3-1.7B](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-lfm3-1-7b.md)\n- [DeepSeek-V3.2 vs occamy-1.0](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-occamy-1-0.md)\n- [DeepSeek V4.1 Flash vs LFM3-1.7B](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-lfm3-1-7b.md)\n- [DeepSeek V4.1 Flash vs occamy-1.0](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-occamy-1-0.md)\n- [DeepSeek-V4-Flash-0731 vs LFM3-1.7B](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-lfm3-1-7b.md)\n- [DeepSeek-V4-Flash-0731 vs occamy-1.0](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-occamy-1-0.md)\n- [Devstral Small 2 vs LFM3-1.7B](https://www.anchorterminal.com/compare/devstral-small-2-vs-lfm3-1-7b.md)\n- [Devstral Small 2 vs occamy-1.0](https://www.anchorterminal.com/compare/devstral-small-2-vs-occamy-1-0.md)\n- [DiffusionGemma 26B A4B IT vs LFM3-1.7B](https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-lfm3-1-7b.md)\n- [DiffusionGemma 26B A4B IT vs occamy-1.0](https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-occamy-1-0.md)\n- [EXAONE 4.5 33B vs LFM3-1.7B](https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-lfm3-1-7b.md)\n- [EXAONE 4.5 33B vs occamy-1.0](https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-occamy-1-0.md)\n- [Gemma 4 12B IT vs LFM3-1.7B](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-lfm3-1-7b.md)\n- [Gemma 4 12B IT vs occamy-1.0](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-occamy-1-0.md)\n- [Gemma 4 26B A4B IT vs LFM3-1.7B](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-lfm3-1-7b.md)\n- [Gemma 4 26B A4B IT vs occamy-1.0](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-occamy-1-0.md)\n- [Gemma 4 31B IT vs LFM3-1.7B](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-lfm3-1-7b.md)\n- [Gemma 4 31B IT vs occamy-1.0](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-occamy-1-0.md)\n- [Gemma 4 E2B IT vs LFM3-1.7B](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-lfm3-1-7b.md)\n- [Gemma 4 E2B IT vs occamy-1.0](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-occamy-1-0.md)\n- [Gemma 4 E4B IT vs LFM3-1.7B](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-lfm3-1-7b.md)\n- [Gemma 4 E4B IT vs occamy-1.0](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-occamy-1-0.md)\n- [GLM-4.5-Air vs LFM3-1.7B](https://www.anchorterminal.com/compare/glm-4-5-air-vs-lfm3-1-7b.md)\n- [GLM-4.5-Air vs occamy-1.0](https://www.anchorterminal.com/compare/glm-4-5-air-vs-occamy-1-0.md)\n- [GLM-4.7-Flash vs LFM3-1.7B](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-lfm3-1-7b.md)\n- [GLM-4.7-Flash vs occamy-1.0](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-occamy-1-0.md)\n- [GLM-5.3-Flash vs LFM3-1.7B](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-lfm3-1-7b.md)\n- [GLM-5.3-Flash vs occamy-1.0](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-occamy-1-0.md)\n- [GLM-5.3 vs LFM3-1.7B](https://www.anchorterminal.com/compare/glm-5-3-vs-lfm3-1-7b.md)\n- [GLM-5.3 vs occamy-1.0](https://www.anchorterminal.com/compare/glm-5-3-vs-occamy-1-0.md)\n- [gpt-oss-120b vs LFM3-1.7B](https://www.anchorterminal.com/compare/gpt-oss-120b-vs-lfm3-1-7b.md)\n- [gpt-oss-120b vs occamy-1.0](https://www.anchorterminal.com/compare/gpt-oss-120b-vs-occamy-1-0.md)\n- [gpt-oss-20b vs LFM3-1.7B](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-lfm3-1-7b.md)\n- [gpt-oss-20b vs occamy-1.0](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-occamy-1-0.md)\n- [Granite 4.2 30B vs LFM3-1.7B](https://www.anchorterminal.com/compare/granite-4-2-30b-vs-lfm3-1-7b.md)\n- [Granite 4.2 30B vs occamy-1.0](https://www.anchorterminal.com/compare/granite-4-2-30b-vs-occamy-1-0.md)\n- [Granite 4.2 3B vs LFM3-1.7B](https://www.anchorterminal.com/compare/granite-4-2-3b-vs-lfm3-1-7b.md)\n- [Granite 4.2 3B vs occamy-1.0](https://www.anchorterminal.com/compare/granite-4-2-3b-vs-occamy-1-0.md)\n- [Granite 4.2 8B vs LFM3-1.7B](https://www.anchorterminal.com/compare/granite-4-2-8b-vs-lfm3-1-7b.md)\n- [Granite 4.2 8B vs occamy-1.0](https://www.anchorterminal.com/compare/granite-4-2-8b-vs-occamy-1-0.md)\n- [Hy-Memory-30B-A3B vs LFM3-1.7B](https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-lfm3-1-7b.md)\n- [Hy-Memory-30B-A3B vs occamy-1.0](https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-occamy-1-0.md)\n- [Hy3 vs LFM3-1.7B](https://www.anchorterminal.com/compare/hy3-vs-lfm3-1-7b.md)\n- [Hy3 vs occamy-1.0](https://www.anchorterminal.com/compare/hy3-vs-occamy-1-0.md)\n- [Inkling-Small vs LFM3-1.7B](https://www.anchorterminal.com/compare/inkling-small-vs-lfm3-1-7b.md)\n- [Inkling-Small vs occamy-1.0](https://www.anchorterminal.com/compare/inkling-small-vs-occamy-1-0.md)\n- [Inkling vs LFM3-1.7B](https://www.anchorterminal.com/compare/inkling-vs-lfm3-1-7b.md)\n- [Inkling vs occamy-1.0](https://www.anchorterminal.com/compare/inkling-vs-occamy-1-0.md)\n- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs LFM3-1.7B](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-lfm3-1-7b.md)\n- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs occamy-1.0](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-occamy-1-0.md)\n- [Kimi K3.1 Pro vs LFM3-1.7B](https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-lfm3-1-7b.md)\n- [Kimi K3.1 Pro vs occamy-1.0](https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-occamy-1-0.md)\n- [Kimi K3 vs LFM3-1.7B](https://www.anchorterminal.com/compare/kimi-k3-vs-lfm3-1-7b.md)\n- [Kimi K3 vs occamy-1.0](https://www.anchorterminal.com/compare/kimi-k3-vs-occamy-1-0.md)\n- [Kolibri-1 vs LFM3-1.7B](https://www.anchorterminal.com/compare/kolibri-1-vs-lfm3-1-7b.md)\n- [Kolibri-1 vs occamy-1.0](https://www.anchorterminal.com/compare/kolibri-1-vs-occamy-1-0.md)\n- [Kronos vs LFM3-1.7B](https://www.anchorterminal.com/compare/kronos-vs-lfm3-1-7b.md)\n- [Kronos vs occamy-1.0](https://www.anchorterminal.com/compare/kronos-vs-occamy-1-0.md)\n- [Laguna M.1 vs LFM3-1.7B](https://www.anchorterminal.com/compare/laguna-m-1-vs-lfm3-1-7b.md)\n- [Laguna M.1 vs occamy-1.0](https://www.anchorterminal.com/compare/laguna-m-1-vs-occamy-1-0.md)\n- [Laguna S 2.1 vs LFM3-1.7B](https://www.anchorterminal.com/compare/laguna-s-2-1-vs-lfm3-1-7b.md)\n- [Laguna S 2.1 vs occamy-1.0](https://www.anchorterminal.com/compare/laguna-s-2-1-vs-occamy-1-0.md)\n- [Laguna XS 2.1 vs LFM3-1.7B](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-lfm3-1-7b.md)\n- [Laguna XS 2.1 vs occamy-1.0](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-occamy-1-0.md)\n- [LBS-27B vs LFM3-1.7B](https://www.anchorterminal.com/compare/lbs-27b-vs-lfm3-1-7b.md)\n- [LBS-27B vs occamy-1.0](https://www.anchorterminal.com/compare/lbs-27b-vs-occamy-1-0.md)\n- [LFM2.5-2.6B vs LFM3-1.7B](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-lfm3-1-7b.md)\n- [LFM2.5-2.6B vs occamy-1.0](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-occamy-1-0.md)\n- [LFM3-1.7B vs LFM3-Audio-2B](https://www.anchorterminal.com/compare/lfm3-1-7b-vs-lfm3-audio-2b.md)\n- [LFM3-1.7B vs LFM3-Omni-2B](https://www.anchorterminal.com/compare/lfm3-1-7b-vs-lfm3-omni-2b.md)\n- [LFM3-1.7B vs LFM3-VL-2B](https://www.anchorterminal.com/compare/lfm3-1-7b-vs-lfm3-vl-2b.md)\n- [LFM3-1.7B vs Ling-3.0-tiny](https://www.anchorterminal.com/compare/lfm3-1-7b-vs-ling-3-0-tiny.md)\n- [LFM3-1.7B vs Ling-3.1-flash-1019](https://www.anchorterminal.com/compare/lfm3-1-7b-vs-ling-3-1-flash-1019.md)\n- [LFM3-1.7B vs LongCat-2.5](https://www.anchorterminal.com/compare/lfm3-1-7b-vs-longcat-2-5.md)\n- [LFM3-1.7B vs MiMo-V2.6 Distill Qwen 9B](https://www.anchorterminal.com/compare/lfm3-1-7b-vs-mimo-v2-6-distill-qwen-9b.md)\n- [LFM3-1.7B vs MiMo-V2.6-Flash-RL](https://www.anchorterminal.com/compare/lfm3-1-7b-vs-mimo-v2-6-flash-rl.md)\n- [LFM3-1.7B vs MiniCPM-o 4.5](https://www.anchorterminal.com/compare/lfm3-1-7b-vs-minicpm-o-4-5.md)\n- [LFM3-1.7B vs MiniCPM-V 4.7 1B](https://www.anchorterminal.com/compare/lfm3-1-7b-vs-minicpm-v-4-7-1b.md)\n- [LFM3-1.7B vs MiniCPM-V 4.7 35B-A3B](https://www.anchorterminal.com/compare/lfm3-1-7b-vs-minicpm-v-4-7-35b-a3b.md)\n- [LFM3-1.7B vs MiniCPM5-2B](https://www.anchorterminal.com/compare/lfm3-1-7b-vs-minicpm5-2b.md)\n- [LFM3-1.7B vs MiniMax-M2.5](https://www.anchorterminal.com/compare/lfm3-1-7b-vs-minimax-m2-5.md)\n- [LFM3-1.7B vs MiniMax-M2.7](https://www.anchorterminal.com/compare/lfm3-1-7b-vs-minimax-m2-7.md)\n- [LFM3-1.7B vs MiniMax-M3.1-Flash](https://www.anchorterminal.com/compare/lfm3-1-7b-vs-minimax-m3-1-flash.md)\n- [LFM3-1.7B vs Mistral Large 4](https://www.anchorterminal.com/compare/lfm3-1-7b-vs-mistral-large-4.md)\n- [LFM3-1.7B vs Muse Glimmer 30B](https://www.anchorterminal.com/compare/lfm3-1-7b-vs-muse-glimmer-30b.md)\n- [LFM3-1.7B vs Nanbeige4.2-3B](https://www.anchorterminal.com/compare/lfm3-1-7b-vs-nanbeige-4-2-3b.md)\n- [LFM3-1.7B vs Nanbeige4.3-33B](https://www.anchorterminal.com/compare/lfm3-1-7b-vs-nanbeige-4-3-33b.md)\n- [LFM3-1.7B vs NVIDIA Nemotron 3.5 Bolt E4B 4GB](https://www.anchorterminal.com/compare/lfm3-1-7b-vs-nemotron-3-5-bolt-e4b-4gb.md)\n- [LFM3-1.7B vs Nemotron 3.5 Lightning 30B A3B](https://www.anchorterminal.com/compare/lfm3-1-7b-vs-nemotron-3-5-lightning-30b-a3b.md)\n- [LFM3-1.7B vs Nemotron 3 Nano 30B A3B](https://www.anchorterminal.com/compare/lfm3-1-7b-vs-nemotron-3-nano-30b-a3b.md)\n- [LFM3-1.7B vs Nemotron 3 Nano 4B](https://www.anchorterminal.com/compare/lfm3-1-7b-vs-nemotron-3-nano-4b.md)\n- [LFM3-1.7B vs Nemotron 3 Nano Omni 30B A3B Reasoning](https://www.anchorterminal.com/compare/lfm3-1-7b-vs-nemotron-3-nano-omni-30b-a3b-reasoning.md)\n- [LFM3-1.7B vs Nemotron 3 Super 120B A12B](https://www.anchorterminal.com/compare/lfm3-1-7b-vs-nemotron-3-super-120b-a12b.md)\n- [LFM3-1.7B vs Nemotron 3 Ultra 550B A55B](https://www.anchorterminal.com/compare/lfm3-1-7b-vs-nemotron-3-ultra-550b-a55b.md)\n- [LFM3-1.7B vs North Mini Code 1.0](https://www.anchorterminal.com/compare/lfm3-1-7b-vs-north-mini-code-1-0.md)\n- [LFM3-1.7B vs Olmo 3 7B Think](https://www.anchorterminal.com/compare/lfm3-1-7b-vs-olmo-3-7b-think.md)\n- [LFM3-1.7B vs Ornith-1.5-122B](https://www.anchorterminal.com/compare/lfm3-1-7b-vs-ornith-1-5-122b.md)\n- [LFM3-1.7B vs Ornith-1.5-35B-A3B](https://www.anchorterminal.com/compare/lfm3-1-7b-vs-ornith-1-5-35b-a3b.md)\n- [LFM3-1.7B vs Ornith-1.5-397B](https://www.anchorterminal.com/compare/lfm3-1-7b-vs-ornith-1-5-397b.md)\n- [LFM3-1.7B vs Ornith-1.5-9B](https://www.anchorterminal.com/compare/lfm3-1-7b-vs-ornith-1-5-9b.md)\n- [LFM3-1.7B vs OTel-2.0-LLM-31B-IT](https://www.anchorterminal.com/compare/lfm3-1-7b-vs-otel-2-0-llm-31b-it.md)\n- [LFM3-1.7B vs Qwen-AgentWorld-35B-A3B](https://www.anchorterminal.com/compare/lfm3-1-7b-vs-qwen-agentworld-35b-a3b.md)\n- [LFM3-1.7B vs Qwen3-30B-A3B-Instruct-2507](https://www.anchorterminal.com/compare/lfm3-1-7b-vs-qwen3-30b-a3b-instruct-2507.md)\n- [LFM3-1.7B vs Qwen3-4B-Instruct-2507](https://www.anchorterminal.com/compare/lfm3-1-7b-vs-qwen3-4b-instruct-2507.md)\n- [LFM3-1.7B vs Qwen3.5-0.8B](https://www.anchorterminal.com/compare/lfm3-1-7b-vs-qwen3-5-0-8b.md)\n- [LFM3-1.7B vs Qwen3.5-122B-A10B](https://www.anchorterminal.com/compare/lfm3-1-7b-vs-qwen3-5-122b-a10b.md)\n- [LFM3-1.7B vs Qwen3.5-2B](https://www.anchorterminal.com/compare/lfm3-1-7b-vs-qwen3-5-2b.md)\n- [LFM3-1.7B vs Qwen3.5-397B-A17B](https://www.anchorterminal.com/compare/lfm3-1-7b-vs-qwen3-5-397b-a17b.md)\n- [LFM3-1.7B vs Qwen3.5-4B](https://www.anchorterminal.com/compare/lfm3-1-7b-vs-qwen3-5-4b.md)\n- [LFM3-1.7B vs Qwen3.5-9B](https://www.anchorterminal.com/compare/lfm3-1-7b-vs-qwen3-5-9b.md)\n- [LFM3-1.7B vs Qwen3.6-27B](https://www.anchorterminal.com/compare/lfm3-1-7b-vs-qwen3-6-27b.md)\n- [LFM3-1.7B vs Qwen3.6-35B-A3B](https://www.anchorterminal.com/compare/lfm3-1-7b-vs-qwen3-6-35b-a3b.md)\n- [LFM3-1.7B vs Qwen3.8-27B](https://www.anchorterminal.com/compare/lfm3-1-7b-vs-qwen3-8-27b.md)\n- [LFM3-1.7B vs Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/lfm3-1-7b-vs-qwen3-8-flash-next.md)\n- [LFM3-1.7B vs Qwen3-Coder-30B-A3B-Instruct](https://www.anchorterminal.com/compare/lfm3-1-7b-vs-qwen3-coder-30b-a3b-instruct.md)\n- [LFM3-1.7B vs Qwen3-Coder-Next](https://www.anchorterminal.com/compare/lfm3-1-7b-vs-qwen3-coder-next.md)\n- [LFM3-1.7B vs Qwen3-Omni-30B-A3B-Instruct](https://www.anchorterminal.com/compare/lfm3-1-7b-vs-qwen3-omni-30b-a3b-instruct.md)\n- [LFM3-1.7B vs 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Preview](https://www.anchorterminal.com/compare/occamy-1-0-vs-ripple-65b-preview.md)\n- [occamy-1.0 vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/occamy-1-0-vs-sensenova-loop-9b-preview.md)\n- [occamy-1.0 vs SmolLM3-3B](https://www.anchorterminal.com/compare/occamy-1-0-vs-smollm3-3b.md)\n- [occamy-1.0 vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/occamy-1-0-vs-spark-x2-6-4b.md)\n- [occamy-1.0 vs Step 3.7 Flash](https://www.anchorterminal.com/compare/occamy-1-0-vs-step-3-7-flash.md)\n- [occamy-1.0 vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/occamy-1-0-vs-swift-1-5-qwen3-8-27b.md)\n- [occamy-1.0 vs Swift 1.5 Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/occamy-1-0-vs-swift-1-5-qwen3-8-flash-next.md)\n- [occamy-1.0 vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/occamy-1-0-vs-thinkingcap-qwen3-8-27b.md)\n- [occamy-1.0 vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/occamy-1-0-vs-unlimited-ocr-2.md)\n- [occamy-1.0 vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/occamy-1-0-vs-wevisone-vl-2b.md)\n- [occamy-1.0 vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/occamy-1-0-vs-wevisone-vl-4b.md)\n- [occamy-1.0 vs ZAYA1-8B](https://www.anchorterminal.com/compare/occamy-1-0-vs-zaya1-8b.md)\n", "meta": { "attribution": "Anchor Terminal (https://www.anchorterminal.com)", "attributionRequired": true, "credit": "CC BY 4.0, credit Anchor Terminal and link the page or API address you took it from (https://www.anchorterminal.com/terms/#data)", "docs": "https://www.anchorterminal.com/docs/", "generatedAt": "2026-11-11", "license": "CC-BY-4.0", "method": "https://www.anchorterminal.com/benchmark/", "methodology": "0.7", "openapi": "https://www.anchorterminal.com/openapi.json", "preview": false, "run": "2026-10-01", "runLabel": "October 2026 research run", "terms": 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