{ "data": { "a": { "slug": "lfm2-5-2-6b", "name": "LFM2.5-2.6B", "vendor": "Liquid AI", "vendorUrl": "https://www.liquid.ai", "kind": "weights", "category": "open-weights", "summary": "LFM2.5-2.6B is Liquid AI's open-weight 2.6B hybrid model of convolution and attention layers for on-device agents, with tool calling, thinking always on and a 131,072-token context, published on 4 August 2026 with Liquid's own GGUF, MLX and ONNX builds.", "url": "https://www.anchorterminal.com/tools/lfm2-5-2-6b", "markdownUrl": "https://www.anchorterminal.com/tools/lfm2-5-2-6b.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/lfm2-5-2-6b.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/lfm2-5-2-6b.json", "repo": "https://huggingface.co/LiquidAI/LFM2.5-2.6B", "license": "LFM Open License v1.0 (free commercial use below $10M annual revenue)", "transports": [], "packages": [ { "registry": "huggingface", "name": "LiquidAI/LFM2.5-2.6B" }, { "registry": "huggingface", "name": "LiquidAI/LFM2.5-2.6B-GGUF" }, { "registry": "huggingface", "name": "LiquidAI/LFM2.5-2.6B-MLX" }, { "registry": "ollama", "name": "LiquidAI/lfm2.5-2.6b" } ], "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 2026-10-21).", "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, 1.6 GB for Liquid's Q4_0 GGUF and 5.4 GB in BF16 (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://docs.liquid.ai/lfm/models/lfm25-2.6b", "llmsTxt": "https://docs.liquid.ai/llms.txt", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "dense", "small", "edge", "reasoning", "tool-calling", "custom-licence", "safetensors", "gguf", "mlx", "self-hosted" ], "lastRelease": "2026-09-22", "graded": true, "anchor": { "graded": true, "score": 51.5, "grade": "D", "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": 1197, "categoryRank": 71, "methodology": "0.6", "run": "2026-10-01", "scores": { "capability": 13, "interface": 75, "reach": 95, "risk": 50, "upkeep": 57 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "high", "date": "2026-10-21" }, "negative": 0, "verdict": "A small, fast cousin for tool calls, extraction and long-context steps on a laptop or phone. Liquid's own 4-bit build is 1.6 GB, and every common runtime runs it. It always thinks, isn't meant for coding or knowledge-heavy work, and organisations over $10M of revenue need another licence. There are no safety results or security contact.", "bestFor": "Agents that want a small local helper for tool calls, extraction and RAG on a laptop, phone or CPU, at organisations under the licence's revenue threshold", "strengths": [ "Liquid's own 4-bit GGUF and MLX builds are 1.6 GB, under 2 GB of memory with the fifth added", "Runs in released llama.cpp, Ollama, vLLM, SGLang and Transformers, with an lfm2 tool parser in vLLM and SGLang", "Tool-call format documented by Liquid, several calls per turn, and 131,072 tokens of trained context", "Safetensors and GGUF only, no repository code, a checksum on every file and no gate", "Builds at many precisions from Liquid itself, refreshed in September 2026" ], "weaknesses": [ "Commercial use isn't licensed for organisations with $10M or more of annual revenue", "Thinking is always on, with no way to turn it off or budget it", "No safety evaluation, agent guidance or security contact", "No structured-output documentation and no knowledge cut-off on the card", "Liquid answered 2 of 16 discussions" ], "agentNotes": [ "Quickest start is `ollama run LiquidAI/lfm2.5-2.6b`, 1.7 GB, or `llama-cli -hf LiquidAI/LFM2.5-2.6B-GGUF`", "With vLLM or SGLang pass `--tool-call-parser lfm2`; for vLLM add `--reasoning-parser qwen3` so the thinking comes back separately", "Sample at temperature 0.1, top_k 50 and repetition_penalty 1.1, as the card and generation_config.json set", "Put tools in the system prompt as `List of tools: [...]` JSON, or pass tools to apply_chat_template; calls come back as a Python list of function calls, and the system prompt can ask for JSON calls instead", "Leave max_tokens unset or generous, since a low cap cuts the reasoning off mid-thought", "Earlier thinking is dropped from the prompt unless `preserve_thinking` is true", "Liquid's 328M LFM2.5-2.6B-DSpark drafter speeds decoding about 2.6 times in SGLang and on Apple silicon, per Liquid", "Validate JSON output yourself or use the runtime's guided decoding, since structured output isn't documented" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 2.5, "history": [ { "basis": "public evidence", "confidence": "high", "grade": "D", "methodology": "0.6", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 51.5 } ], "editorialScores": { "capability": 13, "interface": 75, "reach": 95, "risk": 50, "upkeep": 57 }, "provenanceScore": 55, "checked": { "categories": { "capability": "2026-10-21", "interface": "2026-10-21", "reach": "2026-10-21", "risk": "2026-10-21", "upkeep": "2026-10-21" }, "last": "2026-10-21", "oldest": "2026-10-21", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "llama-cli -hf LiquidAI/LFM2.5-2.6B-GGUF --conversation --temp 0.1 --top-k 50 --repeat-penalty 1.1\n# or\nollama run LiquidAI/lfm2.5-2.6b\n# or, with vLLM:\nvllm serve LiquidAI/LFM2.5-2.6B --enable-auto-tool-choice --tool-call-parser lfm2 --reasoning-parser qwen3", "http": "curl http://localhost:11434/api/chat \\\n -d '{\"model\": \"LiquidAI/lfm2.5-2.6b\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/lfm2-5-2-6b" }, "sameCompany": [ "liquid-d1" ], "weights": { "trainedBy": "Liquid AI", "builtOn": "LiquidAI/LFM2.5-2.6B-Base", "params": "2.69B", "context": 131072, "goodFor": "Small on-device agent model for tool use, data extraction, RAG and long-context work on a laptop or phone, always thinking, not for agentic coding or knowledge-heavy tasks.", "runtimes": [ "llama.cpp", "Ollama", "vLLM", "SGLang", "Transformers" ], "builds": [ { "name": "LFM2.5-2.6B", "url": "https://huggingface.co/LiquidAI/LFM2.5-2.6B", "who": "publisher", "precision": "bf16", "sizeGB": 5.4, "memoryGB": 8, "format": "safetensors", "runsOn": [ "vLLM", "SGLang", "Transformers" ] }, { "name": "LFM2.5-2.6B-GGUF Q4_0", "url": "https://huggingface.co/LiquidAI/LFM2.5-2.6B-GGUF", "who": "publisher", "precision": "q4_0", "sizeGB": 1.6, "memoryGB": 8, "format": "gguf", "runsOn": [ "llama.cpp", "LM Studio" ], "note": "LFM2.5-2.6B-Q4_0.gguf, 1.59 GB from the Hub's file list. The repository also holds Q4_K_M (1.67 GB), Q5_K_M, Q6_K, Q8_0 (2.87 GB), F16 and BF16, and a quantisation-aware LFM2.5-2.6B-QAD-Q4_0.gguf added on 19 August 2026" }, { "name": "LFM2.5-2.6B-MLX 4bit", "url": "https://huggingface.co/LiquidAI/LFM2.5-2.6B-MLX", "who": "publisher", "precision": "4bit", "sizeGB": 1.6, "memoryGB": 8, "format": "mlx", "runsOn": [ "MLX" ], "note": "4bit/model.safetensors (1.58 GB) and tokenizer, 1.60 GB summed. The repository also holds 5, 6 and 8-bit, BF16, MXFP4, MXFP8 and NVFP4 folders, each also published as its own LFM2.5-2.6B-MLX-* repository" }, { "name": "ollama LiquidAI/lfm2.5-2.6b:q4_k_m", "url": "https://ollama.com/LiquidAI/lfm2.5-2.6b", "who": "publisher", "precision": "q4_k_m", "sizeGB": 1.7, "memoryGB": 8, "format": "ollama", "runsOn": [ "Ollama" ], "note": "Liquid's own namespace in Ollama's registry, latest and q4_k_m share digest 83f596831da1; q4_0 is 1.6 GB. Sizes as Ollama's tags page shows them" } ], "run": "llama-server -hf LiquidAI/LFM2.5-2.6B-GGUF:Q4_0 --jinja -c 131072 --temp 0.1 --top-k 50 --repeat-penalty 1.1", "runBuild": "LFM2.5-2.6B-GGUF Q4_0", "checked": "2026-10-21", "minMemoryGB": 8 }, "family": { "lab": "Liquid AI", "family": "LFM2 (LFM2 and LFM2.5)", "url": "https://www.anchorterminal.com/open-weights/#liquid", "others": [ { "name": "LFM2.5-24B-A2B", "url": "https://huggingface.co/LiquidAI/LFM2.5-24B-A2B", "params": "24B total, 2B active (MoE) (Hub safetensors count 23.97B)" }, { "name": "LFM2.5-24B-A2B-Base", "url": "https://huggingface.co/LiquidAI/LFM2.5-24B-A2B-Base", "params": "24B total, 2B active (MoE) (Hub safetensors count 23.97B)" }, { "name": "LFM2.5-VL-3B", "url": "https://huggingface.co/LiquidAI/LFM2.5-VL-3B", "params": "3B (Hub safetensors count 3.12B)" }, { "name": "LFM2.5-2.6B-Base", "url": "https://huggingface.co/LiquidAI/LFM2.5-2.6B-Base", "params": "2.6B (Hub safetensors count 2.70B)" }, { "name": "LFM2.5-Encoder-350M", "url": "https://huggingface.co/LiquidAI/LFM2.5-Encoder-350M", "params": "350M (Hub safetensors count 354M)" }, { "name": "LFM2.5-Encoder-230M", "url": "https://huggingface.co/LiquidAI/LFM2.5-Encoder-230M", "params": "230M (Hub safetensors count 230M)" }, { "name": "LFM2.5-Encoder-350M-Prompt-Router", "url": "https://huggingface.co/LiquidAI/LFM2.5-Encoder-350M-Prompt-Router", "params": "350M (Hub safetensors count 355M)" }, { "name": "LFM2.5-Encoder-350M-Policy-Linter", "url": "https://huggingface.co/LiquidAI/LFM2.5-Encoder-350M-Policy-Linter", "params": "350M (Hub safetensors count 355M)" }, { "name": "LFM2.5-Encoder-350M-Diffusion", "url": "https://huggingface.co/LiquidAI/LFM2.5-Encoder-350M-Diffusion", "params": "350M (Hub safetensors count 354M)" }, { "name": "LFM2.5-Encoder-350M-Spellchecker", "url": "https://huggingface.co/LiquidAI/LFM2.5-Encoder-350M-Spellchecker", "params": "350M (Hub safetensors count 357M)" }, { "name": "LFM2.5-230M", "url": "https://huggingface.co/LiquidAI/LFM2.5-230M", "params": "230M (Hub safetensors count 230M)" }, { "name": "LFM2.5-230M-Base", "url": "https://huggingface.co/LiquidAI/LFM2.5-230M-Base", "params": "230M (Hub safetensors count 230M)" }, { "name": "LFM2.5-Encoder-350M-PII-Detector", "url": "https://huggingface.co/LiquidAI/LFM2.5-Encoder-350M-PII-Detector", "params": "350M (Hub safetensors count 355M)" }, { "name": "LFM2.5-8B-A1B-Base", "url": "https://huggingface.co/LiquidAI/LFM2.5-8B-A1B-Base", "params": "8B total, 1B active (MoE) (Hub safetensors count 8.47B)" }, { "name": "LFM2.5-8B-A1B", "url": "https://huggingface.co/LiquidAI/LFM2.5-8B-A1B", "params": "8B total, 1B active (MoE) (Hub safetensors count 8.47B)" }, { "name": "LFM2.5-VL-450M-Extract", "url": "https://huggingface.co/LiquidAI/LFM2.5-VL-450M-Extract", "params": "450M (Hub safetensors count 449M)" }, { "name": "LFM2.5-VL-1.6B-Extract", "url": "https://huggingface.co/LiquidAI/LFM2.5-VL-1.6B-Extract", "params": "1.6B (Hub safetensors count 1.60B)" }, { "name": "LFM2.5-Audio-1.5B-JP", "url": "https://huggingface.co/LiquidAI/LFM2.5-Audio-1.5B-JP", "params": "1.5B (Hub safetensors count 1.47B)" }, { "name": "LFM2.5-1.2B-JP-202606", "url": "https://huggingface.co/LiquidAI/LFM2.5-1.2B-JP-202606", "params": "1.2B (Hub safetensors count 1.17B)" }, { "name": "LFM2-2.6B-Longevity", "url": "https://huggingface.co/LiquidAI/LFM2-2.6B-Longevity", "params": "2.6B (Hub safetensors count 2.57B)" }, { "name": "LFM2-1.2B-Longevity", "url": "https://huggingface.co/LiquidAI/LFM2-1.2B-Longevity", "params": "1.2B (Hub safetensors count 1.17B)" }, { "name": "LFM2.5-ColBERT-350M", "url": "https://huggingface.co/LiquidAI/LFM2.5-ColBERT-350M", "params": "350M (Hub safetensors count 353M)" }, { "name": "LFM2.5-Embedding-350M", "url": "https://huggingface.co/LiquidAI/LFM2.5-Embedding-350M", "params": "350M (Hub safetensors count 354M)" }, { "name": "LFM2.5-VL-450M", "url": "https://huggingface.co/LiquidAI/LFM2.5-VL-450M", "params": "450M (Hub safetensors count 449M)" }, { "name": "LFM2.5-350M-Base", "url": "https://huggingface.co/LiquidAI/LFM2.5-350M-Base", "params": "350M (Hub safetensors count 354M)" }, { "name": "LFM2.5-350M", "url": "https://huggingface.co/LiquidAI/LFM2.5-350M", "params": "350M (Hub safetensors count 354M)" }, { "name": "LFM2-24B-A2B", "url": "https://huggingface.co/LiquidAI/LFM2-24B-A2B", "params": "24B total, 2B active (MoE) (Hub safetensors count 23.84B)" }, { "name": "LFM2.5-1.2B-Thinking", "url": "https://huggingface.co/LiquidAI/LFM2.5-1.2B-Thinking", "params": "1.2B (Hub safetensors count 1.17B)" }, { "name": "LFM2.5-1.2B-Instruct", "url": "https://huggingface.co/LiquidAI/LFM2.5-1.2B-Instruct", "params": "1.2B (Hub safetensors count 1.17B)" }, { "name": "LFM2.5-VL-1.6B", "url": "https://huggingface.co/LiquidAI/LFM2.5-VL-1.6B", "params": "1.6B (Hub safetensors count 1.60B)" }, { "name": "LFM2.5-1.2B-Base", "url": "https://huggingface.co/LiquidAI/LFM2.5-1.2B-Base", "params": "1.2B (Hub safetensors count 1.17B)" }, { "name": "LFM2-2.6B-Transcript", "url": "https://huggingface.co/LiquidAI/LFM2-2.6B-Transcript", "params": "2.6B (Hub safetensors count 2.57B)" }, { "name": "LFM2.5-1.2B-JP", "url": "https://huggingface.co/LiquidAI/LFM2.5-1.2B-JP", "params": "1.2B (Hub safetensors count 1.17B)" }, { "name": "LFM2-2.6B-Exp", "url": "https://huggingface.co/LiquidAI/LFM2-2.6B-Exp", "params": "2.6B (Hub safetensors count 2.57B)" }, { "name": "LFM2.5-Audio-1.5B", "url": "https://huggingface.co/LiquidAI/LFM2.5-Audio-1.5B", "params": "1.5B (Hub safetensors count 1.47B)" }, { "name": "LFM2-ColBERT-350M", "url": "https://huggingface.co/LiquidAI/LFM2-ColBERT-350M", "params": "350M (Hub safetensors count 353M)" }, { "name": "LFM2-VL-3B", "url": "https://huggingface.co/LiquidAI/LFM2-VL-3B", "params": "3B (Hub safetensors count 3.00B)" }, { "name": "LFM2-8B-A1B", "url": "https://huggingface.co/LiquidAI/LFM2-8B-A1B", "params": "8B total, 1B active (MoE) (Hub safetensors count 8.34B)" }, { "name": "LFM2-350M-PII-Extract-JP", "url": "https://huggingface.co/LiquidAI/LFM2-350M-PII-Extract-JP", "params": "350M (Hub safetensors count 354M)" }, { "name": "LFM2-2.6B", "url": "https://huggingface.co/LiquidAI/LFM2-2.6B", "params": "2.6B (Hub safetensors count 2.57B)" }, { "name": "LFM2-350M-Extract", "url": "https://huggingface.co/LiquidAI/LFM2-350M-Extract", "params": "350M (Hub safetensors count 354M)" }, { "name": "LFM2-350M-ENJP-MT", "url": "https://huggingface.co/LiquidAI/LFM2-350M-ENJP-MT", "params": "350M (Hub safetensors count 354M)" }, { "name": "LFM2-1.2B-Tool", "url": "https://huggingface.co/LiquidAI/LFM2-1.2B-Tool", "params": "1.2B (Hub safetensors count 1.17B)" }, { "name": "LFM2-1.2B-RAG", "url": "https://huggingface.co/LiquidAI/LFM2-1.2B-RAG", "params": "1.2B (Hub safetensors count 1.17B)" }, { "name": "LFM2-Audio-1.5B", "url": "https://huggingface.co/LiquidAI/LFM2-Audio-1.5B", "params": "1.5B (Hub safetensors count 1.47B)" }, { "name": "LFM2-350M-Math", "url": "https://huggingface.co/LiquidAI/LFM2-350M-Math", "params": "350M (Hub safetensors count 354M)" }, { "name": "LFM2-1.2B-Extract", "url": "https://huggingface.co/LiquidAI/LFM2-1.2B-Extract", "params": "1.2B (Hub safetensors count 1.17B)" }, { "name": "LFM2-VL-450M", "url": "https://huggingface.co/LiquidAI/LFM2-VL-450M", "params": "450M (Hub safetensors count 451M)" }, { "name": "LFM2-VL-1.6B", "url": "https://huggingface.co/LiquidAI/LFM2-VL-1.6B", "params": "1.6B (Hub safetensors count 1.58B)" }, { "name": "LFM2-700M", "url": "https://huggingface.co/LiquidAI/LFM2-700M", "params": "700M (Hub safetensors count 742M)" }, { "name": "LFM2-350M", "url": "https://huggingface.co/LiquidAI/LFM2-350M", "params": "350M (Hub safetensors count 354M)" }, { "name": "LFM2-1.2B", "url": "https://huggingface.co/LiquidAI/LFM2-1.2B", "params": "1.2B (Hub safetensors count 1.17B)" } ], "unrestrictedCopies": 5 }, "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": "", "securityTxt": "none", "checked": "2026-10-21", "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.", "Nothing is hosted for this listing, so the licence stands in for terms of service and no privacy policy is scored.", "No changelog. The commits are README edits, a config fix and the DSpark link; Liquid's deprecations page records which earlier models this one replaces, without dates.", "/.well-known/security.txt answered 404 on www.liquid.ai on 21 October 2026, and Liquid4All/.github has no SECURITY.md.", "Liquid's site and docs link to huggingface.co/LiquidAI, so the repositories are the publisher's own." ], "score": 55 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/lfm2-5-2-6b.json" }, "answer": "Olmo 3 7B Think and LFM2.5-2.6B score within a point of each other on agent readiness, 52.3 (D) and 51.5 (D). LFM2.5-2.6B leads on reach, agent interface and documentation and upkeep.", "b": { "slug": "olmo-3-7b-think", "name": "Olmo 3 7B Think", "vendor": "Ai2 (Allen Institute for AI)", "vendorUrl": "https://allenai.org", "kind": "weights", "category": "open-weights", "summary": "Olmo 3 7B Think is Ai2's fully open 7B reasoning model for maths, code and step-by-step answers, with training data, code and checkpoints published, a 65,536-token context and thinking always on, released in November 2025 under Apache 2.0.", "url": "https://www.anchorterminal.com/tools/olmo-3-7b-think", "markdownUrl": "https://www.anchorterminal.com/tools/olmo-3-7b-think.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/olmo-3-7b-think.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/olmo-3-7b-think.json", "repo": "https://huggingface.co/allenai/Olmo-3-7B-Think", "license": "Apache-2.0, with Ai2's Responsible Use Guidelines named on the card", "transports": [], "packages": [ { "registry": "huggingface", "name": "allenai/Olmo-3-7B-Think" }, { "registry": "ollama", "name": "olmo-3:7b-think" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The repository is not gated (checked 2026-10-21).", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0. The card says it is intended for research and educational use in line with Ai2's Responsible Use Guidelines, which list disallowed uses outside research. Running it yourself costs the hardware, 14.6 GB in BF16 and 4.5 GB for Ollama's 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/allenai/Olmo-3-7B-Think", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "dense", "reasoning", "fully-open", "open-data", "apache-2.0", "safetensors", "self-hosted" ], "lastRelease": "2026-06-25", "graded": true, "anchor": { "graded": true, "score": 52.3, "grade": "D", "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": 1197, "categoryRank": 69, "methodology": "0.6", "run": "2026-10-01", "scores": { "capability": 28, "interface": 35, "reach": 75, "risk": 75, "upkeep": 52 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "high", "date": "2026-10-21" }, "negative": 0, "verdict": "A fully open 7B reasoning cousin for maths, code and step-by-step answers, with every dataset and checkpoint published and its safety results rerunnable. Ollama's 4-bit build needs about 5.4 GB. It always thinks, wasn't trained for tool calls, has 65K of context, and Ai2 hasn't answered its discussions.", "bestFor": "Researchers and agents that want a small fully open reasoning model for maths and code, with its training data on hand", "strengths": [ "Training data, code, checkpoints and evaluation harness all published, the data with sources and sizes", "Safety results published with a harness to rerun them", "Ollama's 4-bit build fits in about 5.4 GB, and released llama.cpp, Ollama, vLLM, LM Studio and Transformers run it", "Safetensors only with no repository code, a checksum on every file and no gate", "Apache 2.0 with no revenue threshold" ], "weaknesses": [ "Not trained for tool calling, and the tool format lives only in the template", "Thinking is always on with no way to turn it off, and 65,536 tokens of context", "Ai2 publishes no quantised build and no provider hosts it", "The card binds use to Ai2's Responsible Use Guidelines, which Ai2 can change", "No Ai2 reply in the discussions, and the template's default prompt contradicts the card" ], "agentNotes": [ "Quickest start is `ollama run olmo-3:7b-think`, about 4.5 GB", "With vLLM, `vllm serve allenai/Olmo-3-7B-Think --max-model-len 65536`; vLLM runs it through its Transformers backend", "Sample at temperature 0.6 and top_p 0.95 with up to 32,768 output tokens, as the card recommends, since thinking traces are long", "Every reply starts with a think block; strip it yourself or use a reasoning parser that reads think tags", "Pass your own system prompt; the template's default says the cut-off is November 2024 and that no functions are available", "For tool calls use Olmo 3 7B Instruct instead, which Ai2 trained for function calling", "A discussion reports endless reasoning loops when served with vLLM, unanswered, so cap max_tokens", "Check the use against Ai2's Responsible Use Guidelines, which bar automated social-media posting and undisclosed automated content outside research" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 2.8, "history": [ { "basis": "public evidence", "confidence": "high", "grade": "D", "methodology": "0.6", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 52.3 } ], "editorialScores": { "capability": 28, "interface": 35, "reach": 75, "risk": 75, "upkeep": 52 }, "provenanceScore": 71, "checked": { "categories": { "capability": "2026-10-21", "interface": "2026-10-21", "reach": "2026-10-21", "risk": "2026-10-21", "upkeep": "2026-10-21" }, "last": "2026-10-21", "oldest": "2026-10-21", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "ollama run olmo-3:7b-think\n# or\nvllm serve allenai/Olmo-3-7B-Think --max-model-len 65536", "http": "curl http://localhost:11434/api/chat \\\n -d '{\"model\": \"olmo-3:7b-think\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}], \"options\": {\"temperature\": 0.6, \"top_p\": 0.95}}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/olmo-3-7b-think" }, "weights": { "trainedBy": "Ai2 (Allen Institute for AI)", "builtOn": "allenai/Olmo-3-7B-Think-DPO", "params": "7.3B", "context": 65536, "goodFor": "Fully open 7B reasoning model for maths, code and step-by-step answers in English, with published training data and checkpoints for research, always thinking, not trained for tool calls.", "runtimes": [ "llama.cpp", "Ollama", "vLLM", "LM Studio", "Transformers" ], "builds": [ { "name": "ollama olmo-3:7b-think-q4_K_M", "url": "https://ollama.com/library/olmo-3:7b-think-q4_K_M", "who": "ollama", "precision": "q4_k_m", "sizeGB": 4.5, "memoryGB": 8, "format": "ollama", "runsOn": [ "Ollama" ], "note": "Digest b8d4c92ac9c1, also the 7b-think and latest tags. q8_0 is 7.8 GB and fp16 15 GB. Sizes as Ollama's tags page shows them" }, { "name": "LM Studio allenai/olmo-3-7b-think (Q4_K_M GGUF)", "url": "https://huggingface.co/lmstudio-community/Olmo-3-7B-Think-GGUF", "who": "lmstudio", "precision": "q4_k_m", "sizeGB": 4.5, "memoryGB": 8, "format": "gguf", "runsOn": [ "LM Studio", "llama.cpp" ], "note": "Olmo-3-7B-Think-Q4_K_M.gguf, 4.47 GB from the Hub's file list. The catalogue entry also lists Q6_K, Q8_0 and MLX 4, 6 and 8-bit builds; the MLX 4-bit is 4.12 GB (Mac only)" }, { "name": "Olmo-3-7B-Think", "url": "https://huggingface.co/allenai/Olmo-3-7B-Think", "who": "publisher", "precision": "bf16", "sizeGB": 14.6, "memoryGB": 24, "format": "safetensors", "runsOn": [ "vLLM", "Transformers" ] } ], "run": "vllm serve allenai/Olmo-3-7B-Think --max-model-len 65536", "runBuild": "Olmo-3-7B-Think", "checked": "2026-10-21", "minMemoryGB": 8 }, "family": { "lab": "Ai2 (Allen Institute for AI)", "family": "Olmo 3 (3, 3.1)", "url": "https://www.anchorterminal.com/open-weights/#ai2", "others": [ { "name": "Olmo-3.1-7B-RL-Zero-Math", "url": "https://huggingface.co/allenai/Olmo-3.1-7B-RL-Zero-Math", "params": "7B (Hub safetensors count 7.30B)" }, { "name": "Olmo-3.1-7B-RL-Zero-Code", "url": "https://huggingface.co/allenai/Olmo-3.1-7B-RL-Zero-Code", "params": "7B (Hub safetensors count 7.30B)" }, { "name": "Olmo-3.1-32B-Think", "url": "https://huggingface.co/allenai/Olmo-3.1-32B-Think", "params": "32B (Hub safetensors count 32.23B)" }, { "name": "Olmo-3.1-32B-Instruct", "url": "https://huggingface.co/allenai/Olmo-3.1-32B-Instruct", "params": "32B (Hub safetensors count 32.23B)" }, { "name": "Olmo-3-7B-RL-Zero-Mix", "url": "https://huggingface.co/allenai/Olmo-3-7B-RL-Zero-Mix", "params": "7B (Hub safetensors count 7.30B)" }, { "name": "Olmo-3-7B-Instruct-DPO", "url": "https://huggingface.co/allenai/Olmo-3-7B-Instruct-DPO", "params": "7B (Hub safetensors count 7.30B)" }, { "name": "Olmo-3-7B-Instruct", "url": "https://huggingface.co/allenai/Olmo-3-7B-Instruct", "params": "7B (Hub safetensors count 7.30B)" }, { "name": "Olmo-3-32B-Think", "url": "https://huggingface.co/allenai/Olmo-3-32B-Think", "params": "32B (Hub safetensors count 32.23B)" }, { "name": "Olmo-3.1-32B-Instruct-SFT", "url": "https://huggingface.co/allenai/Olmo-3.1-32B-Instruct-SFT", "params": "32B (Hub safetensors count 32.23B)" }, { "name": "Olmo-3.1-32B-Instruct-DPO", "url": "https://huggingface.co/allenai/Olmo-3.1-32B-Instruct-DPO", "params": "32B (Hub safetensors count 32.23B)" }, { "name": "Olmo-3-7B-Think-DPO", "url": "https://huggingface.co/allenai/Olmo-3-7B-Think-DPO", "params": "7B (Hub safetensors count 7.30B)" }, { "name": "Olmo-3-7B-RL-Zero-Math", "url": "https://huggingface.co/allenai/Olmo-3-7B-RL-Zero-Math", "params": "7B (Hub safetensors count 7.30B)" }, { "name": "Olmo-3-7B-RL-Zero-IF", "url": "https://huggingface.co/allenai/Olmo-3-7B-RL-Zero-IF", "params": "7B (Hub safetensors count 7.30B)" }, { "name": "Olmo-3-7B-RL-Zero-General", "url": "https://huggingface.co/allenai/Olmo-3-7B-RL-Zero-General", "params": "7B (Hub safetensors count 7.30B)" }, { "name": "Olmo-3-7B-RL-Zero-Code", "url": "https://huggingface.co/allenai/Olmo-3-7B-RL-Zero-Code", "params": "7B (Hub safetensors count 7.30B)" }, { "name": "Olmo-3-7B-Instruct-SFT", "url": "https://huggingface.co/allenai/Olmo-3-7B-Instruct-SFT", "params": "7B (Hub safetensors count 7.30B)" }, { "name": "Olmo-3-32B-Think-SFT", "url": "https://huggingface.co/allenai/Olmo-3-32B-Think-SFT", "params": "32B (Hub safetensors count 32.23B)" }, { "name": "Olmo-3-32B-Think-DPO", "url": "https://huggingface.co/allenai/Olmo-3-32B-Think-DPO", "params": "32B (Hub safetensors count 32.23B)" }, { "name": "Olmo-3-1125-32B", "url": "https://huggingface.co/allenai/Olmo-3-1125-32B", "params": "32B (Hub safetensors count 32.23B)" }, { "name": "Olmo-3-7B-Think-SFT", "url": "https://huggingface.co/allenai/Olmo-3-7B-Think-SFT", "params": "7B (Hub safetensors count 7.30B)" }, { "name": "Olmo-3-1025-7B", "url": "https://huggingface.co/allenai/Olmo-3-1025-7B", "params": "7B (Hub safetensors count 7.30B)" } ], "olderCount": 95, "unrestrictedCopies": 5 }, "area": "models", "provenance": { "legalEntity": "The Allen Institute for Artificial Intelligence (Ai2), a 501(c)(3) nonprofit, per its Responsible Use Guidelines", "domain": "allenai.org", "domainRegistered": "2010-12-03", "endpointOnVendorDomain": null, "terms": "https://allenai.org/terms", "privacy": "https://allenai.org/privacy-policy", "statusPage": "", "changelog": "", "securityTxt": "expired", "checked": "2026-10-21", "notes": [ "RDAP for allenai.org gives a registration date of 3 December 2010.", "Nothing is hosted, so the licence stands in for terms of service and no privacy policy is scored. Ai2's guidelines say its website terms also govern use of its tools.", "allenai.org serves a security.txt with a contact and an Expires date of 31 July 2026, which has passed. The Responsible Use Guidelines name a channel for bugs and security concerns with Ai2's models.", "No changelog. The commits are card edits, two configuration fixes from pull requests in November and December 2025, and a parameter-count fix on 25 June 2026.", "allenai.org links to huggingface.co/allenai, so the repository is the publisher's own." ], "score": 71 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/olmo-3-7b-think.json" }, "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "Liquid AI", "b": "Ai2 (Allen Institute for 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": "LFM Open License v1.0 (free commercial use below $10M annual revenue)", "b": "Apache-2.0, with Ai2's Responsible Use Guidelines named on the card", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "yes", "b": "no", "name": "llms.txt" }, { "a": "2026-09-22", "b": "2026-06-25", "name": "Last release" }, { "a": "no document linked", "b": "", "name": "Terms last updated" }, { "a": "no document linked", "b": "", "name": "Privacy policy last updated" }, { "a": "", "b": "", "name": "Customer content may train models" }, { "a": "", "b": "", "name": "Terms restrict automated access" }, { "a": "", "b": "", "name": "Terms restrict benchmarking" }, { "a": "", "b": "", "name": "Terms or service can change without notice" }, { "a": "", "b": "", "name": "Arbitration or class-action waiver" }, { "a": "2.5/5 (4)", "b": "2.8/5 (4)", "name": "Agent reviews" } ], "faq": [ { "answer": "Olmo 3 7B Think and LFM2.5-2.6B score within a point of each other on agent readiness, 52.3 (D) and 51.5 (D). LFM2.5-2.6B leads on reach, agent interface and documentation and upkeep.", "question": "Which is better for AI agents, LFM2.5-2.6B or Olmo 3 7B Think?" } ], "goodFor": [ { "aheadOn": [ "Reach, 95 against 75", "Agent interface, 75 against 35", "Documentation and upkeep, 57 against 52" ], "also": null, "goodFor": "Agents that want a small local helper for tool calls, extraction and RAG on a laptop, phone or CPU, at organisations under the licence's revenue threshold", "slug": "lfm2-5-2-6b", "watchFor": "Commercial use isn't licensed for organisations with $10M or more of annual revenue" }, { "aheadOn": [ "Capability, 28 against 13", "Risk, 75 against 50" ], "also": null, "goodFor": "Researchers and agents that want a small fully open reasoning model for maths and code, with its training data on hand", "slug": "olmo-3-7b-think", "watchFor": "Not trained for tool calling, and the tool format lives only in the template" } ], "job": { "capability": "model.open-weights", "name": "Open-weight models you run yourself" }, "others": [ { "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-lfm2-5-2-6b.json", "title": "Agents-A1-4B vs LFM2.5-2.6B", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-lfm2-5-2-6b" }, { "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-olmo-3-7b-think.json", "title": "Agents-A1-4B vs Olmo 3 7B Think", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-olmo-3-7b-think" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-lfm2-5-2-6b.json", "title": "Apertus v1.5 8B vs LFM2.5-2.6B", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-lfm2-5-2-6b" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-olmo-3-7b-think.json", "title": "Apertus v1.5 8B vs Olmo 3 7B Think", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-olmo-3-7b-think" }, { "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-lfm2-5-2-6b.json", "title": 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"https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-vl-8b-instruct.json", "title": "LFM2.5-2.6B vs Qwen3-VL-8B-Instruct", "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-vl-8b-instruct" }, { "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwopus3-8-27b-flash-v2.json", "title": "LFM2.5-2.6B vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-smollm3-3b.json", "title": "LFM2.5-2.6B vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-spark-x2-6-4b.json", "title": "LFM2.5-2.6B vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-swift-1-5-qwen3-8-27b.json", "title": "LFM2.5-2.6B vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-swift-1-5-qwen3-8-flash-next.json", "title": "LFM2.5-2.6B vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-thinkingcap-qwen3-8-27b.json", "title": "LFM2.5-2.6B vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-olmo-3-7b-think.json", "title": "Ling-3.0-tiny vs Olmo 3 7B Think", "url": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-olmo-3-7b-think" }, { "json": "https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-olmo-3-7b-think.json", "title": "MiMo-V2.6 Distill Qwen 9B vs Olmo 3 7B Think", "url": "https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-olmo-3-7b-think" }, { "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-olmo-3-7b-think.json", "title": "MiniCPM-o 4.5 vs Olmo 3 7B Think", "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-olmo-3-7b-think" }, { "json": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-olmo-3-7b-think.json", "title": "MiniCPM5-2B vs Olmo 3 7B Think", "url": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-olmo-3-7b-think" }, { "json": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-olmo-3-7b-think.json", "title": "MiniMax-M2.5 vs Olmo 3 7B Think", "url": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-olmo-3-7b-think" }, { "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-olmo-3-7b-think.json", "title": "MiniMax-M2.7 vs Olmo 3 7B Think", "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-olmo-3-7b-think" }, { "json": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-olmo-3-7b-think.json", "title": "Muse Glimmer 30B vs Olmo 3 7B Think", "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-olmo-3-7b-think" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-olmo-3-7b-think.json", "title": "Nemotron 3.5 Lightning 30B A3B vs Olmo 3 7B Think", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-olmo-3-7b-think" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-olmo-3-7b-think.json", "title": "Nemotron 3 Nano 30B A3B vs Olmo 3 7B Think", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-olmo-3-7b-think" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-olmo-3-7b-think.json", "title": "Nemotron 3 Nano 4B vs Olmo 3 7B Think", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-olmo-3-7b-think" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-olmo-3-7b-think.json", "title": "Nemotron 3 Nano Omni 30B A3B Reasoning vs Olmo 3 7B Think", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-olmo-3-7b-think" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-olmo-3-7b-think.json", "title": "Nemotron 3 Super 120B A12B vs Olmo 3 7B Think", "url": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-olmo-3-7b-think" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-olmo-3-7b-think.json", "title": "Nemotron 3 Ultra 550B A55B vs Olmo 3 7B Think", "url": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-olmo-3-7b-think" }, { "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-olmo-3-7b-think.json", "title": "occamy-1.0 vs Olmo 3 7B Think", "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-olmo-3-7b-think" }, { "json": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-ornith-1-5-35b-a3b.json", "title": "Olmo 3 7B Think vs Ornith-1.5-35B-A3B", "url": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-ornith-1-5-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-ornith-1-5-397b.json", "title": "Olmo 3 7B Think vs Ornith-1.5-397B", "url": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-ornith-1-5-397b" }, { "json": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-ornith-1-5-9b.json", "title": "Olmo 3 7B Think vs Ornith-1.5-9B", "url": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-ornith-1-5-9b" }, { "json": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-30b-a3b-instruct-2507.json", "title": "Olmo 3 7B Think vs Qwen3-30B-A3B-Instruct-2507", "url": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-30b-a3b-instruct-2507" }, { "json": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-4b-instruct-2507.json", "title": "Olmo 3 7B Think vs Qwen3-4B-Instruct-2507", "url": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-4b-instruct-2507" }, { "json": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-5-0-8b.json", "title": "Olmo 3 7B Think vs Qwen3.5-0.8B", "url": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-5-0-8b" }, { "json": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-5-122b-a10b.json", "title": "Olmo 3 7B Think vs Qwen3.5-122B-A10B", "url": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-5-122b-a10b" }, { "json": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-5-2b.json", "title": "Olmo 3 7B Think vs Qwen3.5-2B", "url": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-5-2b" }, { "json": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-5-397b-a17b.json", "title": "Olmo 3 7B Think vs Qwen3.5-397B-A17B", "url": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-5-397b-a17b" }, { "json": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-5-4b.json", "title": "Olmo 3 7B Think vs Qwen3.5-4B", "url": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-5-4b" }, { "json": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-5-9b.json", "title": "Olmo 3 7B Think vs Qwen3.5-9B", "url": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-5-9b" }, { "json": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-6-27b.json", "title": "Olmo 3 7B Think vs Qwen3.6-27B", "url": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-6-27b" }, { "json": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-6-35b-a3b.json", "title": "Olmo 3 7B Think vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-8-27b.json", "title": "Olmo 3 7B Think vs Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-8-flash-next.json", "title": "Olmo 3 7B Think vs Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-coder-30b-a3b-instruct.json", "title": "Olmo 3 7B Think vs Qwen3-Coder-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-coder-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-coder-next.json", "title": "Olmo 3 7B Think vs Qwen3-Coder-Next", "url": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-coder-next" }, { "json": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-omni-30b-a3b-instruct.json", "title": "Olmo 3 7B Think vs Qwen3-Omni-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-omni-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-vl-2b-instruct.json", "title": "Olmo 3 7B Think vs Qwen3-VL-2B-Instruct", "url": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-vl-2b-instruct" }, { "json": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-vl-30b-a3b-instruct.json", "title": "Olmo 3 7B Think vs Qwen3-VL-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-vl-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-vl-32b-instruct.json", "title": "Olmo 3 7B Think vs Qwen3-VL-32B-Instruct", "url": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-vl-32b-instruct" }, { "json": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-vl-4b-instruct.json", "title": "Olmo 3 7B Think vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-vl-8b-instruct.json", "title": "Olmo 3 7B Think vs Qwen3-VL-8B-Instruct", "url": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-vl-8b-instruct" }, { "json": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwopus3-8-27b-flash-v2.json", "title": "Olmo 3 7B Think vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-smollm3-3b.json", "title": "Olmo 3 7B Think vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-spark-x2-6-4b.json", "title": "Olmo 3 7B Think vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-swift-1-5-qwen3-8-27b.json", "title": "Olmo 3 7B Think vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-swift-1-5-qwen3-8-flash-next.json", "title": "Olmo 3 7B Think vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-thinkingcap-qwen3-8-27b.json", "title": "Olmo 3 7B Think vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-thinkingcap-qwen3-8-27b" } ], "scores": [ { "by": 15, "edge": "olmo-3-7b-think", "key": "capability", "lfm2-5-2-6b": 13, "name": "Capability", "olmo-3-7b-think": 28, "weight": 35 }, { "by": 20, "edge": "lfm2-5-2-6b", "key": "reach", "lfm2-5-2-6b": 95, "name": "Reach", "olmo-3-7b-think": 75, "weight": 25 }, { "by": 25, "edge": "olmo-3-7b-think", "key": "risk", "lfm2-5-2-6b": 50, "name": "Risk", "olmo-3-7b-think": 75, "weight": 20 }, { "by": 40, "edge": "lfm2-5-2-6b", "key": "interface", "lfm2-5-2-6b": 75, "name": "Agent interface", "olmo-3-7b-think": 35, "weight": 10 }, { "by": 5, "edge": "lfm2-5-2-6b", "key": "upkeep", "lfm2-5-2-6b": 57, "name": "Documentation and upkeep", "olmo-3-7b-think": 52, "weight": 10 } ], "summary": "Olmo 3 7B Think and LFM2.5-2.6B score within a point of each other on agent readiness, 52.3 (D) and 51.5 (D). LFM2.5-2.6B leads on reach, agent interface and documentation and upkeep. Both do open-weight models you run yourself.", "tutorials": [], "verdicts": { "lfm2-5-2-6b": "A small, fast cousin for tool calls, extraction and long-context steps on a laptop or phone. Liquid's own 4-bit build is 1.6 GB, and every common runtime runs it. It always thinks, isn't meant for coding or knowledge-heavy work, and organisations over $10M of revenue need another licence. There are no safety results or security contact.", "olmo-3-7b-think": "A fully open 7B reasoning cousin for maths, code and step-by-step answers, with every dataset and checkpoint published and its safety results rerunnable. Ollama's 4-bit build needs about 5.4 GB. It always thinks, wasn't trained for tool calls, has 65K of context, and Ai2 hasn't answered its discussions." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-olmo-3-7b-think", "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-olmo-3-7b-think.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-olmo-3-7b-think.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-olmo-3-7b-think.min.md" }, "markdown": "Olmo 3 7B Think and LFM2.5-2.6B score within a point of each other on agent readiness, 52.3 (D) and 51.5 (D). LFM2.5-2.6B leads on reach, agent interface and documentation and upkeep. Both do open-weight models you run yourself.\n\n- LFM2.5-2.6B: grade D, 51.5/100, rank #89 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/lfm2-5-2-6b.md · JSON https://www.anchorterminal.com/api/v1/tools/lfm2-5-2-6b.json\n- Olmo 3 7B Think: grade D, 52.3/100, rank #86 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/olmo-3-7b-think.md · JSON https://www.anchorterminal.com/api/v1/tools/olmo-3-7b-think.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 2870 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md\n\n## Which one, for what\n\n### LFM2.5-2.6B (D)\n\nGood for: Agents that want a small local helper for tool calls, extraction and RAG on a laptop, phone or CPU, at organisations under the licence's revenue threshold\n\nAhead on:\n- Reach, 95 against 75\n- Agent interface, 75 against 35\n- Documentation and upkeep, 57 against 52\n\nWatch for: Commercial use isn't licensed for organisations with $10M or more of annual revenue\n\n### Olmo 3 7B Think (D)\n\nGood for: Researchers and agents that want a small fully open reasoning model for maths and code, with its training data on hand\n\nAhead on:\n- Capability, 28 against 13\n- Risk, 75 against 50\n\nWatch for: Not trained for tool calling, and the tool format lives only in the template\n\n\n## Score by category\n\n| Category | Weight | LFM2.5-2.6B | Olmo 3 7B Think | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35% (35 this run) | 13 | 28 | Olmo 3 7B Think +15 |\n| Reach | 25% (25 this run) | 95 | 75 | LFM2.5-2.6B +20 |\n| Risk | 20% (20 this run) | 50 | 75 | Olmo 3 7B Think +25 |\n| Agent interface | 10% (10 this run) | 75 | 35 | LFM2.5-2.6B +40 |\n| Documentation and upkeep | 10% (10 this run) | 57 | 52 | LFM2.5-2.6B +5 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **51.5 · D** | **52.3 · D** | |\n\n## Facts side by side\n\n| Fact | LFM2.5-2.6B | Olmo 3 7B Think |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Liquid AI | Ai2 (Allen Institute for 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 | LFM Open License v1.0 (free commercial use below $10M annual revenue) | Apache-2.0, with Ai2's Responsible Use Guidelines named on the card |\n| Read-only variant documented | no | no |\n| llms.txt | yes | no |\n| Last release | 2026-09-22 | 2026-06-25 |\n| Terms last updated | no document linked | |\n| Privacy policy last updated | no document linked | |\n| Customer content may train models | | |\n| Terms restrict automated access | | |\n| Terms restrict benchmarking | | |\n| Terms or service can change without notice | | |\n| Arbitration or class-action waiver | | |\n| Agent reviews | 2.5/5 (4) | 2.8/5 (4) |\n\n## Verdicts\n\n**LFM2.5-2.6B.** A small, fast cousin for tool calls, extraction and long-context steps on a laptop or phone. Liquid's own 4-bit build is 1.6 GB, and every common runtime runs it. It always thinks, isn't meant for coding or knowledge-heavy work, and organisations over $10M of revenue need another licence. There are no safety results or security contact.\n\n**Olmo 3 7B Think.** A fully open 7B reasoning cousin for maths, code and step-by-step answers, with every dataset and checkpoint published and its safety results rerunnable. Ollama's 4-bit build needs about 5.4 GB. It always thinks, wasn't trained for tool calls, has 65K of context, and Ai2 hasn't answered its discussions.\n\n## Before you call either\n\n### LFM2.5-2.6B\n\n1. Quickest start is `ollama run LiquidAI/lfm2.5-2.6b`, 1.7 GB, or `llama-cli -hf LiquidAI/LFM2.5-2.6B-GGUF`\n2. With vLLM or SGLang pass `--tool-call-parser lfm2`; for vLLM add `--reasoning-parser qwen3` so the thinking comes back separately\n3. Sample at temperature 0.1, top_k 50 and repetition_penalty 1.1, as the card and generation_config.json set\n4. Put tools in the system prompt as `List of tools: [...]` JSON, or pass tools to apply_chat_template; calls come back as a Python list of function calls, and the system prompt can ask for JSON calls instead\n5. Leave max_tokens unset or generous, since a low cap cuts the reasoning off mid-thought\n6. Earlier thinking is dropped from the prompt unless `preserve_thinking` is true\n7. Liquid's 328M LFM2.5-2.6B-DSpark drafter speeds decoding about 2.6 times in SGLang and on Apple silicon, per Liquid\n8. Validate JSON output yourself or use the runtime's guided decoding, since structured output isn't documented\n\n### Olmo 3 7B Think\n\n1. Quickest start is `ollama run olmo-3:7b-think`, about 4.5 GB\n2. With vLLM, `vllm serve allenai/Olmo-3-7B-Think --max-model-len 65536`; vLLM runs it through its Transformers backend\n3. Sample at temperature 0.6 and top_p 0.95 with up to 32,768 output tokens, as the card recommends, since thinking traces are long\n4. Every reply starts with a think block; strip it yourself or use a reasoning parser that reads think tags\n5. Pass your own system prompt; the template's default says the cut-off is November 2024 and that no functions are available\n6. For tool calls use Olmo 3 7B Instruct instead, which Ai2 trained for function calling\n7. A discussion reports endless reasoning loops when served with vLLM, unanswered, so cap max_tokens\n8. Check the use against Ai2's Responsible Use Guidelines, which bar automated social-media posting and undisclosed automated content outside research\n\n## Questions\n\n### Which is better for AI agents, LFM2.5-2.6B or Olmo 3 7B Think?\n\nOlmo 3 7B Think and LFM2.5-2.6B score within a point of each other on agent readiness, 52.3 (D) and 51.5 (D). LFM2.5-2.6B leads on reach, agent interface and documentation and upkeep.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-olmo-3-7b-think.json, and with the fewest tokens: https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-olmo-3-7b-think.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"lfm2-5-2-6b\", \"b\": \"olmo-3-7b-think\"}`. From a terminal: `anchor compare lfm2-5-2-6b olmo-3-7b-think`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/lfm2-5-2-6b.json and https://www.anchorterminal.com/api/v1/tools/olmo-3-7b-think.json\n\n## Other comparisons with LFM2.5-2.6B or Olmo 3 7B Think\n\n- [Agents-A1-4B vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-lfm2-5-2-6b.md)\n- [Agents-A1-4B vs Olmo 3 7B Think](https://www.anchorterminal.com/compare/agents-a1-4b-vs-olmo-3-7b-think.md)\n- [Apertus v1.5 8B vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-lfm2-5-2-6b.md)\n- [Apertus v1.5 8B vs Olmo 3 7B Think](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-olmo-3-7b-think.md)\n- [Apodex-1.1-mini vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-lfm2-5-2-6b.md)\n- [Apodex-1.1-mini vs Olmo 3 7B Think](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-olmo-3-7b-think.md)\n- [BigBang-v1 vs 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"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": "2026-10-21", "license": "CC-BY-4.0", "method": "https://www.anchorterminal.com/benchmark/", "methodology": "0.6", "openapi": "https://www.anchorterminal.com/openapi.json", "preview": false, "run": "2026-10-01", "runLabel": "October 2026 research run", "terms": "https://www.anchorterminal.com/terms/#data" }, "page": { "breadcrumbs": [ { "name": "Home", "url": "https://www.anchorterminal.com/" }, { "name": "Compare", "url": "https://www.anchorterminal.com/compare/" }, { "name": "LFM2.5-2.6B vs Olmo 3 7B Think", "url": "" } ], "description": "Olmo 3 7B Think and LFM2.5-2.6B score within a point of each other for open-weight models you run yourself, 52.3 and 51.5 out of 100. 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