{ "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": "MiniCPM-o 4.5 scores 61 (C) on agent readiness against LFM2.5-2.6B's 51.5 (D), and leads in 1 of 4 scored categories. LFM2.5-2.6B leads on reach, risk and agent interface.", "b": { "slug": "minicpm-o-4-5", "name": "MiniCPM-o 4.5", "vendor": "OpenBMB", "vendorUrl": "https://openbmb.cn", "kind": "weights", "category": "open-weights", "summary": "MiniCPM-o 4.5 is OpenBMB's open-weight omni-modal model of 9.4B parameters that takes images, video, audio and text and answers in text and speech, including full-duplex live conversation and voice cloning from a reference clip, under Apache 2.0.", "url": "https://www.anchorterminal.com/tools/minicpm-o-4-5", "markdownUrl": "https://www.anchorterminal.com/tools/minicpm-o-4-5.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/minicpm-o-4-5.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/minicpm-o-4-5.json", "repo": "https://huggingface.co/openbmb/MiniCPM-o-4_5", "license": "Apache-2.0; built from Qwen3-8B, SigLIP2, Whisper-medium and CosyVoice2, all Apache 2.0 on their Hub cards", "transports": [], "packages": [ { "registry": "huggingface", "name": "openbmb/MiniCPM-o-4_5" }, { "registry": "huggingface", "name": "openbmb/MiniCPM-o-4_5-gguf" }, { "registry": "huggingface", "name": "openbmb/MiniCPM-o-4_5-awq" }, { "registry": "huggingface", "name": "openbmb/MiniCPM-o-4_5-GPTQ" }, { "registry": "ollama", "name": "openbmb/minicpm-o4.5" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. None of OpenBMB's four repositories for the model is gated (checked 2026-10-21).", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0. OpenBMB also serves it as MiniCPM-O-4.5-9B on its own OpenAI-compatible API at api.modelbest.cn, with a key from platform.modelbest.cn, which isn't graded here. Running it yourself costs the hardware, 20.0 GB for the BF16 repository and 5.9 GB for the Q4_0 GGUF with its vision file (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://github.com/OpenSQZ/MiniCPM-V-CookBook", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "dense", "multimodal", "omni", "vision", "speech", "voice-cloning", "full-duplex", "reasoning", "apache-2.0", "safetensors", "gguf", "awq", "gptq", "self-hosted" ], "lastRelease": "2026-10-16", "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": 1197, "categoryRank": 35, "methodology": "0.6", "run": "2026-10-01", "scores": { "interface": 35, "reach": 90, "risk": 40, "upkeep": 66 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-10-21" }, "negative": 0, "verdict": "A small omni cousin that sees, listens and talks back, with full-duplex speech and voice cloning, in about 7 GB at 4-bit through llama.cpp or Ollama for text and images. Speech output needs OpenBMB's remote code or llama.cpp-omni, and 28 GB of GPU for the full demo. The main risks are repository code and pickle files and no safety results.", "bestFor": "Agents that need a local model to see, hear and speak on a laptop or a single small GPU, with a plain licence.", "strengths": [ "OpenBMB's 4-bit GGUF and Ollama builds fit in about 7 GB", "Images, video, audio and text in, text and streaming speech out, with full-duplex live conversation", "Plain Apache 2.0, no gate, and OpenBMB's own API serves it too", "Released vLLM, llama.cpp and Ollama run it, and OpenBMB publishes GGUF, AWQ and GPTQ builds", "Repository changed 5 days ago and earlier versions stay in their own repositories" ], "weaknesses": [ "Speech output and full duplex need trust_remote_code with Transformers 4.51.0 or OpenBMB's llama.cpp-omni", "Two pickle files (flow.pt and hift.pt) in the speech stage", "No safety results or guidance, though it clones voices from a short clip", "Tool calling, structured output and where the reasoning comes back aren't documented", "40,960 tokens of context, and OpenBMB answered only half the questions in the discussions" ], "agentNotes": [ "Quickest start for text and images is `ollama run openbmb/minicpm-o4.5`, about 6 GB", "With vLLM 0.16.0 or later pass `--trust-remote-code`, and send `stop_token_ids` [151643, 151645] as OpenBMB's guide does", "vLLM, llama.cpp and Ollama return text only; for speech out use the card's Transformers 4.51.0 code or OpenBMB's llama.cpp-omni", "In llama.cpp pass both the language GGUF and MiniCPM-o-4_5-vision-F16.gguf with `--mmproj`, and use `--jinja --reasoning-budget 0` to turn thinking off", "Keep prompts within 40,960 tokens", "Don't rely on tool calls or JSON output, neither is documented; validate any you get" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 2.8, "history": [ { "basis": "public evidence", "confidence": "low", "grade": "C", "methodology": "0.6", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 61 } ], "editorialScores": { "interface": 35, "reach": 90, "risk": 40, "upkeep": 66 }, "provenanceScore": 53, "checked": { "categories": { "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 openbmb/minicpm-o4.5\n# or, with vLLM 0.16.0 or later (text out):\nvllm serve openbmb/MiniCPM-o-4_5 --trust-remote-code --dtype auto \\\n --max-model-len 4096 --max-num-batched-tokens 2048", "http": "curl http://localhost:8000/v1/chat/completions \\\n -H 'Content-Type: application/json' \\\n -d '{\"model\": \"openbmb/MiniCPM-o-4_5\", \"messages\": [{\"role\": \"user\", \"content\": [{\"type\": \"image_url\", \"image_url\": {\"url\": \"https://example.com/photo.jpg\"}}, {\"type\": \"text\", \"text\": \"What is in this picture?\"}]}], \"stop_token_ids\": [151643, 151645]}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/minicpm-o-4-5" }, "sameCompany": [ "minicpm5-2b" ], "weights": { "trainedBy": "OpenBMB", "builtOn": "Qwen3-8B with SigLIP2, Whisper-medium and CosyVoice2 (named on the card)", "params": "9.4B", "context": 40960, "goodFor": "Omni-modal assistant for images, video, speech and text in, text and speech out, with live full-duplex conversation and voice cloning, small enough for a laptop at 4-bit.", "runtimes": [ "vLLM", "llama.cpp", "Ollama" ], "builds": [ { "name": "MiniCPM-o-4_5-gguf Q4_K_M", "url": "https://huggingface.co/openbmb/MiniCPM-o-4_5-gguf", "who": "publisher", "precision": "q4_k_m", "sizeGB": 6.12, "memoryGB": 8, "format": "gguf", "runsOn": [ "llama.cpp" ], "note": "5.03 GB language file plus the 1.10 GB vision file, the pair OpenBMB's llama.cpp guide uses. The audio, TTS and token2wav GGUF files (2.75 GB more) are for OpenBMB's llama.cpp-omni" }, { "name": "MiniCPM-o-4_5-gguf Q4_0", "url": "https://huggingface.co/openbmb/MiniCPM-o-4_5-gguf", "who": "publisher", "precision": "q4_0", "sizeGB": 5.87, "memoryGB": 8, "format": "gguf", "note": "4.77 GB language file plus the 1.10 GB vision file, the smallest 4-bit pair" }, { "name": "ollama openbmb/minicpm-o4.5:latest", "url": "https://ollama.com/openbmb/minicpm-o4.5/tags", "who": "publisher", "precision": "q4_k_m", "sizeGB": 6.1, "memoryGB": 8, "format": "ollama", "runsOn": [ "Ollama" ], "note": "In the openbmb namespace on ollama.com, taken as OpenBMB's, text and image input only, 40K context. q4_0 is 5.9 GB. Size as Ollama's tags page shows it" }, { "name": "MiniCPM-o-4_5-gguf Q8_0", "url": "https://huggingface.co/openbmb/MiniCPM-o-4_5-gguf", "who": "publisher", "precision": "q8_0", "sizeGB": 9.8, "memoryGB": 12, "format": "gguf", "note": "8.71 GB language file plus the 1.10 GB vision file" }, { "name": "MiniCPM-o-4_5-GPTQ", "url": "https://huggingface.co/openbmb/MiniCPM-o-4_5-GPTQ", "who": "publisher", "precision": "gptq-int4", "sizeGB": 9.77, "memoryGB": 12, "format": "safetensors", "note": "4-bit GPTQ, group size 128. Remote code. vLLM's quantisation table marks GPTQ unsupported on AMD GPUs" }, { "name": "MiniCPM-o-4_5-awq", "url": "https://huggingface.co/openbmb/MiniCPM-o-4_5-awq", "who": "publisher", "precision": "awq-int4", "sizeGB": 13.07, "memoryGB": 16, "format": "safetensors", "note": "4-bit AWQ language model with the vision, audio and TTS modules left unquantised, plus a bundled CosyVoice2-0.5B folder. Remote code" }, { "name": "MiniCPM-o-4_5-gguf F16", "url": "https://huggingface.co/openbmb/MiniCPM-o-4_5-gguf", "who": "publisher", "precision": "f16", "sizeGB": 17.48, "memoryGB": 24, "format": "gguf", "note": "16.38 GB language file plus MiniCPM-o-4_5-vision-F16.gguf (1.10 GB) for image input" }, { "name": "MiniCPM-o-4_5", "url": "https://huggingface.co/openbmb/MiniCPM-o-4_5", "who": "publisher", "precision": "bf16", "sizeGB": 20.05, "memoryGB": 32, "format": "safetensors", "runsOn": [ "vLLM", "Transformers (remote code)" ], "note": "Size is the Hub's file list summed, 18.7 GB of safetensors plus the token2wav stage, example media and code. Speech output needs the remote code with Transformers 4.51.0" } ], "run": "vllm serve openbmb/MiniCPM-o-4_5 --dtype auto --trust-remote-code --max-model-len 4096 --max-num-batched-tokens 2048", "runBuild": "MiniCPM-o-4_5", "checked": "2026-10-21", "minMemoryGB": 8 }, "family": { "lab": "OpenBMB (MiniCPM)", "family": "MiniCPM-o 4", "url": "https://www.anchorterminal.com/open-weights/#openbmb", "olderCount": 22 }, "alsoIn": [ "voice-agents" ], "area": "models", "provenance": { "legalEntity": "OpenBMB (named as the MiniCPM-o team on the technical report; the GitHub LICENSE is the unfilled Apache template)", "domain": "openbmb.cn", "domainRegistered": "", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://huggingface.co/openbmb/MiniCPM-o-4_5/commits/main", "securityTxt": "none", "checked": "2026-10-21", "notes": [ "The card links OpenBMB's GitHub repository, whose README links back to this Hugging Face repository. rdap.org has no RDAP service for .cn, so the domain's registration date wasn't checked.", "The listing is the weights, so the Apache 2.0 licence stands in for terms of service and no privacy policy is scored. OpenBMB's API at api.modelbest.cn has its own terms, not read.", "The changelog is the repository's commit history, whose messages are mostly README updates, with code fixes on 4 March and 6 July 2026. OpenBMB's GitHub README carries dated news entries (release on 3 February 2026, technical report on 7 May, API on 17 May).", "openbmb.cn/.well-known/security.txt and modelbest.cn/.well-known/security.txt answered 404 on 21 October 2026, and OpenBMB/MiniCPM-o has no security policy." ], "score": 53 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/minicpm-o-4-5.json" }, "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "Liquid AI", "b": "OpenBMB", "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; built from Qwen3-8B, SigLIP2, Whisper-medium and CosyVoice2, all Apache 2.0 on their Hub cards", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "yes", "b": "no", "name": "llms.txt" }, { "a": "2026-09-22", "b": "2026-10-16", "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": "", "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": "MiniCPM-o 4.5 scores 61 (C) on agent readiness against LFM2.5-2.6B's 51.5 (D), and leads in 1 of 4 scored categories. LFM2.5-2.6B leads on reach, risk and agent interface.", "question": "Which is better for AI agents, LFM2.5-2.6B or MiniCPM-o 4.5?" } ], "goodFor": [ { "aheadOn": [ "Reach, 95 against 90", "Risk, 50 against 40", "Agent interface, 75 against 35" ], "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": [ "Documentation and upkeep, 66 against 57" ], "also": null, "goodFor": "Agents that need a local model to see, hear and speak on a laptop or a single small GPU, with a plain licence.", "slug": "minicpm-o-4-5", "watchFor": "Speech output and full duplex need trust_remote_code with Transformers 4.51.0 or OpenBMB's llama.cpp-omni" } ], "job": { "capability": "model.open-weights", "name": "Open-weight models you run yourself" }, 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"url": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-lfm2-5-2-6b" }, { "json": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-minicpm-o-4-5.json", "title": "gpt-oss-20b vs MiniCPM-o 4.5", "url": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-minicpm-o-4-5" }, { "json": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-lfm2-5-2-6b.json", "title": "Granite 4.2 3B vs LFM2.5-2.6B", "url": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-lfm2-5-2-6b" }, { "json": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-minicpm-o-4-5.json", "title": "Granite 4.2 3B vs MiniCPM-o 4.5", "url": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-minicpm-o-4-5" }, { "json": "https://www.anchorterminal.com/compare/hy3-vs-lfm2-5-2-6b.json", "title": "Hy3 vs LFM2.5-2.6B", "url": "https://www.anchorterminal.com/compare/hy3-vs-lfm2-5-2-6b" }, { "json": "https://www.anchorterminal.com/compare/hy3-vs-minicpm-o-4-5.json", "title": "Hy3 vs MiniCPM-o 4.5", 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"https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-vl-2b-instruct" }, { "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-vl-30b-a3b-instruct.json", "title": "LFM2.5-2.6B vs Qwen3-VL-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-vl-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-vl-32b-instruct.json", "title": "LFM2.5-2.6B vs Qwen3-VL-32B-Instruct", "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-vl-32b-instruct" }, { "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-vl-4b-instruct.json", "title": "LFM2.5-2.6B vs Qwen3-VL-4B-Instruct", "url": "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-minicpm-o-4-5.json", "title": "Ling-3.0-tiny vs MiniCPM-o 4.5", "url": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-minicpm-o-4-5" }, { "json": "https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-minicpm-o-4-5.json", "title": "MiMo-V2.6 Distill Qwen 9B vs MiniCPM-o 4.5", "url": "https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-minicpm-o-4-5" }, { "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-minicpm5-2b.json", "title": "MiniCPM-o 4.5 vs MiniCPM5-2B", "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-minicpm5-2b" }, { "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-minimax-m2-5.json", "title": "MiniCPM-o 4.5 vs MiniMax-M2.5", "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-minimax-m2-5" }, { "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-minimax-m2-7.json", "title": "MiniCPM-o 4.5 vs MiniMax-M2.7", "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-minimax-m2-7" }, { "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-muse-glimmer-30b.json", "title": "MiniCPM-o 4.5 vs Muse Glimmer 30B", "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-muse-glimmer-30b" }, { "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-nemotron-3-5-lightning-30b-a3b.json", "title": "MiniCPM-o 4.5 vs Nemotron 3.5 Lightning 30B A3B", "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-nemotron-3-5-lightning-30b-a3b" }, { "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-nemotron-3-nano-30b-a3b.json", "title": "MiniCPM-o 4.5 vs Nemotron 3 Nano 30B A3B", "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-nemotron-3-nano-30b-a3b" }, { "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-nemotron-3-nano-4b.json", "title": "MiniCPM-o 4.5 vs Nemotron 3 Nano 4B", "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-nemotron-3-nano-4b" }, { "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-nemotron-3-nano-omni-30b-a3b-reasoning.json", "title": "MiniCPM-o 4.5 vs Nemotron 3 Nano Omni 30B A3B Reasoning", "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-nemotron-3-nano-omni-30b-a3b-reasoning" }, { "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-nemotron-3-super-120b-a12b.json", "title": "MiniCPM-o 4.5 vs Nemotron 3 Super 120B A12B", "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-nemotron-3-super-120b-a12b" }, { "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-nemotron-3-ultra-550b-a55b.json", "title": "MiniCPM-o 4.5 vs Nemotron 3 Ultra 550B A55B", "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-nemotron-3-ultra-550b-a55b" }, { "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-occamy-1-0.json", "title": "MiniCPM-o 4.5 vs occamy-1.0", "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-occamy-1-0" }, { "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/minicpm-o-4-5-vs-ornith-1-5-35b-a3b.json", "title": "MiniCPM-o 4.5 vs Ornith-1.5-35B-A3B", "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-ornith-1-5-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-ornith-1-5-397b.json", "title": "MiniCPM-o 4.5 vs Ornith-1.5-397B", "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-ornith-1-5-397b" }, { "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-ornith-1-5-9b.json", "title": "MiniCPM-o 4.5 vs Ornith-1.5-9B", "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-ornith-1-5-9b" }, { "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-30b-a3b-instruct-2507.json", "title": "MiniCPM-o 4.5 vs Qwen3-30B-A3B-Instruct-2507", "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-30b-a3b-instruct-2507" }, { "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-4b-instruct-2507.json", "title": "MiniCPM-o 4.5 vs Qwen3-4B-Instruct-2507", "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-4b-instruct-2507" }, { "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-5-0-8b.json", "title": "MiniCPM-o 4.5 vs Qwen3.5-0.8B", "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-5-0-8b" }, { "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-5-122b-a10b.json", "title": "MiniCPM-o 4.5 vs Qwen3.5-122B-A10B", "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-5-122b-a10b" }, { "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-5-2b.json", "title": "MiniCPM-o 4.5 vs Qwen3.5-2B", "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-5-2b" }, { "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-5-397b-a17b.json", "title": "MiniCPM-o 4.5 vs Qwen3.5-397B-A17B", "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-5-397b-a17b" }, { "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-5-4b.json", "title": "MiniCPM-o 4.5 vs Qwen3.5-4B", "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-5-4b" }, { "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-5-9b.json", "title": "MiniCPM-o 4.5 vs Qwen3.5-9B", "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-5-9b" }, { "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-6-27b.json", "title": "MiniCPM-o 4.5 vs Qwen3.6-27B", "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-6-27b" }, { "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-6-35b-a3b.json", "title": "MiniCPM-o 4.5 vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-8-27b.json", "title": "MiniCPM-o 4.5 vs Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-8-flash-next.json", "title": "MiniCPM-o 4.5 vs Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-coder-30b-a3b-instruct.json", "title": "MiniCPM-o 4.5 vs Qwen3-Coder-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-coder-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-coder-next.json", "title": "MiniCPM-o 4.5 vs Qwen3-Coder-Next", "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-coder-next" }, { "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-omni-30b-a3b-instruct.json", "title": "MiniCPM-o 4.5 vs Qwen3-Omni-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-omni-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-vl-2b-instruct.json", "title": "MiniCPM-o 4.5 vs Qwen3-VL-2B-Instruct", "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-vl-2b-instruct" }, { "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-vl-30b-a3b-instruct.json", "title": "MiniCPM-o 4.5 vs Qwen3-VL-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-vl-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-vl-32b-instruct.json", "title": "MiniCPM-o 4.5 vs Qwen3-VL-32B-Instruct", "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-vl-32b-instruct" }, { "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-vl-4b-instruct.json", "title": "MiniCPM-o 4.5 vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-vl-8b-instruct.json", "title": "MiniCPM-o 4.5 vs Qwen3-VL-8B-Instruct", "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-vl-8b-instruct" }, { "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwopus3-8-27b-flash-v2.json", "title": "MiniCPM-o 4.5 vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-smollm3-3b.json", "title": "MiniCPM-o 4.5 vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-spark-x2-6-4b.json", "title": "MiniCPM-o 4.5 vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-swift-1-5-qwen3-8-27b.json", "title": "MiniCPM-o 4.5 vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-swift-1-5-qwen3-8-flash-next.json", "title": "MiniCPM-o 4.5 vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-thinkingcap-qwen3-8-27b.json", "title": "MiniCPM-o 4.5 vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-thinkingcap-qwen3-8-27b" } ], "scores": [ { "key": "capability", "name": "Capability", "pending": true, "weight": 35 }, { "by": 5, "edge": "lfm2-5-2-6b", "key": "reach", "lfm2-5-2-6b": 95, "minicpm-o-4-5": 90, "name": "Reach", "weight": 25 }, { "by": 10, "edge": "lfm2-5-2-6b", "key": "risk", "lfm2-5-2-6b": 50, "minicpm-o-4-5": 40, "name": "Risk", "weight": 20 }, { "by": 40, "edge": "lfm2-5-2-6b", "key": "interface", "lfm2-5-2-6b": 75, "minicpm-o-4-5": 35, "name": "Agent interface", "weight": 10 }, { "by": 9, "edge": "minicpm-o-4-5", "key": "upkeep", "lfm2-5-2-6b": 57, "minicpm-o-4-5": 66, "name": "Documentation and upkeep", "weight": 10 } ], "summary": "MiniCPM-o 4.5 scores 61 (C) on agent readiness against LFM2.5-2.6B's 51.5 (D), and leads in 1 of 4 scored categories. LFM2.5-2.6B leads on reach, risk and agent interface. 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.", "minicpm-o-4-5": "A small omni cousin that sees, listens and talks back, with full-duplex speech and voice cloning, in about 7 GB at 4-bit through llama.cpp or Ollama for text and images. Speech output needs OpenBMB's remote code or llama.cpp-omni, and 28 GB of GPU for the full demo. The main risks are repository code and pickle files and no safety results." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-minicpm-o-4-5", "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-minicpm-o-4-5.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-minicpm-o-4-5.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-minicpm-o-4-5.min.md" }, "markdown": "MiniCPM-o 4.5 scores 61 (C) on agent readiness against LFM2.5-2.6B's 51.5 (D), and leads in 1 of 4 scored categories. LFM2.5-2.6B leads on reach, risk and agent interface. 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- MiniCPM-o 4.5: grade C, 61/100, rank #42 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/minicpm-o-4-5.md · JSON https://www.anchorterminal.com/api/v1/tools/minicpm-o-4-5.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- Best conversational voice-agent APIs: https://www.anchorterminal.com/best/voice-agents/index.md\n- All 388 voice agents comparisons: https://www.anchorterminal.com/compare/voice-agents/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 90\n- Risk, 50 against 40\n- Agent interface, 75 against 35\n\nWatch for: Commercial use isn't licensed for organisations with $10M or more of annual revenue\n\n### MiniCPM-o 4.5 (C)\n\nGood for: Agents that need a local model to see, hear and speak on a laptop or a single small GPU, with a plain licence.\n\nAhead on:\n- Documentation and upkeep, 66 against 57\n\nWatch for: Speech output and full duplex need trust_remote_code with Transformers 4.51.0 or OpenBMB's llama.cpp-omni\n\n\n## Score by category\n\n| Category | Weight | LFM2.5-2.6B | MiniCPM-o 4.5 | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 95 | 90 | LFM2.5-2.6B +5 |\n| Risk | 20% (20 this run) | 50 | 40 | LFM2.5-2.6B +10 |\n| Agent interface | 10% (10 this run) | 75 | 35 | LFM2.5-2.6B +40 |\n| Documentation and upkeep | 10% (10 this run) | 57 | 66 | MiniCPM-o 4.5 +9 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **51.5 · D** | **61 · C** | |\n\n## Facts side by side\n\n| Fact | LFM2.5-2.6B | MiniCPM-o 4.5 |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Liquid AI | OpenBMB |\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; built from Qwen3-8B, SigLIP2, Whisper-medium and CosyVoice2, all Apache 2.0 on their Hub cards |\n| Read-only variant documented | no | no |\n| llms.txt | yes | no |\n| Last release | 2026-09-22 | 2026-10-16 |\n| Terms last updated | no document linked | no document linked |\n| Privacy policy last updated | no document linked | 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**MiniCPM-o 4.5.** A small omni cousin that sees, listens and talks back, with full-duplex speech and voice cloning, in about 7 GB at 4-bit through llama.cpp or Ollama for text and images. Speech output needs OpenBMB's remote code or llama.cpp-omni, and 28 GB of GPU for the full demo. The main risks are repository code and pickle files and no safety results.\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### MiniCPM-o 4.5\n\n1. Quickest start for text and images is `ollama run openbmb/minicpm-o4.5`, about 6 GB\n2. With vLLM 0.16.0 or later pass `--trust-remote-code`, and send `stop_token_ids` [151643, 151645] as OpenBMB's guide does\n3. vLLM, llama.cpp and Ollama return text only; for speech out use the card's Transformers 4.51.0 code or OpenBMB's llama.cpp-omni\n4. In llama.cpp pass both the language GGUF and MiniCPM-o-4_5-vision-F16.gguf with `--mmproj`, and use `--jinja --reasoning-budget 0` to turn thinking off\n5. Keep prompts within 40,960 tokens\n6. Don't rely on tool calls or JSON output, neither is documented; validate any you get\n\n## Questions\n\n### Which is better for AI agents, LFM2.5-2.6B or MiniCPM-o 4.5?\n\nMiniCPM-o 4.5 scores 61 (C) on agent readiness against LFM2.5-2.6B's 51.5 (D), and leads in 1 of 4 scored categories. LFM2.5-2.6B leads on reach, risk and agent interface.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-minicpm-o-4-5.json, and with the fewest tokens: https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-minicpm-o-4-5.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"lfm2-5-2-6b\", \"b\": \"minicpm-o-4-5\"}`. From a terminal: `anchor compare lfm2-5-2-6b minicpm-o-4-5`\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/minicpm-o-4-5.json\n\n## Other comparisons with LFM2.5-2.6B or MiniCPM-o 4.5\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 MiniCPM-o 4.5](https://www.anchorterminal.com/compare/agents-a1-4b-vs-minicpm-o-4-5.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 MiniCPM-o 4.5](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-minicpm-o-4-5.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 MiniCPM-o 4.5](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-minicpm-o-4-5.md)\n- [BigBang-v1 vs 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