{ "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. Liquid now lists it as deprecated, replaced by LFM3-1.7B.", "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", "dns": "lfm2-5-2-6b.grade.anchorterminal.com", "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": "2026-11-03", "graded": true, "anchor": { "graded": true, "score": 50.3, "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": 1518, "categoryRank": 108, "methodology": "0.7", "run": "2026-10-01", "scores": { "capability": 13, "interface": 75, "reach": 90, "risk": 50, "upkeep": 57 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "high", "date": "2026-11-04" }, "negative": 0, "verdict": "A small, fast model for tool calls, extraction and long-context steps on a laptop or phone, which Liquid now lists as deprecated in favour of LFM3-1.7B. Liquid's own 4-bit build is 1.6 GB and five common runtimes run it. It always thinks, 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": [ "Liquid lists it as deprecated, replaced by LFM3-1.7B; deprecated models stay downloadable but get no further updates", "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" ], "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": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 50.3 } ], "editorialScores": { "capability": 13, "interface": 75, "reach": 90, "risk": 50, "upkeep": 57 }, "provenanceScore": 62, "checked": { "categories": { "capability": "2026-11-04", "interface": "2026-11-04", "reach": "2026-11-04", "risk": "2026-11-04", "upkeep": "2026-11-04" }, "last": "2026-11-04", "lastMoved": "2026-11-04", "oldest": "2026-11-04", "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" }, "supersededBy": [ "lfm3-1-7b" ], "sameCompany": [ "liquid-d1", "lfm3-1-7b", "lfm3-audio-2b", "lfm3-omni-2b", "lfm3-vl-2b" ], "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, 1,593,894,912 bytes by its LFS pointer. The repository also holds Q4_K_M (1,674,455,040 bytes), Q5_K_M, Q6_K, Q8_0, 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, per the 21 October 2026 check" } ], "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": "2026-11-04", "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": "2026-11-04", "notes": [ "The licence names Liquid AI, Inc. as licensor. The domain registration date is carried from the 21 October check, which took it from a registry data service this run doesn't use.", "Nothing is hosted for this listing, so the licence stands in for terms of service and no privacy policy is scored. Liquid's site terms (30 September 2026) cover its hosted services.", "No changelog. The commits are README edits, a config fix and a generation-config fix; Liquid's deprecations page lists this model as deprecated, replaced by LFM3-1.7B, without a date.", "/.well-known/security.txt answered 404 on www.liquid.ai on 4 November 2026.", "Liquid's site and docs link to huggingface.co/LiquidAI, so the repositories are the publisher's own." ], "score": 62 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/lfm2-5-2-6b.json", "live": { "slug": "lfm2-5-2-6b", "securityTxt": { "url": "https://liquid.ai/.well-known/security.txt", "state": "none", "checkedAt": "2026-11-10T16:08:13.778560021Z" }, "llmsTxt": { "url": "https://docs.liquid.ai/llms.txt", "ok": true, "status": 200, "checkedAt": "2026-11-10T15:43:30.139732436Z" }, "domain": { "domain": "liquid.ai", "registered": "2017-12-16", "source": "https://rdap.identitydigital.services/rdap/domain/liquid.ai", "checkedAt": "2026-11-08T14:48:59.460510541Z" }, "updatedAt": "2026-11-10T16:08:13.778560021Z" } }, "answer": "MiniCPM-V 4.7 1B scores 61 (C) on agent readiness against LFM2.5-2.6B's 50.3 (D), and leads in 2 of 4 scored categories. LFM2.5-2.6B leads on agent interface and documentation and upkeep.", "b": { "slug": "minicpm-v-4-7-1b", "name": "MiniCPM-V 4.7 1B", "vendor": "OpenBMB", "vendorUrl": "https://openbmb.cn", "kind": "weights", "category": "open-weights", "summary": "MiniCPM-V 4.7 1B is OpenBMB's small open-weight vision-language model, 1.55B parameters, that reads images and video on a phone or laptop, calls tools, and ships in BF16, 4-bit and GGUF builds under Apache 2.0.", "url": "https://www.anchorterminal.com/tools/minicpm-v-4-7-1b", "markdownUrl": "https://www.anchorterminal.com/tools/minicpm-v-4-7-1b.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/minicpm-v-4-7-1b.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/minicpm-v-4-7-1b.json", "dns": "minicpm-v-4-7-1b.grade.anchorterminal.com", "repo": "https://huggingface.co/openbmb/MiniCPM-V-4.7-1B", "license": "Apache-2.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "openbmb/MiniCPM-V-4.7-1B" }, { "registry": "huggingface", "name": "openbmb/MiniCPM-V-4.7-1B-gguf" }, { "registry": "huggingface", "name": "openbmb/MiniCPM-V-4.7-1B-AWQ" }, { "registry": "huggingface", "name": "openbmb/MiniCPM-V-4.7-1B-GPTQ" }, { "registry": "huggingface", "name": "openbmb/MiniCPM-V-4.7-1B-int4" }, { "registry": "ollama", "name": "openbmb/minicpm-v4.7:1b" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The main repository and OpenBMB's GGUF, AWQ, GPTQ and int4 repositories are not gated (checked 2026-11-10).", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0, named in the card's metadata and carried as a LICENSE file in OpenBMB's GitHub repository. Running it yourself costs the hardware, 3.1 GB for the BF16 file or about 1.5 GB for the IQ4_XS GGUF with its projector (checked 2026-11-10).", "priceSummary": "Free", "where": "local", "x402": { "level": "no", "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-11-10).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-11-10" }, "docsUrl": "https://huggingface.co/openbmb/MiniCPM-V-4.7-1B", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "multimodal", "vision", "video", "on-device", "small", "tool-calling", "apache-2.0", "safetensors", "gguf", "awq", "gptq", "self-hosted" ], "lastRelease": "2026-10-30", "graded": true, "anchor": { "graded": true, "score": 61, "grade": "C", "agentReady": false, "rank": 0, "ranked": false, "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools", "rankOf": 1518, "categoryRank": 60, "methodology": "0.7", "run": "2026-10-01", "scores": { "interface": 70, "reach": 95, "risk": 55, "upkeep": 48 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-11-10" }, "negative": 0, "verdict": "A vision-language cousin small enough for a phone, under plain Apache 2.0, that every runtime we count can load. The projector is bigger than the language model, so plan for about 2 GB. The card says nothing about limits or training data and doesn't show how a tool call comes back.", "bestFor": "Agents that need a cheap local model to read screenshots, photos and short video on a phone, a laptop or beside a bigger model", "strengths": [ "Runs in about 2 GB from OpenBMB's own GGUF with its projector", "Runs in llama.cpp, Ollama, vLLM, SGLang and Transformers from their released versions", "Plain Apache 2.0, ungated, safetensors and GGUF only, no remote code", "Images, several images and video in one small model, with thinking off by default", "OpenBMB publishes 4-bit builds, a GGUF repository and a speculative-decoding head itself" ], "weaknesses": [ "No limitations section, safety results, agent-use guidance or security contact", "Training data isn't described, and the card gives no context length, languages or knowledge cut-off", "The card shows a tool-calling request but not the reply, and names no parser", "Nobody has asked a question on the repository yet, so there's no record of OpenBMB answering for this size", "No inference provider is mapped on Hugging Face" ], "agentNotes": [ "Start it with `llama-server -hf openbmb/MiniCPM-V-4.7-1B-gguf:Q4_K_M --jinja`, or `ollama run openbmb/minicpm-v4.7:1b`", "For image input in llama.cpp add `mmproj-model-f16.gguf`, or the smaller 4x projector", "The GGUF files at the repository root have thinking off. Use the files in `thinking/` when you want the trace", "Use temperature 0.7, top_p 0.95 and top_k 100 in instruct mode, as the card recommends", "Check tool calls in your runtime first, since the card names no parser", "Validate JSON output yourself, since structured output isn't documented" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 0, "avgRating": 0, "history": [ { "basis": "public evidence", "confidence": "low", "grade": "C", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 61 } ], "editorialScores": { "interface": 70, "reach": 95, "risk": 55, "upkeep": 48 }, "provenanceScore": 58, "checked": { "categories": { "interface": "2026-11-10", "reach": "2026-11-10", "risk": "2026-11-10", "upkeep": "2026-11-10" }, "last": "2026-11-10", "oldest": "2026-11-10", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "ollama run openbmb/minicpm-v4.7:1b\n# or, with llama.cpp and OpenBMB's own GGUF:\nllama-server -hf openbmb/MiniCPM-V-4.7-1B-gguf:Q4_K_M --jinja\n# or, with Transformers' server, as the card gives it:\npip install \"transformers[serving]\u003e=5.18.0\"\ntransformers serve openbmb/MiniCPM-V-4.7-1B --port 8000 --host 0.0.0.0", "http": "curl http://localhost:11434/api/chat \\\n -d '{\"model\": \"openbmb/minicpm-v4.7:1b\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/minicpm-v-4-7-1b" }, "sameCompany": [ "minicpm-v-4-7-35b-a3b", "minicpm-o-4-5", "minicpm5-2b" ], "weights": { "trainedBy": "OpenBMB (ModelBest and Tsinghua NLP Lab)", "builtOn": "Qwen3.5 and Qwen3.6 as the language backbone and SigLIP2 as the vision module, per the card's acknowledgements. Not declared in the metadata. The text config is qwen3_5_text with 24 layers", "params": "1.55B", "context": 262144, "goodFor": "Small vision-language model for images, several images and video on a phone or a laptop, with tool calling and thinking off by default.", "runtimes": [ "llama.cpp", "Ollama", "vLLM", "SGLang", "Transformers" ], "builds": [ { "name": "MiniCPM-V-4.7-1B", "url": "https://huggingface.co/openbmb/MiniCPM-V-4.7-1B", "who": "publisher", "precision": "bf16", "sizeGB": 3.11, "memoryGB": 8, "format": "safetensors", "runsOn": [ "vLLM (ROCm)", "SGLang (ROCm)", "Transformers" ] }, { "name": "MiniCPM-V-4.7-1B-AWQ", "url": "https://huggingface.co/openbmb/MiniCPM-V-4.7-1B-AWQ", "who": "publisher", "precision": "w4a16", "sizeGB": 2.37, "memoryGB": 8, "format": "safetensors", "runsOn": [ "vLLM" ], "note": "AWQ, 4-bit weights with 16-bit activations" }, { "name": "MiniCPM-V-4.7-1B-GPTQ", "url": "https://huggingface.co/openbmb/MiniCPM-V-4.7-1B-GPTQ", "who": "publisher", "precision": "w4a16", "sizeGB": 1.87, "memoryGB": 8, "format": "safetensors", "runsOn": [ "vLLM" ], "note": "GPTQ, 4-bit weights with 16-bit activations" }, { "name": "MiniCPM-V-4.7-1B-int4", "url": "https://huggingface.co/openbmb/MiniCPM-V-4.7-1B-int4", "who": "publisher", "precision": "int4", "sizeGB": 1.57, "memoryGB": 8, "format": "safetensors", "runsOn": [ "vLLM" ] }, { "name": "MiniCPM-V-4_7-1B-F16", "url": "https://huggingface.co/openbmb/MiniCPM-V-4.7-1B-gguf/blob/main/MiniCPM-V-4_7-1B-F16.gguf", "who": "publisher", "precision": "f16", "sizeGB": 3.13, "memoryGB": 8, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)" ], "note": "2.02 GB of weights plus the 1.11 GB projector" }, { "name": "MiniCPM-V-4_7-1B-Q8_0", "url": "https://huggingface.co/openbmb/MiniCPM-V-4.7-1B-gguf/blob/main/MiniCPM-V-4_7-1B-Q8_0.gguf", "who": "publisher", "precision": "q8_0", "sizeGB": 2.19, "memoryGB": 8, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)" ], "note": "1.08 GB of weights plus the 1.11 GB projector" }, { "name": "MiniCPM-V-4_7-1B-Q6_K", "url": "https://huggingface.co/openbmb/MiniCPM-V-4.7-1B-gguf/blob/main/MiniCPM-V-4_7-1B-Q6_K.gguf", "who": "publisher", "precision": "q6_k", "sizeGB": 1.95, "memoryGB": 8, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)" ], "note": "0.84 GB of weights plus the 1.11 GB projector" }, { "name": "MiniCPM-V-4_7-1B-Q4_K_M", "url": "https://huggingface.co/openbmb/MiniCPM-V-4.7-1B-gguf/blob/main/MiniCPM-V-4_7-1B-Q4_K_M.gguf", "who": "publisher", "precision": "q4_k_m", "sizeGB": 1.78, "memoryGB": 8, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)" ], "note": "0.67 GB of weights plus mmproj-model-f16.gguf (1.11 GB), or 1.57 GB with the 4x projector (0.90 GB). The same file with thinking on is in the thinking/ folder" }, { "name": "MiniCPM-V-4_7-1B-IQ4_XS", "url": "https://huggingface.co/openbmb/MiniCPM-V-4.7-1B-gguf/blob/main/MiniCPM-V-4_7-1B-IQ4_XS.gguf", "who": "publisher", "precision": "iq4_xs", "sizeGB": 1.54, "memoryGB": 8, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)" ], "note": "0.64 GB of weights plus the 0.90 GB 4x projector, the smallest at 4 bits. The IQ3 files are below the 4-bit line and are not listed" }, { "name": "ollama openbmb/minicpm-v4.7:1b", "url": "https://ollama.com/openbmb/minicpm-v4.7", "who": "publisher", "precision": "q4_k_m", "sizeGB": 1.8, "memoryGB": 8, "format": "ollama", "runsOn": [ "Ollama" ], "note": "OpenBMB's own namespace on Ollama, not Ollama's library. 1.8 GB as the tags page shows it, with q8_0 (2.2 GB) and fp16 (3.1 GB) tags beside it" } ], "run": "llama-server -hf openbmb/MiniCPM-V-4.7-1B-gguf:Q4_K_M --jinja", "runBuild": "MiniCPM-V-4_7-1B-Q4_K_M", "checked": "2026-11-10", "minMemoryGB": 8 }, "family": { "lab": "OpenBMB (MiniCPM)", "family": "MiniCPM-V 4", "url": "https://www.anchorterminal.com/open-weights/#openbmb", "others": [ { "name": "MiniCPM-V 4.7 35B-A3B", "url": "https://huggingface.co/openbmb/MiniCPM-V-4.7-35B-A3B", "params": "35.2B total, 3B active (MoE; 35,212,875,824 per the safetensors count)", "listing": "https://www.anchorterminal.com/tools/minicpm-v-4-7-35b-a3b" } ], "olderCount": 22 }, "area": "models", "provenance": { "legalEntity": "OpenBMB (named in the GitHub LICENSE's copyright line)", "domain": "openbmb.cn", "domainRegistered": "", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://github.com/OpenBMB/MiniCPM-V", "securityTxt": "none", "checked": "2026-11-10", "notes": [ "No legal entity is named on the card. OpenBMB is the open-source organisation of ModelBest and Tsinghua University's NLP lab, as the MiniCPM5-2B listing records.", "Nothing is hosted, so the Apache 2.0 licence stands in for terms of service and no privacy policy is scored.", "The changelog is the GitHub README's dated news list (the release on 12 October 2026, DSpark heads on 15 October) and the repositories' commit messages, which say what changed.", "openbmb.cn/.well-known/security.txt returns 404 and github.com/OpenBMB/MiniCPM-V has no SECURITY.md.", "unchecked: the domain's registration date." ], "score": 58 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/minicpm-v-4-7-1b.json" }, "edges": [ { "by": 9, "category": "upkeep", "leads": "lfm2-5-2-6b", "name": "Documentation and upkeep", "reasons": { "a": "The model card earns 17, with everything but a knowledge cut-off. Training data earns 5 for a general description without a cut-off. The last change earns 20, a card commit on 3 November 2026 naming LFM3-1.7B as the new version. Responsiveness earns 5, with Liquid replies in 2 of 16 discussions. Version policy earns 5, as Liquid now lists the model as deprecated with a replacement but no date. File health earns 5, since the files agree but a report about the card waits without a reply.", "b": "Model card 8, Intended use (image, multi-image and video understanding on device), architecture (early visual-token compression inside the vision encoder) and parameters (1B) are there. Training data 0, no description of the training data on the card. Last change 20, the last commit, on 30 October 2026, updated the card and removed an unused asset folder, eleven days before this check. Responsiveness 0, the repository has no discussions that aren't pull requests (two pull requests in all), so nobody has asked anything yet and the line reads as none answered, as settled in calibration. Version policy 10, MiniCPM-V 4.5 and 4.6 stay in their own repositories and the GitHub README dates each release. File health 10, Config, template and card agree on the architecture and the thinking switch, and no report waits without a reply, since there are no discussions. 8 + 0 + 20 + 0 + 10 + 10 = 48." }, "scores": { "a": 57, "b": 48 } }, { "by": 5, "category": "reach", "leads": "minicpm-v-4-7-1b", "name": "Reach", "reasons": { "a": "Memory earns the full 30, since Liquid's own 4-bit GGUF is 1.59 GB and fits 8 GB with room to spare. Runtimes earn 25, with the lfm2 architecture in released llama.cpp, vLLM, SGLang and Transformers, and in Ollama through the llama.cpp version it pins. Builds earn 15 for Liquid's own GGUF, MLX and ONNX builds at many precisions. Terms earn 10, a custom licence that is free for commercial use under $10M of annual revenue. Download earns 10, with no gate. Hosted earns nothing, as Hugging Face maps no inference provider and Liquid's docs list no hosted API for it.", "b": "Memory 30, OpenBMB's own IQ4_XS GGUF is 0.64 GB, 1.54 GB with the 0.90 GB vision projector, and 1.54 GB × 1.2 = 1.9 GB, which fits 16 GB with room to spare. Runtimes 25, MiniCPMV4_7ForConditionalGeneration in vLLM's registry at v0.33.0, the minicpmv4_7 model type in SGLang's minicpmv.py at v0.5.23, the minicpmv4_7 model in Transformers at v5.22.2, a MiniCPM-V 4.7 guide and conversion code in llama.cpp at b12242, and an openbmb/minicpm-v4.7:1b tag in OpenBMB's namespace on Ollama, read as the 35B-A3B listing's is. Builds 15, OpenBMB publishes AWQ, GPTQ and int4 safetensors builds and a GGUF repository from IQ3_XS to F16, so quantised builds at two precisions or more. Terms 15, Apache 2.0 in the card's metadata and as the LICENSE of OpenBMB's MiniCPM-V repository, an OSI licence. Download 10, gated is false on the main repository and on the GGUF, AWQ, GPTQ and int4 repositories, so the files download with no account or terms. Hosted 0, Hugging Face lists no inference provider for the model (an empty provider mapping on 10 November 2026), and no provider's own list was read. 30 + 25 + 15 + 15 + 10 + 0 = 95." }, "scores": { "a": 90, "b": 95 } }, { "by": 5, "category": "risk", "leads": "minicpm-v-4-7-1b", "name": "Risk", "reasons": { "a": "File safety earns 25, safetensors and GGUF only, and nothing from the repository runs to load it or build prompts. Provenance earns 15, files in the LiquidAI organisation Liquid links to, with a checksum for every file. Safety evaluation and agent guidance earn nothing, since Liquid publishes neither. Licence risk earns 10, losing 5 for the revenue threshold, with no use policy and no change since release. The security programme earns nothing, since no security.txt, disclosure policy or security contact was found.", "b": "File safety 25, safetensors in the main repository and the AWQ, GPTQ and int4 builds, GGUF in the GGUF build, with no .bin, .pt, .pth or .py files in the main repository. Provenance 15, files in the openbmb organisation that OpenBMB's GitHub README links to (10), with a SHA-256 for both large files in the main repository (5). Safety evaluation 0, no safety results on the card. Agent guidance 0, the card has no limitations or disclaimer section, and no prompt-injection guidance, instruction hierarchy or statement of trained refusals. Licence risk 15, Apache 2.0 has no use policy, field-of-use limit, threshold or change clause, nothing is added on the card, and the LICENSE in OpenBMB's GitHub repository resolved on 10 November 2026. Security programme 0, no SECURITY.md in OpenBMB/MiniCPM-V and no security.txt on openbmb.cn (both 404 on 10 November 2026), and no advisories found. 25 + 15 + 0 + 0 + 15 + 0 = 55." }, "scores": { "a": 50, "b": 55 } }, { "by": 5, "category": "interface", "leads": "lfm2-5-2-6b", "name": "Agent interface", "reasons": { "a": "The chat template earns 20 for chat_template.jinja in the repository. The tool-calling format earns 25, as Liquid's card and tool-use page say how tools go in and how calls come back, and its vLLM and SGLang pages name the lfm2 parser. Trained tool calling earns 20, with several calls in one Python list and agentic RL inside agent harnesses. Structured output earns nothing, as JSON output isn't documented for the model. Reasoning control earns nothing, since the model always thinks. Context earns 10 for 131,072 tokens trained.", "b": "Chat template 20, chat_template.jinja in the repository, 7,249 characters. Tool-calling format 10, the card shows tools declared in the OpenAI tools array in a request to Transformers' server, but not how a call comes back, and names no parser for vLLM or SGLang. Trained tool calling 20, the template loops over tool_calls, so it renders several calls in one assistant turn, and the card gives a tool-calling request as a supported use. Structured output 0, Neither JSON nor JSON Schema output is documented or claimed for the model on the card. Reasoning control 10, the card passes enable_thinking per request in its examples, the template has been off by default since 19 October 2026, and the GGUF repository keeps thinking builds in their own folder. Context 10, 262,144 tokens, max_position_embeddings in the text config. 20 + 10 + 20 + 0 + 10 + 10 = 70." }, "scores": { "a": 75, "b": 70 } } ], "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", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "yes", "b": "no", "name": "llms.txt" }, { "a": "2026-11-03", "b": "2026-10-30", "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": "2.5/5 (4)", "b": "none", "name": "Agent reviews" } ], "faq": [ { "answer": "MiniCPM-V 4.7 1B scores 61 (C) on agent readiness against LFM2.5-2.6B's 50.3 (D), and leads in 2 of 4 scored categories. LFM2.5-2.6B leads on agent interface and documentation and upkeep.", "question": "Which is better for AI agents, LFM2.5-2.6B or MiniCPM-V 4.7 1B?" } ], "goodFor": [ { "aheadOn": [ "Agent interface, 75 against 70", "Documentation and upkeep, 57 against 48" ], "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": "Liquid lists it as deprecated, replaced by LFM3-1.7B; deprecated models stay downloadable but get no further updates" }, { "aheadOn": [ "Reach, 95 against 90", "Risk, 55 against 50" ], "also": null, "goodFor": "Agents that need a cheap local model to read screenshots, photos and short video on a phone, a laptop or beside a bigger model", "slug": "minicpm-v-4-7-1b", "watchFor": "No limitations section, safety results, agent-use guidance or security contact" } ], "history": { "lfm2-5-2-6b": [ { "causes": [ "correction" ], "date": "2026-11-04", "delta": -1.2, "from": 51.5, "fromGrade": "D", "to": 50.3, "toGrade": "D" } ], "minicpm-v-4-7-1b": null }, "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-minicpm-v-4-7-1b.json", "title": "Agents-A1-4B vs MiniCPM-V 4.7 1B", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-minicpm-v-4-7-1b" }, { "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-minicpm-v-4-7-1b.json", "title": "Apertus v1.5 8B vs MiniCPM-V 4.7 1B", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-minicpm-v-4-7-1b" }, { "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-lfm2-5-2-6b.json", "title": "Apodex-1.1-mini vs LFM2.5-2.6B", "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-lfm2-5-2-6b" }, { "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-minicpm-v-4-7-1b.json", "title": "Apodex-1.1-mini vs MiniCPM-V 4.7 1B", "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-minicpm-v-4-7-1b" }, { "json": "https://www.anchorterminal.com/compare/arete-27b-vs-lfm2-5-2-6b.json", "title": "Arete-27B vs LFM2.5-2.6B", "url": "https://www.anchorterminal.com/compare/arete-27b-vs-lfm2-5-2-6b" }, { "json": "https://www.anchorterminal.com/compare/arete-27b-vs-minicpm-v-4-7-1b.json", "title": "Arete-27B vs MiniCPM-V 4.7 1B", "url": "https://www.anchorterminal.com/compare/arete-27b-vs-minicpm-v-4-7-1b" }, { "json": 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{ "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-wevisone-vl-4b.json", "title": "LFM2.5-2.6B vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-zaya1-8b.json", "title": "LFM2.5-2.6B vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-zaya1-8b" }, { "json": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-minicpm-v-4-7-1b.json", "title": "LFM3-1.7B vs MiniCPM-V 4.7 1B", "url": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-minicpm-v-4-7-1b" }, { "json": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-minicpm-v-4-7-1b.json", "title": "LFM3-Audio-2B vs MiniCPM-V 4.7 1B", "url": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-minicpm-v-4-7-1b" }, { "json": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-minicpm-v-4-7-1b.json", "title": "LFM3-Omni-2B vs MiniCPM-V 4.7 1B", "url": 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"https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-qwen3-6-35b-a3b.json", "title": "MiniCPM-V 4.7 1B vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-qwen3-8-27b.json", "title": "MiniCPM-V 4.7 1B vs Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-qwen3-8-flash-next.json", "title": "MiniCPM-V 4.7 1B vs Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-qwen3-coder-30b-a3b-instruct.json", "title": "MiniCPM-V 4.7 1B vs Qwen3-Coder-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-qwen3-coder-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-qwen3-coder-next.json", "title": "MiniCPM-V 4.7 1B vs Qwen3-Coder-Next", "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-qwen3-coder-next" }, { "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-qwen3-omni-30b-a3b-instruct.json", "title": "MiniCPM-V 4.7 1B vs Qwen3-Omni-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-qwen3-omni-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-qwen3-vl-2b-instruct.json", "title": "MiniCPM-V 4.7 1B vs Qwen3-VL-2B-Instruct", "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-qwen3-vl-2b-instruct" }, { "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-qwen3-vl-30b-a3b-instruct.json", "title": "MiniCPM-V 4.7 1B vs Qwen3-VL-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-qwen3-vl-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-qwen3-vl-32b-instruct.json", "title": "MiniCPM-V 4.7 1B vs Qwen3-VL-32B-Instruct", "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-qwen3-vl-32b-instruct" }, { "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-qwen3-vl-4b-instruct.json", "title": "MiniCPM-V 4.7 1B vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-qwen3-vl-8b-instruct.json", "title": "MiniCPM-V 4.7 1B vs Qwen3-VL-8B-Instruct", "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-qwen3-vl-8b-instruct" }, { "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-qwopus3-8-27b-flash-v2.json", "title": "MiniCPM-V 4.7 1B vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-recursive-flint.json", "title": "MiniCPM-V 4.7 1B vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-ripple-65b-preview.json", "title": "MiniCPM-V 4.7 1B vs Ripple-65B Preview", "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-ripple-65b-preview" }, { "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-sensenova-loop-9b-preview.json", "title": "MiniCPM-V 4.7 1B vs SenseNova-Loop-9B-Preview", "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-sensenova-loop-9b-preview" }, { "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-smollm3-3b.json", "title": "MiniCPM-V 4.7 1B vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-spark-x2-6-4b.json", "title": "MiniCPM-V 4.7 1B vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-step-3-7-flash.json", "title": "MiniCPM-V 4.7 1B vs Step 3.7 Flash", "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-step-3-7-flash" }, { "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-swift-1-5-qwen3-8-27b.json", "title": "MiniCPM-V 4.7 1B vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-swift-1-5-qwen3-8-flash-next.json", "title": "MiniCPM-V 4.7 1B vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-thinkingcap-qwen3-8-27b.json", "title": "MiniCPM-V 4.7 1B vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-unlimited-ocr-2.json", "title": "MiniCPM-V 4.7 1B vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-wevisone-vl-2b.json", "title": "MiniCPM-V 4.7 1B vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-wevisone-vl-4b.json", "title": "MiniCPM-V 4.7 1B vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-zaya1-8b.json", "title": "MiniCPM-V 4.7 1B vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-zaya1-8b" } ], "panel": null, "scores": [ { "key": "capability", "name": "Capability", "pending": true, "weight": 35 }, { "by": 5, "edge": "minicpm-v-4-7-1b", "key": "reach", "lfm2-5-2-6b": 90, "minicpm-v-4-7-1b": 95, "name": "Reach", "weight": 25 }, { "by": 5, "edge": "minicpm-v-4-7-1b", "key": "risk", "lfm2-5-2-6b": 50, "minicpm-v-4-7-1b": 55, "name": "Risk", "weight": 20 }, { "by": 5, "edge": "lfm2-5-2-6b", "key": "interface", "lfm2-5-2-6b": 75, "minicpm-v-4-7-1b": 70, "name": "Agent interface", "weight": 10 }, { "by": 9, "edge": "lfm2-5-2-6b", "key": "upkeep", "lfm2-5-2-6b": 57, "minicpm-v-4-7-1b": 48, "name": "Documentation and upkeep", "weight": 10 } ], "summary": "MiniCPM-V 4.7 1B scores 61 (C) on agent readiness against LFM2.5-2.6B's 50.3 (D), and leads in 2 of 4 scored categories. LFM2.5-2.6B leads on agent interface and documentation and upkeep. Both do open-weight models you run yourself.", "tasks": null, "tutorials": [], "verdicts": { "lfm2-5-2-6b": "A small, fast model for tool calls, extraction and long-context steps on a laptop or phone, which Liquid now lists as deprecated in favour of LFM3-1.7B. Liquid's own 4-bit build is 1.6 GB and five common runtimes run it. It always thinks, and organisations over $10M of revenue need another licence. There are no safety results or security contact.", "minicpm-v-4-7-1b": "A vision-language cousin small enough for a phone, under plain Apache 2.0, that every runtime we count can load. The projector is bigger than the language model, so plan for about 2 GB. The card says nothing about limits or training data and doesn't show how a tool call comes back." } }, "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-v-4-7-1b", "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-minicpm-v-4-7-1b.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-minicpm-v-4-7-1b.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-minicpm-v-4-7-1b.min.md" }, "markdown": "MiniCPM-V 4.7 1B scores 61 (C) on agent readiness against LFM2.5-2.6B's 50.3 (D), and leads in 2 of 4 scored categories. LFM2.5-2.6B leads on agent interface and documentation and upkeep. Both do open-weight models you run yourself.\n\n- LFM2.5-2.6B: grade D, 50.3/100, rank #146 of 160 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-V 4.7 1B: grade C, 61/100, rank #76 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/minicpm-v-4-7-1b.md · JSON https://www.anchorterminal.com/api/v1/tools/minicpm-v-4-7-1b.json\n- Best open-weight models you download and run yourself for AI agents: https://www.anchorterminal.com/best/open-weights/index.md\n- All 6359 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md\n\n## Which one, for what\n\n### 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- Agent interface, 75 against 70\n- Documentation and upkeep, 57 against 48\n\nWatch for: Liquid lists it as deprecated, replaced by LFM3-1.7B; deprecated models stay downloadable but get no further updates\n\n### MiniCPM-V 4.7 1B (C)\n\nGood for: Agents that need a cheap local model to read screenshots, photos and short video on a phone, a laptop or beside a bigger model\n\nAhead on:\n- Reach, 95 against 90\n- Risk, 55 against 50\n\nWatch for: No limitations section, safety results, agent-use guidance or security contact\n\n\n## Why the edge\n\n### Documentation and upkeep: LFM2.5-2.6B +9\n\n- LFM2.5-2.6B (57): The model card earns 17, with everything but a knowledge cut-off. Training data earns 5 for a general description without a cut-off. The last change earns 20, a card commit on 3 November 2026 naming LFM3-1.7B as the new version. Responsiveness earns 5, with Liquid replies in 2 of 16 discussions. Version policy earns 5, as Liquid now lists the model as deprecated with a replacement but no date. File health earns 5, since the files agree but a report about the card waits without a reply.\n- MiniCPM-V 4.7 1B (48): Model card 8, Intended use (image, multi-image and video understanding on device), architecture (early visual-token compression inside the vision encoder) and parameters (1B) are there. Training data 0, no description of the training data on the card. Last change 20, the last commit, on 30 October 2026, updated the card and removed an unused asset folder, eleven days before this check. Responsiveness 0, the repository has no discussions that aren't pull requests (two pull requests in all), so nobody has asked anything yet and the line reads as none answered, as settled in calibration. Version policy 10, MiniCPM-V 4.5 and 4.6 stay in their own repositories and the GitHub README dates each release. File health 10, Config, template and card agree on the architecture and the thinking switch, and no report waits without a reply, since there are no discussions. 8 + 0 + 20 + 0 + 10 + 10 = 48.\n\n### Reach: MiniCPM-V 4.7 1B +5\n\n- LFM2.5-2.6B (90): Memory earns the full 30, since Liquid's own 4-bit GGUF is 1.59 GB and fits 8 GB with room to spare. Runtimes earn 25, with the lfm2 architecture in released llama.cpp, vLLM, SGLang and Transformers, and in Ollama through the llama.cpp version it pins. Builds earn 15 for Liquid's own GGUF, MLX and ONNX builds at many precisions. Terms earn 10, a custom licence that is free for commercial use under $10M of annual revenue. Download earns 10, with no gate. Hosted earns nothing, as Hugging Face maps no inference provider and Liquid's docs list no hosted API for it.\n- MiniCPM-V 4.7 1B (95): Memory 30, OpenBMB's own IQ4_XS GGUF is 0.64 GB, 1.54 GB with the 0.90 GB vision projector, and 1.54 GB × 1.2 = 1.9 GB, which fits 16 GB with room to spare. Runtimes 25, MiniCPMV4_7ForConditionalGeneration in vLLM's registry at v0.33.0, the minicpmv4_7 model type in SGLang's minicpmv.py at v0.5.23, the minicpmv4_7 model in Transformers at v5.22.2, a MiniCPM-V 4.7 guide and conversion code in llama.cpp at b12242, and an openbmb/minicpm-v4.7:1b tag in OpenBMB's namespace on Ollama, read as the 35B-A3B listing's is. Builds 15, OpenBMB publishes AWQ, GPTQ and int4 safetensors builds and a GGUF repository from IQ3_XS to F16, so quantised builds at two precisions or more. Terms 15, Apache 2.0 in the card's metadata and as the LICENSE of OpenBMB's MiniCPM-V repository, an OSI licence. Download 10, gated is false on the main repository and on the GGUF, AWQ, GPTQ and int4 repositories, so the files download with no account or terms. Hosted 0, Hugging Face lists no inference provider for the model (an empty provider mapping on 10 November 2026), and no provider's own list was read. 30 + 25 + 15 + 15 + 10 + 0 = 95.\n\n### Risk: MiniCPM-V 4.7 1B +5\n\n- LFM2.5-2.6B (50): File safety earns 25, safetensors and GGUF only, and nothing from the repository runs to load it or build prompts. Provenance earns 15, files in the LiquidAI organisation Liquid links to, with a checksum for every file. Safety evaluation and agent guidance earn nothing, since Liquid publishes neither. Licence risk earns 10, losing 5 for the revenue threshold, with no use policy and no change since release. The security programme earns nothing, since no security.txt, disclosure policy or security contact was found.\n- MiniCPM-V 4.7 1B (55): File safety 25, safetensors in the main repository and the AWQ, GPTQ and int4 builds, GGUF in the GGUF build, with no .bin, .pt, .pth or .py files in the main repository. Provenance 15, files in the openbmb organisation that OpenBMB's GitHub README links to (10), with a SHA-256 for both large files in the main repository (5). Safety evaluation 0, no safety results on the card. Agent guidance 0, the card has no limitations or disclaimer section, and no prompt-injection guidance, instruction hierarchy or statement of trained refusals. Licence risk 15, Apache 2.0 has no use policy, field-of-use limit, threshold or change clause, nothing is added on the card, and the LICENSE in OpenBMB's GitHub repository resolved on 10 November 2026. Security programme 0, no SECURITY.md in OpenBMB/MiniCPM-V and no security.txt on openbmb.cn (both 404 on 10 November 2026), and no advisories found. 25 + 15 + 0 + 0 + 15 + 0 = 55.\n\n### Agent interface: LFM2.5-2.6B +5\n\n- LFM2.5-2.6B (75): The chat template earns 20 for chat_template.jinja in the repository. The tool-calling format earns 25, as Liquid's card and tool-use page say how tools go in and how calls come back, and its vLLM and SGLang pages name the lfm2 parser. Trained tool calling earns 20, with several calls in one Python list and agentic RL inside agent harnesses. Structured output earns nothing, as JSON output isn't documented for the model. Reasoning control earns nothing, since the model always thinks. Context earns 10 for 131,072 tokens trained.\n- MiniCPM-V 4.7 1B (70): Chat template 20, chat_template.jinja in the repository, 7,249 characters. Tool-calling format 10, the card shows tools declared in the OpenAI tools array in a request to Transformers' server, but not how a call comes back, and names no parser for vLLM or SGLang. Trained tool calling 20, the template loops over tool_calls, so it renders several calls in one assistant turn, and the card gives a tool-calling request as a supported use. Structured output 0, Neither JSON nor JSON Schema output is documented or claimed for the model on the card. Reasoning control 10, the card passes enable_thinking per request in its examples, the template has been off by default since 19 October 2026, and the GGUF repository keeps thinking builds in their own folder. Context 10, 262,144 tokens, max_position_embeddings in the text config. 20 + 10 + 20 + 0 + 10 + 10 = 70.\n\n## Score by category\n\n| Category | Weight | LFM2.5-2.6B | MiniCPM-V 4.7 1B | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 90 | 95 | MiniCPM-V 4.7 1B +5 |\n| Risk | 20% (20 this run) | 50 | 55 | MiniCPM-V 4.7 1B +5 |\n| Agent interface | 10% (10 this run) | 75 | 70 | LFM2.5-2.6B +5 |\n| Documentation and upkeep | 10% (10 this run) | 57 | 48 | LFM2.5-2.6B +9 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **50.3 · D** | **61 · C** | |\n\n## Facts side by side\n\n| Fact | LFM2.5-2.6B | MiniCPM-V 4.7 1B |\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 |\n| Read-only variant documented | no | no |\n| llms.txt | yes | no |\n| Last release | 2026-11-03 | 2026-10-30 |\n| Terms last updated | no document linked | no document linked |\n| Privacy policy last updated | no document linked | no document linked |\n| Agent reviews | 2.5/5 (4) | none |\n\n## How the scores have moved\n\n### LFM2.5-2.6B\n\n- -1.2 on 4 November 2026: correction\n\n### MiniCPM-V 4.7 1B\n\nNo score change since it was listed.\n\n## Verdicts\n\n**LFM2.5-2.6B.** A small, fast model for tool calls, extraction and long-context steps on a laptop or phone, which Liquid now lists as deprecated in favour of LFM3-1.7B. Liquid's own 4-bit build is 1.6 GB and five common runtimes run it. It always thinks, and organisations over $10M of revenue need another licence. There are no safety results or security contact.\n\n**MiniCPM-V 4.7 1B.** A vision-language cousin small enough for a phone, under plain Apache 2.0, that every runtime we count can load. The projector is bigger than the language model, so plan for about 2 GB. The card says nothing about limits or training data and doesn't show how a tool call comes back.\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-V 4.7 1B\n\n1. Start it with `llama-server -hf openbmb/MiniCPM-V-4.7-1B-gguf:Q4_K_M --jinja`, or `ollama run openbmb/minicpm-v4.7:1b`\n2. For image input in llama.cpp add `mmproj-model-f16.gguf`, or the smaller 4x projector\n3. The GGUF files at the repository root have thinking off. Use the files in `thinking/` when you want the trace\n4. Use temperature 0.7, top_p 0.95 and top_k 100 in instruct mode, as the card recommends\n5. Check tool calls in your runtime first, since the card names no parser\n6. Validate JSON output yourself, since structured output isn't documented\n\n## Questions\n\n### Which is better for AI agents, LFM2.5-2.6B or MiniCPM-V 4.7 1B?\n\nMiniCPM-V 4.7 1B scores 61 (C) on agent readiness against LFM2.5-2.6B's 50.3 (D), and leads in 2 of 4 scored categories. LFM2.5-2.6B leads on 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-minicpm-v-4-7-1b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-minicpm-v-4-7-1b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"lfm2-5-2-6b\", \"b\": \"minicpm-v-4-7-1b\"}`. From a terminal: `anchor compare lfm2-5-2-6b minicpm-v-4-7-1b`\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-v-4-7-1b.json\n\n## Other comparisons with LFM2.5-2.6B or MiniCPM-V 4.7 1B\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-V 4.7 1B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-minicpm-v-4-7-1b.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-V 4.7 1B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-minicpm-v-4-7-1b.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-V 4.7 1B](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-minicpm-v-4-7-1b.md)\n- [Arete-27B vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/arete-27b-vs-lfm2-5-2-6b.md)\n- [Arete-27B vs MiniCPM-V 4.7 1B](https://www.anchorterminal.com/compare/arete-27b-vs-minicpm-v-4-7-1b.md)\n- [BigBang-v1 vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/bigbang-v1-vs-lfm2-5-2-6b.md)\n- [BigBang-v1 vs MiniCPM-V 4.7 1B](https://www.anchorterminal.com/compare/bigbang-v1-vs-minicpm-v-4-7-1b.md)\n- [Carbon-X vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/carbon-x-vs-lfm2-5-2-6b.md)\n- [Carbon-X vs MiniCPM-V 4.7 1B](https://www.anchorterminal.com/compare/carbon-x-vs-minicpm-v-4-7-1b.md)\n- [Cola2 vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/cola2-vs-lfm2-5-2-6b.md)\n- [Cola2 vs MiniCPM-V 4.7 1B](https://www.anchorterminal.com/compare/cola2-vs-minicpm-v-4-7-1b.md)\n- [Cosmos-Reason2-2B vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-lfm2-5-2-6b.md)\n- [Cosmos-Reason2-2B vs MiniCPM-V 4.7 1B](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-minicpm-v-4-7-1b.md)\n- [Darwin-36B-Opus vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-lfm2-5-2-6b.md)\n- [Darwin-36B-Opus vs MiniCPM-V 4.7 1B](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-minicpm-v-4-7-1b.md)\n- [DeepSeek-V3.2 vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-lfm2-5-2-6b.md)\n- [DeepSeek-V3.2 vs MiniCPM-V 4.7 1B](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-minicpm-v-4-7-1b.md)\n- [DeepSeek V4.1 Flash vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-lfm2-5-2-6b.md)\n- [DeepSeek V4.1 Flash vs MiniCPM-V 4.7 1B](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-minicpm-v-4-7-1b.md)\n- [DeepSeek-V4-Flash-0731 vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-lfm2-5-2-6b.md)\n- [DeepSeek-V4-Flash-0731 vs MiniCPM-V 4.7 1B](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-minicpm-v-4-7-1b.md)\n- [Devstral Small 2 vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/devstral-small-2-vs-lfm2-5-2-6b.md)\n- [Devstral Small 2 vs MiniCPM-V 4.7 1B](https://www.anchorterminal.com/compare/devstral-small-2-vs-minicpm-v-4-7-1b.md)\n- [DiffusionGemma 26B A4B IT vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-lfm2-5-2-6b.md)\n- [DiffusionGemma 26B A4B IT vs MiniCPM-V 4.7 1B](https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-minicpm-v-4-7-1b.md)\n- [EXAONE 4.5 33B vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-lfm2-5-2-6b.md)\n- [EXAONE 4.5 33B vs MiniCPM-V 4.7 1B](https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-minicpm-v-4-7-1b.md)\n- [Gemma 4 12B IT vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-lfm2-5-2-6b.md)\n- [Gemma 4 12B IT vs MiniCPM-V 4.7 1B](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-minicpm-v-4-7-1b.md)\n- [Gemma 4 26B A4B IT vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-lfm2-5-2-6b.md)\n- [Gemma 4 26B A4B IT vs MiniCPM-V 4.7 1B](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-minicpm-v-4-7-1b.md)\n- [Gemma 4 31B IT vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-lfm2-5-2-6b.md)\n- [Gemma 4 31B IT vs MiniCPM-V 4.7 1B](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-minicpm-v-4-7-1b.md)\n- [Gemma 4 E2B IT vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-lfm2-5-2-6b.md)\n- [Gemma 4 E2B IT vs MiniCPM-V 4.7 1B](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-minicpm-v-4-7-1b.md)\n- [Gemma 4 E4B IT vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-lfm2-5-2-6b.md)\n- [Gemma 4 E4B IT vs MiniCPM-V 4.7 1B](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-minicpm-v-4-7-1b.md)\n- [GLM-4.5-Air vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/glm-4-5-air-vs-lfm2-5-2-6b.md)\n- [GLM-4.5-Air vs MiniCPM-V 4.7 1B](https://www.anchorterminal.com/compare/glm-4-5-air-vs-minicpm-v-4-7-1b.md)\n- [GLM-4.7-Flash vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-lfm2-5-2-6b.md)\n- [GLM-4.7-Flash vs MiniCPM-V 4.7 1B](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-minicpm-v-4-7-1b.md)\n- [GLM-5.3-Flash vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-lfm2-5-2-6b.md)\n- [GLM-5.3-Flash vs MiniCPM-V 4.7 1B](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-minicpm-v-4-7-1b.md)\n- [GLM-5.3 vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/glm-5-3-vs-lfm2-5-2-6b.md)\n- [GLM-5.3 vs MiniCPM-V 4.7 1B](https://www.anchorterminal.com/compare/glm-5-3-vs-minicpm-v-4-7-1b.md)\n- [gpt-oss-120b vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/gpt-oss-120b-vs-lfm2-5-2-6b.md)\n- [gpt-oss-120b vs MiniCPM-V 4.7 1B](https://www.anchorterminal.com/compare/gpt-oss-120b-vs-minicpm-v-4-7-1b.md)\n- [gpt-oss-20b vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-lfm2-5-2-6b.md)\n- [gpt-oss-20b vs MiniCPM-V 4.7 1B](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-minicpm-v-4-7-1b.md)\n- [Granite 4.2 30B vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/granite-4-2-30b-vs-lfm2-5-2-6b.md)\n- [Granite 4.2 30B vs MiniCPM-V 4.7 1B](https://www.anchorterminal.com/compare/granite-4-2-30b-vs-minicpm-v-4-7-1b.md)\n- [Granite 4.2 3B vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/granite-4-2-3b-vs-lfm2-5-2-6b.md)\n- [Granite 4.2 3B vs MiniCPM-V 4.7 1B](https://www.anchorterminal.com/compare/granite-4-2-3b-vs-minicpm-v-4-7-1b.md)\n- [Granite 4.2 8B vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/granite-4-2-8b-vs-lfm2-5-2-6b.md)\n- [Granite 4.2 8B vs MiniCPM-V 4.7 1B](https://www.anchorterminal.com/compare/granite-4-2-8b-vs-minicpm-v-4-7-1b.md)\n- [Hy-Memory-30B-A3B vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-lfm2-5-2-6b.md)\n- [Hy-Memory-30B-A3B vs MiniCPM-V 4.7 1B](https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-minicpm-v-4-7-1b.md)\n- [Hy3 vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/hy3-vs-lfm2-5-2-6b.md)\n- [Hy3 vs MiniCPM-V 4.7 1B](https://www.anchorterminal.com/compare/hy3-vs-minicpm-v-4-7-1b.md)\n- [Inkling-Small vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/inkling-small-vs-lfm2-5-2-6b.md)\n- [Inkling-Small vs MiniCPM-V 4.7 1B](https://www.anchorterminal.com/compare/inkling-small-vs-minicpm-v-4-7-1b.md)\n- [Inkling vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/inkling-vs-lfm2-5-2-6b.md)\n- [Inkling vs MiniCPM-V 4.7 1B](https://www.anchorterminal.com/compare/inkling-vs-minicpm-v-4-7-1b.md)\n- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-lfm2-5-2-6b.md)\n- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs MiniCPM-V 4.7 1B](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-minicpm-v-4-7-1b.md)\n- [Kimi K3.1 Pro vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-lfm2-5-2-6b.md)\n- [Kimi K3.1 Pro vs MiniCPM-V 4.7 1B](https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-minicpm-v-4-7-1b.md)\n- [Kimi K3 vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/kimi-k3-vs-lfm2-5-2-6b.md)\n- [Kimi K3 vs MiniCPM-V 4.7 1B](https://www.anchorterminal.com/compare/kimi-k3-vs-minicpm-v-4-7-1b.md)\n- [Kolibri-1 vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/kolibri-1-vs-lfm2-5-2-6b.md)\n- [Kolibri-1 vs MiniCPM-V 4.7 1B](https://www.anchorterminal.com/compare/kolibri-1-vs-minicpm-v-4-7-1b.md)\n- [Kronos vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/kronos-vs-lfm2-5-2-6b.md)\n- [Kronos vs MiniCPM-V 4.7 1B](https://www.anchorterminal.com/compare/kronos-vs-minicpm-v-4-7-1b.md)\n- [Laguna M.1 vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/laguna-m-1-vs-lfm2-5-2-6b.md)\n- [Laguna M.1 vs MiniCPM-V 4.7 1B](https://www.anchorterminal.com/compare/laguna-m-1-vs-minicpm-v-4-7-1b.md)\n- [Laguna S 2.1 vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/laguna-s-2-1-vs-lfm2-5-2-6b.md)\n- [Laguna S 2.1 vs MiniCPM-V 4.7 1B](https://www.anchorterminal.com/compare/laguna-s-2-1-vs-minicpm-v-4-7-1b.md)\n- [Laguna XS 2.1 vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-lfm2-5-2-6b.md)\n- [Laguna XS 2.1 vs MiniCPM-V 4.7 1B](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-minicpm-v-4-7-1b.md)\n- [LBS-27B vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/lbs-27b-vs-lfm2-5-2-6b.md)\n- [LBS-27B vs MiniCPM-V 4.7 1B](https://www.anchorterminal.com/compare/lbs-27b-vs-minicpm-v-4-7-1b.md)\n- [LFM2.5-2.6B vs LFM3-1.7B](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-lfm3-1-7b.md)\n- [LFM2.5-2.6B vs LFM3-Audio-2B](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-lfm3-audio-2b.md)\n- [LFM2.5-2.6B vs LFM3-Omni-2B](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-lfm3-omni-2b.md)\n- [LFM2.5-2.6B vs LFM3-VL-2B](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-lfm3-vl-2b.md)\n- [LFM2.5-2.6B vs Ling-3.0-tiny](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-ling-3-0-tiny.md)\n- [LFM2.5-2.6B vs Ling-3.1-flash-1019](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-ling-3-1-flash-1019.md)\n- [LFM2.5-2.6B vs LongCat-2.5](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-longcat-2-5.md)\n- [LFM2.5-2.6B vs MiMo-V2.6 Distill Qwen 9B](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-mimo-v2-6-distill-qwen-9b.md)\n- [LFM2.5-2.6B vs MiMo-V2.6-Flash-RL](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-mimo-v2-6-flash-rl.md)\n- [LFM2.5-2.6B vs MiniCPM-o 4.5](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-minicpm-o-4-5.md)\n- [LFM2.5-2.6B vs MiniCPM-V 4.7 35B-A3B](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-minicpm-v-4-7-35b-a3b.md)\n- [LFM2.5-2.6B vs MiniCPM5-2B](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-minicpm5-2b.md)\n- [LFM2.5-2.6B vs MiniMax-M2.5](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-minimax-m2-5.md)\n- [LFM2.5-2.6B vs MiniMax-M2.7](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-minimax-m2-7.md)\n- [LFM2.5-2.6B vs MiniMax-M3.1-Flash](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-minimax-m3-1-flash.md)\n- [LFM2.5-2.6B vs Mistral Large 4](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-mistral-large-4.md)\n- [LFM2.5-2.6B vs Muse Glimmer 30B](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-muse-glimmer-30b.md)\n- [LFM2.5-2.6B vs Nanbeige4.2-3B](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-nanbeige-4-2-3b.md)\n- [LFM2.5-2.6B vs Nanbeige4.3-33B](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-nanbeige-4-3-33b.md)\n- [LFM2.5-2.6B vs NVIDIA Nemotron 3.5 Bolt E4B 4GB](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-nemotron-3-5-bolt-e4b-4gb.md)\n- [LFM2.5-2.6B vs Nemotron 3.5 Lightning 30B A3B](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-nemotron-3-5-lightning-30b-a3b.md)\n- [LFM2.5-2.6B vs Nemotron 3 Nano 30B A3B](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-nemotron-3-nano-30b-a3b.md)\n- [LFM2.5-2.6B vs Nemotron 3 Nano 4B](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-nemotron-3-nano-4b.md)\n- [LFM2.5-2.6B vs Nemotron 3 Nano Omni 30B A3B Reasoning](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-nemotron-3-nano-omni-30b-a3b-reasoning.md)\n- [LFM2.5-2.6B vs Nemotron 3 Super 120B A12B](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-nemotron-3-super-120b-a12b.md)\n- [LFM2.5-2.6B vs Nemotron 3 Ultra 550B 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Unlimited-OCR-2](https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-unlimited-ocr-2.md)\n- [MiniCPM-V 4.7 1B vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-wevisone-vl-2b.md)\n- [MiniCPM-V 4.7 1B vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-wevisone-vl-4b.md)\n- [MiniCPM-V 4.7 1B vs ZAYA1-8B](https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-zaya1-8b.md)\n", "meta": { "attribution": "Anchor Terminal (https://www.anchorterminal.com)", "attributionRequired": true, "credit": "CC BY 4.0, credit Anchor Terminal and link the page or API address you took it from (https://www.anchorterminal.com/terms/#data)", "docs": "https://www.anchorterminal.com/docs/", "generatedAt": "2026-11-11", "license": "CC-BY-4.0", "method": "https://www.anchorterminal.com/benchmark/", "methodology": "0.7", "openapi": "https://www.anchorterminal.com/openapi.json", "preview": false, "run": "2026-10-01", "runLabel": "October 2026 research 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