{ "data": { "a": { "slug": "lfm3-vl-2b", "name": "LFM3-VL-2B", "vendor": "Liquid AI", "vendorUrl": "https://www.liquid.ai", "kind": "weights", "category": "open-weights", "summary": "LFM3-VL-2B is Liquid AI's open-weight 1.92B vision-language model for on-device use, taking text, images and video with thinking, tool calling and a 128K-token context, released on 27 October 2026 with Liquid's own GGUF, FP8, NVFP4, MLX and ONNX builds.", "url": "https://www.anchorterminal.com/tools/lfm3-vl-2b", "markdownUrl": "https://www.anchorterminal.com/tools/lfm3-vl-2b.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/lfm3-vl-2b.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/lfm3-vl-2b.json", "dns": "lfm3-vl-2b.grade.anchorterminal.com", "repo": "https://huggingface.co/LiquidAI/LFM3-VL-2B", "license": "LFM Open License v1.0 (free commercial use below $10M annual revenue)", "transports": [], "packages": [ { "registry": "huggingface", "name": "LiquidAI/LFM3-VL-2B" }, { "registry": "huggingface", "name": "LiquidAI/LFM3-VL-2B-GGUF" }, { "registry": "huggingface", "name": "LiquidAI/LFM3-VL-2B-MLX-4bit" }, { "registry": "ollama", "name": "lfm3-vl" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The repository and Liquid's GGUF, MLX and ONNX repositories are not gated (checked 2026-11-10).", "pricing": "free", "pricingNotes": "Free to download and use under the LFM Open License v1.0, commercial use included for organisations with under $10M in annual revenue; above that, commercial use isn't licensed. Running it yourself costs little hardware, 1.10 GB for Liquid's Q4_K_M GGUF with its 0.20 GB projector file and 3.84 GB in BF16. Liquid's API also serves it, at prices shown in its console (checked 2026-11-10).", "priceSummary": "Free", "where": "local", "x402": { "level": "no", "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-11-10).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-11-10" }, "docsUrl": "https://docs.liquid.ai/lfm/models/lfm3-vl-2b", "llmsTxt": "https://docs.liquid.ai/llms.txt", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "vision", "video", "small", "edge", "reasoning", "tool-calling", "custom-licence", "safetensors", "gguf", "mlx", "onnx", "self-hosted" ], "lastRelease": "2026-11-09", "graded": true, "anchor": { "graded": true, "score": 61, "grade": "C", "agentReady": false, "rank": 0, "ranked": false, "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools", "rankOf": 1518, "categoryRank": 58, "methodology": "0.7", "run": "2026-10-01", "scores": { "interface": 90, "reach": 85, "risk": 40, "upkeep": 77 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-11-10" }, "negative": 0, "verdict": "A 1.92B vision model that reads images and video on an 8 GB machine, with a documented tool-call format and thinking. It runs today in llama.cpp, Ollama and LM Studio, while vLLM and SGLang support is still unreleased. Transformers needs repository code, and there are no safety results, security contact or data sources.", "bestFor": "Agents on a laptop, phone or robot that need to read screens, documents, images and video from a model under 2 GB at 4 bits", "strengths": [ "Reads images and video on an 8 GB machine from Liquid's own GGUF", "Tool calling, deferred tools and thinking in a 1.92B vision model", "A 128K-token context", "Liquid answered most questions on the repository", "Liquid's own API serves it" ], "weaknesses": [ "vLLM and SGLang support is unreleased, which the card says itself", "Loading in Transformers runs code from the repository", "No safety results and no security contact", "How to ask for JSON isn't documented for this model", "The licence stops free commercial use at $10M in annual revenue", "Not recommended for coding, multi-turn conversations or complex reasoning" ], "agentNotes": [ "Start it with `llama-server -hf LiquidAI/LFM3-VL-2B-GGUF:Q4_K_M --jinja` or `ollama run lfm3-vl`, with llama.cpp b11960 or later", "Use temperature 0.1, top_k 50 and repetition penalty 1.1, per the card", "Video is sampled at 2 frames a second up to 768 frames, and each frame costs context. Video input in llama.cpp needs ffmpeg", "Don't plan on vLLM or SGLang until a release carries LFM3-VL", "Validate JSON output yourself", "Liquid doesn't recommend it for coding, multi-turn conversations or complex reasoning" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 0, "avgRating": 0, "history": [ { "basis": "public evidence", "confidence": "low", "grade": "C", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 61 } ], "editorialScores": { "interface": 90, "reach": 85, "risk": 40, "upkeep": 77 }, "provenanceScore": 73, "checked": { "categories": { "interface": "2026-11-10", "reach": "2026-11-10", "risk": "2026-11-10", "upkeep": "2026-11-10" }, "last": "2026-11-10", "oldest": "2026-11-10", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "llama-server -hf LiquidAI/LFM3-VL-2B-GGUF:Q4_K_M --jinja\n# or\nollama run lfm3-vl", "http": "curl http://localhost:8080/v1/chat/completions \\\n -H 'Content-Type: application/json' \\\n -d '{\"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/lfm3-vl-2b" }, "sameCompany": [ "liquid-d1", "lfm2-5-2-6b", "lfm3-1-7b", "lfm3-audio-2b", "lfm3-omni-2b" ], "weights": { "trainedBy": "Liquid AI", "builtOn": "LiquidAI/LFM3-1.7B-Base", "params": "1.92B", "context": 128000, "goodFor": "Small on-device vision model for questions about video, documents, charts and app screens, finding objects and spatial reasoning, not for coding, multi-turn conversations or complex reasoning.", "runtimes": [ "llama.cpp", "Ollama", "LM Studio" ], "builds": [ { "name": "LFM3-VL-2B", "url": "https://huggingface.co/LiquidAI/LFM3-VL-2B", "who": "publisher", "precision": "bf16", "sizeGB": 3.84, "memoryGB": 8, "format": "safetensors", "runsOn": [ "Transformers" ], "note": "Loads in Transformers with the repository's own code. Liquid also publishes FP8 (2.59 GB) and NVFP4 (2.21 GB) safetensors builds and an ONNX repository" }, { "name": "LFM3-VL-2B-GGUF Q8_0", "url": "https://huggingface.co/LiquidAI/LFM3-VL-2B-GGUF", "who": "publisher", "precision": "q8_0", "sizeGB": 2.05, "memoryGB": 8, "format": "gguf", "runsOn": [ "llama.cpp", "LM Studio" ], "note": "LFM3-VL-2B-Q8_0.gguf (1.85 GB) plus mmproj-LFM3-VL-2B-Q8_0.gguf (0.20 GB)" }, { "name": "LFM3-VL-2B-GGUF Q4_K_M", "url": "https://huggingface.co/LiquidAI/LFM3-VL-2B-GGUF", "who": "publisher", "precision": "q4_k_m", "sizeGB": 1.3, "memoryGB": 8, "format": "gguf", "runsOn": [ "llama.cpp", "LM Studio" ], "note": "LFM3-VL-2B-Q4_K_M.gguf (1.10 GB) plus Liquid's projector file mmproj-LFM3-VL-2B-Q8_0.gguf (0.20 GB), from the Hub's file list. The repository also holds Q4_0, Q8_0, F16 and BF16 files" }, { "name": "LFM3-VL-2B-MLX-4bit", "url": "https://huggingface.co/LiquidAI/LFM3-VL-2B-MLX-4bit", "who": "publisher", "precision": "4bit", "sizeGB": 1.59, "memoryGB": 8, "format": "mlx", "runsOn": [ "MLX-VLM" ], "note": "Liquid also publishes 5, 6 and 8-bit and BF16 MLX builds" }, { "name": "ollama lfm3-vl", "url": "https://ollama.com/library/lfm3-vl", "who": "ollama", "precision": "q4_k_m", "sizeGB": 1.5, "memoryGB": 8, "format": "ollama", "runsOn": [ "Ollama" ], "note": "Ollama's library build, size as its tags page shows it on 10 November 2026. The library also has q8_0, bf16, mxfp8 and nvfp4 tags" } ], "run": "llama-server -hf LiquidAI/LFM3-VL-2B-GGUF:Q4_K_M --jinja", "runBuild": "LFM3-VL-2B-GGUF Q4_K_M", "checked": "2026-11-10", "minMemoryGB": 8 }, "family": { "lab": "Liquid AI", "family": "LFM3", "url": "https://www.anchorterminal.com/open-weights/#liquid", "others": [ { "name": "LFM3-1.7B-Base", "url": "https://huggingface.co/LiquidAI/LFM3-1.7B-Base", "params": "1.73B" }, { "name": "LFM3-1.7B", "url": "https://huggingface.co/LiquidAI/LFM3-1.7B", "params": "1.73B", "listing": "https://www.anchorterminal.com/tools/lfm3-1-7b" }, { "name": "LFM3-Omni-2B", "url": "https://huggingface.co/LiquidAI/LFM3-Omni-2B", "params": "2.17B", "listing": "https://www.anchorterminal.com/tools/lfm3-omni-2b" }, { "name": "LFM3-Audio-2B", "url": "https://huggingface.co/LiquidAI/LFM3-Audio-2B", "params": "2.00B", "listing": "https://www.anchorterminal.com/tools/lfm3-audio-2b" } ] }, "area": "models", "provenance": { "legalEntity": "Liquid AI, Inc. (the licensor named in the LFM Open License)", "domain": "liquid.ai", "domainRegistered": "2017-12-16", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://huggingface.co/LiquidAI/LFM3-VL-2B/commits/main", "securityTxt": "none", "checked": "2026-11-10", "notes": [ "The licence names Liquid AI, Inc. as licensor. The domain's registration date is as recorded on the liquid-d1 listing on 8 October 2026", "www.liquid.ai/.well-known/security.txt answered 404 on 10 November 2026" ], "score": 73 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/lfm3-vl-2b.json" }, "answer": "LFM3-VL-2B scores 61 (C) on agent readiness against Qwen3-VL-4B-Instruct's 52.6 (D), and leads in 2 of 4 scored categories. Qwen3-VL-4B-Instruct leads on reach and risk.", "b": { "slug": "qwen3-vl-4b-instruct", "name": "Qwen3-VL-4B-Instruct", "vendor": "Alibaba (Qwen team)", "vendorUrl": "https://qwen.ai", "kind": "weights", "category": "open-weights", "summary": "Qwen3-VL-4B-Instruct is Alibaba's open-weight dense vision-language model of about 4B parameters, taking text, images and video with a 262,144-token context, published on Hugging Face in BF16, FP8 and GGUF under Apache 2.0.", "url": "https://www.anchorterminal.com/tools/qwen3-vl-4b-instruct", "markdownUrl": "https://www.anchorterminal.com/tools/qwen3-vl-4b-instruct.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/qwen3-vl-4b-instruct.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/qwen3-vl-4b-instruct.json", "dns": "qwen3-vl-4b-instruct.grade.anchorterminal.com", "repo": "https://huggingface.co/Qwen/Qwen3-VL-4B-Instruct", "license": "Apache-2.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "Qwen/Qwen3-VL-4B-Instruct" }, { "registry": "huggingface", "name": "Qwen/Qwen3-VL-4B-Instruct-FP8" }, { "registry": "huggingface", "name": "Qwen/Qwen3-VL-4B-Instruct-GGUF" }, { "registry": "ollama", "name": "qwen3-vl:4b-instruct" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. None of the three repositories is gated (checked 2026-10-21).", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0. Running it yourself costs the hardware, 8.89 GB for the BF16 files, 6.04 GB for Alibaba's FP8 build and 2.5 GB plus a 0.45 GB vision file for its Q4_K_M GGUF (checked 2026-10-21).", "priceSummary": "Free", "where": "local", "x402": { "level": "no", "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-10-21).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-10-21" }, "docsUrl": "https://huggingface.co/Qwen/Qwen3-VL-4B-Instruct", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "dense", "multimodal", "vision", "video", "tool-calling", "apache-2.0", "safetensors", "fp8", "gguf", "self-hosted", "edge" ], "lastRelease": "2025-11-01", "graded": true, "anchor": { "graded": true, "score": 52.6, "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": 102, "methodology": "0.7", "run": "2026-10-01", "scores": { "capability": 25, "interface": 60, "reach": 95, "risk": 55, "upkeep": 31 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "high", "date": "2026-11-04" }, "negative": 0, "verdict": "Qwen3-VL-4B-Instruct reads screenshots, documents, charts and video, and Alibaba's Q4_K_M GGUF needs about 3.5 GB. Transformers, vLLM, SGLang, llama.cpp and Ollama run it under plain Apache 2.0. It has no thinking mode, documents tool calls only in the template, and Qwen hasn't answered repository questions or published safety work, so add guardrails before it drives a GUI.", "bestFor": "An agent with 4 GB or more to spare that needs a local model to read images, documents and video", "strengths": [ "About 3.5 GB of memory for Alibaba's own Q4_K_M GGUF with its vision file, with FP8, Q8_0 and F16 builds as well", "Runs in Transformers, vLLM, SGLang, llama.cpp and Ollama in released versions", "Apache 2.0 with no use policy, downloadable with no account", "262,144 tokens of native context over text, images and video", "Safetensors and GGUF only, with a chat template in the repository" ], "weaknesses": [ "No safety evaluation, agent-use guidance or security contact", "Tool calling isn't documented beyond the template, and Alibaba names no parser", "No thinking mode in this edition and no documented JSON output", "No reply from Qwen to any of the 14 Hugging Face discussions that aren't pull requests, counted on 4 November 2026", "The card leaves out limitations, languages, knowledge cut-off and memory needs", "Only Featherless was found serving it for agents without the hardware" ], "agentNotes": [ "Start it with `llama-server -hf Qwen/Qwen3-VL-4B-Instruct-GGUF:Q4_K_M --jinja -c 32768`, or `ollama run qwen3-vl:4b-instruct`", "In Ollama the plain `qwen3-vl:4b` tag is the Thinking edition, so name `4b-instruct`", "With the GGUF, load the mmproj vision file too, or images won't be read", "Parse tool calls as JSON inside `\u003ctool_call\u003e` tags, or use SGLang's `--tool-call-parser qwen`", "Use the card's sampling, `temperature=0.7, top_p=0.8, top_k=20, presence_penalty=1.5` for vision and `temperature=1.0, top_p=1.0, top_k=40, presence_penalty=2.0` for text", "Validate JSON output yourself, since structured output isn't documented", "vLLM needs 0.11.0 or later and Transformers 4.57.0 or later" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 2.8, "history": [ { "basis": "public evidence", "confidence": "high", "grade": "D", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 52.6 } ], "editorialScores": { "capability": 25, "interface": 60, "reach": 95, "risk": 55, "upkeep": 31 }, "provenanceScore": 54, "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", "oldest": "2026-11-04", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "ollama run qwen3-vl:4b-instruct\n# or with vLLM 0.11.0 or later, per Qwen's README:\nvllm serve Qwen/Qwen3-VL-4B-Instruct-FP8\n# or with SGLang, using the tool parser its cookbook names for Qwen3-VL:\npython -m sglang.launch_server --model-path Qwen/Qwen3-VL-4B-Instruct --tool-call-parser qwen", "http": "curl http://localhost:11434/api/chat \\\n -d '{\"model\": \"qwen3-vl:4b-instruct\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/qwen3-vl-4b-instruct" }, "sameCompany": [ "qwen3-embedding-0-6b", "qwen3-embedding-4b", "qwen3-embedding-8b", "qwen3-vl-embedding-2b", "qwen3-vl-embedding-8b", "qwen3-asr-0-6b", "qwen3-asr-1-7b", "qwen3-tts-1-7b-customvoice", "qwen-agentworld-35b-a3b", "qwen3-30b-a3b-instruct-2507", "qwen3-4b-instruct-2507", "qwen3-coder-30b-a3b-instruct", "qwen3-coder-next", "qwen3-omni-30b-a3b-instruct", "qwen3-vl-2b-instruct", "qwen3-vl-30b-a3b-instruct", "qwen3-vl-32b-instruct", "qwen3-vl-8b-instruct", "qwen3-5-0-8b", "qwen3-5-122b-a10b", "qwen3-5-2b", "qwen3-5-397b-a17b", "qwen3-5-4b", "qwen3-5-9b", "qwen3-6-27b", "qwen3-6-35b-a3b", "qwen3-8-27b", "qwen3-8-flash-next" ], "weights": { "trainedBy": "Alibaba (Qwen team)", "params": "4.44B", "context": 262144, "goodFor": "Small vision-language model for images, documents and video with text out, fits in 8 GB at 4-bit, no thinking mode in this Instruct edition.", "runtimes": [ "llama.cpp", "Ollama", "vLLM", "SGLang", "Transformers" ], "builds": [ { "name": "Qwen3-VL-4B-Instruct-FP8", "url": "https://huggingface.co/Qwen/Qwen3-VL-4B-Instruct-FP8", "who": "publisher", "precision": "fp8", "sizeGB": 6.04, "memoryGB": 8, "format": "safetensors" }, { "name": "Qwen3VL-4B-Instruct-Q8_0", "url": "https://huggingface.co/Qwen/Qwen3-VL-4B-Instruct-GGUF/blob/main/Qwen3VL-4B-Instruct-Q8_0.gguf", "who": "publisher", "precision": "q8_0", "sizeGB": 4.73, "memoryGB": 8, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)" ], "note": "Size includes mmproj-Qwen3VL-4B-Instruct-Q8_0.gguf (0.45 GB), the vision file" }, { "name": "Qwen3VL-4B-Instruct-Q4_K_M", "url": "https://huggingface.co/Qwen/Qwen3-VL-4B-Instruct-GGUF/blob/main/Qwen3VL-4B-Instruct-Q4_K_M.gguf", "who": "publisher", "precision": "q4_k_m", "sizeGB": 2.95, "memoryGB": 8, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)" ], "note": "2.5 GB language file plus mmproj-Qwen3VL-4B-Instruct-Q8_0.gguf (0.45 GB) for image input" }, { "name": "ollama qwen3-vl:4b-instruct", "url": "https://ollama.com/library/qwen3-vl:4b-instruct", "who": "ollama", "precision": "q4_k_m", "sizeGB": 3.3, "memoryGB": 8, "format": "ollama", "runsOn": [ "Ollama" ], "note": "Q4_K_M with its vision projector (digest 84f7ed8042fa, updated the day before our check). qwen3-vl:4b, the library default, is the Thinking edition. Size as Ollama's tags page shows it" }, { "name": "Qwen3-VL-4B-Instruct", "url": "https://huggingface.co/Qwen/Qwen3-VL-4B-Instruct", "who": "publisher", "precision": "bf16", "sizeGB": 8.89, "memoryGB": 12, "format": "safetensors", "runsOn": [ "vLLM (ROCm)", "SGLang (ROCm)" ] }, { "name": "Qwen3VL-4B-Instruct-F16", "url": "https://huggingface.co/Qwen/Qwen3-VL-4B-Instruct-GGUF/blob/main/Qwen3VL-4B-Instruct-F16.gguf", "who": "publisher", "precision": "f16", "sizeGB": 8.89, "memoryGB": 12, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)" ], "note": "Size includes mmproj-Qwen3VL-4B-Instruct-F16.gguf (0.84 GB), the vision file" } ], "run": "llama-server -hf Qwen/Qwen3-VL-4B-Instruct-GGUF:Q4_K_M --jinja -c 32768", "runBuild": "Qwen3VL-4B-Instruct-Q4_K_M", "checked": "2026-11-04", "minMemoryGB": 8 }, "family": { "lab": "Alibaba (Qwen)", "family": "Qwen3", "url": "https://www.anchorterminal.com/open-weights/#qwen", "others": [ { "name": "Qwen3-0.6B", "url": "https://huggingface.co/Qwen/Qwen3-0.6B", "params": "0.6B" }, { "name": "Qwen3-1.7B", "url": "https://huggingface.co/Qwen/Qwen3-1.7B", "params": "1.7B" }, { "name": "Qwen3-14B", "url": "https://huggingface.co/Qwen/Qwen3-14B", "params": "14B" }, { "name": "Qwen3-235B-A22B", "url": "https://huggingface.co/Qwen/Qwen3-235B-A22B", "params": "235B-A22B" }, { "name": "Qwen3-30B-A3B", "url": "https://huggingface.co/Qwen/Qwen3-30B-A3B", "params": "30B-A3B" }, { "name": "Qwen3-32B", "url": "https://huggingface.co/Qwen/Qwen3-32B", "params": "32B" }, { "name": "Qwen3-4B", "url": "https://huggingface.co/Qwen/Qwen3-4B", "params": "4B" }, { "name": "Qwen3-8B", "url": "https://huggingface.co/Qwen/Qwen3-8B", "params": "8B" }, { "name": "Qwen3-0.6B-Base", "url": "https://huggingface.co/Qwen/Qwen3-0.6B-Base", "params": "0.6B" }, { "name": "Qwen3-1.7B-Base", "url": "https://huggingface.co/Qwen/Qwen3-1.7B-Base", "params": "1.7B" }, { "name": "Qwen3-14B-Base", "url": "https://huggingface.co/Qwen/Qwen3-14B-Base", "params": "14B" }, { "name": "Qwen3-30B-A3B-Base", "url": "https://huggingface.co/Qwen/Qwen3-30B-A3B-Base", "params": "30B-A3B" }, { "name": "Qwen3-4B-Base", "url": "https://huggingface.co/Qwen/Qwen3-4B-Base", "params": "4B" }, { "name": "Qwen3-8B-Base", "url": "https://huggingface.co/Qwen/Qwen3-8B-Base", "params": "8B" }, { "name": "Qwen3-Reranker-0.6B", "url": "https://huggingface.co/Qwen/Qwen3-Reranker-0.6B", "params": "0.6B" }, { "name": "Qwen3-Reranker-8B", "url": "https://huggingface.co/Qwen/Qwen3-Reranker-8B", "params": "8B" }, { "name": "Qwen3-Embedding-0.6B", "url": "https://huggingface.co/Qwen/Qwen3-Embedding-0.6B", "params": "0.6B", "listing": "https://www.anchorterminal.com/tools/qwen3-embedding-0-6b" }, { "name": "Qwen3-Embedding-4B", "url": "https://huggingface.co/Qwen/Qwen3-Embedding-4B", "params": "4B", "listing": "https://www.anchorterminal.com/tools/qwen3-embedding-4b" }, { "name": "Qwen3-Embedding-8B", "url": "https://huggingface.co/Qwen/Qwen3-Embedding-8B", "params": "8B", "listing": "https://www.anchorterminal.com/tools/qwen3-embedding-8b" }, { "name": "Qwen3-Reranker-4B", "url": "https://huggingface.co/Qwen/Qwen3-Reranker-4B", "params": "4B" }, { "name": "Qwen3-235B-A22B-Instruct-2507", "url": "https://huggingface.co/Qwen/Qwen3-235B-A22B-Instruct-2507", "params": "235B-A22B" }, { "name": "Qwen3-Coder-480B-A35B-Instruct", "url": "https://huggingface.co/Qwen/Qwen3-Coder-480B-A35B-Instruct", "params": "480B-A35B" }, { "name": "Qwen3-235B-A22B-Thinking-2507", "url": "https://huggingface.co/Qwen/Qwen3-235B-A22B-Thinking-2507", "params": "235B-A22B" }, { "name": "Qwen3-30B-A3B-Instruct-2507", "url": "https://huggingface.co/Qwen/Qwen3-30B-A3B-Instruct-2507", "params": "30B-A3B", "listing": "https://www.anchorterminal.com/tools/qwen3-30b-a3b-instruct-2507" }, { "name": "Qwen3-30B-A3B-Thinking-2507", "url": "https://huggingface.co/Qwen/Qwen3-30B-A3B-Thinking-2507", "params": "30B-A3B" }, { "name": "Qwen3-Coder-30B-A3B-Instruct", "url": "https://huggingface.co/Qwen/Qwen3-Coder-30B-A3B-Instruct", "params": "30B-A3B", "listing": "https://www.anchorterminal.com/tools/qwen3-coder-30b-a3b-instruct" }, { "name": "Qwen3-4B-Instruct-2507", "url": "https://huggingface.co/Qwen/Qwen3-4B-Instruct-2507", "params": "4B", "listing": "https://www.anchorterminal.com/tools/qwen3-4b-instruct-2507" }, { "name": "Qwen3-4B-Thinking-2507", "url": "https://huggingface.co/Qwen/Qwen3-4B-Thinking-2507", "params": "4B" }, { "name": "Qwen3-Next-80B-A3B-Instruct", "url": "https://huggingface.co/Qwen/Qwen3-Next-80B-A3B-Instruct", "params": "80B-A3B" }, { "name": "Qwen3-Next-80B-A3B-Thinking", "url": "https://huggingface.co/Qwen/Qwen3-Next-80B-A3B-Thinking", "params": "80B-A3B" }, { "name": "Qwen3-Omni-30B-A3B-Captioner", "url": "https://huggingface.co/Qwen/Qwen3-Omni-30B-A3B-Captioner", "params": "30B-A3B" }, { "name": "Qwen3-Omni-30B-A3B-Thinking", "url": "https://huggingface.co/Qwen/Qwen3-Omni-30B-A3B-Thinking", "params": "30B-A3B" }, { "name": "Qwen3-Omni-30B-A3B-Instruct", "url": "https://huggingface.co/Qwen/Qwen3-Omni-30B-A3B-Instruct", "params": "30B-A3B", "listing": "https://www.anchorterminal.com/tools/qwen3-omni-30b-a3b-instruct" }, { "name": "Qwen3-VL-235B-A22B-Instruct", "url": "https://huggingface.co/Qwen/Qwen3-VL-235B-A22B-Instruct", "params": "235B-A22B" }, { "name": "Qwen3-VL-235B-A22B-Thinking", "url": "https://huggingface.co/Qwen/Qwen3-VL-235B-A22B-Thinking", "params": "235B-A22B" }, { "name": "Qwen3Guard-Gen-0.6B", "url": "https://huggingface.co/Qwen/Qwen3Guard-Gen-0.6B", "params": "0.6B" }, { "name": "Qwen3Guard-Gen-4B", "url": "https://huggingface.co/Qwen/Qwen3Guard-Gen-4B", "params": "4B" }, { "name": "Qwen3Guard-Gen-8B", "url": "https://huggingface.co/Qwen/Qwen3Guard-Gen-8B", "params": "8B" }, { "name": "Qwen3Guard-Stream-0.6B", "url": "https://huggingface.co/Qwen/Qwen3Guard-Stream-0.6B", "params": "0.6B" }, { "name": "Qwen3Guard-Stream-4B", "url": "https://huggingface.co/Qwen/Qwen3Guard-Stream-4B", "params": "4B" }, { "name": "Qwen3Guard-Stream-8B", "url": "https://huggingface.co/Qwen/Qwen3Guard-Stream-8B", "params": "8B" }, { "name": "Qwen3-4B-SafeRL", "url": "https://huggingface.co/Qwen/Qwen3-4B-SafeRL", "params": "4B" }, { "name": "Qwen3-VL-30B-A3B-Instruct", "url": "https://huggingface.co/Qwen/Qwen3-VL-30B-A3B-Instruct", "params": "30B-A3B", "listing": "https://www.anchorterminal.com/tools/qwen3-vl-30b-a3b-instruct" }, { "name": "Qwen3-VL-30B-A3B-Thinking", "url": "https://huggingface.co/Qwen/Qwen3-VL-30B-A3B-Thinking", "params": "30B-A3B" }, { "name": "Qwen3-VL-4B-Thinking", "url": "https://huggingface.co/Qwen/Qwen3-VL-4B-Thinking", "params": "4B" }, { "name": "Qwen3-VL-8B-Instruct", "url": "https://huggingface.co/Qwen/Qwen3-VL-8B-Instruct", "params": "8B", "listing": "https://www.anchorterminal.com/tools/qwen3-vl-8b-instruct" }, { "name": "Qwen3-VL-8B-Thinking", "url": "https://huggingface.co/Qwen/Qwen3-VL-8B-Thinking", "params": "8B" }, { "name": "Qwen3-VL-2B-Instruct", "url": "https://huggingface.co/Qwen/Qwen3-VL-2B-Instruct", "params": "2B", "listing": "https://www.anchorterminal.com/tools/qwen3-vl-2b-instruct" }, { "name": "Qwen3-VL-2B-Thinking", "url": "https://huggingface.co/Qwen/Qwen3-VL-2B-Thinking", "params": "2B" }, { "name": "Qwen3-VL-32B-Instruct", "url": "https://huggingface.co/Qwen/Qwen3-VL-32B-Instruct", "params": "32B", "listing": "https://www.anchorterminal.com/tools/qwen3-vl-32b-instruct" }, { "name": "Qwen3-VL-32B-Thinking", "url": "https://huggingface.co/Qwen/Qwen3-VL-32B-Thinking", "params": "32B" }, { "name": "Qwen3-VL-Embedding-2B", "url": "https://huggingface.co/Qwen/Qwen3-VL-Embedding-2B", "params": "2B", "listing": "https://www.anchorterminal.com/tools/qwen3-vl-embedding-2b" }, { "name": "Qwen3-VL-Embedding-8B", "url": "https://huggingface.co/Qwen/Qwen3-VL-Embedding-8B", "params": "8B", "listing": "https://www.anchorterminal.com/tools/qwen3-vl-embedding-8b" }, { "name": "Qwen3-VL-Reranker-2B", "url": "https://huggingface.co/Qwen/Qwen3-VL-Reranker-2B", "params": "2B" }, { "name": "Qwen3-VL-Reranker-8B", "url": "https://huggingface.co/Qwen/Qwen3-VL-Reranker-8B", "params": "8B" }, { "name": "Qwen3-TTS-12Hz-0.6B-Base", "url": "https://huggingface.co/Qwen/Qwen3-TTS-12Hz-0.6B-Base", "params": "0.6B" }, { "name": "Qwen3-TTS-12Hz-0.6B-CustomVoice", "url": "https://huggingface.co/Qwen/Qwen3-TTS-12Hz-0.6B-CustomVoice", "params": "0.6B" }, { "name": "Qwen3-TTS-12Hz-1.7B-Base", "url": "https://huggingface.co/Qwen/Qwen3-TTS-12Hz-1.7B-Base", "params": "1.7B" }, { "name": "Qwen3-TTS-12Hz-1.7B-CustomVoice", "url": "https://huggingface.co/Qwen/Qwen3-TTS-12Hz-1.7B-CustomVoice", "params": "1.7B", "listing": "https://www.anchorterminal.com/tools/qwen3-tts-1-7b-customvoice" }, { "name": "Qwen3-TTS-12Hz-1.7B-VoiceDesign", "url": "https://huggingface.co/Qwen/Qwen3-TTS-12Hz-1.7B-VoiceDesign", "params": "1.7B" }, { "name": "Qwen3-TTS-Tokenizer-12Hz", "url": "https://huggingface.co/Qwen/Qwen3-TTS-Tokenizer-12Hz", "params": "not in name" }, { "name": "Qwen3-ASR-0.6B", "url": "https://huggingface.co/Qwen/Qwen3-ASR-0.6B", "params": "0.6B", "listing": "https://www.anchorterminal.com/tools/qwen3-asr-0-6b" }, { "name": "Qwen3-ASR-1.7B", "url": "https://huggingface.co/Qwen/Qwen3-ASR-1.7B", "params": "1.7B", "listing": "https://www.anchorterminal.com/tools/qwen3-asr-1-7b" }, { "name": "Qwen3-ForcedAligner-0.6B", "url": "https://huggingface.co/Qwen/Qwen3-ForcedAligner-0.6B", "params": "0.6B" }, { "name": "Qwen3-Coder-Next", "url": "https://huggingface.co/Qwen/Qwen3-Coder-Next", "params": "not in name; Hub safetensors total 79.7B", "listing": "https://www.anchorterminal.com/tools/qwen3-coder-next" }, { "name": "Qwen3-Coder-Next-Base", "url": "https://huggingface.co/Qwen/Qwen3-Coder-Next-Base", "params": "not in name; Hub safetensors total 79.7B" }, { "name": "WebWorld-14B", "url": "https://huggingface.co/Qwen/WebWorld-14B", "params": "14B" }, { "name": "WebWorld-32B", "url": "https://huggingface.co/Qwen/WebWorld-32B", "params": "32B" }, { "name": "WebWorld-8B", "url": "https://huggingface.co/Qwen/WebWorld-8B", "params": "8B" }, { "name": "Qwen3.5-397B-A17B", "url": "https://huggingface.co/Qwen/Qwen3.5-397B-A17B", "params": "397B-A17B", "listing": "https://www.anchorterminal.com/tools/qwen3-5-397b-a17b" }, { "name": "Qwen3.5-122B-A10B", "url": "https://huggingface.co/Qwen/Qwen3.5-122B-A10B", "params": "122B-A10B", "listing": "https://www.anchorterminal.com/tools/qwen3-5-122b-a10b" }, { "name": "Qwen3.5-27B", "url": "https://huggingface.co/Qwen/Qwen3.5-27B", "params": "27B" }, { "name": "Qwen3.5-35B-A3B", "url": "https://huggingface.co/Qwen/Qwen3.5-35B-A3B", "params": "35B-A3B" }, { "name": "Qwen3.5-35B-A3B-Base", "url": "https://huggingface.co/Qwen/Qwen3.5-35B-A3B-Base", "params": "35B-A3B" }, { "name": "Qwen3.5-9B-Base", "url": "https://huggingface.co/Qwen/Qwen3.5-9B-Base", "params": "9B" }, { "name": "Qwen3.5-4B", "url": "https://huggingface.co/Qwen/Qwen3.5-4B", "params": "4B", "listing": "https://www.anchorterminal.com/tools/qwen3-5-4b" }, { "name": "Qwen3.5-4B-Base", "url": "https://huggingface.co/Qwen/Qwen3.5-4B-Base", "params": "4B" }, { "name": "Qwen3.5-9B", "url": "https://huggingface.co/Qwen/Qwen3.5-9B", "params": "9B", "listing": "https://www.anchorterminal.com/tools/qwen3-5-9b" }, { "name": "Qwen3.5-0.8B", "url": "https://huggingface.co/Qwen/Qwen3.5-0.8B", "params": "0.8B", "listing": "https://www.anchorterminal.com/tools/qwen3-5-0-8b" }, { "name": "Qwen3.5-0.8B-Base", "url": "https://huggingface.co/Qwen/Qwen3.5-0.8B-Base", "params": "0.8B" }, { "name": "Qwen3.5-2B", "url": "https://huggingface.co/Qwen/Qwen3.5-2B", "params": "2B", "listing": "https://www.anchorterminal.com/tools/qwen3-5-2b" }, { "name": "Qwen3.5-2B-Base", "url": "https://huggingface.co/Qwen/Qwen3.5-2B-Base", "params": "2B" }, { "name": "Qwen3.6-35B-A3B", "url": "https://huggingface.co/Qwen/Qwen3.6-35B-A3B", "params": "35B-A3B", "listing": "https://www.anchorterminal.com/tools/qwen3-6-35b-a3b" }, { "name": "Qwen3.6-27B", "url": "https://huggingface.co/Qwen/Qwen3.6-27B", "params": "27B", "listing": "https://www.anchorterminal.com/tools/qwen3-6-27b" }, { "name": "Qwen-AgentWorld-35B-A3B", "url": "https://huggingface.co/Qwen/Qwen-AgentWorld-35B-A3B", "params": "35B-A3B" }, { "name": "Qwen3.8-27B", "url": "https://huggingface.co/Qwen/Qwen3.8-27B", "params": "27B", "listing": "https://www.anchorterminal.com/tools/qwen3-8-27b" }, { "name": "Qwen3.8-2.4T-A95B", "url": "https://huggingface.co/Qwen/Qwen3.8-2.4T-A95B", "params": "2.4T-A95B" }, { "name": "Qwen3.8-Flash-Next", "url": "https://huggingface.co/Qwen/Qwen3.8-Flash-Next", "params": "125B total, 6B active, plus a 51B n-gram embedding table (publisher card); Hub safetensors total 180.0B", "listing": "https://www.anchorterminal.com/tools/qwen3-8-flash-next" }, { "name": "Qwen-Drive-1.0-4B", "url": "https://huggingface.co/Qwen/Qwen-Drive-1.0-4B", "params": "4B" } ], "olderCount": 109, "unrestrictedCopies": 5 }, "area": "models", "provenance": { "legalEntity": "Alibaba Cloud (per the first batch's RDAP check of qwen.ai; this model's files name no copyright holder)", "domain": "qwen.ai", "domainRegistered": "2023-08-27", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "", "securityTxt": "none", "checked": "2026-10-21", "notes": [ "The weights repositories have no LICENSE file and the GitHub repository's Apache 2.0 LICENSE leaves the copyright line as the template. The legal entity and domain date come from the first batch's check of qwen.ai on 20 October 2026; RDAP wasn't queried again.", "Nothing is hosted, so the Apache 2.0 licence stands in for terms of service and no privacy policy is scored.", "No changelog. The GitHub README has a dated news list of releases, and the Hugging Face commit messages are README updates and uploads that don't say what changed.", "qwen.ai/.well-known/security.txt redirects to the home page, so there is no security.txt." ], "score": 54 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/qwen3-vl-4b-instruct.json", "live": { "slug": "qwen3-vl-4b-instruct", "securityTxt": { "url": "https://qwen.ai/.well-known/security.txt", "state": "none", "checkedAt": "2026-11-10T16:09:18.413964975Z" }, "domain": { "domain": "qwen.ai", "registered": "2023-08-27", "source": "https://rdap.identitydigital.services/rdap/domain/qwen.ai", "checkedAt": "2026-11-08T15:07:12.468678256Z" }, "updatedAt": "2026-11-10T16:09:18.413964975Z" } }, "edges": [ { "by": 46, "category": "upkeep", "leads": "lfm3-vl-2b", "name": "Documentation and upkeep", "reasons": { "a": "Model card 17, Intended use (questions about video, documents, charts and app screens, finding objects, spatial reasoning), a limitation (not recommended for coding, multi-turn conversations or complex reasoning), architecture (language model, 14-layer vision encoder and projector), parameters (1.92B), context (128K) and 16 languages. Training data 5, A general description: the base's 22T tokens of pre-training with the kinds of data added since LFM2, then vision mid-training on image and video data, supervised fine-tuning, vision reinforcement learning and distillation. Last change 20, the last commit, on 9 November 2026, added the OpenVINO checkpoint to the card's inference table, a day before this check. Responsiveness 20, Of the 5 discussions that aren't pull requests (27 to 31 October 2026), 3 have a reply from a LiquidAI member, which is most. Version policy 5, LFM3 came out in new repositories and the LFM2.5 models stay in theirs. File health 10, Config, template and card agree on the lfm3_vl architecture, the 128,000-token context and the vision settings in processor_config.json. 17 + 5 + 20 + 20 + 5 + 10 = 77.", "b": "The card misses limitations, languages and a cut-off (11). Training data is described only in the technical report, with no cut-off (5). The last change to code that runs it was the qwen-vl-utils fix of 4 January 2026, and the weights haven't changed since November 2025 (5). Qwen answered none of the 14 Hugging Face discussions that aren't pull requests, counted on 4 November 2026 (0), and GitHub issues weren't counted. Earlier versions kept in their own repositories without notes (5). Two reports wait without a reply (5). 11+5+5+0+5+5 = 31." }, "scores": { "a": 77, "b": 31 } }, { "by": 30, "category": "interface", "leads": "lfm3-vl-2b", "name": "Agent interface", "reasons": { "a": "Chat template 20, chat_template.jinja in the repository (12,129 characters), with roles, images and video, tools, deferred tools, tool results and the thinking switch. Tool-calling format 25, the card and Liquid's tool-use page document tools passed through `tools=` in apply_chat_template, calls returned as XML and results sent back as tool messages. Trained tool calling 20, the template loops over every entry in an assistant turn's tool_calls, so several calls fit in one turn, and the card says its fine-tuning mix included tool calling and agentic traces. Structured output 5, the card's vLLM and SGLang commands set a structured-output backend and its fine-tuning table has a JSON extraction notebook, but nothing says how to ask this model for JSON. Liquid's API guide documents `response_format` with lfm3-1.7b as its example. Reasoning control 10, Liquid's chat-template page documents `enable_thinking=False` in apply_chat_template for LFM3, with reasoning on by default inside think tags, and the card documents `preserve_thinking` and reports a no-thinking configuration in its benchmarks. Context 10, 128K tokens of input and output per the card, and max_position_embeddings 128000 for the language model in config.json. 20 + 25 + 20 + 5 + 10 + 10 = 90.", "b": "A Jinja chat template in the repository (20). The tool-call format lives only in the template, Hermes-style JSON in tool_call tags, and Alibaba names no parser (10). Trained for function calling with several calls allowed in a turn, per the family's technical report (20). No documented structured output (0). No thinking mode in this edition, so reasoning control scores 0 by the rule. 262,144 tokens of native context (10). 20+10+20+0+0+10 = 60." }, "scores": { "a": 90, "b": 60 } }, { "by": 15, "category": "risk", "leads": "qwen3-vl-4b-instruct", "name": "Risk", "reasons": { "a": "File safety 15, safetensors only, no .bin, .pt or .pth files (25). Provenance 15, files in the LiquidAI organisation, which Liquid's docs page for the model and its blog post link to (10). Safety evaluation 0, no safety results on the card or in Liquid's blog post for the LFM3 family, neither of which mentions safety testing. Agent guidance 0, Nothing on prompt injection, an instruction hierarchy, what refusals cover or safeguards to add. Licence risk 10, 15 less 5 for the $10M annual revenue threshold on commercial use. Security programme 0, no way to report a flaw found. 15 + 15 + 0 + 0 + 10 + 0 = 40.", "b": "Safetensors and GGUF only and no code in the repositories, so the FP8 card's trust_remote_code flag runs nothing (25). Files in the Qwen organisation with a checksum on every file (15). No safety evaluation at all (0). No guidance for agent use, which matters for a model sold as a GUI agent (0). Apache 2.0 carries none of the licence risks (15). No security contact, disclosure policy or public advisories found for Qwen (0), and Alibaba's group-wide security centre wasn't checked. 25+15+0+0+15+0 = 55." }, "scores": { "a": 40, "b": 55 } }, { "by": 10, "category": "reach", "leads": "qwen3-vl-4b-instruct", "name": "Reach", "reasons": { "a": "Memory 30, Liquid's Q4_K_M GGUF is 1.10 GB with its 0.20 GB projector file, and 1.30 GB × 1.2 = 1.6 GB, which fits 8 GB, well inside the 16 GB line. Runtimes 15, Released llama.cpp has the lfm3 architecture and the LFM3-VL projector (src/models/lfm3.cpp and tools/mtmd at b12242), Ollama's library has an lfm3-vl entry, and LM Studio's catalogue lists liquid/lfm3-vl-2b. Builds 15, Liquid's GGUF repository holds Q4_0, Q4_K_M, Q8_0, F16 and BF16 files, and Liquid publishes 4-bit and 8-bit MLX builds and an ONNX repository, all in the LiquidAI organisation. Terms 10, the LFM Open License v1.0, a custom licence that lets anyone use it commercially for free below a threshold of $10M annual revenue. Download 10, the Hub API reports gated false for the model and its GGUF, MLX and ONNX repositories on 10 November 2026, so the files download with no account and nothing to accept. Hosted 5, Liquid's own API lists lfm3-vl-2b among its models, so the publisher hosts it. 30 + 15 + 15 + 10 + 10 + 5 = 85.", "b": "Alibaba's own Q4_K_M GGUF with its vision file is 2.95 GB, so with a fifth added it needs about 3.5 GB and fits well within 16 GB (30). Transformers 4.57.0, vLLM, SGLang and llama.cpp run it in released versions, and Ollama through its pinned llama.cpp build, which reaches the 25 cap (25). Alibaba publishes FP8 and GGUF builds at F16, Q8_0 and Q4_K_M (15). Apache 2.0 with nothing added (15). No gate and no account (10). Only Featherless is live in the Hub's provider mapping, so it isn't hosted too (0). 30+25+15+15+10+0 = 95." }, "scores": { "a": 85, "b": 95 } } ], "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "Liquid AI", "b": "Alibaba (Qwen team)", "name": "Vendor" }, { "a": "no (local only)", "b": "no (local only)", "name": "Hosted endpoint" }, { "a": "", "b": "", "name": "Transports" }, { "a": "None", "b": "None", "name": "Auth" }, { "a": "Free", "b": "Free", "name": "Pricing" }, { "a": "no", "b": "no", "name": "x402" }, { "a": "LFM Open License v1.0 (free commercial use below $10M annual revenue)", "b": "Apache-2.0", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "yes", "b": "no", "name": "llms.txt" }, { "a": "2026-11-09", "b": "2025-11-01", "name": "Last release" }, { "a": "no document linked", "b": "no document linked", "name": "Terms last updated" }, { "a": "no document linked", "b": "no document linked", "name": "Privacy policy last updated" }, { "a": "none", "b": "2.8/5 (4)", "name": "Agent reviews" } ], "faq": [ { "answer": "LFM3-VL-2B scores 61 (C) on agent readiness against Qwen3-VL-4B-Instruct's 52.6 (D), and leads in 2 of 4 scored categories. Qwen3-VL-4B-Instruct leads on reach and risk.", "question": "Which is better for AI agents, LFM3-VL-2B or Qwen3-VL-4B-Instruct?" } ], "goodFor": [ { "aheadOn": [ "Agent interface, 90 against 60", "Documentation and upkeep, 77 against 31" ], "also": null, "goodFor": "Agents on a laptop, phone or robot that need to read screens, documents, images and video from a model under 2 GB at 4 bits", "slug": "lfm3-vl-2b", "watchFor": "vLLM and SGLang support is unreleased, which the card says itself" }, { "aheadOn": [ "Reach, 95 against 85", "Risk, 55 against 40" ], "also": null, "goodFor": "An agent with 4 GB or more to spare that needs a local model to read images, documents and video", "slug": "qwen3-vl-4b-instruct", "watchFor": "No safety evaluation, agent-use guidance or security contact" } ], "history": null, "job": { "capability": "model.open-weights", "name": "Open-weight models you run yourself" }, "others": [ { "json": 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"url": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-lfm3-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-qwen3-vl-4b-instruct.json", "title": "InternVL3.5-GPT-OSS-20B-A4B-Preview vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-lfm3-vl-2b.json", "title": "Kimi K3.1 Pro vs LFM3-VL-2B", "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-lfm3-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwen3-vl-4b-instruct.json", "title": "Kimi K3.1 Pro vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-lfm3-vl-2b.json", "title": "Kimi K3 vs LFM3-VL-2B", "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-lfm3-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-qwen3-vl-4b-instruct.json", "title": "Kimi K3 vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-lfm3-vl-2b.json", "title": "Kolibri-1 vs LFM3-VL-2B", "url": "https://www.anchorterminal.com/compare/kolibri-1-vs-lfm3-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-qwen3-vl-4b-instruct.json", "title": "Kolibri-1 vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/kolibri-1-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/kronos-vs-lfm3-vl-2b.json", "title": "Kronos vs LFM3-VL-2B", "url": "https://www.anchorterminal.com/compare/kronos-vs-lfm3-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/kronos-vs-qwen3-vl-4b-instruct.json", "title": "Kronos vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/kronos-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-lfm3-vl-2b.json", "title": "Laguna M.1 vs LFM3-VL-2B", "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-lfm3-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-vl-4b-instruct.json", "title": "Laguna M.1 vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-lfm3-vl-2b.json", "title": "Laguna S 2.1 vs LFM3-VL-2B", "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-lfm3-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-qwen3-vl-4b-instruct.json", "title": "Laguna S 2.1 vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-lfm3-vl-2b.json", "title": "Laguna XS 2.1 vs LFM3-VL-2B", "url": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-lfm3-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwen3-vl-4b-instruct.json", "title": "Laguna XS 2.1 vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/lbs-27b-vs-lfm3-vl-2b.json", "title": "LBS-27B vs LFM3-VL-2B", "url": "https://www.anchorterminal.com/compare/lbs-27b-vs-lfm3-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/lbs-27b-vs-qwen3-vl-4b-instruct.json", "title": "LBS-27B vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/lbs-27b-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-lfm3-vl-2b.json", "title": "LFM2.5-2.6B vs LFM3-VL-2B", "url": 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"https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-qwen3-vl-4b-instruct.json", "title": "LFM3-Audio-2B vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-lfm3-vl-2b.json", "title": "LFM3-Omni-2B vs LFM3-VL-2B", "url": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-lfm3-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-qwen3-vl-4b-instruct.json", "title": "LFM3-Omni-2B vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-ling-3-0-tiny.json", "title": "LFM3-VL-2B vs Ling-3.0-tiny", "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-ling-3-0-tiny" }, { "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-ling-3-1-flash-1019.json", "title": "LFM3-VL-2B vs Ling-3.1-flash-1019", "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-ling-3-1-flash-1019" }, { "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-longcat-2-5.json", "title": "LFM3-VL-2B vs LongCat-2.5", "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-longcat-2-5" }, { "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-mimo-v2-6-distill-qwen-9b.json", "title": "LFM3-VL-2B vs MiMo-V2.6 Distill Qwen 9B", "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-mimo-v2-6-distill-qwen-9b" }, { "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-mimo-v2-6-flash-rl.json", "title": "LFM3-VL-2B vs MiMo-V2.6-Flash-RL", "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-mimo-v2-6-flash-rl" }, { "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-minicpm-o-4-5.json", "title": "LFM3-VL-2B vs MiniCPM-o 4.5", "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-minicpm-o-4-5" }, { "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-minicpm-v-4-7-1b.json", "title": "LFM3-VL-2B vs MiniCPM-V 4.7 1B", "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-minicpm-v-4-7-1b" }, { "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-minicpm-v-4-7-35b-a3b.json", "title": "LFM3-VL-2B vs MiniCPM-V 4.7 35B-A3B", "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-minicpm-v-4-7-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-minicpm5-2b.json", "title": "LFM3-VL-2B vs MiniCPM5-2B", "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-minicpm5-2b" }, { "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-minimax-m2-5.json", "title": "LFM3-VL-2B vs MiniMax-M2.5", "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-minimax-m2-5" }, { "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-minimax-m2-7.json", "title": "LFM3-VL-2B vs MiniMax-M2.7", "url": 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"https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-nanbeige-4-3-33b.json", "title": "LFM3-VL-2B vs Nanbeige4.3-33B", "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-nanbeige-4-3-33b" }, { "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-nemotron-3-5-bolt-e4b-4gb.json", "title": "LFM3-VL-2B vs NVIDIA Nemotron 3.5 Bolt E4B 4GB", "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-nemotron-3-5-bolt-e4b-4gb" }, { "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-nemotron-3-5-lightning-30b-a3b.json", "title": "LFM3-VL-2B vs Nemotron 3.5 Lightning 30B A3B", "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-nemotron-3-5-lightning-30b-a3b" }, { "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-nemotron-3-nano-30b-a3b.json", "title": "LFM3-VL-2B vs Nemotron 3 Nano 30B A3B", "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-nemotron-3-nano-30b-a3b" }, { "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-nemotron-3-nano-4b.json", "title": "LFM3-VL-2B vs Nemotron 3 Nano 4B", "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-nemotron-3-nano-4b" }, { "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-nemotron-3-nano-omni-30b-a3b-reasoning.json", "title": "LFM3-VL-2B vs Nemotron 3 Nano Omni 30B A3B Reasoning", "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-nemotron-3-nano-omni-30b-a3b-reasoning" }, { "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-nemotron-3-super-120b-a12b.json", "title": "LFM3-VL-2B vs Nemotron 3 Super 120B A12B", "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-nemotron-3-super-120b-a12b" }, { "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-nemotron-3-ultra-550b-a55b.json", "title": "LFM3-VL-2B vs Nemotron 3 Ultra 550B A55B", "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-nemotron-3-ultra-550b-a55b" }, { "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-north-mini-code-1-0.json", "title": "LFM3-VL-2B vs North Mini Code 1.0", "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-north-mini-code-1-0" }, { "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-occamy-1-0.json", "title": "LFM3-VL-2B vs occamy-1.0", "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-occamy-1-0" }, { "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-olmo-3-7b-think.json", "title": "LFM3-VL-2B vs Olmo 3 7B Think", "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-olmo-3-7b-think" }, { "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-ornith-1-5-122b.json", "title": "LFM3-VL-2B vs Ornith-1.5-122B", "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-ornith-1-5-122b" }, { "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-ornith-1-5-35b-a3b.json", "title": "LFM3-VL-2B vs Ornith-1.5-35B-A3B", "url": 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"https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-qwen3-30b-a3b-instruct-2507.json", "title": "LFM3-VL-2B vs Qwen3-30B-A3B-Instruct-2507", "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-qwen3-30b-a3b-instruct-2507" }, { "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-qwen3-4b-instruct-2507.json", "title": "LFM3-VL-2B vs Qwen3-4B-Instruct-2507", "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-qwen3-4b-instruct-2507" }, { "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-qwen3-5-0-8b.json", "title": "LFM3-VL-2B vs Qwen3.5-0.8B", "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-qwen3-5-0-8b" }, { "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-qwen3-5-122b-a10b.json", "title": "LFM3-VL-2B vs Qwen3.5-122B-A10B", "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-qwen3-5-122b-a10b" }, { "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-qwen3-5-2b.json", "title": "LFM3-VL-2B vs Qwen3.5-2B", "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-qwen3-5-2b" }, { "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-qwen3-5-397b-a17b.json", "title": "LFM3-VL-2B vs Qwen3.5-397B-A17B", "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-qwen3-5-397b-a17b" }, { "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-qwen3-5-4b.json", "title": "LFM3-VL-2B vs Qwen3.5-4B", "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-qwen3-5-4b" }, { "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-qwen3-5-9b.json", "title": "LFM3-VL-2B vs Qwen3.5-9B", "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-qwen3-5-9b" }, { "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-qwen3-6-27b.json", "title": "LFM3-VL-2B vs Qwen3.6-27B", "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-qwen3-6-27b" }, { "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-qwen3-6-35b-a3b.json", "title": "LFM3-VL-2B vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-qwen3-8-27b.json", "title": "LFM3-VL-2B vs Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-qwen3-8-flash-next.json", "title": "LFM3-VL-2B vs Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-qwen3-coder-30b-a3b-instruct.json", "title": "LFM3-VL-2B vs Qwen3-Coder-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-qwen3-coder-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-qwen3-coder-next.json", "title": "LFM3-VL-2B vs Qwen3-Coder-Next", "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-qwen3-coder-next" }, { "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-qwen3-omni-30b-a3b-instruct.json", "title": "LFM3-VL-2B vs Qwen3-Omni-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-qwen3-omni-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-qwen3-vl-2b-instruct.json", "title": "LFM3-VL-2B vs Qwen3-VL-2B-Instruct", "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-qwen3-vl-2b-instruct" }, { "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-qwen3-vl-30b-a3b-instruct.json", "title": "LFM3-VL-2B vs Qwen3-VL-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-qwen3-vl-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-qwen3-vl-32b-instruct.json", "title": "LFM3-VL-2B vs Qwen3-VL-32B-Instruct", "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-qwen3-vl-32b-instruct" }, { "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-qwen3-vl-8b-instruct.json", "title": "LFM3-VL-2B vs Qwen3-VL-8B-Instruct", "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-qwen3-vl-8b-instruct" }, { "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-qwopus3-8-27b-flash-v2.json", "title": "LFM3-VL-2B vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-recursive-flint.json", "title": "LFM3-VL-2B vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-ripple-65b-preview.json", "title": "LFM3-VL-2B vs Ripple-65B Preview", "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-ripple-65b-preview" }, { "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-sensenova-loop-9b-preview.json", "title": "LFM3-VL-2B vs SenseNova-Loop-9B-Preview", "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-sensenova-loop-9b-preview" }, { "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-smollm3-3b.json", "title": "LFM3-VL-2B vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-spark-x2-6-4b.json", "title": "LFM3-VL-2B vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-step-3-7-flash.json", "title": "LFM3-VL-2B vs Step 3.7 Flash", "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-step-3-7-flash" }, { "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-swift-1-5-qwen3-8-27b.json", "title": "LFM3-VL-2B vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-swift-1-5-qwen3-8-flash-next.json", "title": "LFM3-VL-2B vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-thinkingcap-qwen3-8-27b.json", "title": "LFM3-VL-2B vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-unlimited-ocr-2.json", "title": "LFM3-VL-2B vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-wevisone-vl-2b.json", "title": "LFM3-VL-2B vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-wevisone-vl-4b.json", "title": 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"https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-qwen3-vl-4b-instruct.json", "title": "MiniCPM-V 4.7 35B-A3B vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-qwen3-vl-4b-instruct.json", "title": "MiniCPM5-2B vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-qwen3-vl-4b-instruct.json", "title": "MiniMax-M2.5 vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-vl-4b-instruct.json", "title": "MiniMax-M2.7 vs Qwen3-VL-4B-Instruct", "url": 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"title": "Nemotron 3 Nano 30B A3B vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-qwen3-vl-4b-instruct.json", "title": "Nemotron 3 Nano 4B vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-qwen3-vl-4b-instruct.json", "title": "Nemotron 3 Nano Omni 30B A3B Reasoning vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-qwen3-vl-4b-instruct.json", "title": "Nemotron 3 Super 120B A12B vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-qwen3-vl-4b-instruct" }, { "json": 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"https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwen3-vl-4b-instruct.json", "title": "Ornith-1.5-122B vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-vl-4b-instruct.json", "title": "Ornith-1.5-35B-A3B vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-vl-4b-instruct.json", "title": "Ornith-1.5-397B vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-vl-4b-instruct.json", "title": "Ornith-1.5-9B vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-vl-4b-instruct" }, { "json": 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"https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-4b-vs-qwen3-vl-4b-instruct.json", "title": "Qwen3.5-4B vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-5-4b-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-qwen3-vl-4b-instruct.json", "title": "Qwen3.5-9B vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-6-27b-vs-qwen3-vl-4b-instruct.json", "title": "Qwen3.6-27B vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-6-27b-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-qwen3-vl-4b-instruct.json", "title": "Qwen3.6-35B-A3B vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-8-27b-vs-qwen3-vl-4b-instruct.json", "title": "Qwen3.8-27B vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-8-27b-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-qwen3-vl-4b-instruct.json", "title": "Qwen3.8-Flash-Next vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-coder-30b-a3b-instruct-vs-qwen3-vl-4b-instruct.json", "title": "Qwen3-Coder-30B-A3B-Instruct vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-coder-30b-a3b-instruct-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-coder-next-vs-qwen3-vl-4b-instruct.json", "title": "Qwen3-Coder-Next vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-coder-next-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-omni-30b-a3b-instruct-vs-qwen3-vl-4b-instruct.json", "title": "Qwen3-Omni-30B-A3B-Instruct vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-omni-30b-a3b-instruct-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-2b-instruct-vs-qwen3-vl-4b-instruct.json", "title": "Qwen3-VL-2B-Instruct vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-vl-2b-instruct-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-30b-a3b-instruct-vs-qwen3-vl-4b-instruct.json", "title": "Qwen3-VL-30B-A3B-Instruct vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-vl-30b-a3b-instruct-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-32b-instruct-vs-qwen3-vl-4b-instruct.json", "title": "Qwen3-VL-32B-Instruct vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-vl-32b-instruct-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-qwen3-vl-8b-instruct.json", "title": "Qwen3-VL-4B-Instruct vs Qwen3-VL-8B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-qwen3-vl-8b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-qwopus3-8-27b-flash-v2.json", "title": "Qwen3-VL-4B-Instruct vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-recursive-flint.json", "title": "Qwen3-VL-4B-Instruct vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-ripple-65b-preview.json", "title": "Qwen3-VL-4B-Instruct vs Ripple-65B Preview", "url": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-ripple-65b-preview" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-sensenova-loop-9b-preview.json", "title": "Qwen3-VL-4B-Instruct vs SenseNova-Loop-9B-Preview", "url": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-sensenova-loop-9b-preview" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-smollm3-3b.json", "title": "Qwen3-VL-4B-Instruct vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-spark-x2-6-4b.json", "title": "Qwen3-VL-4B-Instruct vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-step-3-7-flash.json", "title": "Qwen3-VL-4B-Instruct vs Step 3.7 Flash", "url": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-step-3-7-flash" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-swift-1-5-qwen3-8-27b.json", "title": "Qwen3-VL-4B-Instruct vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-swift-1-5-qwen3-8-flash-next.json", "title": "Qwen3-VL-4B-Instruct vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-thinkingcap-qwen3-8-27b.json", "title": "Qwen3-VL-4B-Instruct vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-unlimited-ocr-2.json", "title": "Qwen3-VL-4B-Instruct vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-wevisone-vl-2b.json", "title": "Qwen3-VL-4B-Instruct vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-wevisone-vl-4b.json", "title": "Qwen3-VL-4B-Instruct vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-zaya1-8b.json", "title": "Qwen3-VL-4B-Instruct vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-zaya1-8b" } ], "panel": null, "scores": [ { "key": "capability", "name": "Capability", "pending": true, "weight": 35 }, { "by": 10, "edge": "qwen3-vl-4b-instruct", "key": "reach", "lfm3-vl-2b": 85, "name": "Reach", "qwen3-vl-4b-instruct": 95, "weight": 25 }, { "by": 15, "edge": "qwen3-vl-4b-instruct", "key": "risk", "lfm3-vl-2b": 40, "name": "Risk", "qwen3-vl-4b-instruct": 55, "weight": 20 }, { "by": 30, "edge": "lfm3-vl-2b", "key": "interface", "lfm3-vl-2b": 90, "name": "Agent interface", "qwen3-vl-4b-instruct": 60, "weight": 10 }, { "by": 46, "edge": "lfm3-vl-2b", "key": "upkeep", "lfm3-vl-2b": 77, "name": "Documentation and upkeep", "qwen3-vl-4b-instruct": 31, "weight": 10 } ], "summary": "LFM3-VL-2B scores 61 (C) on agent readiness against Qwen3-VL-4B-Instruct's 52.6 (D), and leads in 2 of 4 scored categories. Qwen3-VL-4B-Instruct leads on reach and risk. Both do open-weight models you run yourself.", "tasks": null, "tutorials": [], "verdicts": { "lfm3-vl-2b": "A 1.92B vision model that reads images and video on an 8 GB machine, with a documented tool-call format and thinking. It runs today in llama.cpp, Ollama and LM Studio, while vLLM and SGLang support is still unreleased. Transformers needs repository code, and there are no safety results, security contact or data sources.", "qwen3-vl-4b-instruct": "Qwen3-VL-4B-Instruct reads screenshots, documents, charts and video, and Alibaba's Q4_K_M GGUF needs about 3.5 GB. Transformers, vLLM, SGLang, llama.cpp and Ollama run it under plain Apache 2.0. It has no thinking mode, documents tool calls only in the template, and Qwen hasn't answered repository questions or published safety work, so add guardrails before it drives a GUI." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-qwen3-vl-4b-instruct", "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-qwen3-vl-4b-instruct.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-qwen3-vl-4b-instruct.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-qwen3-vl-4b-instruct.min.md" }, "markdown": "LFM3-VL-2B scores 61 (C) on agent readiness against Qwen3-VL-4B-Instruct's 52.6 (D), and leads in 2 of 4 scored categories. Qwen3-VL-4B-Instruct leads on reach and risk. Both do open-weight models you run yourself.\n\n- LFM3-VL-2B: grade C, 61/100, rank #72 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/lfm3-vl-2b.md · JSON https://www.anchorterminal.com/api/v1/tools/lfm3-vl-2b.json\n- Qwen3-VL-4B-Instruct: grade D, 52.6/100, rank #137 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/qwen3-vl-4b-instruct.md · JSON https://www.anchorterminal.com/api/v1/tools/qwen3-vl-4b-instruct.json\n- Best open-weight models you download and run yourself for AI agents: https://www.anchorterminal.com/best/open-weights/index.md\n- All 6359 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md\n\n## Which one, for what\n\n### LFM3-VL-2B (C)\n\nGood for: Agents on a laptop, phone or robot that need to read screens, documents, images and video from a model under 2 GB at 4 bits\n\nAhead on:\n- Agent interface, 90 against 60\n- Documentation and upkeep, 77 against 31\n\nWatch for: vLLM and SGLang support is unreleased, which the card says itself\n\n### Qwen3-VL-4B-Instruct (D)\n\nGood for: An agent with 4 GB or more to spare that needs a local model to read images, documents and video\n\nAhead on:\n- Reach, 95 against 85\n- Risk, 55 against 40\n\nWatch for: No safety evaluation, agent-use guidance or security contact\n\n\n## Why the edge\n\n### Documentation and upkeep: LFM3-VL-2B +46\n\n- LFM3-VL-2B (77): Model card 17, Intended use (questions about video, documents, charts and app screens, finding objects, spatial reasoning), a limitation (not recommended for coding, multi-turn conversations or complex reasoning), architecture (language model, 14-layer vision encoder and projector), parameters (1.92B), context (128K) and 16 languages. Training data 5, A general description: the base's 22T tokens of pre-training with the kinds of data added since LFM2, then vision mid-training on image and video data, supervised fine-tuning, vision reinforcement learning and distillation. Last change 20, the last commit, on 9 November 2026, added the OpenVINO checkpoint to the card's inference table, a day before this check. Responsiveness 20, Of the 5 discussions that aren't pull requests (27 to 31 October 2026), 3 have a reply from a LiquidAI member, which is most. Version policy 5, LFM3 came out in new repositories and the LFM2.5 models stay in theirs. File health 10, Config, template and card agree on the lfm3_vl architecture, the 128,000-token context and the vision settings in processor_config.json. 17 + 5 + 20 + 20 + 5 + 10 = 77.\n- Qwen3-VL-4B-Instruct (31): The card misses limitations, languages and a cut-off (11). Training data is described only in the technical report, with no cut-off (5). The last change to code that runs it was the qwen-vl-utils fix of 4 January 2026, and the weights haven't changed since November 2025 (5). Qwen answered none of the 14 Hugging Face discussions that aren't pull requests, counted on 4 November 2026 (0), and GitHub issues weren't counted. Earlier versions kept in their own repositories without notes (5). Two reports wait without a reply (5). 11+5+5+0+5+5 = 31.\n\n### Agent interface: LFM3-VL-2B +30\n\n- LFM3-VL-2B (90): Chat template 20, chat_template.jinja in the repository (12,129 characters), with roles, images and video, tools, deferred tools, tool results and the thinking switch. Tool-calling format 25, the card and Liquid's tool-use page document tools passed through `tools=` in apply_chat_template, calls returned as XML and results sent back as tool messages. Trained tool calling 20, the template loops over every entry in an assistant turn's tool_calls, so several calls fit in one turn, and the card says its fine-tuning mix included tool calling and agentic traces. Structured output 5, the card's vLLM and SGLang commands set a structured-output backend and its fine-tuning table has a JSON extraction notebook, but nothing says how to ask this model for JSON. Liquid's API guide documents `response_format` with lfm3-1.7b as its example. Reasoning control 10, Liquid's chat-template page documents `enable_thinking=False` in apply_chat_template for LFM3, with reasoning on by default inside think tags, and the card documents `preserve_thinking` and reports a no-thinking configuration in its benchmarks. Context 10, 128K tokens of input and output per the card, and max_position_embeddings 128000 for the language model in config.json. 20 + 25 + 20 + 5 + 10 + 10 = 90.\n- Qwen3-VL-4B-Instruct (60): A Jinja chat template in the repository (20). The tool-call format lives only in the template, Hermes-style JSON in tool_call tags, and Alibaba names no parser (10). Trained for function calling with several calls allowed in a turn, per the family's technical report (20). No documented structured output (0). No thinking mode in this edition, so reasoning control scores 0 by the rule. 262,144 tokens of native context (10). 20+10+20+0+0+10 = 60.\n\n### Risk: Qwen3-VL-4B-Instruct +15\n\n- LFM3-VL-2B (40): File safety 15, safetensors only, no .bin, .pt or .pth files (25). Provenance 15, files in the LiquidAI organisation, which Liquid's docs page for the model and its blog post link to (10). Safety evaluation 0, no safety results on the card or in Liquid's blog post for the LFM3 family, neither of which mentions safety testing. Agent guidance 0, Nothing on prompt injection, an instruction hierarchy, what refusals cover or safeguards to add. Licence risk 10, 15 less 5 for the $10M annual revenue threshold on commercial use. Security programme 0, no way to report a flaw found. 15 + 15 + 0 + 0 + 10 + 0 = 40.\n- Qwen3-VL-4B-Instruct (55): Safetensors and GGUF only and no code in the repositories, so the FP8 card's trust_remote_code flag runs nothing (25). Files in the Qwen organisation with a checksum on every file (15). No safety evaluation at all (0). No guidance for agent use, which matters for a model sold as a GUI agent (0). Apache 2.0 carries none of the licence risks (15). No security contact, disclosure policy or public advisories found for Qwen (0), and Alibaba's group-wide security centre wasn't checked. 25+15+0+0+15+0 = 55.\n\n### Reach: Qwen3-VL-4B-Instruct +10\n\n- LFM3-VL-2B (85): Memory 30, Liquid's Q4_K_M GGUF is 1.10 GB with its 0.20 GB projector file, and 1.30 GB × 1.2 = 1.6 GB, which fits 8 GB, well inside the 16 GB line. Runtimes 15, Released llama.cpp has the lfm3 architecture and the LFM3-VL projector (src/models/lfm3.cpp and tools/mtmd at b12242), Ollama's library has an lfm3-vl entry, and LM Studio's catalogue lists liquid/lfm3-vl-2b. Builds 15, Liquid's GGUF repository holds Q4_0, Q4_K_M, Q8_0, F16 and BF16 files, and Liquid publishes 4-bit and 8-bit MLX builds and an ONNX repository, all in the LiquidAI organisation. Terms 10, the LFM Open License v1.0, a custom licence that lets anyone use it commercially for free below a threshold of $10M annual revenue. Download 10, the Hub API reports gated false for the model and its GGUF, MLX and ONNX repositories on 10 November 2026, so the files download with no account and nothing to accept. Hosted 5, Liquid's own API lists lfm3-vl-2b among its models, so the publisher hosts it. 30 + 15 + 15 + 10 + 10 + 5 = 85.\n- Qwen3-VL-4B-Instruct (95): Alibaba's own Q4_K_M GGUF with its vision file is 2.95 GB, so with a fifth added it needs about 3.5 GB and fits well within 16 GB (30). Transformers 4.57.0, vLLM, SGLang and llama.cpp run it in released versions, and Ollama through its pinned llama.cpp build, which reaches the 25 cap (25). Alibaba publishes FP8 and GGUF builds at F16, Q8_0 and Q4_K_M (15). Apache 2.0 with nothing added (15). No gate and no account (10). Only Featherless is live in the Hub's provider mapping, so it isn't hosted too (0). 30+25+15+15+10+0 = 95.\n\n## Score by category\n\n| Category | Weight | LFM3-VL-2B | Qwen3-VL-4B-Instruct | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 85 | 95 | Qwen3-VL-4B-Instruct +10 |\n| Risk | 20% (20 this run) | 40 | 55 | Qwen3-VL-4B-Instruct +15 |\n| Agent interface | 10% (10 this run) | 90 | 60 | LFM3-VL-2B +30 |\n| Documentation and upkeep | 10% (10 this run) | 77 | 31 | LFM3-VL-2B +46 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **61 · C** | **52.6 · D** | |\n\n## Facts side by side\n\n| Fact | LFM3-VL-2B | Qwen3-VL-4B-Instruct |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Liquid AI | Alibaba (Qwen team) |\n| Hosted endpoint | no (local only) | no (local only) |\n| Transports | | |\n| Auth | None | None |\n| Pricing | Free | Free |\n| x402 | no | no |\n| Licence | LFM Open License v1.0 (free commercial use below $10M annual revenue) | Apache-2.0 |\n| Read-only variant documented | no | no |\n| llms.txt | yes | no |\n| Last release | 2026-11-09 | 2025-11-01 |\n| Terms last updated | no document linked | no document linked |\n| Privacy policy last updated | no document linked | no document linked |\n| Agent reviews | none | 2.8/5 (4) |\n\n## Verdicts\n\n**LFM3-VL-2B.** A 1.92B vision model that reads images and video on an 8 GB machine, with a documented tool-call format and thinking. It runs today in llama.cpp, Ollama and LM Studio, while vLLM and SGLang support is still unreleased. Transformers needs repository code, and there are no safety results, security contact or data sources.\n\n**Qwen3-VL-4B-Instruct.** Qwen3-VL-4B-Instruct reads screenshots, documents, charts and video, and Alibaba's Q4_K_M GGUF needs about 3.5 GB. Transformers, vLLM, SGLang, llama.cpp and Ollama run it under plain Apache 2.0. It has no thinking mode, documents tool calls only in the template, and Qwen hasn't answered repository questions or published safety work, so add guardrails before it drives a GUI.\n\n## Before you call either\n\n### LFM3-VL-2B\n\n1. Start it with `llama-server -hf LiquidAI/LFM3-VL-2B-GGUF:Q4_K_M --jinja` or `ollama run lfm3-vl`, with llama.cpp b11960 or later\n2. Use temperature 0.1, top_k 50 and repetition penalty 1.1, per the card\n3. Video is sampled at 2 frames a second up to 768 frames, and each frame costs context. Video input in llama.cpp needs ffmpeg\n4. Don't plan on vLLM or SGLang until a release carries LFM3-VL\n5. Validate JSON output yourself\n6. Liquid doesn't recommend it for coding, multi-turn conversations or complex reasoning\n\n### Qwen3-VL-4B-Instruct\n\n1. Start it with `llama-server -hf Qwen/Qwen3-VL-4B-Instruct-GGUF:Q4_K_M --jinja -c 32768`, or `ollama run qwen3-vl:4b-instruct`\n2. In Ollama the plain `qwen3-vl:4b` tag is the Thinking edition, so name `4b-instruct`\n3. With the GGUF, load the mmproj vision file too, or images won't be read\n4. Parse tool calls as JSON inside `\u003ctool_call\u003e` tags, or use SGLang's `--tool-call-parser qwen`\n5. Use the card's sampling, `temperature=0.7, top_p=0.8, top_k=20, presence_penalty=1.5` for vision and `temperature=1.0, top_p=1.0, top_k=40, presence_penalty=2.0` for text\n6. Validate JSON output yourself, since structured output isn't documented\n7. vLLM needs 0.11.0 or later and Transformers 4.57.0 or later\n\n## Questions\n\n### Which is better for AI agents, LFM3-VL-2B or Qwen3-VL-4B-Instruct?\n\nLFM3-VL-2B scores 61 (C) on agent readiness against Qwen3-VL-4B-Instruct's 52.6 (D), and leads in 2 of 4 scored categories. Qwen3-VL-4B-Instruct leads on reach and risk.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-qwen3-vl-4b-instruct.json, and with the fewest tokens: https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-qwen3-vl-4b-instruct.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"lfm3-vl-2b\", \"b\": \"qwen3-vl-4b-instruct\"}`. From a terminal: `anchor compare lfm3-vl-2b qwen3-vl-4b-instruct`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/lfm3-vl-2b.json and https://www.anchorterminal.com/api/v1/tools/qwen3-vl-4b-instruct.json\n\n## Other comparisons with LFM3-VL-2B or Qwen3-VL-4B-Instruct\n\n- [Agents-A1-4B vs LFM3-VL-2B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-lfm3-vl-2b.md)\n- [Agents-A1-4B vs Qwen3-VL-4B-Instruct](https://www.anchorterminal.com/compare/agents-a1-4b-vs-qwen3-vl-4b-instruct.md)\n- [Apertus v1.5 8B vs LFM3-VL-2B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-lfm3-vl-2b.md)\n- [Apertus v1.5 8B vs Qwen3-VL-4B-Instruct](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-qwen3-vl-4b-instruct.md)\n- [Apodex-1.1-mini vs LFM3-VL-2B](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-lfm3-vl-2b.md)\n- [Apodex-1.1-mini vs Qwen3-VL-4B-Instruct](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-qwen3-vl-4b-instruct.md)\n- [Arete-27B vs LFM3-VL-2B](https://www.anchorterminal.com/compare/arete-27b-vs-lfm3-vl-2b.md)\n- [Arete-27B vs Qwen3-VL-4B-Instruct](https://www.anchorterminal.com/compare/arete-27b-vs-qwen3-vl-4b-instruct.md)\n- [BigBang-v1 vs LFM3-VL-2B](https://www.anchorterminal.com/compare/bigbang-v1-vs-lfm3-vl-2b.md)\n- [BigBang-v1 vs Qwen3-VL-4B-Instruct](https://www.anchorterminal.com/compare/bigbang-v1-vs-qwen3-vl-4b-instruct.md)\n- [Carbon-X vs LFM3-VL-2B](https://www.anchorterminal.com/compare/carbon-x-vs-lfm3-vl-2b.md)\n- [Carbon-X vs Qwen3-VL-4B-Instruct](https://www.anchorterminal.com/compare/carbon-x-vs-qwen3-vl-4b-instruct.md)\n- [Cola2 vs LFM3-VL-2B](https://www.anchorterminal.com/compare/cola2-vs-lfm3-vl-2b.md)\n- [Cola2 vs Qwen3-VL-4B-Instruct](https://www.anchorterminal.com/compare/cola2-vs-qwen3-vl-4b-instruct.md)\n- [Cosmos-Reason2-2B vs LFM3-VL-2B](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-lfm3-vl-2b.md)\n- [Cosmos-Reason2-2B vs Qwen3-VL-4B-Instruct](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-qwen3-vl-4b-instruct.md)\n- [Darwin-36B-Opus vs LFM3-VL-2B](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-lfm3-vl-2b.md)\n- [Darwin-36B-Opus vs Qwen3-VL-4B-Instruct](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-qwen3-vl-4b-instruct.md)\n- [DeepSeek-V3.2 vs LFM3-VL-2B](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-lfm3-vl-2b.md)\n- [DeepSeek-V3.2 vs Qwen3-VL-4B-Instruct](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-qwen3-vl-4b-instruct.md)\n- [DeepSeek V4.1 Flash vs LFM3-VL-2B](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-lfm3-vl-2b.md)\n- [DeepSeek V4.1 Flash vs Qwen3-VL-4B-Instruct](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-vl-4b-instruct.md)\n- [DeepSeek-V4-Flash-0731 vs LFM3-VL-2B](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-lfm3-vl-2b.md)\n- [DeepSeek-V4-Flash-0731 vs Qwen3-VL-4B-Instruct](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-qwen3-vl-4b-instruct.md)\n- [Devstral Small 2 vs LFM3-VL-2B](https://www.anchorterminal.com/compare/devstral-small-2-vs-lfm3-vl-2b.md)\n- [Devstral Small 2 vs Qwen3-VL-4B-Instruct](https://www.anchorterminal.com/compare/devstral-small-2-vs-qwen3-vl-4b-instruct.md)\n- [DiffusionGemma 26B A4B IT vs LFM3-VL-2B](https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-lfm3-vl-2b.md)\n- [DiffusionGemma 26B A4B IT vs Qwen3-VL-4B-Instruct](https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-qwen3-vl-4b-instruct.md)\n- [EXAONE 4.5 33B vs LFM3-VL-2B](https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-lfm3-vl-2b.md)\n- [EXAONE 4.5 33B vs Qwen3-VL-4B-Instruct](https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-qwen3-vl-4b-instruct.md)\n- [Gemma 4 12B IT vs LFM3-VL-2B](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-lfm3-vl-2b.md)\n- [Gemma 4 12B IT vs Qwen3-VL-4B-Instruct](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-qwen3-vl-4b-instruct.md)\n- [Gemma 4 26B A4B IT vs LFM3-VL-2B](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-lfm3-vl-2b.md)\n- [Gemma 4 26B A4B IT vs Qwen3-VL-4B-Instruct](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-qwen3-vl-4b-instruct.md)\n- [Gemma 4 31B IT vs LFM3-VL-2B](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-lfm3-vl-2b.md)\n- [Gemma 4 31B IT vs Qwen3-VL-4B-Instruct](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-vl-4b-instruct.md)\n- [Gemma 4 E2B IT vs LFM3-VL-2B](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-lfm3-vl-2b.md)\n- [Gemma 4 E2B IT vs Qwen3-VL-4B-Instruct](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-qwen3-vl-4b-instruct.md)\n- [Gemma 4 E4B IT vs LFM3-VL-2B](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-lfm3-vl-2b.md)\n- [Gemma 4 E4B IT vs Qwen3-VL-4B-Instruct](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-qwen3-vl-4b-instruct.md)\n- [GLM-4.5-Air vs LFM3-VL-2B](https://www.anchorterminal.com/compare/glm-4-5-air-vs-lfm3-vl-2b.md)\n- [GLM-4.5-Air vs Qwen3-VL-4B-Instruct](https://www.anchorterminal.com/compare/glm-4-5-air-vs-qwen3-vl-4b-instruct.md)\n- [GLM-4.7-Flash vs LFM3-VL-2B](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-lfm3-vl-2b.md)\n- [GLM-4.7-Flash vs Qwen3-VL-4B-Instruct](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwen3-vl-4b-instruct.md)\n- [GLM-5.3-Flash vs LFM3-VL-2B](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-lfm3-vl-2b.md)\n- [GLM-5.3-Flash vs Qwen3-VL-4B-Instruct](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-qwen3-vl-4b-instruct.md)\n- [GLM-5.3 vs LFM3-VL-2B](https://www.anchorterminal.com/compare/glm-5-3-vs-lfm3-vl-2b.md)\n- [GLM-5.3 vs Qwen3-VL-4B-Instruct](https://www.anchorterminal.com/compare/glm-5-3-vs-qwen3-vl-4b-instruct.md)\n- [gpt-oss-120b vs LFM3-VL-2B](https://www.anchorterminal.com/compare/gpt-oss-120b-vs-lfm3-vl-2b.md)\n- [gpt-oss-120b vs Qwen3-VL-4B-Instruct](https://www.anchorterminal.com/compare/gpt-oss-120b-vs-qwen3-vl-4b-instruct.md)\n- [gpt-oss-20b vs LFM3-VL-2B](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-lfm3-vl-2b.md)\n- [gpt-oss-20b vs Qwen3-VL-4B-Instruct](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-qwen3-vl-4b-instruct.md)\n- [Granite 4.2 30B vs LFM3-VL-2B](https://www.anchorterminal.com/compare/granite-4-2-30b-vs-lfm3-vl-2b.md)\n- [Granite 4.2 30B vs Qwen3-VL-4B-Instruct](https://www.anchorterminal.com/compare/granite-4-2-30b-vs-qwen3-vl-4b-instruct.md)\n- [Granite 4.2 3B vs LFM3-VL-2B](https://www.anchorterminal.com/compare/granite-4-2-3b-vs-lfm3-vl-2b.md)\n- [Granite 4.2 3B vs Qwen3-VL-4B-Instruct](https://www.anchorterminal.com/compare/granite-4-2-3b-vs-qwen3-vl-4b-instruct.md)\n- [Granite 4.2 8B vs LFM3-VL-2B](https://www.anchorterminal.com/compare/granite-4-2-8b-vs-lfm3-vl-2b.md)\n- [Granite 4.2 8B vs Qwen3-VL-4B-Instruct](https://www.anchorterminal.com/compare/granite-4-2-8b-vs-qwen3-vl-4b-instruct.md)\n- [Hy-Memory-30B-A3B vs LFM3-VL-2B](https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-lfm3-vl-2b.md)\n- [Hy-Memory-30B-A3B vs Qwen3-VL-4B-Instruct](https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-vl-4b-instruct.md)\n- [Hy3 vs LFM3-VL-2B](https://www.anchorterminal.com/compare/hy3-vs-lfm3-vl-2b.md)\n- [Hy3 vs Qwen3-VL-4B-Instruct](https://www.anchorterminal.com/compare/hy3-vs-qwen3-vl-4b-instruct.md)\n- [Inkling-Small vs LFM3-VL-2B](https://www.anchorterminal.com/compare/inkling-small-vs-lfm3-vl-2b.md)\n- [Inkling-Small vs Qwen3-VL-4B-Instruct](https://www.anchorterminal.com/compare/inkling-small-vs-qwen3-vl-4b-instruct.md)\n- [Inkling vs LFM3-VL-2B](https://www.anchorterminal.com/compare/inkling-vs-lfm3-vl-2b.md)\n- [Inkling vs Qwen3-VL-4B-Instruct](https://www.anchorterminal.com/compare/inkling-vs-qwen3-vl-4b-instruct.md)\n- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs LFM3-VL-2B](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-lfm3-vl-2b.md)\n- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs Qwen3-VL-4B-Instruct](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-qwen3-vl-4b-instruct.md)\n- [Kimi K3.1 Pro vs LFM3-VL-2B](https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-lfm3-vl-2b.md)\n- [Kimi K3.1 Pro vs Qwen3-VL-4B-Instruct](https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwen3-vl-4b-instruct.md)\n- [Kimi K3 vs LFM3-VL-2B](https://www.anchorterminal.com/compare/kimi-k3-vs-lfm3-vl-2b.md)\n- [Kimi K3 vs Qwen3-VL-4B-Instruct](https://www.anchorterminal.com/compare/kimi-k3-vs-qwen3-vl-4b-instruct.md)\n- [Kolibri-1 vs LFM3-VL-2B](https://www.anchorterminal.com/compare/kolibri-1-vs-lfm3-vl-2b.md)\n- [Kolibri-1 vs Qwen3-VL-4B-Instruct](https://www.anchorterminal.com/compare/kolibri-1-vs-qwen3-vl-4b-instruct.md)\n- [Kronos vs LFM3-VL-2B](https://www.anchorterminal.com/compare/kronos-vs-lfm3-vl-2b.md)\n- [Kronos vs Qwen3-VL-4B-Instruct](https://www.anchorterminal.com/compare/kronos-vs-qwen3-vl-4b-instruct.md)\n- [Laguna M.1 vs LFM3-VL-2B](https://www.anchorterminal.com/compare/laguna-m-1-vs-lfm3-vl-2b.md)\n- [Laguna M.1 vs Qwen3-VL-4B-Instruct](https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-vl-4b-instruct.md)\n- [Laguna S 2.1 vs LFM3-VL-2B](https://www.anchorterminal.com/compare/laguna-s-2-1-vs-lfm3-vl-2b.md)\n- [Laguna S 2.1 vs Qwen3-VL-4B-Instruct](https://www.anchorterminal.com/compare/laguna-s-2-1-vs-qwen3-vl-4b-instruct.md)\n- [Laguna XS 2.1 vs LFM3-VL-2B](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-lfm3-vl-2b.md)\n- [Laguna XS 2.1 vs Qwen3-VL-4B-Instruct](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwen3-vl-4b-instruct.md)\n- [LBS-27B vs LFM3-VL-2B](https://www.anchorterminal.com/compare/lbs-27b-vs-lfm3-vl-2b.md)\n- [LBS-27B vs Qwen3-VL-4B-Instruct](https://www.anchorterminal.com/compare/lbs-27b-vs-qwen3-vl-4b-instruct.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 Qwen3-VL-4B-Instruct](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-vl-4b-instruct.md)\n- [LFM3-1.7B vs LFM3-VL-2B](https://www.anchorterminal.com/compare/lfm3-1-7b-vs-lfm3-vl-2b.md)\n- [LFM3-1.7B vs Qwen3-VL-4B-Instruct](https://www.anchorterminal.com/compare/lfm3-1-7b-vs-qwen3-vl-4b-instruct.md)\n- [LFM3-Audio-2B vs LFM3-VL-2B](https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-lfm3-vl-2b.md)\n- [LFM3-Audio-2B vs Qwen3-VL-4B-Instruct](https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-qwen3-vl-4b-instruct.md)\n- [LFM3-Omni-2B vs LFM3-VL-2B](https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-lfm3-vl-2b.md)\n- [LFM3-Omni-2B vs Qwen3-VL-4B-Instruct](https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-qwen3-vl-4b-instruct.md)\n- [LFM3-VL-2B vs Ling-3.0-tiny](https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-ling-3-0-tiny.md)\n- [LFM3-VL-2B vs Ling-3.1-flash-1019](https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-ling-3-1-flash-1019.md)\n- [LFM3-VL-2B vs LongCat-2.5](https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-longcat-2-5.md)\n- [LFM3-VL-2B vs MiMo-V2.6 Distill Qwen 9B](https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-mimo-v2-6-distill-qwen-9b.md)\n- [LFM3-VL-2B vs MiMo-V2.6-Flash-RL](https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-mimo-v2-6-flash-rl.md)\n- [LFM3-VL-2B vs MiniCPM-o 4.5](https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-minicpm-o-4-5.md)\n- [LFM3-VL-2B vs MiniCPM-V 4.7 1B](https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-minicpm-v-4-7-1b.md)\n- [LFM3-VL-2B vs MiniCPM-V 4.7 35B-A3B](https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-minicpm-v-4-7-35b-a3b.md)\n- [LFM3-VL-2B vs MiniCPM5-2B](https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-minicpm5-2b.md)\n- [LFM3-VL-2B vs MiniMax-M2.5](https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-minimax-m2-5.md)\n- [LFM3-VL-2B vs MiniMax-M2.7](https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-minimax-m2-7.md)\n- [LFM3-VL-2B vs MiniMax-M3.1-Flash](https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-minimax-m3-1-flash.md)\n- [LFM3-VL-2B vs Mistral Large 4](https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-mistral-large-4.md)\n- [LFM3-VL-2B vs Muse Glimmer 30B](https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-muse-glimmer-30b.md)\n- [LFM3-VL-2B vs Nanbeige4.2-3B](https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-nanbeige-4-2-3b.md)\n- [LFM3-VL-2B vs Nanbeige4.3-33B](https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-nanbeige-4-3-33b.md)\n- [LFM3-VL-2B vs NVIDIA Nemotron 3.5 Bolt E4B 4GB](https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-nemotron-3-5-bolt-e4b-4gb.md)\n- [LFM3-VL-2B vs Nemotron 3.5 Lightning 30B A3B](https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-nemotron-3-5-lightning-30b-a3b.md)\n- [LFM3-VL-2B vs Nemotron 3 Nano 30B A3B](https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-nemotron-3-nano-30b-a3b.md)\n- [LFM3-VL-2B vs Nemotron 3 Nano 4B](https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-nemotron-3-nano-4b.md)\n- [LFM3-VL-2B vs Nemotron 3 Nano Omni 30B A3B Reasoning](https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-nemotron-3-nano-omni-30b-a3b-reasoning.md)\n- [LFM3-VL-2B vs Nemotron 3 Super 120B A12B](https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-nemotron-3-super-120b-a12b.md)\n- [LFM3-VL-2B vs Nemotron 3 Ultra 550B A55B](https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-nemotron-3-ultra-550b-a55b.md)\n- [LFM3-VL-2B vs North Mini Code 1.0](https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-north-mini-code-1-0.md)\n- [LFM3-VL-2B vs occamy-1.0](https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-occamy-1-0.md)\n- 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Qwen3-4B-Instruct-2507](https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-qwen3-4b-instruct-2507.md)\n- [LFM3-VL-2B vs Qwen3.5-0.8B](https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-qwen3-5-0-8b.md)\n- [LFM3-VL-2B vs Qwen3.5-122B-A10B](https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-qwen3-5-122b-a10b.md)\n- [LFM3-VL-2B vs Qwen3.5-2B](https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-qwen3-5-2b.md)\n- [LFM3-VL-2B vs Qwen3.5-397B-A17B](https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-qwen3-5-397b-a17b.md)\n- [LFM3-VL-2B vs Qwen3.5-4B](https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-qwen3-5-4b.md)\n- [LFM3-VL-2B vs Qwen3.5-9B](https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-qwen3-5-9b.md)\n- [LFM3-VL-2B vs Qwen3.6-27B](https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-qwen3-6-27b.md)\n- [LFM3-VL-2B vs Qwen3.6-35B-A3B](https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-qwen3-6-35b-a3b.md)\n- [LFM3-VL-2B vs 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"attributionRequired": true, "credit": "CC BY 4.0, credit Anchor Terminal and link the page or API address you took it from (https://www.anchorterminal.com/terms/#data)", "docs": "https://www.anchorterminal.com/docs/", "generatedAt": "2026-11-11", "license": "CC-BY-4.0", "method": "https://www.anchorterminal.com/benchmark/", "methodology": "0.7", "openapi": "https://www.anchorterminal.com/openapi.json", "preview": false, "run": "2026-10-01", "runLabel": "October 2026 research run", "terms": "https://www.anchorterminal.com/terms/#data" }, "page": { "breadcrumbs": [ { "name": "Home", "url": "https://www.anchorterminal.com/" }, { "name": "Compare", "url": "https://www.anchorterminal.com/compare/" }, { "name": "LFM3-VL-2B vs Qwen3-VL-4B-Instruct", "url": "" } ], "description": "LFM3-VL-2B scores 61 (C) to Qwen3-VL-4B-Instruct's 52.6 (D) for open-weight models you run yourself. 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