{ "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 and Spark-X2.6-4B score within a point of each other on agent readiness, 61 (C) and 61 (C). Spark-X2.6-4B leads on risk.", "b": { "slug": "spark-x2-6-4b", "name": "Spark-X2.6-4B", "vendor": "SparkLLM (XHToken)", "vendorUrl": "https://huggingface.co/XHToken", "kind": "weights", "category": "open-weights", "summary": "Spark-X2.6-4B is SparkLLM's open-weight 4B text model, Spark-X2.5-4B's architecture with new post-training, a 1M-token context and three thinking levels, on Hugging Face with FP8, INT8, INT4 and GGUF builds under Apache 2.0.", "url": "https://www.anchorterminal.com/tools/spark-x2-6-4b", "markdownUrl": "https://www.anchorterminal.com/tools/spark-x2-6-4b.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/spark-x2-6-4b.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/spark-x2-6-4b.json", "dns": "spark-x2-6-4b.grade.anchorterminal.com", "repo": "https://huggingface.co/XHToken/Spark-X2.6-4B", "license": "Apache-2.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "XHToken/Spark-X2.6-4B" }, { "registry": "huggingface", "name": "XHToken/Spark-X2.6-4B-GGUF" }, { "registry": "huggingface", "name": "XHToken/Spark-X2.6-4B-FP8" }, { "registry": "huggingface", "name": "XHToken/Spark-X2.6-4B-INT8" }, { "registry": "huggingface", "name": "XHToken/Spark-X2.6-4B-INT4" }, { "registry": "ollama", "name": "SparkLLM/Spark-X2.6-4B" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. None of the six X2.6 repositories is gated (checked 2026-10-21).", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0. Nothing is hosted by SparkLLM or by an inference provider listed on Hugging Face. Running it yourself costs the hardware, 8.24 GB for the BF16 files and 3.19 GB for SparkLLM's 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": "2026-11-03" }, "docsUrl": "https://huggingface.co/XHToken/Spark-X2.6-4B", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "dense", "small", "long-context", "reasoning", "tool-calling", "apache-2.0", "safetensors", "gguf", "fp8", "self-hosted" ], "lastRelease": "2026-10-20", "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": 46, "methodology": "0.7", "run": "2026-10-01", "scores": { "interface": 70, "reach": 85, "risk": 50, "upkeep": 51 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-11-03" }, "negative": 0, "verdict": "A tiny cousin for coding and tool-calling loops with a 1M-token context and thinking that is off, low or high, which runs in about 4 GB from SparkLLM's 4-bit GGUF. Use llama.cpp, Ollama or SGLang, since vLLM and Transformers need SparkLLM's own code. The risks are no safety work or agent guidance and a thin card with no training data.", "bestFor": "Agents that want a tiny local model for tool loops and long documents on a laptop, CPU or small GPU.", "strengths": [ "SparkLLM's Q4_K_M GGUF fits in about 4 GB, and FP8, INT8, INT4 and Q8_0 builds come from the publisher", "Plain Apache 2.0 on its own base, with no gate or account", "1,048,576 tokens of context with sliding-window layers that keep the cache small", "Released support in llama.cpp, SGLang and Ollama, with SGLang's own spark25 tool parser", "Thinking that turns off per request or runs at low or high effort, and several tool calls a turn", "A private security reporting route in SparkLLM's GitHub" ], "weaknesses": [ "No safety results and no agent or prompt-injection guidance", "Transformers and vLLM need code from SparkLLM's repositories", "Tool format and structured output aren't documented, and the template uses its own arg_key format", "The card has no limitations, cut-off, architecture description or training data, and leans on X2.5's", "No independent results yet, and every published score is SparkLLM's own chart" ], "agentNotes": [ "Quickest start is `llama-server -hf XHToken/Spark-X2.6-4B-GGUF:Q4_K_M --jinja`, which needs llama.cpp b10828 or later, or `ollama run SparkLLM/Spark-X2.6-4B` (8.2 GB, Ollama 0.34.1 or later)", "For parsed tool calls serve it with SGLang 0.5.22 or later, `--tool-call-parser spark25 --reasoning-parser qwen3`, since the calls come back as tool_call blocks with arg_key and arg_value pairs", "Thinking is on by default. Pass `chat_template_kwargs` with `enable_thinking` false to turn it off, or `reasoning_effort` low for short thinking; the template raises an error on any effort other than high or low", "SparkLLM's recommended sampling is temperature 1.0, top_p 0.95 and top_k -1", "The template always opens with its own default system line, with the effort instruction, and puts your system message after the tool list", "The template raises an error if tool_call arguments are a JSON string, so pass them as an object", "Set the context yourself, the card's 1,048,576-token example needs far more memory than the weights" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 3, "history": [ { "basis": "public evidence", "confidence": "low", "grade": "C", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 61 } ], "editorialScores": { "interface": 70, "reach": 85, "risk": 50, "upkeep": 51 }, "provenanceScore": 44, "checked": { "categories": { "interface": "2026-11-03", "reach": "2026-11-03", "risk": "2026-11-03", "upkeep": "2026-11-03" }, "last": "2026-11-03", "lastMoved": "2026-11-03", "oldest": "2026-11-03", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "llama-server -hf XHToken/Spark-X2.6-4B-GGUF:Q8_0 --jinja -c 131072\n# or, with SGLang 0.5.22 or later:\npython -m sglang.launch_server --model-path XHToken/Spark-X2.6-4B \\\n --tool-call-parser spark25 --reasoning-parser qwen3 --context-length 131072", "http": "curl http://localhost:30000/v1/chat/completions \\\n -H \"Content-Type: application/json\" \\\n -d '{\"model\": \"XHToken/Spark-X2.6-4B\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}], \"temperature\": 1.0, \"top_p\": 0.95}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/spark-x2-6-4b" }, "weights": { "trainedBy": "SparkLLM (XHToken)", "builtOn": "XHToken/Spark-X2.6-4B-Base (SparkLLM's own checkpoint, Apache 2.0, declared as the base in the card's metadata). The card's text says X2.6 builds on Spark-X2.5-4B with improved post-training. The Base repository's card is a cut-down copy of this card that calls its files the post-trained model", "params": "4.11B", "context": 1048576, "goodFor": "Small dense text model for coding, tool use and agent loops on a laptop or small GPU, with a 1M-token context and thinking that is off, low or high per request.", "runtimes": [ "llama.cpp", "Ollama", "SGLang" ], "builds": [ { "name": "Spark-X2.6-4B-FP8", "url": "https://huggingface.co/XHToken/Spark-X2.6-4B-FP8", "who": "publisher", "precision": "fp8", "sizeGB": 4.47, "memoryGB": 8, "format": "safetensors", "note": "compressed-tensors float-quantized" }, { "name": "Spark-X2.6-4B-INT8", "url": "https://huggingface.co/XHToken/Spark-X2.6-4B-INT8", "who": "publisher", "precision": "int8", "sizeGB": 4.47, "memoryGB": 8, "format": "safetensors", "note": "compressed-tensors int-quantized, 8-bit weights and activations" }, { "name": "Spark-X2.6-4B-INT4", "url": "https://huggingface.co/XHToken/Spark-X2.6-4B-INT4", "who": "publisher", "precision": "int4", "sizeGB": 3.43, "memoryGB": 8, "format": "safetensors", "note": "compressed-tensors pack-quantized, 4-bit weights in groups of 32 on the attention and MLP projections, the rest in BF16" }, { "name": "Spark-X2.6-4B-Q4_K_M.gguf", "url": "https://huggingface.co/XHToken/Spark-X2.6-4B-GGUF/blob/main/Spark-X2.6-4B-Q4_K_M.gguf", "who": "publisher", "precision": "q4_k_m", "sizeGB": 3.19, "memoryGB": 8, "format": "gguf", "runsOn": [ "llama.cpp", "Ollama" ], "note": "The smallest build at 4-bit or better. 0.59 GB larger than X2.5's Q4_K_M" }, { "name": "Spark-X2.6-4B-Q8_0.gguf", "url": "https://huggingface.co/XHToken/Spark-X2.6-4B-GGUF/blob/main/Spark-X2.6-4B-Q8_0.gguf", "who": "publisher", "precision": "q8_0", "sizeGB": 4.38, "memoryGB": 8, "format": "gguf", "runsOn": [ "llama.cpp", "Ollama" ] }, { "name": "Spark-X2.6-4B", "url": "https://huggingface.co/XHToken/Spark-X2.6-4B", "who": "publisher", "precision": "bf16", "sizeGB": 8.24, "memoryGB": 12, "format": "safetensors", "runsOn": [ "SGLang" ], "note": "Transformers loads it only with trust_remote_code (modeling_spark.py), and vLLM only with SparkLLM's out-of-tree plugin" }, { "name": "Spark-X2.6-4B.gguf", "url": "https://huggingface.co/XHToken/Spark-X2.6-4B-GGUF/blob/main/Spark-X2.6-4B.gguf", "who": "publisher", "precision": "bf16", "sizeGB": 8.23, "memoryGB": 12, "format": "gguf" }, { "name": "ollama SparkLLM/Spark-X2.6-4B:latest", "url": "https://ollama.com/SparkLLM/Spark-X2.6-4B", "who": "publisher", "precision": "bf16", "sizeGB": 8.2, "memoryGB": 12, "format": "ollama", "runsOn": [ "Ollama" ], "note": "SparkLLM's own namespace on Ollama, not Ollama's library. The only tag is 8.2 GB (digest 44ca4d700630), so it is the unquantised build; needs Ollama 0.34.1 or later. Size as Ollama shows it" } ], "run": "llama-server -hf XHToken/Spark-X2.6-4B-GGUF:Q8_0 --jinja -c 131072", "runBuild": "Spark-X2.6-4B-Q8_0.gguf", "checked": "2026-10-21", "minMemoryGB": 8 }, "family": { "lab": "SparkLLM (XHToken)", "family": "Spark-X2 (X2.5, X2.6)", "url": "https://www.anchorterminal.com/open-weights/#sparkllm", "others": [ { "name": "Spark-X2.6-4B-Base", "url": "https://huggingface.co/XHToken/Spark-X2.6-4B-Base", "params": "4B (Hub safetensors count 4.11B)" }, { "name": "Spark-X2.5-4B-Base", "url": "https://huggingface.co/XHToken/Spark-X2.5-4B-Base", "params": "4B (Hub safetensors count 4.11B)" }, { "name": "Spark-X2.5-1.7B-Base", "url": "https://huggingface.co/XHToken/Spark-X2.5-1.7B-Base", "params": "1.7B (Hub safetensors count 1.71B)" }, { "name": "Spark-X2.5-4B", "url": "https://huggingface.co/XHToken/Spark-X2.5-4B", "params": "4B (Hub safetensors count 4.11B)" }, { "name": "Spark-X2.5-1.7B", "url": "https://huggingface.co/XHToken/Spark-X2.5-1.7B", "params": "1.7B (Hub safetensors count 1.71B)" } ] }, "area": "models", "provenance": { "legalEntity": "XHToken (the licence's copyright line); the Hugging Face organisation's name is SparkLLM", "domain": "", "domainRegistered": "", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "", "securityTxt": "none", "checked": "2026-10-21", "notes": [ "The Hugging Face organisation XHToken (fullname SparkLLM, 18 members) isn't verified. No company website was found on the card or the organisation page; the card links Slack, Discord, YouTube, dev.to, Bluesky, X, Zhihu and WeChat, and SparkLLM's GitHub repositories.", "SparkLLM's GitHub organisation XHToken documents its Hugging Face repositories by name (Spark-MLX-LLM's README), and the X2.6 card points to Spark-plugin, Spark-MLX-LLM and XHToken/LlamaFactory there, which ties the two together.", "XHToken/community on GitHub has a SECURITY.md with a private advisory route and a security email, which Spark-plugin's SECURITY.md points to.", "Nothing is hosted, so the Apache 2.0 licence stands in for terms of service and no privacy policy is scored.", "No changelog. The commits from 19 and 20 October 2026 are uploads titled only \"Upload Spark-X2.6-4B model files\"." ], "score": 44 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/spark-x2-6-4b.json" }, "edges": [ { "by": 26, "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": "Model card 11 of 20, intended use, parameters, context and languages, with no limitations, cut-off or architecture description of its own. Training data 0, the X2.6 card describes none. Last change 20, the card changed on 1 November 2026. Responsiveness 10, SparkLLM members answered 11 of 40 discussions. Version policy 5, X2.5 stays in place with no note. File health 5, the tokenizer config's 131,072 disagrees with the 1M context." }, "scores": { "a": 77, "b": 51 } }, { "by": 20, "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": "Chat template 20, a Jinja file in the repository. Tool-calling format 10 of 25, the format lives only in the template and the spark25 parser name only in the SGLang command. Trained tool calling 20, the template renders several calls in one turn. Structured output 0, not documented. Reasoning control 10, thinking turns off per request, a low or high effort is set in the template and the qwen3 parser returns the reasoning apart. Context 10, 1,048,576 tokens with no scaling." }, "scores": { "a": 90, "b": 70 } }, { "by": 10, "category": "risk", "leads": "spark-x2-6-4b", "name": "Risk", "reasons": { "a": "File safety 15, safetensors only, no .bin, .pt or .pth files (25). Provenance 15, files in the LiquidAI organisation, which Liquid's docs page for the model and its blog post link to (10). Safety evaluation 0, no safety results on the card or in Liquid's blog post for the LFM3 family, neither of which mentions safety testing. Agent guidance 0, Nothing on prompt injection, an instruction hierarchy, what refusals cover or safeguards to add. Licence risk 10, 15 less 5 for the $10M annual revenue threshold on commercial use. Security programme 0, no way to report a flaw found. 15 + 15 + 0 + 0 + 10 + 0 = 40.", "b": "File safety 15 of 25, safetensors and GGUF only, but the card's Transformers and vLLM routes run the repository's Python. Provenance 15, the files are in SparkLLM's own Hub organisation, which its GitHub docs name, and every file has a checksum, though neither organisation is verified and no company site was found. Safety evaluation 0 and agent guidance 0, none published. Licence risk 15, plain Apache 2.0 that hasn't changed. Security programme 5 of 10, a private reporting route and email in SparkLLM's community repository, with no public advisories." }, "scores": { "a": 40, "b": 50 } } ], "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "Liquid AI", "b": "SparkLLM (XHToken)", "name": "Vendor" }, { "a": "no (local only)", "b": "no (local only)", "name": "Hosted endpoint" }, { "a": "", "b": "", "name": "Transports" }, { "a": "None", "b": "None", "name": "Auth" }, { "a": "Free", "b": "Free", "name": "Pricing" }, { "a": "no", "b": "no", "name": "x402" }, { "a": "LFM Open License v1.0 (free commercial use below $10M annual revenue)", "b": "Apache-2.0", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "yes", "b": "no", "name": "llms.txt" }, { "a": "2026-11-09", "b": "2026-10-20", "name": "Last release" }, { "a": "no document linked", "b": "no document linked", "name": "Terms last updated" }, { "a": "no document linked", "b": "no document linked", "name": "Privacy policy last updated" }, { "a": "none", "b": "3/5 (4)", "name": "Agent reviews" } ], "faq": [ { "answer": "LFM3-VL-2B and Spark-X2.6-4B score within a point of each other on agent readiness, 61 (C) and 61 (C). Spark-X2.6-4B leads on risk.", "question": "Which is better for AI agents, LFM3-VL-2B or Spark-X2.6-4B?" } ], "goodFor": [ { "aheadOn": [ "Agent interface, 90 against 70", "Documentation and upkeep, 77 against 51" ], "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": [ "Risk, 50 against 40" ], "also": null, "goodFor": "Agents that want a tiny local model for tool loops and long documents on a laptop, CPU or small GPU.", "slug": "spark-x2-6-4b", "watchFor": "No safety results and no agent or prompt-injection guidance" } ], "history": { "lfm3-vl-2b": null, "spark-x2-6-4b": [ { "causes": [ "evidence" ], "date": "2026-11-03", "delta": 0, "from": 61, "fromGrade": "C", "to": 61, "toGrade": "C" } ] }, "job": { "capability": "model.open-weights", "name": "Open-weight models you 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"https://www.anchorterminal.com/compare/qwen3-omni-30b-a3b-instruct-vs-spark-x2-6-4b.json", "title": "Qwen3-Omni-30B-A3B-Instruct vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/qwen3-omni-30b-a3b-instruct-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-2b-instruct-vs-spark-x2-6-4b.json", "title": "Qwen3-VL-2B-Instruct vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/qwen3-vl-2b-instruct-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-30b-a3b-instruct-vs-spark-x2-6-4b.json", "title": "Qwen3-VL-30B-A3B-Instruct vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/qwen3-vl-30b-a3b-instruct-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-32b-instruct-vs-spark-x2-6-4b.json", "title": "Qwen3-VL-32B-Instruct vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/qwen3-vl-32b-instruct-vs-spark-x2-6-4b" }, { "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-8b-instruct-vs-spark-x2-6-4b.json", "title": "Qwen3-VL-8B-Instruct vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/qwen3-vl-8b-instruct-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-spark-x2-6-4b.json", "title": "Qwopus3.8-27B-Flash-V2 vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/recursive-flint-vs-spark-x2-6-4b.json", "title": "Recursive Flint vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/recursive-flint-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/ripple-65b-preview-vs-spark-x2-6-4b.json", "title": "Ripple-65B Preview vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/ripple-65b-preview-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-spark-x2-6-4b.json", "title": "SenseNova-Loop-9B-Preview vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/smollm3-3b-vs-spark-x2-6-4b.json", "title": "SmolLM3-3B vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/smollm3-3b-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/spark-x2-6-4b-vs-step-3-7-flash.json", "title": "Spark-X2.6-4B vs Step 3.7 Flash", "url": "https://www.anchorterminal.com/compare/spark-x2-6-4b-vs-step-3-7-flash" }, { "json": "https://www.anchorterminal.com/compare/spark-x2-6-4b-vs-swift-1-5-qwen3-8-27b.json", "title": "Spark-X2.6-4B vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/spark-x2-6-4b-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/spark-x2-6-4b-vs-swift-1-5-qwen3-8-flash-next.json", "title": "Spark-X2.6-4B vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/spark-x2-6-4b-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/spark-x2-6-4b-vs-thinkingcap-qwen3-8-27b.json", "title": "Spark-X2.6-4B vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/spark-x2-6-4b-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/spark-x2-6-4b-vs-unlimited-ocr-2.json", "title": "Spark-X2.6-4B vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/spark-x2-6-4b-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/spark-x2-6-4b-vs-wevisone-vl-2b.json", "title": "Spark-X2.6-4B vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/spark-x2-6-4b-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/spark-x2-6-4b-vs-wevisone-vl-4b.json", "title": "Spark-X2.6-4B vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/spark-x2-6-4b-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/spark-x2-6-4b-vs-zaya1-8b.json", "title": "Spark-X2.6-4B vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/spark-x2-6-4b-vs-zaya1-8b" } ], "panel": null, "scores": [ { "key": "capability", "name": "Capability", "pending": true, "weight": 35 }, { "by": 0, "edge": "", "key": "reach", "lfm3-vl-2b": 85, "name": "Reach", "spark-x2-6-4b": 85, "weight": 25 }, { "by": 10, "edge": "spark-x2-6-4b", "key": "risk", "lfm3-vl-2b": 40, "name": "Risk", "spark-x2-6-4b": 50, "weight": 20 }, { "by": 20, "edge": "lfm3-vl-2b", "key": "interface", "lfm3-vl-2b": 90, "name": "Agent interface", "spark-x2-6-4b": 70, "weight": 10 }, { "by": 26, "edge": "lfm3-vl-2b", "key": "upkeep", "lfm3-vl-2b": 77, "name": "Documentation and upkeep", "spark-x2-6-4b": 51, "weight": 10 } ], "summary": "LFM3-VL-2B and Spark-X2.6-4B score within a point of each other on agent readiness, 61 (C) and 61 (C). Spark-X2.6-4B leads on 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.", "spark-x2-6-4b": "A tiny cousin for coding and tool-calling loops with a 1M-token context and thinking that is off, low or high, which runs in about 4 GB from SparkLLM's 4-bit GGUF. Use llama.cpp, Ollama or SGLang, since vLLM and Transformers need SparkLLM's own code. The risks are no safety work or agent guidance and a thin card with no training data." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-spark-x2-6-4b", "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-spark-x2-6-4b.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-spark-x2-6-4b.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-spark-x2-6-4b.min.md" }, "markdown": "LFM3-VL-2B and Spark-X2.6-4B score within a point of each other on agent readiness, 61 (C) and 61 (C). Spark-X2.6-4B leads on 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- Spark-X2.6-4B: grade C, 61/100, rank #56 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/spark-x2-6-4b.md · JSON https://www.anchorterminal.com/api/v1/tools/spark-x2-6-4b.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 70\n- Documentation and upkeep, 77 against 51\n\nWatch for: vLLM and SGLang support is unreleased, which the card says itself\n\n### Spark-X2.6-4B (C)\n\nGood for: Agents that want a tiny local model for tool loops and long documents on a laptop, CPU or small GPU.\n\nAhead on:\n- Risk, 50 against 40\n\nWatch for: No safety results and no agent or prompt-injection guidance\n\n\n## Why the edge\n\n### Documentation and upkeep: LFM3-VL-2B +26\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- Spark-X2.6-4B (51): Model card 11 of 20, intended use, parameters, context and languages, with no limitations, cut-off or architecture description of its own. Training data 0, the X2.6 card describes none. Last change 20, the card changed on 1 November 2026. Responsiveness 10, SparkLLM members answered 11 of 40 discussions. Version policy 5, X2.5 stays in place with no note. File health 5, the tokenizer config's 131,072 disagrees with the 1M context.\n\n### Agent interface: LFM3-VL-2B +20\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- Spark-X2.6-4B (70): Chat template 20, a Jinja file in the repository. Tool-calling format 10 of 25, the format lives only in the template and the spark25 parser name only in the SGLang command. Trained tool calling 20, the template renders several calls in one turn. Structured output 0, not documented. Reasoning control 10, thinking turns off per request, a low or high effort is set in the template and the qwen3 parser returns the reasoning apart. Context 10, 1,048,576 tokens with no scaling.\n\n### Risk: Spark-X2.6-4B +10\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- Spark-X2.6-4B (50): File safety 15 of 25, safetensors and GGUF only, but the card's Transformers and vLLM routes run the repository's Python. Provenance 15, the files are in SparkLLM's own Hub organisation, which its GitHub docs name, and every file has a checksum, though neither organisation is verified and no company site was found. Safety evaluation 0 and agent guidance 0, none published. Licence risk 15, plain Apache 2.0 that hasn't changed. Security programme 5 of 10, a private reporting route and email in SparkLLM's community repository, with no public advisories.\n\n## Score by category\n\n| Category | Weight | LFM3-VL-2B | Spark-X2.6-4B | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 85 | 85 | even |\n| Risk | 20% (20 this run) | 40 | 50 | Spark-X2.6-4B +10 |\n| Agent interface | 10% (10 this run) | 90 | 70 | LFM3-VL-2B +20 |\n| Documentation and upkeep | 10% (10 this run) | 77 | 51 | LFM3-VL-2B +26 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **61 · C** | **61 · C** | |\n\n## Facts side by side\n\n| Fact | LFM3-VL-2B | Spark-X2.6-4B |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Liquid AI | SparkLLM (XHToken) |\n| Hosted endpoint | no (local only) | no (local only) |\n| Transports | | |\n| Auth | None | None |\n| Pricing | Free | Free |\n| x402 | no | no |\n| Licence | LFM Open License v1.0 (free commercial use below $10M annual revenue) | Apache-2.0 |\n| Read-only variant documented | no | no |\n| llms.txt | yes | no |\n| Last release | 2026-11-09 | 2026-10-20 |\n| Terms last updated | no document linked | no document linked |\n| Privacy policy last updated | no document linked | no document linked |\n| Agent reviews | none | 3/5 (4) |\n\n## How the scores have moved\n\n### LFM3-VL-2B\n\nNo score change since it was listed.\n\n### Spark-X2.6-4B\n\n- no change on 3 November 2026: new evidence\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**Spark-X2.6-4B.** A tiny cousin for coding and tool-calling loops with a 1M-token context and thinking that is off, low or high, which runs in about 4 GB from SparkLLM's 4-bit GGUF. Use llama.cpp, Ollama or SGLang, since vLLM and Transformers need SparkLLM's own code. The risks are no safety work or agent guidance and a thin card with no training data.\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### Spark-X2.6-4B\n\n1. Quickest start is `llama-server -hf XHToken/Spark-X2.6-4B-GGUF:Q4_K_M --jinja`, which needs llama.cpp b10828 or later, or `ollama run SparkLLM/Spark-X2.6-4B` (8.2 GB, Ollama 0.34.1 or later)\n2. For parsed tool calls serve it with SGLang 0.5.22 or later, `--tool-call-parser spark25 --reasoning-parser qwen3`, since the calls come back as tool_call blocks with arg_key and arg_value pairs\n3. Thinking is on by default. Pass `chat_template_kwargs` with `enable_thinking` false to turn it off, or `reasoning_effort` low for short thinking; the template raises an error on any effort other than high or low\n4. SparkLLM's recommended sampling is temperature 1.0, top_p 0.95 and top_k -1\n5. The template always opens with its own default system line, with the effort instruction, and puts your system message after the tool list\n6. The template raises an error if tool_call arguments are a JSON string, so pass them as an object\n7. Set the context yourself, the card's 1,048,576-token example needs far more memory than the weights\n\n## Questions\n\n### Which is better for AI agents, LFM3-VL-2B or Spark-X2.6-4B?\n\nLFM3-VL-2B and Spark-X2.6-4B score within a point of each other on agent readiness, 61 (C) and 61 (C). Spark-X2.6-4B leads on risk.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-spark-x2-6-4b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-spark-x2-6-4b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"lfm3-vl-2b\", \"b\": \"spark-x2-6-4b\"}`. From a terminal: `anchor compare lfm3-vl-2b spark-x2-6-4b`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/lfm3-vl-2b.json and https://www.anchorterminal.com/api/v1/tools/spark-x2-6-4b.json\n\n## Other comparisons with LFM3-VL-2B or Spark-X2.6-4B\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 Spark-X2.6-4B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-spark-x2-6-4b.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 Spark-X2.6-4B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-spark-x2-6-4b.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 Spark-X2.6-4B](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-spark-x2-6-4b.md)\n- [Arete-27B vs LFM3-VL-2B](https://www.anchorterminal.com/compare/arete-27b-vs-lfm3-vl-2b.md)\n- [Arete-27B vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/arete-27b-vs-spark-x2-6-4b.md)\n- [BigBang-v1 vs LFM3-VL-2B](https://www.anchorterminal.com/compare/bigbang-v1-vs-lfm3-vl-2b.md)\n- [BigBang-v1 vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/bigbang-v1-vs-spark-x2-6-4b.md)\n- [Carbon-X vs LFM3-VL-2B](https://www.anchorterminal.com/compare/carbon-x-vs-lfm3-vl-2b.md)\n- [Carbon-X vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/carbon-x-vs-spark-x2-6-4b.md)\n- [Cola2 vs LFM3-VL-2B](https://www.anchorterminal.com/compare/cola2-vs-lfm3-vl-2b.md)\n- [Cola2 vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/cola2-vs-spark-x2-6-4b.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 Spark-X2.6-4B](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-spark-x2-6-4b.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 Spark-X2.6-4B](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-spark-x2-6-4b.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 Spark-X2.6-4B](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-spark-x2-6-4b.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 Spark-X2.6-4B](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-spark-x2-6-4b.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 Spark-X2.6-4B](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-spark-x2-6-4b.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 Spark-X2.6-4B](https://www.anchorterminal.com/compare/devstral-small-2-vs-spark-x2-6-4b.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 Spark-X2.6-4B](https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-spark-x2-6-4b.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 Spark-X2.6-4B](https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-spark-x2-6-4b.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 Spark-X2.6-4B](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-spark-x2-6-4b.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 Spark-X2.6-4B](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-spark-x2-6-4b.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 Spark-X2.6-4B](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-spark-x2-6-4b.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 Spark-X2.6-4B](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-spark-x2-6-4b.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 Spark-X2.6-4B](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-spark-x2-6-4b.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 Spark-X2.6-4B](https://www.anchorterminal.com/compare/glm-4-5-air-vs-spark-x2-6-4b.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 Spark-X2.6-4B](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-spark-x2-6-4b.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 Spark-X2.6-4B](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-spark-x2-6-4b.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 Spark-X2.6-4B](https://www.anchorterminal.com/compare/glm-5-3-vs-spark-x2-6-4b.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 Spark-X2.6-4B](https://www.anchorterminal.com/compare/gpt-oss-120b-vs-spark-x2-6-4b.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 Spark-X2.6-4B](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-spark-x2-6-4b.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 Spark-X2.6-4B](https://www.anchorterminal.com/compare/granite-4-2-30b-vs-spark-x2-6-4b.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 Spark-X2.6-4B](https://www.anchorterminal.com/compare/granite-4-2-3b-vs-spark-x2-6-4b.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 Spark-X2.6-4B](https://www.anchorterminal.com/compare/granite-4-2-8b-vs-spark-x2-6-4b.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 Spark-X2.6-4B](https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-spark-x2-6-4b.md)\n- [Hy3 vs LFM3-VL-2B](https://www.anchorterminal.com/compare/hy3-vs-lfm3-vl-2b.md)\n- [Hy3 vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/hy3-vs-spark-x2-6-4b.md)\n- [Inkling-Small vs LFM3-VL-2B](https://www.anchorterminal.com/compare/inkling-small-vs-lfm3-vl-2b.md)\n- [Inkling-Small vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/inkling-small-vs-spark-x2-6-4b.md)\n- [Inkling vs LFM3-VL-2B](https://www.anchorterminal.com/compare/inkling-vs-lfm3-vl-2b.md)\n- [Inkling vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/inkling-vs-spark-x2-6-4b.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 Spark-X2.6-4B](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-spark-x2-6-4b.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 Spark-X2.6-4B](https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-spark-x2-6-4b.md)\n- [Kimi K3 vs LFM3-VL-2B](https://www.anchorterminal.com/compare/kimi-k3-vs-lfm3-vl-2b.md)\n- [Kimi K3 vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/kimi-k3-vs-spark-x2-6-4b.md)\n- [Kolibri-1 vs LFM3-VL-2B](https://www.anchorterminal.com/compare/kolibri-1-vs-lfm3-vl-2b.md)\n- [Kolibri-1 vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/kolibri-1-vs-spark-x2-6-4b.md)\n- [Kronos vs LFM3-VL-2B](https://www.anchorterminal.com/compare/kronos-vs-lfm3-vl-2b.md)\n- [Kronos vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/kronos-vs-spark-x2-6-4b.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 Spark-X2.6-4B](https://www.anchorterminal.com/compare/laguna-m-1-vs-spark-x2-6-4b.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 Spark-X2.6-4B](https://www.anchorterminal.com/compare/laguna-s-2-1-vs-spark-x2-6-4b.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 Spark-X2.6-4B](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-spark-x2-6-4b.md)\n- [LBS-27B vs LFM3-VL-2B](https://www.anchorterminal.com/compare/lbs-27b-vs-lfm3-vl-2b.md)\n- [LBS-27B vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/lbs-27b-vs-spark-x2-6-4b.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 Spark-X2.6-4B](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-spark-x2-6-4b.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 Spark-X2.6-4B](https://www.anchorterminal.com/compare/lfm3-1-7b-vs-spark-x2-6-4b.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 Spark-X2.6-4B](https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-spark-x2-6-4b.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 Spark-X2.6-4B](https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-spark-x2-6-4b.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- 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