{ "data": { "a": { "slug": "lfm3-1-7b", "name": "LFM3-1.7B", "vendor": "Liquid AI", "vendorUrl": "https://www.liquid.ai", "kind": "weights", "category": "open-weights", "summary": "LFM3-1.7B is Liquid AI's open-weight 1.73B text model for on-device agents, with tool calling, deferred tools, switchable thinking and a 256K-token context, released on 27 October 2026 with Liquid's own GGUF, FP8, NVFP4, MLX and ONNX builds.", "url": "https://www.anchorterminal.com/tools/lfm3-1-7b", "markdownUrl": "https://www.anchorterminal.com/tools/lfm3-1-7b.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/lfm3-1-7b.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/lfm3-1-7b.json", "dns": "lfm3-1-7b.grade.anchorterminal.com", "repo": "https://huggingface.co/LiquidAI/LFM3-1.7B", "license": "LFM Open License v1.0 (free commercial use below $10M annual revenue)", "transports": [], "packages": [ { "registry": "huggingface", "name": "LiquidAI/LFM3-1.7B" }, { "registry": "huggingface", "name": "LiquidAI/LFM3-1.7B-GGUF" }, { "registry": "huggingface", "name": "LiquidAI/LFM3-1.7B-MLX-4bit" }, { "registry": "ollama", "name": "lfm3" } ], "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 and 3.46 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/lfm3-1.7b", "llmsTxt": "https://docs.liquid.ai/llms.txt", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "dense", "small", "edge", "reasoning", "tool-calling", "long-context", "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": 55, "methodology": "0.7", "run": "2026-10-01", "scores": { "interface": 100, "reach": 85, "risk": 40, "upkeep": 67 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-11-10" }, "negative": 0, "verdict": "A 1.73B model that fits an 8 GB machine with room to spare, with a documented tool-call format, deferred tools, a thinking switch and a 256K context. It runs today in llama.cpp, Ollama and LM Studio only, loads in Transformers through repository code, and has no safety results, security contact or data sources.", "bestFor": "Agents on a laptop, phone or small server that need tool calling and a long context from a model under 2 GB at 4 bits", "strengths": [ "Fits an 8 GB machine from Liquid's own GGUF, at 1.10 GB for Q4_K_M", "The card and docs document the tool-call format, deferred tools and tool search", "A 256K-token context, trained in mid-training per the card", "Thinking can be turned off per request", "Liquid's own API serves it, and its guide documents JSON Schema output" ], "weaknesses": [ "vLLM and SGLang have no released support, though the card lists both", "Loading in Transformers runs code from the repository", "No safety results and no security contact", "Liquid says turning thinking off can be inconsistent", "The licence stops free commercial use at $10M in annual revenue", "Training data is described without sources or a cut-off" ], "agentNotes": [ "Start it with `llama-server -hf LiquidAI/LFM3-1.7B-GGUF:Q4_K_M --jinja` or `ollama run lfm3`", "Use temperature 0.1, top_k 50 and repetition penalty 1.1 for chat, tools and agents, per the card", "Pass `enable_thinking=False` for direct answers, and expect it to reason anyway on hard tasks", "For a large tool catalogue mark tools `defer_loading` and give it a search tool", "Don't plan on vLLM or SGLang until a release carries LFM3", "Liquid doesn't recommend it for agentic coding or complex multi-step 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": 100, "reach": 85, "risk": 40, "upkeep": 67 }, "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/LFM3-1.7B-GGUF:Q4_K_M --jinja\n# or\nollama run lfm3", "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/lfm3-1-7b" }, "sameCompany": [ "liquid-d1", "lfm2-5-2-6b", "lfm3-audio-2b", "lfm3-omni-2b", "lfm3-vl-2b" ], "weights": { "trainedBy": "Liquid AI", "builtOn": "LiquidAI/LFM3-1.7B-Base", "params": "1.73B", "context": 262144, "goodFor": "Small on-device agent model for tool calling, tool search over large catalogues, structured output and long-context work on a laptop or phone, not for agentic coding or complex multi-step reasoning.", "runtimes": [ "llama.cpp", "Ollama", "LM Studio" ], "builds": [ { "name": "LFM3-1.7B", "url": "https://huggingface.co/LiquidAI/LFM3-1.7B", "who": "publisher", "precision": "bf16", "sizeGB": 3.46, "memoryGB": 8, "format": "safetensors", "runsOn": [ "Transformers" ], "note": "Loads in Transformers with the repository's own code. Liquid also publishes FP8 (2.21 GB) and NVFP4 (1.83 GB) safetensors builds, which the card pairs with SGLang, and an ONNX repository" }, { "name": "LFM3-1.7B-GGUF Q8_0", "url": "https://huggingface.co/LiquidAI/LFM3-1.7B-GGUF", "who": "publisher", "precision": "q8_0", "sizeGB": 1.85, "memoryGB": 8, "format": "gguf", "runsOn": [ "llama.cpp", "LM Studio" ] }, { "name": "LFM3-1.7B-GGUF Q4_K_M", "url": "https://huggingface.co/LiquidAI/LFM3-1.7B-GGUF", "who": "publisher", "precision": "q4_k_m", "sizeGB": 1.1, "memoryGB": 8, "format": "gguf", "runsOn": [ "llama.cpp", "LM Studio" ], "note": "LFM3-1.7B-Q4_K_M.gguf, from the Hub's file list. The repository also holds a quantisation-aware LFM3-1.7B-QAD-Q4_0.gguf (1.05 GB), Q8_0, F16 and BF16 files" }, { "name": "LFM3-1.7B-MLX-4bit", "url": "https://huggingface.co/LiquidAI/LFM3-1.7B-MLX-4bit", "who": "publisher", "precision": "4bit", "sizeGB": 0.97, "memoryGB": 8, "format": "mlx", "runsOn": [ "MLX" ], "note": "Liquid also publishes 5, 6 and 8-bit and BF16 MLX builds" }, { "name": "ollama lfm3", "url": "https://ollama.com/library/lfm3", "who": "ollama", "precision": "q4_k_m", "sizeGB": 1.1, "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/LFM3-1.7B-GGUF:Q4_K_M --jinja", "runBuild": "LFM3-1.7B-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-VL-2B", "url": "https://huggingface.co/LiquidAI/LFM3-VL-2B", "params": "1.92B", "listing": "https://www.anchorterminal.com/tools/lfm3-vl-2b" }, { "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/LFM3-1.7B/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/lfm3-1-7b.json" }, "answer": "LFM3-1.7B and WeVisOne-VL-4B score within a point of each other on agent readiness, 61 (C) and 61 (C). WeVisOne-VL-4B leads on risk.", "b": { "slug": "wevisone-vl-4b", "name": "WeVisOne-VL-4B", "vendor": "Tencent (WeChat Vision)", "vendorUrl": "https://www.tencent.com", "kind": "weights", "category": "open-weights", "summary": "WeVisOne-VL-4B is an open-weight perception model of 4.8B parameters from Tencent's WeChat Vision team, built on Qwen3-VL. Name something in an image and it returns boxes, points, counts, text, masks or alpha mattes. Apache 2.0.", "url": "https://www.anchorterminal.com/tools/wevisone-vl-4b", "markdownUrl": "https://www.anchorterminal.com/tools/wevisone-vl-4b.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/wevisone-vl-4b.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/wevisone-vl-4b.json", "dns": "wevisone-vl-4b.grade.anchorterminal.com", "repo": "https://huggingface.co/tencent/WeVisOne-VL-4B", "license": "Apache-2.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "tencent/WeVisOne-VL-4B" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The repository is not gated (checked 2026-11-10).", "pricing": "free", "pricingNotes": "Free to download and use under Apache 2.0. Running it yourself costs the hardware, 9.66 GB in BF16 (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://github.com/Tencent/WeVisOne-VL", "capabilities": [ "model.open-weights", "docs.ocr" ], "tags": [ "open-weights", "model", "vision", "grounding", "object-detection", "segmentation", "matting", "ocr", "counting", "apache-2.0", "safetensors", "self-hosted" ], "lastRelease": "2026-10-27", "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": 66, "methodology": "0.7", "run": "2026-10-01", "scores": { "interface": 70, "reach": 70, "risk": 55, "upkeep": 48 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-11-10" }, "negative": 0, "verdict": "A small Apache 2.0 perception model that finds, counts, reads and cuts out what you name in an image, and says None when it isn't there. Boxes and points run in vLLM, SGLang or Transformers. Masks and mattes need Tencent's package and a pickle file. Two weeks old, with no questions answered yet and no safety results.", "bestFor": "Agents that need to locate, count or cut out objects in images on a small GPU, under a permissive licence", "strengths": [ "Seven perception tasks in one model, from boxes to alpha mattes", "Returns None for a target that isn't in the image", "Apache 2.0, ungated", "Each task's input and output is documented", "9.66 GB in BF16, so it fits a 12 GB machine" ], "weaknesses": [ "The pixel decoder ships as a pickle file", "Masks and mattes need Tencent's package from GitHub", "No quantised build from Tencent", "One image and one turn at a time, no video", "No reply from Tencent on the repository's discussions", "No context length, languages, safety results or data sources on the card" ], "agentNotes": [ "Use Tencent's package: `WeVisOneVL(\"tencent/WeVisOne-VL-4B\", backend=\"vllm\")` then `model.predict(image, task=..., query=...)`", "Tasks are detection, referring, point, count_box, count_point, ocr, ocr_grounding, segmentation and matting", "A None answer means the target isn't in the image. Treat it as a result, not an error", "mask_decoder.pt is a pickle. Load it only from the tencent repository, and check its SHA-256 against the Hub's", "Send one image a turn. Video grids are rejected by the decoder", "The card says it isn't for identifying individuals or for high-impact decisions on its own" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 0, "avgRating": 0, "history": [ { "basis": "public evidence", "confidence": "low", "grade": "C", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 61 } ], "editorialScores": { "interface": 70, "reach": 70, "risk": 55, "upkeep": 48 }, "provenanceScore": 68, "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": "git clone https://github.com/Tencent/WeVisOne-VL.git \u0026\u0026 cd WeVisOne-VL\npip install -r requirements.txt \u0026\u0026 pip install -e .\n# then, in Python: WeVisOneVL(\"tencent/WeVisOne-VL-4B\", backend=\"transformers\") or backend=\"vllm\"", "http": "# No HTTP API of its own. Tencent's package calls model.predict(image, task=\"detection\", query=[...]).\n# For boxes and points only, vLLM serves the backbone at http://localhost:8000/v1 with: vllm serve tencent/WeVisOne-VL-4B" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/wevisone-vl-4b" }, "sameCompany": [ "hy-memory", "tencentdb-agent-memory", "hunyuanocr", "hy-memory-30b-a3b", "hy3", "wevisone-vl-2b" ], "weights": { "trainedBy": "Tencent WeChat Vision", "params": "4.8B", "goodFor": "Finding, counting, reading and cutting out what you name in an image, on a 12 GB machine.", "runtimes": [ "vLLM", "SGLang", "Transformers" ], "builds": [ { "name": "WeVisOne-VL-4B (BF16 safetensors)", "url": "https://huggingface.co/tencent/WeVisOne-VL-4B/blob/main/model.safetensors", "who": "publisher", "precision": "bf16", "sizeGB": 9.66, "memoryGB": 12, "format": "safetensors", "runsOn": [ "vLLM", "SGLang", "Transformers" ], "note": "The only build. Masks and mattes also need mask_decoder.pt and Tencent's package" } ], "run": "vllm serve tencent/WeVisOne-VL-4B", "runBuild": "WeVisOne-VL-4B (BF16 safetensors)", "checked": "2026-11-10", "minMemoryGB": 12 }, "family": { "lab": "Tencent (Hunyuan / Hy)", "family": "WeVisOne-VL", "url": "https://www.anchorterminal.com/open-weights/#tencent", "others": [ { "name": "WeVisOne-VL-2B", "url": "https://huggingface.co/tencent/WeVisOne-VL-2B", "params": "2.4B (2,441,744,384 by the Hub's safetensors count)", "listing": "https://www.anchorterminal.com/tools/wevisone-vl-2b" } ], "olderCount": 22 }, "area": "models", "provenance": { "legalEntity": "Tencent (named in the LICENSE file's copyright line)", "domain": "tencent.com", "domainRegistered": "1998-09-14", "endpointOnVendorDomain": null, "terms": "https://huggingface.co/tencent/WeVisOne-VL-4B/blob/main/LICENSE", "privacy": "", "statusPage": "", "changelog": "https://huggingface.co/tencent/WeVisOne-VL-4B/commits/main", "securityTxt": "none", "checked": "2026-11-10", "notes": [ "The card names WeChat Vision, Tencent Inc. The domain's registration date is carried from our other Tencent listings.", "Nothing is hosted under this listing, so the model licence stands in for terms of service and no privacy policy is scored.", "The changelog is the commit history.", "Tencent's Security Response Center takes reports company-wide, per the Hy3 listing; nothing on the card links it." ], "score": 68 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/wevisone-vl-4b.json" }, "edges": [ { "by": 30, "category": "interface", "leads": "lfm3-1-7b", "name": "Agent interface", "reasons": { "a": "Chat template 20, chat_template.jinja in the repository (12,129 characters), with roles, 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 the model was trained on tool calls and agentic traces and with reinforcement learning inside agent harnesses. Structured output 15, Liquid's API guide documents `response_format` with a JSON Schema, with lfm3-1.7b as the model in its examples, and says the schema is checked before generation starts. Reasoning control 10, the card documents `enable_thinking=False` in the chat template to turn reasoning off per request, with reasoning on by default inside think tags, and `preserve_thinking` for earlier turns. Context 10, 256K tokens per the card, which says the mid-training stage extended the context window to 256K, and max_position_embeddings 262144 in config.json. 20 + 25 + 20 + 15 + 10 + 10 = 100.", "b": "Chat template 20, chat_template.jinja in the repository (5,292 characters), Qwen3-VL's template. Tool-calling format 25, Read for a perception model as what it takes and returns. Trained tool calling 10, Read for a perception model as the inputs it was trained to use. Structured output 15, Read for a perception model as typed outputs. Reasoning control 0, the card says it writes structured output directly with no chain-of-thought reasoning. Context 0, Tencent states no context length for it. 20 + 25 + 10 + 15 + 0 + 0 = 70." }, "scores": { "a": 100, "b": 70 } }, { "by": 19, "category": "upkeep", "leads": "lfm3-1-7b", "name": "Documentation and upkeep", "reasons": { "a": "Model card 17, Intended use (local agents, tool calling, structured outputs, fine-tuning), a limitation (not recommended for agentic coding or complex multi-step reasoning), architecture (24 layers: 13 gated short convolutions, 5 sliding-window and 6 global attention), parameters (1.73B), context (256K) and 16 languages. Training data 5, A general description: 22T tokens of pre-training in two stages with the kinds of data added since LFM2, 1.75T tokens of supervised fine-tuning, then mixed reinforcement learning, distillation from six specialist teachers and agentic reinforcement learning. 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 10, Of the 22 discussions that aren't pull requests (27 October to 8 November 2026), 7 have a reply from a LiquidAI member, which is some. 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 architecture, the 262,144-token context and the thinking switch. 17 + 5 + 20 + 10 + 5 + 10 = 67.", "b": "Model card 8, Intended use, limitations and the architecture are on the card. Training data 5, A general description in the README of a data engine and over 100 million annotated images, with no sources named and no cut-off. Last change 20, the last commit, on 27 October 2026, added links to the paper and project page, 14 days before this check. Responsiveness 0, Neither of the 2 discussions that aren't pull requests (23 and 26 October 2026) has a reply from a tencent member, counted on 10 November by author flags and dates with a script, text not read. Version policy 5, A first release, 14 days old. File health 10, Config, template and card agree on the Qwen3-VL classes, and the card names mask_decoder.pt as required and it's there. 8 + 5 + 20 + 0 + 5 + 10 = 48." }, "scores": { "a": 67, "b": 48 } }, { "by": 15, "category": "reach", "leads": "lfm3-1-7b", "name": "Reach", "reasons": { "a": "Memory 30, Liquid's Q4_K_M GGUF is 1.10 GB, and 1.10 GB × 1.2 = 1.3 GB, which fits 8 GB, well inside the 16 GB line. Runtimes 15, Released llama.cpp has the lfm3 architecture (src/models/lfm3.cpp at b12242), Ollama's library has an lfm3 entry, and LM Studio's catalogue runs the architecture (its liquid/lfm3-vl-2b entry; lmstudio-community publishes a GGUF of this model). 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 lfm3-1.7b among its models, so the publisher hosts it. 30 + 15 + 15 + 10 + 10 + 5 = 85.", "b": "Memory 30, the only build from Tencent is the BF16 safetensors file with its decoder, 9.7 GB, and 9.7 GB × 1.2 = 11.6 GB, within 16 GB. No runtime library has a build. Runtimes 15, the architecture is Qwen3VLForConditionalGeneration, which vLLM 0.33.0, SGLang 0.5.23 and Transformers 5.22.2 run from the safetensors file, three at 5 each. Builds 0, Tencent publishes the BF16 weights only. Terms 15, Apache 2.0 in the card's metadata with the LICENSE file in the repository, and nothing added. Download 10, the Hub API reports gated false on 10 November 2026, so the files download with no account and nothing to accept. Hosted 0, Hugging Face maps no inference provider to it. 30 + 15 + 0 + 15 + 10 + 0 = 70." }, "scores": { "a": 85, "b": 70 } }, { "by": 15, "category": "risk", "leads": "wevisone-vl-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, the backbone is safetensors, but mask_decoder.pt, a PyTorch pickle the card calls a required component, sits beside it. Provenance 15, the files sit in the tencent organisation, which Tencent's GitHub repository for the model links to (10), and the Hub gives a SHA-256 for the large files (5). Safety evaluation 0, no safety results on the card or in the README. Agent guidance 5, General cautions: the card says the release is for research, isn't for autonomous high-impact decisions or identifying individuals, and lists where masks go wrong. Licence risk 15, Apache 2.0 with no use policy, no threshold and no right to change the terms. Security programme 5, Tencent's Security Response Center takes vulnerability reports company-wide, as the Hy3 listing records (5). 15 + 15 + 0 + 5 + 15 + 5 = 55." }, "scores": { "a": 40, "b": 55 } } ], "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "Liquid AI", "b": "Tencent (WeChat Vision)", "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": "2026-10-27", "name": "Last release" }, { "a": "no document linked", "b": "not read yet", "name": "Terms last updated" }, { "a": "no document linked", "b": "no document linked", "name": "Privacy policy last updated" } ], "faq": [ { "answer": "LFM3-1.7B and WeVisOne-VL-4B score within a point of each other on agent readiness, 61 (C) and 61 (C). WeVisOne-VL-4B leads on risk.", "question": "Which is better for AI agents, LFM3-1.7B or WeVisOne-VL-4B?" } ], "goodFor": [ { "aheadOn": [ "Reach, 85 against 70", "Agent interface, 100 against 70", "Documentation and upkeep, 67 against 48" ], "also": null, "goodFor": "Agents on a laptop, phone or small server that need tool calling and a long context from a model under 2 GB at 4 bits", "slug": "lfm3-1-7b", "watchFor": "vLLM and SGLang have no released support, though the card lists both" }, { "aheadOn": [ "Risk, 55 against 40" ], "also": null, "goodFor": "Agents that need to locate, count or cut out objects in images on a small GPU, under a permissive licence", "slug": "wevisone-vl-4b", "watchFor": "The pixel decoder ships as a pickle file" } ], "history": null, "job": { "capability": "model.open-weights", "name": "Open-weight models you run yourself" }, "others": [ { "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-lfm3-1-7b.json", "title": "Agents-A1-4B vs LFM3-1.7B", 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}, { "json": "https://www.anchorterminal.com/compare/carbon-x-vs-lfm3-1-7b.json", "title": "Carbon-X vs LFM3-1.7B", "url": "https://www.anchorterminal.com/compare/carbon-x-vs-lfm3-1-7b" }, { "json": "https://www.anchorterminal.com/compare/carbon-x-vs-wevisone-vl-4b.json", "title": "Carbon-X vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/carbon-x-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/cola2-vs-lfm3-1-7b.json", "title": "Cola2 vs LFM3-1.7B", "url": "https://www.anchorterminal.com/compare/cola2-vs-lfm3-1-7b" }, { "json": "https://www.anchorterminal.com/compare/cola2-vs-wevisone-vl-4b.json", "title": "Cola2 vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/cola2-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-lfm3-1-7b.json", "title": "Cosmos-Reason2-2B vs LFM3-1.7B", "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-lfm3-1-7b" }, { "json": 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{ "json": "https://www.anchorterminal.com/compare/devstral-small-2-vs-lfm3-1-7b.json", "title": "Devstral Small 2 vs LFM3-1.7B", "url": "https://www.anchorterminal.com/compare/devstral-small-2-vs-lfm3-1-7b" }, { "json": "https://www.anchorterminal.com/compare/devstral-small-2-vs-wevisone-vl-4b.json", "title": "Devstral Small 2 vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/devstral-small-2-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-lfm3-1-7b.json", "title": "DiffusionGemma 26B A4B IT vs LFM3-1.7B", "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-lfm3-1-7b" }, { "json": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-wevisone-vl-4b.json", "title": "DiffusionGemma 26B A4B IT vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-wevisone-vl-4b" }, { "json": 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"https://www.anchorterminal.com/compare/qwen3-8-27b-vs-wevisone-vl-4b.json", "title": "Qwen3.8-27B vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/qwen3-8-27b-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-wevisone-vl-4b.json", "title": "Qwen3.8-Flash-Next vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-coder-30b-a3b-instruct-vs-wevisone-vl-4b.json", "title": "Qwen3-Coder-30B-A3B-Instruct vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/qwen3-coder-30b-a3b-instruct-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-coder-next-vs-wevisone-vl-4b.json", "title": "Qwen3-Coder-Next vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/qwen3-coder-next-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-omni-30b-a3b-instruct-vs-wevisone-vl-4b.json", "title": "Qwen3-Omni-30B-A3B-Instruct vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/qwen3-omni-30b-a3b-instruct-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-2b-instruct-vs-wevisone-vl-4b.json", "title": "Qwen3-VL-2B-Instruct vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/qwen3-vl-2b-instruct-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-30b-a3b-instruct-vs-wevisone-vl-4b.json", "title": "Qwen3-VL-30B-A3B-Instruct vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/qwen3-vl-30b-a3b-instruct-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-32b-instruct-vs-wevisone-vl-4b.json", "title": "Qwen3-VL-32B-Instruct vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/qwen3-vl-32b-instruct-vs-wevisone-vl-4b" }, { "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-8b-instruct-vs-wevisone-vl-4b.json", "title": "Qwen3-VL-8B-Instruct vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/qwen3-vl-8b-instruct-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-wevisone-vl-4b.json", "title": "Qwopus3.8-27B-Flash-V2 vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/recursive-flint-vs-wevisone-vl-4b.json", "title": "Recursive Flint vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/recursive-flint-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/ripple-65b-preview-vs-wevisone-vl-4b.json", "title": "Ripple-65B Preview vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/ripple-65b-preview-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-wevisone-vl-4b.json", "title": "SenseNova-Loop-9B-Preview vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/smollm3-3b-vs-wevisone-vl-4b.json", "title": "SmolLM3-3B vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/smollm3-3b-vs-wevisone-vl-4b" }, { "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/step-3-7-flash-vs-wevisone-vl-4b.json", "title": "Step 3.7 Flash vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/step-3-7-flash-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/swift-1-5-qwen3-8-27b-vs-wevisone-vl-4b.json", "title": "Swift 1.5 Qwen3.8-27B vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/swift-1-5-qwen3-8-27b-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/swift-1-5-qwen3-8-flash-next-vs-wevisone-vl-4b.json", "title": "Swift 1.5 Qwen3.8-Flash-Next vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/swift-1-5-qwen3-8-flash-next-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/thinkingcap-qwen3-8-27b-vs-wevisone-vl-4b.json", "title": "ThinkingCap (Qwen3.8 27B) vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/thinkingcap-qwen3-8-27b-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/wevisone-vl-2b-vs-wevisone-vl-4b.json", "title": "WeVisOne-VL-2B vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/wevisone-vl-2b-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/wevisone-vl-4b-vs-zaya1-8b.json", "title": "WeVisOne-VL-4B vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/wevisone-vl-4b-vs-zaya1-8b" }, { "json": "https://www.anchorterminal.com/compare/unlimited-ocr-2-vs-wevisone-vl-4b.json", "title": "Baidu Unlimited-OCR-2 vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/unlimited-ocr-2-vs-wevisone-vl-4b" } ], "panel": null, "scores": [ { "key": "capability", "name": "Capability", "pending": true, "weight": 35 }, { "by": 15, "edge": "lfm3-1-7b", "key": "reach", "lfm3-1-7b": 85, "name": "Reach", "weight": 25, "wevisone-vl-4b": 70 }, { "by": 15, "edge": "wevisone-vl-4b", "key": "risk", "lfm3-1-7b": 40, "name": "Risk", "weight": 20, "wevisone-vl-4b": 55 }, { "by": 30, "edge": "lfm3-1-7b", "key": "interface", "lfm3-1-7b": 100, "name": "Agent interface", "weight": 10, "wevisone-vl-4b": 70 }, { "by": 19, "edge": "lfm3-1-7b", "key": "upkeep", "lfm3-1-7b": 67, "name": "Documentation and upkeep", "weight": 10, "wevisone-vl-4b": 48 } ], "summary": "LFM3-1.7B and WeVisOne-VL-4B score within a point of each other on agent readiness, 61 (C) and 61 (C). WeVisOne-VL-4B leads on risk. Both do open-weight models you run yourself.", "tasks": null, "tutorials": [], "verdicts": { "lfm3-1-7b": "A 1.73B model that fits an 8 GB machine with room to spare, with a documented tool-call format, deferred tools, a thinking switch and a 256K context. It runs today in llama.cpp, Ollama and LM Studio only, loads in Transformers through repository code, and has no safety results, security contact or data sources.", "wevisone-vl-4b": "A small Apache 2.0 perception model that finds, counts, reads and cuts out what you name in an image, and says None when it isn't there. Boxes and points run in vLLM, SGLang or Transformers. Masks and mattes need Tencent's package and a pickle file. Two weeks old, with no questions answered yet and no safety results." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-wevisone-vl-4b", "json": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-wevisone-vl-4b.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-wevisone-vl-4b.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-wevisone-vl-4b.min.md" }, "markdown": "LFM3-1.7B and WeVisOne-VL-4B score within a point of each other on agent readiness, 61 (C) and 61 (C). WeVisOne-VL-4B leads on risk. Both do open-weight models you run yourself.\n\n- LFM3-1.7B: grade C, 61/100, rank #69 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/lfm3-1-7b.md · JSON https://www.anchorterminal.com/api/v1/tools/lfm3-1-7b.json\n- WeVisOne-VL-4B: grade C, 61/100, rank #84 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/wevisone-vl-4b.md · JSON https://www.anchorterminal.com/api/v1/tools/wevisone-vl-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### LFM3-1.7B (C)\n\nGood for: Agents on a laptop, phone or small server that need tool calling and a long context from a model under 2 GB at 4 bits\n\nAhead on:\n- Reach, 85 against 70\n- Agent interface, 100 against 70\n- Documentation and upkeep, 67 against 48\n\nWatch for: vLLM and SGLang have no released support, though the card lists both\n\n### WeVisOne-VL-4B (C)\n\nGood for: Agents that need to locate, count or cut out objects in images on a small GPU, under a permissive licence\n\nAhead on:\n- Risk, 55 against 40\n\nWatch for: The pixel decoder ships as a pickle file\n\n\n## Why the edge\n\n### Agent interface: LFM3-1.7B +30\n\n- LFM3-1.7B (100): Chat template 20, chat_template.jinja in the repository (12,129 characters), with roles, 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 the model was trained on tool calls and agentic traces and with reinforcement learning inside agent harnesses. Structured output 15, Liquid's API guide documents `response_format` with a JSON Schema, with lfm3-1.7b as the model in its examples, and says the schema is checked before generation starts. Reasoning control 10, the card documents `enable_thinking=False` in the chat template to turn reasoning off per request, with reasoning on by default inside think tags, and `preserve_thinking` for earlier turns. Context 10, 256K tokens per the card, which says the mid-training stage extended the context window to 256K, and max_position_embeddings 262144 in config.json. 20 + 25 + 20 + 15 + 10 + 10 = 100.\n- WeVisOne-VL-4B (70): Chat template 20, chat_template.jinja in the repository (5,292 characters), Qwen3-VL's template. Tool-calling format 25, Read for a perception model as what it takes and returns. Trained tool calling 10, Read for a perception model as the inputs it was trained to use. Structured output 15, Read for a perception model as typed outputs. Reasoning control 0, the card says it writes structured output directly with no chain-of-thought reasoning. Context 0, Tencent states no context length for it. 20 + 25 + 10 + 15 + 0 + 0 = 70.\n\n### Documentation and upkeep: LFM3-1.7B +19\n\n- LFM3-1.7B (67): Model card 17, Intended use (local agents, tool calling, structured outputs, fine-tuning), a limitation (not recommended for agentic coding or complex multi-step reasoning), architecture (24 layers: 13 gated short convolutions, 5 sliding-window and 6 global attention), parameters (1.73B), context (256K) and 16 languages. Training data 5, A general description: 22T tokens of pre-training in two stages with the kinds of data added since LFM2, 1.75T tokens of supervised fine-tuning, then mixed reinforcement learning, distillation from six specialist teachers and agentic reinforcement learning. 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 10, Of the 22 discussions that aren't pull requests (27 October to 8 November 2026), 7 have a reply from a LiquidAI member, which is some. 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 architecture, the 262,144-token context and the thinking switch. 17 + 5 + 20 + 10 + 5 + 10 = 67.\n- WeVisOne-VL-4B (48): Model card 8, Intended use, limitations and the architecture are on the card. Training data 5, A general description in the README of a data engine and over 100 million annotated images, with no sources named and no cut-off. Last change 20, the last commit, on 27 October 2026, added links to the paper and project page, 14 days before this check. Responsiveness 0, Neither of the 2 discussions that aren't pull requests (23 and 26 October 2026) has a reply from a tencent member, counted on 10 November by author flags and dates with a script, text not read. Version policy 5, A first release, 14 days old. File health 10, Config, template and card agree on the Qwen3-VL classes, and the card names mask_decoder.pt as required and it's there. 8 + 5 + 20 + 0 + 5 + 10 = 48.\n\n### Reach: LFM3-1.7B +15\n\n- LFM3-1.7B (85): Memory 30, Liquid's Q4_K_M GGUF is 1.10 GB, and 1.10 GB × 1.2 = 1.3 GB, which fits 8 GB, well inside the 16 GB line. Runtimes 15, Released llama.cpp has the lfm3 architecture (src/models/lfm3.cpp at b12242), Ollama's library has an lfm3 entry, and LM Studio's catalogue runs the architecture (its liquid/lfm3-vl-2b entry; lmstudio-community publishes a GGUF of this model). 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 lfm3-1.7b among its models, so the publisher hosts it. 30 + 15 + 15 + 10 + 10 + 5 = 85.\n- WeVisOne-VL-4B (70): Memory 30, the only build from Tencent is the BF16 safetensors file with its decoder, 9.7 GB, and 9.7 GB × 1.2 = 11.6 GB, within 16 GB. No runtime library has a build. Runtimes 15, the architecture is Qwen3VLForConditionalGeneration, which vLLM 0.33.0, SGLang 0.5.23 and Transformers 5.22.2 run from the safetensors file, three at 5 each. Builds 0, Tencent publishes the BF16 weights only. Terms 15, Apache 2.0 in the card's metadata with the LICENSE file in the repository, and nothing added. Download 10, the Hub API reports gated false on 10 November 2026, so the files download with no account and nothing to accept. Hosted 0, Hugging Face maps no inference provider to it. 30 + 15 + 0 + 15 + 10 + 0 = 70.\n\n### Risk: WeVisOne-VL-4B +15\n\n- LFM3-1.7B (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- WeVisOne-VL-4B (55): File safety 15, the backbone is safetensors, but mask_decoder.pt, a PyTorch pickle the card calls a required component, sits beside it. Provenance 15, the files sit in the tencent organisation, which Tencent's GitHub repository for the model links to (10), and the Hub gives a SHA-256 for the large files (5). Safety evaluation 0, no safety results on the card or in the README. Agent guidance 5, General cautions: the card says the release is for research, isn't for autonomous high-impact decisions or identifying individuals, and lists where masks go wrong. Licence risk 15, Apache 2.0 with no use policy, no threshold and no right to change the terms. Security programme 5, Tencent's Security Response Center takes vulnerability reports company-wide, as the Hy3 listing records (5). 15 + 15 + 0 + 5 + 15 + 5 = 55.\n\n## Score by category\n\n| Category | Weight | LFM3-1.7B | WeVisOne-VL-4B | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 85 | 70 | LFM3-1.7B +15 |\n| Risk | 20% (20 this run) | 40 | 55 | WeVisOne-VL-4B +15 |\n| Agent interface | 10% (10 this run) | 100 | 70 | LFM3-1.7B +30 |\n| Documentation and upkeep | 10% (10 this run) | 67 | 48 | LFM3-1.7B +19 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **61 · C** | **61 · C** | |\n\n## Facts side by side\n\n| Fact | LFM3-1.7B | WeVisOne-VL-4B |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Liquid AI | Tencent (WeChat Vision) |\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 | 2026-10-27 |\n| Terms last updated | no document linked | not read yet |\n| Privacy policy last updated | no document linked | no document linked |\n\n## Verdicts\n\n**LFM3-1.7B.** A 1.73B model that fits an 8 GB machine with room to spare, with a documented tool-call format, deferred tools, a thinking switch and a 256K context. It runs today in llama.cpp, Ollama and LM Studio only, loads in Transformers through repository code, and has no safety results, security contact or data sources.\n\n**WeVisOne-VL-4B.** A small Apache 2.0 perception model that finds, counts, reads and cuts out what you name in an image, and says None when it isn't there. Boxes and points run in vLLM, SGLang or Transformers. Masks and mattes need Tencent's package and a pickle file. Two weeks old, with no questions answered yet and no safety results.\n\n## Before you call either\n\n### LFM3-1.7B\n\n1. Start it with `llama-server -hf LiquidAI/LFM3-1.7B-GGUF:Q4_K_M --jinja` or `ollama run lfm3`\n2. Use temperature 0.1, top_k 50 and repetition penalty 1.1 for chat, tools and agents, per the card\n3. Pass `enable_thinking=False` for direct answers, and expect it to reason anyway on hard tasks\n4. For a large tool catalogue mark tools `defer_loading` and give it a search tool\n5. Don't plan on vLLM or SGLang until a release carries LFM3\n6. Liquid doesn't recommend it for agentic coding or complex multi-step reasoning\n\n### WeVisOne-VL-4B\n\n1. Use Tencent's package: `WeVisOneVL(\"tencent/WeVisOne-VL-4B\", backend=\"vllm\")` then `model.predict(image, task=..., query=...)`\n2. Tasks are detection, referring, point, count_box, count_point, ocr, ocr_grounding, segmentation and matting\n3. A None answer means the target isn't in the image. Treat it as a result, not an error\n4. mask_decoder.pt is a pickle. Load it only from the tencent repository, and check its SHA-256 against the Hub's\n5. Send one image a turn. Video grids are rejected by the decoder\n6. The card says it isn't for identifying individuals or for high-impact decisions on its own\n\n## Questions\n\n### Which is better for AI agents, LFM3-1.7B or WeVisOne-VL-4B?\n\nLFM3-1.7B and WeVisOne-VL-4B score within a point of each other on agent readiness, 61 (C) and 61 (C). WeVisOne-VL-4B leads on risk.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/lfm3-1-7b-vs-wevisone-vl-4b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/lfm3-1-7b-vs-wevisone-vl-4b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"lfm3-1-7b\", \"b\": \"wevisone-vl-4b\"}`. From a terminal: `anchor compare lfm3-1-7b wevisone-vl-4b`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/lfm3-1-7b.json and https://www.anchorterminal.com/api/v1/tools/wevisone-vl-4b.json\n\n## Other comparisons with LFM3-1.7B or WeVisOne-VL-4B\n\n- [Agents-A1-4B vs LFM3-1.7B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-lfm3-1-7b.md)\n- [Agents-A1-4B vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-wevisone-vl-4b.md)\n- [Apertus v1.5 8B vs LFM3-1.7B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-lfm3-1-7b.md)\n- [Apertus v1.5 8B vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-wevisone-vl-4b.md)\n- [Apodex-1.1-mini vs LFM3-1.7B](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-lfm3-1-7b.md)\n- [Apodex-1.1-mini vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-wevisone-vl-4b.md)\n- [Arete-27B vs LFM3-1.7B](https://www.anchorterminal.com/compare/arete-27b-vs-lfm3-1-7b.md)\n- [Arete-27B vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/arete-27b-vs-wevisone-vl-4b.md)\n- [BigBang-v1 vs LFM3-1.7B](https://www.anchorterminal.com/compare/bigbang-v1-vs-lfm3-1-7b.md)\n- [BigBang-v1 vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/bigbang-v1-vs-wevisone-vl-4b.md)\n- [Carbon-X vs LFM3-1.7B](https://www.anchorterminal.com/compare/carbon-x-vs-lfm3-1-7b.md)\n- [Carbon-X vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/carbon-x-vs-wevisone-vl-4b.md)\n- [Cola2 vs LFM3-1.7B](https://www.anchorterminal.com/compare/cola2-vs-lfm3-1-7b.md)\n- [Cola2 vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/cola2-vs-wevisone-vl-4b.md)\n- [Cosmos-Reason2-2B vs LFM3-1.7B](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-lfm3-1-7b.md)\n- [Cosmos-Reason2-2B vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-wevisone-vl-4b.md)\n- [Darwin-36B-Opus vs LFM3-1.7B](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-lfm3-1-7b.md)\n- [Darwin-36B-Opus vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-wevisone-vl-4b.md)\n- [DeepSeek-V3.2 vs LFM3-1.7B](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-lfm3-1-7b.md)\n- [DeepSeek-V3.2 vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-wevisone-vl-4b.md)\n- [DeepSeek V4.1 Flash vs LFM3-1.7B](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-lfm3-1-7b.md)\n- [DeepSeek V4.1 Flash vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-wevisone-vl-4b.md)\n- [DeepSeek-V4-Flash-0731 vs LFM3-1.7B](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-lfm3-1-7b.md)\n- [DeepSeek-V4-Flash-0731 vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-wevisone-vl-4b.md)\n- [Devstral Small 2 vs LFM3-1.7B](https://www.anchorterminal.com/compare/devstral-small-2-vs-lfm3-1-7b.md)\n- [Devstral Small 2 vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/devstral-small-2-vs-wevisone-vl-4b.md)\n- [DiffusionGemma 26B A4B IT vs LFM3-1.7B](https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-lfm3-1-7b.md)\n- [DiffusionGemma 26B A4B IT vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-wevisone-vl-4b.md)\n- [EXAONE 4.5 33B vs LFM3-1.7B](https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-lfm3-1-7b.md)\n- [EXAONE 4.5 33B vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-wevisone-vl-4b.md)\n- [Gemma 4 12B IT vs LFM3-1.7B](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-lfm3-1-7b.md)\n- [Gemma 4 12B IT vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-wevisone-vl-4b.md)\n- [Gemma 4 26B A4B IT vs LFM3-1.7B](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-lfm3-1-7b.md)\n- [Gemma 4 26B A4B IT vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-wevisone-vl-4b.md)\n- [Gemma 4 31B IT vs LFM3-1.7B](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-lfm3-1-7b.md)\n- [Gemma 4 31B IT vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-wevisone-vl-4b.md)\n- [Gemma 4 E2B IT vs LFM3-1.7B](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-lfm3-1-7b.md)\n- [Gemma 4 E2B IT vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-wevisone-vl-4b.md)\n- [Gemma 4 E4B IT vs LFM3-1.7B](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-lfm3-1-7b.md)\n- [Gemma 4 E4B IT vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-wevisone-vl-4b.md)\n- [GLM-4.5-Air vs LFM3-1.7B](https://www.anchorterminal.com/compare/glm-4-5-air-vs-lfm3-1-7b.md)\n- [GLM-4.5-Air vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/glm-4-5-air-vs-wevisone-vl-4b.md)\n- [GLM-4.7-Flash vs LFM3-1.7B](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-lfm3-1-7b.md)\n- [GLM-4.7-Flash vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-wevisone-vl-4b.md)\n- [GLM-5.3-Flash vs LFM3-1.7B](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-lfm3-1-7b.md)\n- [GLM-5.3-Flash vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-wevisone-vl-4b.md)\n- [GLM-5.3 vs LFM3-1.7B](https://www.anchorterminal.com/compare/glm-5-3-vs-lfm3-1-7b.md)\n- [GLM-5.3 vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/glm-5-3-vs-wevisone-vl-4b.md)\n- [gpt-oss-120b vs LFM3-1.7B](https://www.anchorterminal.com/compare/gpt-oss-120b-vs-lfm3-1-7b.md)\n- [gpt-oss-120b vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/gpt-oss-120b-vs-wevisone-vl-4b.md)\n- [gpt-oss-20b vs LFM3-1.7B](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-lfm3-1-7b.md)\n- [gpt-oss-20b vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-wevisone-vl-4b.md)\n- [Granite 4.2 30B vs LFM3-1.7B](https://www.anchorterminal.com/compare/granite-4-2-30b-vs-lfm3-1-7b.md)\n- [Granite 4.2 30B vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/granite-4-2-30b-vs-wevisone-vl-4b.md)\n- [Granite 4.2 3B vs LFM3-1.7B](https://www.anchorterminal.com/compare/granite-4-2-3b-vs-lfm3-1-7b.md)\n- [Granite 4.2 3B vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/granite-4-2-3b-vs-wevisone-vl-4b.md)\n- [Granite 4.2 8B vs LFM3-1.7B](https://www.anchorterminal.com/compare/granite-4-2-8b-vs-lfm3-1-7b.md)\n- [Granite 4.2 8B vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/granite-4-2-8b-vs-wevisone-vl-4b.md)\n- [Hy-Memory-30B-A3B vs LFM3-1.7B](https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-lfm3-1-7b.md)\n- [Hy-Memory-30B-A3B vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-wevisone-vl-4b.md)\n- [Hy3 vs LFM3-1.7B](https://www.anchorterminal.com/compare/hy3-vs-lfm3-1-7b.md)\n- [Hy3 vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/hy3-vs-wevisone-vl-4b.md)\n- [Inkling-Small vs LFM3-1.7B](https://www.anchorterminal.com/compare/inkling-small-vs-lfm3-1-7b.md)\n- [Inkling-Small vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/inkling-small-vs-wevisone-vl-4b.md)\n- [Inkling vs LFM3-1.7B](https://www.anchorterminal.com/compare/inkling-vs-lfm3-1-7b.md)\n- [Inkling vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/inkling-vs-wevisone-vl-4b.md)\n- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs LFM3-1.7B](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-lfm3-1-7b.md)\n- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-wevisone-vl-4b.md)\n- [Kimi K3.1 Pro vs LFM3-1.7B](https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-lfm3-1-7b.md)\n- [Kimi K3.1 Pro vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-wevisone-vl-4b.md)\n- [Kimi K3 vs LFM3-1.7B](https://www.anchorterminal.com/compare/kimi-k3-vs-lfm3-1-7b.md)\n- [Kimi K3 vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/kimi-k3-vs-wevisone-vl-4b.md)\n- [Kolibri-1 vs LFM3-1.7B](https://www.anchorterminal.com/compare/kolibri-1-vs-lfm3-1-7b.md)\n- [Kolibri-1 vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/kolibri-1-vs-wevisone-vl-4b.md)\n- [Kronos vs LFM3-1.7B](https://www.anchorterminal.com/compare/kronos-vs-lfm3-1-7b.md)\n- [Kronos vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/kronos-vs-wevisone-vl-4b.md)\n- [Laguna M.1 vs LFM3-1.7B](https://www.anchorterminal.com/compare/laguna-m-1-vs-lfm3-1-7b.md)\n- [Laguna M.1 vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/laguna-m-1-vs-wevisone-vl-4b.md)\n- [Laguna S 2.1 vs LFM3-1.7B](https://www.anchorterminal.com/compare/laguna-s-2-1-vs-lfm3-1-7b.md)\n- [Laguna S 2.1 vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/laguna-s-2-1-vs-wevisone-vl-4b.md)\n- [Laguna XS 2.1 vs LFM3-1.7B](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-lfm3-1-7b.md)\n- [Laguna XS 2.1 vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-wevisone-vl-4b.md)\n- [LBS-27B vs LFM3-1.7B](https://www.anchorterminal.com/compare/lbs-27b-vs-lfm3-1-7b.md)\n- [LBS-27B vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/lbs-27b-vs-wevisone-vl-4b.md)\n- [LFM2.5-2.6B vs LFM3-1.7B](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-lfm3-1-7b.md)\n- [LFM2.5-2.6B vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-wevisone-vl-4b.md)\n- [LFM3-1.7B vs LFM3-Audio-2B](https://www.anchorterminal.com/compare/lfm3-1-7b-vs-lfm3-audio-2b.md)\n- [LFM3-1.7B vs LFM3-Omni-2B](https://www.anchorterminal.com/compare/lfm3-1-7b-vs-lfm3-omni-2b.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 Ling-3.0-tiny](https://www.anchorterminal.com/compare/lfm3-1-7b-vs-ling-3-0-tiny.md)\n- [LFM3-1.7B vs Ling-3.1-flash-1019](https://www.anchorterminal.com/compare/lfm3-1-7b-vs-ling-3-1-flash-1019.md)\n- [LFM3-1.7B vs LongCat-2.5](https://www.anchorterminal.com/compare/lfm3-1-7b-vs-longcat-2-5.md)\n- [LFM3-1.7B vs MiMo-V2.6 Distill Qwen 9B](https://www.anchorterminal.com/compare/lfm3-1-7b-vs-mimo-v2-6-distill-qwen-9b.md)\n- [LFM3-1.7B vs MiMo-V2.6-Flash-RL](https://www.anchorterminal.com/compare/lfm3-1-7b-vs-mimo-v2-6-flash-rl.md)\n- [LFM3-1.7B vs MiniCPM-o 4.5](https://www.anchorterminal.com/compare/lfm3-1-7b-vs-minicpm-o-4-5.md)\n- [LFM3-1.7B vs MiniCPM-V 4.7 1B](https://www.anchorterminal.com/compare/lfm3-1-7b-vs-minicpm-v-4-7-1b.md)\n- [LFM3-1.7B vs MiniCPM-V 4.7 35B-A3B](https://www.anchorterminal.com/compare/lfm3-1-7b-vs-minicpm-v-4-7-35b-a3b.md)\n- [LFM3-1.7B vs MiniCPM5-2B](https://www.anchorterminal.com/compare/lfm3-1-7b-vs-minicpm5-2b.md)\n- [LFM3-1.7B vs MiniMax-M2.5](https://www.anchorterminal.com/compare/lfm3-1-7b-vs-minimax-m2-5.md)\n- [LFM3-1.7B vs MiniMax-M2.7](https://www.anchorterminal.com/compare/lfm3-1-7b-vs-minimax-m2-7.md)\n- [LFM3-1.7B vs MiniMax-M3.1-Flash](https://www.anchorterminal.com/compare/lfm3-1-7b-vs-minimax-m3-1-flash.md)\n- [LFM3-1.7B vs Mistral Large 4](https://www.anchorterminal.com/compare/lfm3-1-7b-vs-mistral-large-4.md)\n- [LFM3-1.7B vs Muse Glimmer 30B](https://www.anchorterminal.com/compare/lfm3-1-7b-vs-muse-glimmer-30b.md)\n- [LFM3-1.7B vs Nanbeige4.2-3B](https://www.anchorterminal.com/compare/lfm3-1-7b-vs-nanbeige-4-2-3b.md)\n- [LFM3-1.7B vs Nanbeige4.3-33B](https://www.anchorterminal.com/compare/lfm3-1-7b-vs-nanbeige-4-3-33b.md)\n- [LFM3-1.7B vs NVIDIA Nemotron 3.5 Bolt E4B 4GB](https://www.anchorterminal.com/compare/lfm3-1-7b-vs-nemotron-3-5-bolt-e4b-4gb.md)\n- [LFM3-1.7B vs Nemotron 3.5 Lightning 30B A3B](https://www.anchorterminal.com/compare/lfm3-1-7b-vs-nemotron-3-5-lightning-30b-a3b.md)\n- [LFM3-1.7B vs Nemotron 3 Nano 30B A3B](https://www.anchorterminal.com/compare/lfm3-1-7b-vs-nemotron-3-nano-30b-a3b.md)\n- [LFM3-1.7B vs Nemotron 3 Nano 4B](https://www.anchorterminal.com/compare/lfm3-1-7b-vs-nemotron-3-nano-4b.md)\n- [LFM3-1.7B vs Nemotron 3 Nano Omni 30B A3B Reasoning](https://www.anchorterminal.com/compare/lfm3-1-7b-vs-nemotron-3-nano-omni-30b-a3b-reasoning.md)\n- [LFM3-1.7B vs Nemotron 3 Super 120B A12B](https://www.anchorterminal.com/compare/lfm3-1-7b-vs-nemotron-3-super-120b-a12b.md)\n- [LFM3-1.7B vs Nemotron 3 Ultra 550B A55B](https://www.anchorterminal.com/compare/lfm3-1-7b-vs-nemotron-3-ultra-550b-a55b.md)\n- [LFM3-1.7B vs North Mini Code 1.0](https://www.anchorterminal.com/compare/lfm3-1-7b-vs-north-mini-code-1-0.md)\n- [LFM3-1.7B vs occamy-1.0](https://www.anchorterminal.com/compare/lfm3-1-7b-vs-occamy-1-0.md)\n- [LFM3-1.7B vs Olmo 3 7B Think](https://www.anchorterminal.com/compare/lfm3-1-7b-vs-olmo-3-7b-think.md)\n- [LFM3-1.7B vs Ornith-1.5-122B](https://www.anchorterminal.com/compare/lfm3-1-7b-vs-ornith-1-5-122b.md)\n- [LFM3-1.7B vs Ornith-1.5-35B-A3B](https://www.anchorterminal.com/compare/lfm3-1-7b-vs-ornith-1-5-35b-a3b.md)\n- [LFM3-1.7B vs Ornith-1.5-397B](https://www.anchorterminal.com/compare/lfm3-1-7b-vs-ornith-1-5-397b.md)\n- [LFM3-1.7B vs Ornith-1.5-9B](https://www.anchorterminal.com/compare/lfm3-1-7b-vs-ornith-1-5-9b.md)\n- [LFM3-1.7B vs OTel-2.0-LLM-31B-IT](https://www.anchorterminal.com/compare/lfm3-1-7b-vs-otel-2-0-llm-31b-it.md)\n- [LFM3-1.7B vs Qwen-AgentWorld-35B-A3B](https://www.anchorterminal.com/compare/lfm3-1-7b-vs-qwen-agentworld-35b-a3b.md)\n- [LFM3-1.7B vs Qwen3-30B-A3B-Instruct-2507](https://www.anchorterminal.com/compare/lfm3-1-7b-vs-qwen3-30b-a3b-instruct-2507.md)\n- [LFM3-1.7B vs Qwen3-4B-Instruct-2507](https://www.anchorterminal.com/compare/lfm3-1-7b-vs-qwen3-4b-instruct-2507.md)\n- [LFM3-1.7B vs Qwen3.5-0.8B](https://www.anchorterminal.com/compare/lfm3-1-7b-vs-qwen3-5-0-8b.md)\n- [LFM3-1.7B vs Qwen3.5-122B-A10B](https://www.anchorterminal.com/compare/lfm3-1-7b-vs-qwen3-5-122b-a10b.md)\n- [LFM3-1.7B vs Qwen3.5-2B](https://www.anchorterminal.com/compare/lfm3-1-7b-vs-qwen3-5-2b.md)\n- [LFM3-1.7B vs Qwen3.5-397B-A17B](https://www.anchorterminal.com/compare/lfm3-1-7b-vs-qwen3-5-397b-a17b.md)\n- [LFM3-1.7B vs Qwen3.5-4B](https://www.anchorterminal.com/compare/lfm3-1-7b-vs-qwen3-5-4b.md)\n- [LFM3-1.7B vs Qwen3.5-9B](https://www.anchorterminal.com/compare/lfm3-1-7b-vs-qwen3-5-9b.md)\n- [LFM3-1.7B vs Qwen3.6-27B](https://www.anchorterminal.com/compare/lfm3-1-7b-vs-qwen3-6-27b.md)\n- [LFM3-1.7B vs Qwen3.6-35B-A3B](https://www.anchorterminal.com/compare/lfm3-1-7b-vs-qwen3-6-35b-a3b.md)\n- [LFM3-1.7B vs Qwen3.8-27B](https://www.anchorterminal.com/compare/lfm3-1-7b-vs-qwen3-8-27b.md)\n- [LFM3-1.7B vs Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/lfm3-1-7b-vs-qwen3-8-flash-next.md)\n- [LFM3-1.7B vs Qwen3-Coder-30B-A3B-Instruct](https://www.anchorterminal.com/compare/lfm3-1-7b-vs-qwen3-coder-30b-a3b-instruct.md)\n- [LFM3-1.7B vs Qwen3-Coder-Next](https://www.anchorterminal.com/compare/lfm3-1-7b-vs-qwen3-coder-next.md)\n- [LFM3-1.7B vs Qwen3-Omni-30B-A3B-Instruct](https://www.anchorterminal.com/compare/lfm3-1-7b-vs-qwen3-omni-30b-a3b-instruct.md)\n- [LFM3-1.7B vs Qwen3-VL-2B-Instruct](https://www.anchorterminal.com/compare/lfm3-1-7b-vs-qwen3-vl-2b-instruct.md)\n- [LFM3-1.7B vs Qwen3-VL-30B-A3B-Instruct](https://www.anchorterminal.com/compare/lfm3-1-7b-vs-qwen3-vl-30b-a3b-instruct.md)\n- [LFM3-1.7B vs Qwen3-VL-32B-Instruct](https://www.anchorterminal.com/compare/lfm3-1-7b-vs-qwen3-vl-32b-instruct.md)\n- [LFM3-1.7B vs 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[WeVisOne-VL-4B vs ZAYA1-8B](https://www.anchorterminal.com/compare/wevisone-vl-4b-vs-zaya1-8b.md)\n- [Baidu Unlimited-OCR-2 vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/unlimited-ocr-2-vs-wevisone-vl-4b.md)\n", "meta": { "attribution": "Anchor Terminal (https://www.anchorterminal.com)", "attributionRequired": true, "credit": "CC BY 4.0, credit Anchor Terminal and link the page or API address you took it from (https://www.anchorterminal.com/terms/#data)", "docs": "https://www.anchorterminal.com/docs/", "generatedAt": "2026-11-11", "license": "CC-BY-4.0", "method": "https://www.anchorterminal.com/benchmark/", "methodology": "0.7", "openapi": "https://www.anchorterminal.com/openapi.json", "preview": false, "run": "2026-10-01", "runLabel": "October 2026 research run", "terms": "https://www.anchorterminal.com/terms/#data" }, "page": { "breadcrumbs": [ { "name": "Home", "url": "https://www.anchorterminal.com/" }, { "name": "Compare", "url": 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