{ "data": { "a": { "slug": "internvl3-5-gpt-oss-20b-a4b-preview", "name": "InternVL3.5-GPT-OSS-20B-A4B-Preview", "vendor": "OpenGVLab (Shanghai AI Laboratory)", "vendorUrl": "https://github.com/OpenGVLab", "kind": "weights", "category": "open-weights", "summary": "InternVL3.5-GPT-OSS-20B-A4B-Preview is OpenGVLab's open-weight vision-language preview that joins OpenAI's gpt-oss-20b mixture-of-experts model with the InternViT-300M vision encoder, 21.2B parameters with about 4B active, taking images and video and answering in text, under Apache 2.0.", "url": "https://www.anchorterminal.com/tools/internvl3-5-gpt-oss-20b-a4b-preview", "markdownUrl": "https://www.anchorterminal.com/tools/internvl3-5-gpt-oss-20b-a4b-preview.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/internvl3-5-gpt-oss-20b-a4b-preview.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/internvl3-5-gpt-oss-20b-a4b-preview.json", "dns": "internvl3-5-gpt-oss-20b-a4b-preview.grade.anchorterminal.com", "repo": "https://huggingface.co/OpenGVLab/InternVL3_5-GPT-OSS-20B-A4B-Preview-HF", "license": "Apache-2.0; a merge of openai/gpt-oss-20b (Apache 2.0 with OpenAI's usage policy) and OpenGVLab/InternViT-300M-448px-V2_5 (MIT)", "transports": [], "packages": [ { "registry": "huggingface", "name": "OpenGVLab/InternVL3_5-GPT-OSS-20B-A4B-Preview-HF" }, { "registry": "huggingface", "name": "OpenGVLab/InternVL3_5-GPT-OSS-20B-A4B-Preview" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. Neither the Transformers-format repository nor the GitHub-format one is gated (checked 2026-10-21).", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0, with gpt-oss-20b's Apache 2.0 and usage policy and InternViT's MIT licence carried with it. Running it costs the hardware, 42.5 GB of BF16 files in either format, since the gpt-oss experts were expanded from MXFP4 to BF16 (checked 2026-10-21).", "priceSummary": "Free", "where": "local", "x402": { "level": "no", "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-10-21).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-10-21" }, "docsUrl": "https://huggingface.co/OpenGVLab/InternVL3_5-GPT-OSS-20B-A4B-Preview-HF", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "moe", "vision", "video", "reasoning", "merge", "preview", "apache-2.0", "safetensors", "self-hosted" ], "lastRelease": "2025-09-08", "graded": true, "anchor": { "graded": true, "score": 53.2, "grade": "D", "agentReady": false, "rank": 0, "ranked": false, "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools", "rankOf": 1518, "categoryRank": 101, "methodology": "0.7", "run": "2026-10-01", "scores": { "interface": 45, "reach": 65, "risk": 55, "upkeep": 28 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-11-04" }, "negative": 0, "verdict": "An image and video reader on gpt-oss-20b with an optional thinking mode, a preview untouched for over a year. It needs about 51 GB in BF16, since OpenGVLab publishes no quantised build, and runs in Transformers and vLLM. The main risk is a chat template in the Transformers-format repository that doesn't match its tokenizer, with no safety results.", "bestFor": "Agents with 64 GB to spare that want an Apache-licensed image and video reader with optional thinking and can live with a stale preview.", "strengths": [ "Plain Apache 2.0 over Apache and MIT bases, with no gate", "Safetensors only and no repository code in the Transformers-format files", "Loads in released Transformers and vLLM, and the GitHub-format twin in SGLang, llama.cpp and Ollama through its llama.cpp pin", "Thinking turned on per request with a documented system prompt", "About 4B active parameters per token on a 21.2B mixture of experts" ], "weaknesses": [ "No quantised build from OpenGVLab, so about 51 GB at BF16", "Chat template uses markers its tokenizer lacks and differs from the twin's harmony template", "No change to the files since September 2025, and it's still a preview", "No safety results, agent guidance or security contact", "No documented tool calling or structured output, and trained at 32K tokens" ], "agentNotes": [ "Serve with `vllm serve OpenGVLab/InternVL3_5-GPT-OSS-20B-A4B-Preview-HF --max-model-len 32768`, which is our command; the card gives none for vLLM", "Check the rendered prompt before relying on it, since the shipped template's \u003c|im_start|\u003e markers aren't tokens in the gpt-oss tokenizer; the GitHub-format repository's harmony template is the alternative", "Use Transformers 4.55.0 or later, as the card requires for this size", "For thinking, send the card's thinking system prompt and parse the reasoning out of the \u003cthink\u003e tags, with temperature 0.6 and sampling on as the card advises", "Keep inputs within 32K tokens, the length it was trained at, whatever the config says" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 1.8, "history": [ { "basis": "public evidence", "confidence": "low", "grade": "D", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 53.2 } ], "editorialScores": { "interface": 45, "reach": 65, "risk": 55, "upkeep": 28 }, "provenanceScore": 61, "checked": { "categories": { "interface": "2026-11-04", "reach": "2026-11-04", "risk": "2026-11-04", "upkeep": "2026-11-04" }, "last": "2026-11-04", "lastMoved": "2026-11-04", "oldest": "2026-11-04", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "vllm serve OpenGVLab/InternVL3_5-GPT-OSS-20B-A4B-Preview-HF --max-model-len 32768", "http": "curl http://localhost:8000/v1/chat/completions \\\n -H 'Content-Type: application/json' \\\n -d '{\"model\": \"OpenGVLab/InternVL3_5-GPT-OSS-20B-A4B-Preview-HF\", \"messages\": [{\"role\": \"user\", \"content\": [{\"type\": \"image_url\", \"image_url\": {\"url\": \"https://example.com/chart.png\"}}, {\"type\": \"text\", \"text\": \"Describe this chart.\"}]}]}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/internvl3-5-gpt-oss-20b-a4b-preview" }, "weights": { "trainedBy": "OpenGVLab (Shanghai AI Laboratory)", "builtOn": "openai/gpt-oss-20b and OpenGVLab/InternViT-300M-448px-V2_5 (declared as a merge)", "params": "21.2B", "active": "4B", "context": 32768, "goodFor": "Vision-language preview for images, documents, charts and video with text out and an optional thinking mode, on the gpt-oss-20b mixture-of-experts backbone.", "runtimes": [ "Transformers", "vLLM", "SGLang", "llama.cpp", "Ollama" ], "builds": [ { "name": "InternVL3_5-GPT-OSS-20B-A4B-Preview-HF", "url": "https://huggingface.co/OpenGVLab/InternVL3_5-GPT-OSS-20B-A4B-Preview-HF", "who": "publisher", "precision": "bf16", "sizeGB": 42.5, "memoryGB": 64, "format": "safetensors", "runsOn": [ "Transformers", "vLLM" ], "note": "Transformers' native InternVL format, no repository code. The shipped chat template uses markers that aren't in the tokenizer" }, { "name": "InternVL3_5-GPT-OSS-20B-A4B-Preview", "url": "https://huggingface.co/OpenGVLab/InternVL3_5-GPT-OSS-20B-A4B-Preview", "who": "publisher", "precision": "bf16", "sizeGB": 42.5, "memoryGB": 64, "format": "safetensors", "runsOn": [ "SGLang", "llama.cpp (after conversion)", "Ollama (through its llama.cpp pin, after conversion)", "Transformers (remote code)" ], "note": "The same weights in OpenGVLab's GitHub format (InternVLChatModel) with modeling code for trust_remote_code and gpt-oss's harmony chat template" } ], "run": "vllm serve OpenGVLab/InternVL3_5-GPT-OSS-20B-A4B-Preview-HF --max-model-len 32768", "runBuild": "InternVL3_5-GPT-OSS-20B-A4B-Preview-HF", "checked": "2026-11-04", "minMemoryGB": 64 }, "family": { "lab": "OpenGVLab (Shanghai AI Laboratory)", "family": "InternVL3 (3.0, 3.5)", "url": "https://www.anchorterminal.com/open-weights/#opengvlab", "others": [ { "name": "InternVL3_5-8B-Flash", "url": "https://huggingface.co/OpenGVLab/InternVL3_5-8B-Flash", "params": "8B (Hub safetensors count 8.81B)" }, { "name": "InternVL3_5-4B-Flash", "url": "https://huggingface.co/OpenGVLab/InternVL3_5-4B-Flash", "params": "4B (Hub safetensors count 4.91B)" }, { "name": "InternVL3_5-38B-Flash", "url": "https://huggingface.co/OpenGVLab/InternVL3_5-38B-Flash", "params": "38B (Hub safetensors count 39.56B)" }, { "name": "InternVL3_5-30B-A3B-Flash", "url": "https://huggingface.co/OpenGVLab/InternVL3_5-30B-A3B-Flash", "params": "30B total, 3B active (MoE) (Hub safetensors count 30.99B)" }, { "name": "InternVL3_5-2B-Flash", "url": "https://huggingface.co/OpenGVLab/InternVL3_5-2B-Flash", "params": "2B (Hub safetensors count 2.49B)" }, { "name": "InternVL3_5-241B-A28B-Flash", "url": "https://huggingface.co/OpenGVLab/InternVL3_5-241B-A28B-Flash", "params": "241B total, 28B active (MoE) (Hub safetensors count 241.71B)" }, { "name": "InternVL3_5-1B-Flash", "url": "https://huggingface.co/OpenGVLab/InternVL3_5-1B-Flash", "params": "1B (Hub safetensors count 1.15B)" }, { "name": "InternVL3_5-14B-Flash", "url": "https://huggingface.co/OpenGVLab/InternVL3_5-14B-Flash", "params": "14B (Hub safetensors count 15.50B)" }, { "name": "InternVL3_5-8B-Pretrained", "url": "https://huggingface.co/OpenGVLab/InternVL3_5-8B-Pretrained", "params": "8B (Hub safetensors count 8.53B)" }, { "name": "InternVL3_5-8B-MPO", "url": "https://huggingface.co/OpenGVLab/InternVL3_5-8B-MPO", "params": "8B (Hub safetensors count 8.53B)" }, { "name": "InternVL3_5-8B-Instruct", "url": "https://huggingface.co/OpenGVLab/InternVL3_5-8B-Instruct", "params": "8B (Hub safetensors count 8.53B)" }, { "name": "InternVL3_5-8B", "url": "https://huggingface.co/OpenGVLab/InternVL3_5-8B", "params": "8B (Hub safetensors count 8.53B)" }, { "name": "InternVL3_5-4B-Pretrained", "url": "https://huggingface.co/OpenGVLab/InternVL3_5-4B-Pretrained", "params": "4B (Hub safetensors count 4.73B)" }, { "name": "InternVL3_5-4B-MPO", "url": "https://huggingface.co/OpenGVLab/InternVL3_5-4B-MPO", "params": "4B (Hub safetensors count 4.73B)" }, { "name": "InternVL3_5-4B-Instruct", "url": "https://huggingface.co/OpenGVLab/InternVL3_5-4B-Instruct", "params": "4B (Hub safetensors count 4.73B)" }, { "name": "InternVL3_5-4B", "url": "https://huggingface.co/OpenGVLab/InternVL3_5-4B", "params": "4B (Hub safetensors count 4.73B)" }, { "name": "InternVL3_5-38B-Pretrained", "url": "https://huggingface.co/OpenGVLab/InternVL3_5-38B-Pretrained", "params": "38B (Hub safetensors count 38.39B)" }, { "name": "InternVL3_5-38B-MPO", "url": "https://huggingface.co/OpenGVLab/InternVL3_5-38B-MPO", "params": "38B (Hub safetensors count 38.39B)" }, { "name": "InternVL3_5-38B-Instruct", "url": "https://huggingface.co/OpenGVLab/InternVL3_5-38B-Instruct", "params": "38B (Hub safetensors count 38.39B)" }, { "name": "InternVL3_5-38B", "url": "https://huggingface.co/OpenGVLab/InternVL3_5-38B", "params": "38B (Hub safetensors count 38.39B)" }, { "name": "InternVL3_5-30B-A3B-Pretrained", "url": "https://huggingface.co/OpenGVLab/InternVL3_5-30B-A3B-Pretrained", "params": "30B total, 3B active (MoE) (Hub safetensors count 30.85B)" }, { "name": "InternVL3_5-30B-A3B-MPO", "url": "https://huggingface.co/OpenGVLab/InternVL3_5-30B-A3B-MPO", "params": "30B total, 3B active (MoE) (Hub safetensors count 30.85B)" }, { "name": "InternVL3_5-30B-A3B-Instruct", "url": "https://huggingface.co/OpenGVLab/InternVL3_5-30B-A3B-Instruct", "params": "30B total, 3B active (MoE) (Hub safetensors count 30.85B)" }, { "name": "InternVL3_5-30B-A3B", "url": "https://huggingface.co/OpenGVLab/InternVL3_5-30B-A3B", "params": "30B total, 3B active (MoE) (Hub safetensors count 30.85B)" }, { "name": "InternVL3_5-2B-Pretrained", "url": "https://huggingface.co/OpenGVLab/InternVL3_5-2B-Pretrained", "params": "2B (Hub safetensors count 2.35B)" }, { "name": "InternVL3_5-2B-MPO", "url": "https://huggingface.co/OpenGVLab/InternVL3_5-2B-MPO", "params": "2B (Hub safetensors count 2.35B)" }, { "name": "InternVL3_5-2B-Instruct", "url": "https://huggingface.co/OpenGVLab/InternVL3_5-2B-Instruct", "params": "2B (Hub safetensors count 2.35B)" }, { "name": "InternVL3_5-2B", "url": "https://huggingface.co/OpenGVLab/InternVL3_5-2B", "params": "2B (Hub safetensors count 2.35B)" }, { "name": "InternVL3_5-241B-A28B-Pretrained", "url": "https://huggingface.co/OpenGVLab/InternVL3_5-241B-A28B-Pretrained", "params": "241B total, 28B active (MoE) (Hub safetensors count 240.70B)" }, { "name": "InternVL3_5-241B-A28B-MPO", "url": "https://huggingface.co/OpenGVLab/InternVL3_5-241B-A28B-MPO", "params": "241B total, 28B active (MoE) (Hub safetensors count 240.70B)" }, { "name": "InternVL3_5-241B-A28B-Instruct", "url": "https://huggingface.co/OpenGVLab/InternVL3_5-241B-A28B-Instruct", "params": "241B total, 28B active (MoE) (Hub safetensors count 240.70B)" }, { "name": "InternVL3_5-241B-A28B", "url": "https://huggingface.co/OpenGVLab/InternVL3_5-241B-A28B", "params": "241B total, 28B active (MoE) (Hub safetensors count 240.70B)" }, { "name": "InternVL3_5-1B-Pretrained", "url": "https://huggingface.co/OpenGVLab/InternVL3_5-1B-Pretrained", "params": "1B (Hub safetensors count 1.06B)" }, { "name": "InternVL3_5-1B-MPO", "url": "https://huggingface.co/OpenGVLab/InternVL3_5-1B-MPO", "params": "1B (Hub safetensors count 1.06B)" }, { "name": "InternVL3_5-1B-Instruct", "url": "https://huggingface.co/OpenGVLab/InternVL3_5-1B-Instruct", "params": "1B (Hub safetensors count 1.06B)" }, { "name": "InternVL3_5-1B", "url": "https://huggingface.co/OpenGVLab/InternVL3_5-1B", "params": "1B (Hub safetensors count 1.06B)" }, { "name": "InternVL3_5-14B-Pretrained", "url": "https://huggingface.co/OpenGVLab/InternVL3_5-14B-Pretrained", "params": "14B (Hub safetensors count 15.12B)" }, { "name": "InternVL3_5-14B-MPO", "url": "https://huggingface.co/OpenGVLab/InternVL3_5-14B-MPO", "params": "14B (Hub safetensors count 15.12B)" }, { "name": "InternVL3_5-14B-Instruct", "url": "https://huggingface.co/OpenGVLab/InternVL3_5-14B-Instruct", "params": "14B (Hub safetensors count 15.12B)" }, { "name": "InternVL3_5-14B", "url": "https://huggingface.co/OpenGVLab/InternVL3_5-14B", "params": "14B (Hub safetensors count 15.12B)" }, { "name": "InternVL3-9B-Pretrained", "url": "https://huggingface.co/OpenGVLab/InternVL3-9B-Pretrained", "params": "9B (Hub safetensors count 9.14B)" }, { "name": "InternVL3-8B-Pretrained", "url": "https://huggingface.co/OpenGVLab/InternVL3-8B-Pretrained", "params": "8B (Hub safetensors count 7.94B)" }, { "name": "InternVL3-78B-Pretrained", "url": "https://huggingface.co/OpenGVLab/InternVL3-78B-Pretrained", "params": "78B (Hub safetensors count 78.41B)" }, { "name": "InternVL3-38B-Pretrained", "url": "https://huggingface.co/OpenGVLab/InternVL3-38B-Pretrained", "params": "38B (Hub safetensors count 38.39B)" }, { "name": "InternVL3-2B-Pretrained", "url": "https://huggingface.co/OpenGVLab/InternVL3-2B-Pretrained", "params": "2B (Hub safetensors count 2.09B)" }, { "name": "InternVL3-1B-Pretrained", "url": "https://huggingface.co/OpenGVLab/InternVL3-1B-Pretrained", "params": "1B (Hub safetensors count 938M)" }, { "name": "InternVL3-14B-Pretrained", "url": "https://huggingface.co/OpenGVLab/InternVL3-14B-Pretrained", "params": "14B (Hub safetensors count 15.12B)" }, { "name": "InternVL3-9B-Instruct", "url": "https://huggingface.co/OpenGVLab/InternVL3-9B-Instruct", "params": "9B (Hub safetensors count 9.14B)" }, { "name": "InternVL3-8B-Instruct", "url": "https://huggingface.co/OpenGVLab/InternVL3-8B-Instruct", "params": "8B (Hub safetensors count 7.94B)" }, { "name": "InternVL3-78B-Instruct", "url": "https://huggingface.co/OpenGVLab/InternVL3-78B-Instruct", "params": "78B (Hub safetensors count 78.41B)" }, { "name": "InternVL3-38B-Instruct", "url": "https://huggingface.co/OpenGVLab/InternVL3-38B-Instruct", "params": "38B (Hub safetensors count 38.39B)" }, { "name": "InternVL3-2B-Instruct", "url": "https://huggingface.co/OpenGVLab/InternVL3-2B-Instruct", "params": "2B (Hub safetensors count 2.09B)" }, { "name": "InternVL3-1B-Instruct", "url": "https://huggingface.co/OpenGVLab/InternVL3-1B-Instruct", "params": "1B (Hub safetensors count 938M)" }, { "name": "InternVL3-14B-Instruct", "url": "https://huggingface.co/OpenGVLab/InternVL3-14B-Instruct", "params": "14B (Hub safetensors count 15.12B)" }, { "name": "InternVL3-9B", "url": "https://huggingface.co/OpenGVLab/InternVL3-9B", "params": "9B (Hub safetensors count 9.14B)" }, { "name": "InternVL3-8B", "url": "https://huggingface.co/OpenGVLab/InternVL3-8B", "params": "8B (Hub safetensors count 7.94B)" }, { "name": "InternVL3-78B", "url": "https://huggingface.co/OpenGVLab/InternVL3-78B", "params": "78B (Hub safetensors count 78.41B)" }, { "name": "InternVL3-38B", "url": "https://huggingface.co/OpenGVLab/InternVL3-38B", "params": "38B (Hub safetensors count 38.39B)" }, { "name": "InternVL3-2B", "url": "https://huggingface.co/OpenGVLab/InternVL3-2B", "params": "2B (Hub safetensors count 2.09B)" }, { "name": "InternVL3-1B", "url": "https://huggingface.co/OpenGVLab/InternVL3-1B", "params": "1B (Hub safetensors count 938M)" }, { "name": "InternVL3-14B", "url": "https://huggingface.co/OpenGVLab/InternVL3-14B", "params": "14B (Hub safetensors count 15.12B)" } ], "olderCount": 41 }, "area": "models", "provenance": { "legalEntity": "Shanghai AI Laboratory (OpenGVLab), not named in the repository", "domain": "github.com/OpenGVLab", "domainRegistered": "", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://huggingface.co/OpenGVLab/InternVL3_5-GPT-OSS-20B-A4B-Preview-HF/commits/main", "securityTxt": "none", "checked": "2026-11-04", "notes": [ "OpenGVLab's GitHub organisation profile links its Hugging Face organisation. The repositories carry no LICENSE file or copyright line, and shlab.org.cn's registration date wasn't checked.", "Nothing is hosted, so the Apache 2.0 licence stands in for terms of service and no privacy policy is scored.", "The changelog is the commit history, five commits from 29 August to 8 September 2025, all titled Upload folder using huggingface_hub, which don't say what changed. Unchanged on 4 November 2026.", "www.shlab.org.cn and internvl.github.io answered 404 for security.txt on 21 October 2026, and OpenGVLab/InternVL has no security policy." ], "score": 61 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/internvl3-5-gpt-oss-20b-a4b-preview.json", "live": { "slug": "internvl3-5-gpt-oss-20b-a4b-preview", "domain": { "domain": "github.com/opengvlab", "checkedAt": "2026-11-08T15:08:02.394346699Z" }, "pages": [ { "url": "https://huggingface.co/OpenGVLab/InternVL3_5-GPT-OSS-20B-A4B-Preview-HF/commits/main", "kind": "changelog", "status": 200, "checkedAt": "2026-11-10T16:22:18.761399377Z", "changedAt": "2026-11-10T16:22:18.761399377Z", "fingerprint": "e63419d9bf08" } ], "updatedAt": "2026-11-10T16:22:18.761399377Z" } }, "answer": "LFM3-VL-2B scores 61 (C) on agent readiness against InternVL3.5-GPT-OSS-20B-A4B-Preview's 53.2 (D), and leads in 3 of 4 scored categories. InternVL3.5-GPT-OSS-20B-A4B-Preview leads on risk.", "b": { "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" }, "edges": [ { "by": 49, "category": "upkeep", "leads": "lfm3-vl-2b", "name": "Documentation and upkeep", "reasons": { "a": "Model card 8 of 20, the family card misses limitations, context, languages and a cut-off. Training data 5, described without a cut-off. Last change 0, the last commit to either repository is 8 September 2025 and to OpenGVLab's reference code 22 September 2025, over a year before 4 November 2026. Responsiveness 10, OpenGVLab answered 3 of 8 questions across the two repositories, none since September 2025. Version policy 5, a first release. File health 0, the template uses markers the tokenizer doesn't have and contradicts the twin's template, and the card names the wrong language model in its licence line.", "b": "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." }, "scores": { "a": 28, "b": 77 } }, { "by": 45, "category": "interface", "leads": "lfm3-vl-2b", "name": "Agent interface", "reasons": { "a": "Chat template 20, a Jinja file in the repository, though it doesn't match the tokenizer. Tool-calling format 10 of 25, only the twin's inherited harmony template mentions tools. Trained tool calling 0, no evidence it was trained to call tools. Structured output 0. Reasoning control 10, a system prompt turns thinking on and the card says the reasoning comes back in think tags. Context 5, trained at 32K tokens.", "b": "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." }, "scores": { "a": 45, "b": 90 } }, { "by": 20, "category": "reach", "leads": "lfm3-vl-2b", "name": "Reach", "reasons": { "a": "Memory 15 of 30, the only builds are BF16 at 42.5 GB by the repository's safetensors index, or 51 GB with a fifth added, which fits 64 GB. Runtimes 25, Transformers and vLLM load this repository, and SGLang, llama.cpp and Ollama (through llama.cpp b11963, which Ollama v0.40.14 pins) run the same weights in their GitHub format. Builds 0, OpenGVLab publishes no quantised build. Terms 15, Apache 2.0 with nothing added by the bases. Download 10, not gated. Hosted 0, no provider serves it.", "b": "Memory 30, Liquid's Q4_K_M GGUF is 1.10 GB with its 0.20 GB projector file, and 1.30 GB × 1.2 = 1.6 GB, which fits 8 GB, well inside the 16 GB line. Runtimes 15, Released llama.cpp has the lfm3 architecture and the LFM3-VL projector (src/models/lfm3.cpp and tools/mtmd at b12242), Ollama's library has an lfm3-vl entry, and LM Studio's catalogue lists liquid/lfm3-vl-2b. Builds 15, Liquid's GGUF repository holds Q4_0, Q4_K_M, Q8_0, F16 and BF16 files, and Liquid publishes 4-bit and 8-bit MLX builds and an ONNX repository, all in the LiquidAI organisation. Terms 10, the LFM Open License v1.0, a custom licence that lets anyone use it commercially for free below a threshold of $10M annual revenue. Download 10, the Hub API reports gated false for the model and its GGUF, MLX and ONNX repositories on 10 November 2026, so the files download with no account and nothing to accept. Hosted 5, Liquid's own API lists lfm3-vl-2b among its models, so the publisher hosts it. 30 + 15 + 15 + 10 + 10 + 5 = 85." }, "scores": { "a": 65, "b": 85 } }, { "by": 15, "category": "risk", "leads": "internvl3-5-gpt-oss-20b-a4b-preview", "name": "Risk", "reasons": { "a": "File safety 25, safetensors only and no code in the repository. Provenance 15, the files are in the OpenGVLab organisation its GitHub links, with a checksum on every file. Safety evaluation 0 and agent guidance 0, nothing published. Licence risk 15, Apache 2.0 over Apache and MIT bases, with gpt-oss's usage policy asking only for lawful use. Security programme 0, no policy, security.txt or advisories.", "b": "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." }, "scores": { "a": 55, "b": 40 } } ], "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "OpenGVLab (Shanghai AI Laboratory)", "b": "Liquid AI", "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": "Apache-2.0; a merge of openai/gpt-oss-20b (Apache 2.0 with OpenAI's usage policy) and OpenGVLab/InternViT-300M-448px-V2_5 (MIT)", "b": "LFM Open License v1.0 (free commercial use below $10M annual revenue)", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "no", "b": "yes", "name": "llms.txt" }, { "a": "2025-09-08", "b": "2026-11-09", "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": "1.8/5 (4)", "b": "none", "name": "Agent reviews" } ], "faq": [ { "answer": "LFM3-VL-2B scores 61 (C) on agent readiness against InternVL3.5-GPT-OSS-20B-A4B-Preview's 53.2 (D), and leads in 3 of 4 scored categories. InternVL3.5-GPT-OSS-20B-A4B-Preview leads on risk.", "question": "Which is better for AI agents, InternVL3.5-GPT-OSS-20B-A4B-Preview or LFM3-VL-2B?" } ], "goodFor": [ { "aheadOn": [ "Risk, 55 against 40" ], "also": null, "goodFor": "Agents with 64 GB to spare that want an Apache-licensed image and video reader with optional thinking and can live with a stale preview.", "slug": "internvl3-5-gpt-oss-20b-a4b-preview", "watchFor": "No quantised build from OpenGVLab, so about 51 GB at BF16" }, { "aheadOn": [ "Reach, 85 against 65", "Agent interface, 90 against 45", "Documentation and upkeep, 77 against 28" ], "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" } ], "history": { "internvl3-5-gpt-oss-20b-a4b-preview": [ { "causes": [ "correction" ], "date": "2026-11-04", "delta": 2, "from": 51.2, "fromGrade": "D", "to": 53.2, "toGrade": "D" } ], "lfm3-vl-2b": null }, "job": { "capability": "model.open-weights", "name": "Open-weight models you run yourself" }, "others": [ { "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-internvl3-5-gpt-oss-20b-a4b-preview.json", "title": "Agents-A1-4B vs InternVL3.5-GPT-OSS-20B-A4B-Preview", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-internvl3-5-gpt-oss-20b-a4b-preview" }, { "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-lfm3-vl-2b.json", "title": "Agents-A1-4B vs LFM3-VL-2B", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-lfm3-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-internvl3-5-gpt-oss-20b-a4b-preview.json", "title": "Apertus v1.5 8B vs InternVL3.5-GPT-OSS-20B-A4B-Preview", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-internvl3-5-gpt-oss-20b-a4b-preview" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-lfm3-vl-2b.json", "title": "Apertus v1.5 8B vs LFM3-VL-2B", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-lfm3-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-internvl3-5-gpt-oss-20b-a4b-preview.json", "title": "Apodex-1.1-mini vs InternVL3.5-GPT-OSS-20B-A4B-Preview", "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-internvl3-5-gpt-oss-20b-a4b-preview" }, { "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-lfm3-vl-2b.json", "title": "Apodex-1.1-mini vs LFM3-VL-2B", "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-lfm3-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/arete-27b-vs-internvl3-5-gpt-oss-20b-a4b-preview.json", "title": "Arete-27B vs InternVL3.5-GPT-OSS-20B-A4B-Preview", "url": "https://www.anchorterminal.com/compare/arete-27b-vs-internvl3-5-gpt-oss-20b-a4b-preview" }, { "json": "https://www.anchorterminal.com/compare/arete-27b-vs-lfm3-vl-2b.json", "title": "Arete-27B vs LFM3-VL-2B", "url": "https://www.anchorterminal.com/compare/arete-27b-vs-lfm3-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-internvl3-5-gpt-oss-20b-a4b-preview.json", "title": "BigBang-v1 vs InternVL3.5-GPT-OSS-20B-A4B-Preview", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-internvl3-5-gpt-oss-20b-a4b-preview" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-lfm3-vl-2b.json", "title": "BigBang-v1 vs LFM3-VL-2B", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-lfm3-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/carbon-x-vs-internvl3-5-gpt-oss-20b-a4b-preview.json", "title": "Carbon-X vs InternVL3.5-GPT-OSS-20B-A4B-Preview", "url": "https://www.anchorterminal.com/compare/carbon-x-vs-internvl3-5-gpt-oss-20b-a4b-preview" }, { "json": "https://www.anchorterminal.com/compare/carbon-x-vs-lfm3-vl-2b.json", "title": "Carbon-X vs LFM3-VL-2B", "url": "https://www.anchorterminal.com/compare/carbon-x-vs-lfm3-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/cola2-vs-internvl3-5-gpt-oss-20b-a4b-preview.json", "title": "Cola2 vs InternVL3.5-GPT-OSS-20B-A4B-Preview", "url": "https://www.anchorterminal.com/compare/cola2-vs-internvl3-5-gpt-oss-20b-a4b-preview" }, { "json": "https://www.anchorterminal.com/compare/cola2-vs-lfm3-vl-2b.json", "title": "Cola2 vs LFM3-VL-2B", "url": "https://www.anchorterminal.com/compare/cola2-vs-lfm3-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-internvl3-5-gpt-oss-20b-a4b-preview.json", "title": "Cosmos-Reason2-2B vs InternVL3.5-GPT-OSS-20B-A4B-Preview", "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-internvl3-5-gpt-oss-20b-a4b-preview" }, { "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-lfm3-vl-2b.json", "title": "Cosmos-Reason2-2B vs LFM3-VL-2B", "url": 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{ "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-lfm3-vl-2b.json", "title": "Gemma 4 12B IT vs LFM3-VL-2B", "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-lfm3-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-internvl3-5-gpt-oss-20b-a4b-preview.json", "title": "Gemma 4 26B A4B IT vs InternVL3.5-GPT-OSS-20B-A4B-Preview", "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-internvl3-5-gpt-oss-20b-a4b-preview" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-lfm3-vl-2b.json", "title": "Gemma 4 26B A4B IT vs LFM3-VL-2B", "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-lfm3-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-internvl3-5-gpt-oss-20b-a4b-preview.json", "title": "Gemma 4 31B IT vs InternVL3.5-GPT-OSS-20B-A4B-Preview", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-internvl3-5-gpt-oss-20b-a4b-preview" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-lfm3-vl-2b.json", "title": "Gemma 4 31B IT vs LFM3-VL-2B", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-lfm3-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-internvl3-5-gpt-oss-20b-a4b-preview.json", "title": "Gemma 4 E2B IT vs InternVL3.5-GPT-OSS-20B-A4B-Preview", "url": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-internvl3-5-gpt-oss-20b-a4b-preview" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-lfm3-vl-2b.json", "title": "Gemma 4 E2B IT vs LFM3-VL-2B", "url": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-lfm3-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-internvl3-5-gpt-oss-20b-a4b-preview.json", "title": "Gemma 4 E4B IT vs InternVL3.5-GPT-OSS-20B-A4B-Preview", "url": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-internvl3-5-gpt-oss-20b-a4b-preview" }, { "json": 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vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-qwen3-8-27b.json", "title": "LFM3-VL-2B vs Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-qwen3-8-flash-next.json", "title": "LFM3-VL-2B vs Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-qwen3-coder-30b-a3b-instruct.json", "title": "LFM3-VL-2B vs Qwen3-Coder-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-qwen3-coder-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-qwen3-coder-next.json", "title": "LFM3-VL-2B vs Qwen3-Coder-Next", "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-qwen3-coder-next" }, { "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-qwen3-omni-30b-a3b-instruct.json", "title": "LFM3-VL-2B vs Qwen3-Omni-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-qwen3-omni-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-qwen3-vl-2b-instruct.json", "title": "LFM3-VL-2B vs Qwen3-VL-2B-Instruct", "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-qwen3-vl-2b-instruct" }, { "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-qwen3-vl-30b-a3b-instruct.json", "title": "LFM3-VL-2B vs Qwen3-VL-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-qwen3-vl-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-qwen3-vl-32b-instruct.json", "title": "LFM3-VL-2B vs Qwen3-VL-32B-Instruct", "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-qwen3-vl-32b-instruct" }, { "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-qwen3-vl-4b-instruct.json", "title": "LFM3-VL-2B vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-qwen3-vl-8b-instruct.json", "title": "LFM3-VL-2B vs Qwen3-VL-8B-Instruct", "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-qwen3-vl-8b-instruct" }, { "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-qwopus3-8-27b-flash-v2.json", "title": "LFM3-VL-2B vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-recursive-flint.json", "title": "LFM3-VL-2B vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-ripple-65b-preview.json", "title": "LFM3-VL-2B vs Ripple-65B Preview", "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-ripple-65b-preview" }, { "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-sensenova-loop-9b-preview.json", "title": "LFM3-VL-2B vs SenseNova-Loop-9B-Preview", "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-sensenova-loop-9b-preview" }, { "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-smollm3-3b.json", "title": "LFM3-VL-2B vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-spark-x2-6-4b.json", "title": "LFM3-VL-2B vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-step-3-7-flash.json", "title": "LFM3-VL-2B vs Step 3.7 Flash", "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-step-3-7-flash" }, { "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-swift-1-5-qwen3-8-27b.json", "title": "LFM3-VL-2B vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-swift-1-5-qwen3-8-flash-next.json", "title": "LFM3-VL-2B vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-thinkingcap-qwen3-8-27b.json", "title": "LFM3-VL-2B vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-unlimited-ocr-2.json", "title": "LFM3-VL-2B vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-wevisone-vl-2b.json", "title": "LFM3-VL-2B vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-wevisone-vl-4b.json", "title": "LFM3-VL-2B vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-zaya1-8b.json", "title": "LFM3-VL-2B vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-zaya1-8b" } ], "panel": null, "scores": [ { "key": "capability", "name": "Capability", "pending": true, "weight": 35 }, { "by": 20, "edge": "lfm3-vl-2b", "internvl3-5-gpt-oss-20b-a4b-preview": 65, "key": "reach", "lfm3-vl-2b": 85, "name": "Reach", "weight": 25 }, { "by": 15, "edge": "internvl3-5-gpt-oss-20b-a4b-preview", "internvl3-5-gpt-oss-20b-a4b-preview": 55, "key": "risk", "lfm3-vl-2b": 40, "name": "Risk", "weight": 20 }, { "by": 45, "edge": "lfm3-vl-2b", "internvl3-5-gpt-oss-20b-a4b-preview": 45, "key": "interface", "lfm3-vl-2b": 90, "name": "Agent interface", "weight": 10 }, { "by": 49, "edge": "lfm3-vl-2b", "internvl3-5-gpt-oss-20b-a4b-preview": 28, "key": "upkeep", "lfm3-vl-2b": 77, "name": "Documentation and upkeep", "weight": 10 } ], "summary": "LFM3-VL-2B scores 61 (C) on agent readiness against InternVL3.5-GPT-OSS-20B-A4B-Preview's 53.2 (D), and leads in 3 of 4 scored categories. InternVL3.5-GPT-OSS-20B-A4B-Preview leads on risk. Both do open-weight models you run yourself.", "tasks": null, "tutorials": [], "verdicts": { "internvl3-5-gpt-oss-20b-a4b-preview": "An image and video reader on gpt-oss-20b with an optional thinking mode, a preview untouched for over a year. It needs about 51 GB in BF16, since OpenGVLab publishes no quantised build, and runs in Transformers and vLLM. The main risk is a chat template in the Transformers-format repository that doesn't match its tokenizer, with no safety results.", "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." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-lfm3-vl-2b", "json": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-lfm3-vl-2b.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-lfm3-vl-2b.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-lfm3-vl-2b.min.md" }, "markdown": "LFM3-VL-2B scores 61 (C) on agent readiness against InternVL3.5-GPT-OSS-20B-A4B-Preview's 53.2 (D), and leads in 3 of 4 scored categories. InternVL3.5-GPT-OSS-20B-A4B-Preview leads on risk. Both do open-weight models you run yourself.\n\n- InternVL3.5-GPT-OSS-20B-A4B-Preview: grade D, 53.2/100, rank #136 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/internvl3-5-gpt-oss-20b-a4b-preview.md · JSON https://www.anchorterminal.com/api/v1/tools/internvl3-5-gpt-oss-20b-a4b-preview.json\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- 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### InternVL3.5-GPT-OSS-20B-A4B-Preview (D)\n\nGood for: Agents with 64 GB to spare that want an Apache-licensed image and video reader with optional thinking and can live with a stale preview.\n\nAhead on:\n- Risk, 55 against 40\n\nWatch for: No quantised build from OpenGVLab, so about 51 GB at BF16\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- Reach, 85 against 65\n- Agent interface, 90 against 45\n- Documentation and upkeep, 77 against 28\n\nWatch for: vLLM and SGLang support is unreleased, which the card says itself\n\n\n## Why the edge\n\n### Documentation and upkeep: LFM3-VL-2B +49\n\n- InternVL3.5-GPT-OSS-20B-A4B-Preview (28): Model card 8 of 20, the family card misses limitations, context, languages and a cut-off. Training data 5, described without a cut-off. Last change 0, the last commit to either repository is 8 September 2025 and to OpenGVLab's reference code 22 September 2025, over a year before 4 November 2026. Responsiveness 10, OpenGVLab answered 3 of 8 questions across the two repositories, none since September 2025. Version policy 5, a first release. File health 0, the template uses markers the tokenizer doesn't have and contradicts the twin's template, and the card names the wrong language model in its licence line.\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\n### Agent interface: LFM3-VL-2B +45\n\n- InternVL3.5-GPT-OSS-20B-A4B-Preview (45): Chat template 20, a Jinja file in the repository, though it doesn't match the tokenizer. Tool-calling format 10 of 25, only the twin's inherited harmony template mentions tools. Trained tool calling 0, no evidence it was trained to call tools. Structured output 0. Reasoning control 10, a system prompt turns thinking on and the card says the reasoning comes back in think tags. Context 5, trained at 32K tokens.\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\n### Reach: LFM3-VL-2B +20\n\n- InternVL3.5-GPT-OSS-20B-A4B-Preview (65): Memory 15 of 30, the only builds are BF16 at 42.5 GB by the repository's safetensors index, or 51 GB with a fifth added, which fits 64 GB. Runtimes 25, Transformers and vLLM load this repository, and SGLang, llama.cpp and Ollama (through llama.cpp b11963, which Ollama v0.40.14 pins) run the same weights in their GitHub format. Builds 0, OpenGVLab publishes no quantised build. Terms 15, Apache 2.0 with nothing added by the bases. Download 10, not gated. Hosted 0, no provider serves it.\n- LFM3-VL-2B (85): Memory 30, Liquid's Q4_K_M GGUF is 1.10 GB with its 0.20 GB projector file, and 1.30 GB × 1.2 = 1.6 GB, which fits 8 GB, well inside the 16 GB line. Runtimes 15, Released llama.cpp has the lfm3 architecture and the LFM3-VL projector (src/models/lfm3.cpp and tools/mtmd at b12242), Ollama's library has an lfm3-vl entry, and LM Studio's catalogue lists liquid/lfm3-vl-2b. Builds 15, Liquid's GGUF repository holds Q4_0, Q4_K_M, Q8_0, F16 and BF16 files, and Liquid publishes 4-bit and 8-bit MLX builds and an ONNX repository, all in the LiquidAI organisation. Terms 10, the LFM Open License v1.0, a custom licence that lets anyone use it commercially for free below a threshold of $10M annual revenue. Download 10, the Hub API reports gated false for the model and its GGUF, MLX and ONNX repositories on 10 November 2026, so the files download with no account and nothing to accept. Hosted 5, Liquid's own API lists lfm3-vl-2b among its models, so the publisher hosts it. 30 + 15 + 15 + 10 + 10 + 5 = 85.\n\n### Risk: InternVL3.5-GPT-OSS-20B-A4B-Preview +15\n\n- InternVL3.5-GPT-OSS-20B-A4B-Preview (55): File safety 25, safetensors only and no code in the repository. Provenance 15, the files are in the OpenGVLab organisation its GitHub links, with a checksum on every file. Safety evaluation 0 and agent guidance 0, nothing published. Licence risk 15, Apache 2.0 over Apache and MIT bases, with gpt-oss's usage policy asking only for lawful use. Security programme 0, no policy, security.txt or advisories.\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\n## Score by category\n\n| Category | Weight | InternVL3.5-GPT-OSS-20B-A4B-Preview | LFM3-VL-2B | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 65 | 85 | LFM3-VL-2B +20 |\n| Risk | 20% (20 this run) | 55 | 40 | InternVL3.5-GPT-OSS-20B-A4B-Preview +15 |\n| Agent interface | 10% (10 this run) | 45 | 90 | LFM3-VL-2B +45 |\n| Documentation and upkeep | 10% (10 this run) | 28 | 77 | LFM3-VL-2B +49 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **53.2 · D** | **61 · C** | |\n\n## Facts side by side\n\n| Fact | InternVL3.5-GPT-OSS-20B-A4B-Preview | LFM3-VL-2B |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | OpenGVLab (Shanghai AI Laboratory) | Liquid AI |\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 | Apache-2.0; a merge of openai/gpt-oss-20b (Apache 2.0 with OpenAI's usage policy) and OpenGVLab/InternViT-300M-448px-V2_5 (MIT) | LFM Open License v1.0 (free commercial use below $10M annual revenue) |\n| Read-only variant documented | no | no |\n| llms.txt | no | yes |\n| Last release | 2025-09-08 | 2026-11-09 |\n| Terms last updated | no document linked | no document linked |\n| Privacy policy last updated | no document linked | no document linked |\n| Agent reviews | 1.8/5 (4) | none |\n\n## How the scores have moved\n\n### InternVL3.5-GPT-OSS-20B-A4B-Preview\n\n- +2.0 on 4 November 2026: correction\n\n### LFM3-VL-2B\n\nNo score change since it was listed.\n\n## Verdicts\n\n**InternVL3.5-GPT-OSS-20B-A4B-Preview.** An image and video reader on gpt-oss-20b with an optional thinking mode, a preview untouched for over a year. It needs about 51 GB in BF16, since OpenGVLab publishes no quantised build, and runs in Transformers and vLLM. The main risk is a chat template in the Transformers-format repository that doesn't match its tokenizer, with no safety results.\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## Before you call either\n\n### InternVL3.5-GPT-OSS-20B-A4B-Preview\n\n1. Serve with `vllm serve OpenGVLab/InternVL3_5-GPT-OSS-20B-A4B-Preview-HF --max-model-len 32768`, which is our command; the card gives none for vLLM\n2. Check the rendered prompt before relying on it, since the shipped template's \u003c|im_start|\u003e markers aren't tokens in the gpt-oss tokenizer; the GitHub-format repository's harmony template is the alternative\n3. Use Transformers 4.55.0 or later, as the card requires for this size\n4. For thinking, send the card's thinking system prompt and parse the reasoning out of the \u003cthink\u003e tags, with temperature 0.6 and sampling on as the card advises\n5. Keep inputs within 32K tokens, the length it was trained at, whatever the config says\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## Questions\n\n### Which is better for AI agents, InternVL3.5-GPT-OSS-20B-A4B-Preview or LFM3-VL-2B?\n\nLFM3-VL-2B scores 61 (C) on agent readiness against InternVL3.5-GPT-OSS-20B-A4B-Preview's 53.2 (D), and leads in 3 of 4 scored categories. InternVL3.5-GPT-OSS-20B-A4B-Preview leads on risk.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-lfm3-vl-2b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-lfm3-vl-2b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"internvl3-5-gpt-oss-20b-a4b-preview\", \"b\": \"lfm3-vl-2b\"}`. From a terminal: `anchor compare internvl3-5-gpt-oss-20b-a4b-preview lfm3-vl-2b`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/internvl3-5-gpt-oss-20b-a4b-preview.json and https://www.anchorterminal.com/api/v1/tools/lfm3-vl-2b.json\n\n## Other comparisons with InternVL3.5-GPT-OSS-20B-A4B-Preview or LFM3-VL-2B\n\n- [Agents-A1-4B vs InternVL3.5-GPT-OSS-20B-A4B-Preview](https://www.anchorterminal.com/compare/agents-a1-4b-vs-internvl3-5-gpt-oss-20b-a4b-preview.md)\n- [Agents-A1-4B vs LFM3-VL-2B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-lfm3-vl-2b.md)\n- [Apertus v1.5 8B vs InternVL3.5-GPT-OSS-20B-A4B-Preview](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-internvl3-5-gpt-oss-20b-a4b-preview.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- [Apodex-1.1-mini vs InternVL3.5-GPT-OSS-20B-A4B-Preview](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-internvl3-5-gpt-oss-20b-a4b-preview.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- [Arete-27B vs InternVL3.5-GPT-OSS-20B-A4B-Preview](https://www.anchorterminal.com/compare/arete-27b-vs-internvl3-5-gpt-oss-20b-a4b-preview.md)\n- [Arete-27B vs LFM3-VL-2B](https://www.anchorterminal.com/compare/arete-27b-vs-lfm3-vl-2b.md)\n- [BigBang-v1 vs InternVL3.5-GPT-OSS-20B-A4B-Preview](https://www.anchorterminal.com/compare/bigbang-v1-vs-internvl3-5-gpt-oss-20b-a4b-preview.md)\n- [BigBang-v1 vs LFM3-VL-2B](https://www.anchorterminal.com/compare/bigbang-v1-vs-lfm3-vl-2b.md)\n- [Carbon-X vs InternVL3.5-GPT-OSS-20B-A4B-Preview](https://www.anchorterminal.com/compare/carbon-x-vs-internvl3-5-gpt-oss-20b-a4b-preview.md)\n- [Carbon-X vs LFM3-VL-2B](https://www.anchorterminal.com/compare/carbon-x-vs-lfm3-vl-2b.md)\n- [Cola2 vs InternVL3.5-GPT-OSS-20B-A4B-Preview](https://www.anchorterminal.com/compare/cola2-vs-internvl3-5-gpt-oss-20b-a4b-preview.md)\n- [Cola2 vs LFM3-VL-2B](https://www.anchorterminal.com/compare/cola2-vs-lfm3-vl-2b.md)\n- [Cosmos-Reason2-2B vs InternVL3.5-GPT-OSS-20B-A4B-Preview](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-internvl3-5-gpt-oss-20b-a4b-preview.md)\n- [Cosmos-Reason2-2B vs LFM3-VL-2B](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-lfm3-vl-2b.md)\n- [Darwin-36B-Opus vs InternVL3.5-GPT-OSS-20B-A4B-Preview](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-internvl3-5-gpt-oss-20b-a4b-preview.md)\n- [Darwin-36B-Opus vs LFM3-VL-2B](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-lfm3-vl-2b.md)\n- [DeepSeek-V3.2 vs InternVL3.5-GPT-OSS-20B-A4B-Preview](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-internvl3-5-gpt-oss-20b-a4b-preview.md)\n- [DeepSeek-V3.2 vs LFM3-VL-2B](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-lfm3-vl-2b.md)\n- [DeepSeek V4.1 Flash vs InternVL3.5-GPT-OSS-20B-A4B-Preview](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-internvl3-5-gpt-oss-20b-a4b-preview.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-Flash-0731 vs InternVL3.5-GPT-OSS-20B-A4B-Preview](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-internvl3-5-gpt-oss-20b-a4b-preview.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- [Devstral Small 2 vs InternVL3.5-GPT-OSS-20B-A4B-Preview](https://www.anchorterminal.com/compare/devstral-small-2-vs-internvl3-5-gpt-oss-20b-a4b-preview.md)\n- [Devstral Small 2 vs LFM3-VL-2B](https://www.anchorterminal.com/compare/devstral-small-2-vs-lfm3-vl-2b.md)\n- [DiffusionGemma 26B A4B IT vs InternVL3.5-GPT-OSS-20B-A4B-Preview](https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-internvl3-5-gpt-oss-20b-a4b-preview.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- [EXAONE 4.5 33B vs InternVL3.5-GPT-OSS-20B-A4B-Preview](https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-internvl3-5-gpt-oss-20b-a4b-preview.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- [Gemma 4 12B IT vs InternVL3.5-GPT-OSS-20B-A4B-Preview](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-internvl3-5-gpt-oss-20b-a4b-preview.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 26B A4B IT vs InternVL3.5-GPT-OSS-20B-A4B-Preview](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-internvl3-5-gpt-oss-20b-a4b-preview.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 31B IT vs InternVL3.5-GPT-OSS-20B-A4B-Preview](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-internvl3-5-gpt-oss-20b-a4b-preview.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 E2B IT vs InternVL3.5-GPT-OSS-20B-A4B-Preview](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-internvl3-5-gpt-oss-20b-a4b-preview.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 E4B IT vs InternVL3.5-GPT-OSS-20B-A4B-Preview](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-internvl3-5-gpt-oss-20b-a4b-preview.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- [GLM-4.5-Air vs InternVL3.5-GPT-OSS-20B-A4B-Preview](https://www.anchorterminal.com/compare/glm-4-5-air-vs-internvl3-5-gpt-oss-20b-a4b-preview.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-4.7-Flash vs InternVL3.5-GPT-OSS-20B-A4B-Preview](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-internvl3-5-gpt-oss-20b-a4b-preview.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-5.3-Flash vs InternVL3.5-GPT-OSS-20B-A4B-Preview](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-internvl3-5-gpt-oss-20b-a4b-preview.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 vs InternVL3.5-GPT-OSS-20B-A4B-Preview](https://www.anchorterminal.com/compare/glm-5-3-vs-internvl3-5-gpt-oss-20b-a4b-preview.md)\n- [GLM-5.3 vs LFM3-VL-2B](https://www.anchorterminal.com/compare/glm-5-3-vs-lfm3-vl-2b.md)\n- [gpt-oss-120b vs InternVL3.5-GPT-OSS-20B-A4B-Preview](https://www.anchorterminal.com/compare/gpt-oss-120b-vs-internvl3-5-gpt-oss-20b-a4b-preview.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-20b vs InternVL3.5-GPT-OSS-20B-A4B-Preview](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-internvl3-5-gpt-oss-20b-a4b-preview.md)\n- [gpt-oss-20b vs LFM3-VL-2B](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-lfm3-vl-2b.md)\n- [Granite 4.2 30B vs InternVL3.5-GPT-OSS-20B-A4B-Preview](https://www.anchorterminal.com/compare/granite-4-2-30b-vs-internvl3-5-gpt-oss-20b-a4b-preview.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 3B vs InternVL3.5-GPT-OSS-20B-A4B-Preview](https://www.anchorterminal.com/compare/granite-4-2-3b-vs-internvl3-5-gpt-oss-20b-a4b-preview.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 8B vs InternVL3.5-GPT-OSS-20B-A4B-Preview](https://www.anchorterminal.com/compare/granite-4-2-8b-vs-internvl3-5-gpt-oss-20b-a4b-preview.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- [Hy-Memory-30B-A3B vs InternVL3.5-GPT-OSS-20B-A4B-Preview](https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-internvl3-5-gpt-oss-20b-a4b-preview.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- [Hy3 vs InternVL3.5-GPT-OSS-20B-A4B-Preview](https://www.anchorterminal.com/compare/hy3-vs-internvl3-5-gpt-oss-20b-a4b-preview.md)\n- [Hy3 vs LFM3-VL-2B](https://www.anchorterminal.com/compare/hy3-vs-lfm3-vl-2b.md)\n- [Inkling-Small vs InternVL3.5-GPT-OSS-20B-A4B-Preview](https://www.anchorterminal.com/compare/inkling-small-vs-internvl3-5-gpt-oss-20b-a4b-preview.md)\n- [Inkling-Small vs LFM3-VL-2B](https://www.anchorterminal.com/compare/inkling-small-vs-lfm3-vl-2b.md)\n- [Inkling vs InternVL3.5-GPT-OSS-20B-A4B-Preview](https://www.anchorterminal.com/compare/inkling-vs-internvl3-5-gpt-oss-20b-a4b-preview.md)\n- [Inkling vs LFM3-VL-2B](https://www.anchorterminal.com/compare/inkling-vs-lfm3-vl-2b.md)\n- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs Kimi K3](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-kimi-k3.md)\n- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs Kimi K3.1 Pro](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-kimi-k3-1-pro.md)\n- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs Kolibri-1](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-kolibri-1.md)\n- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs Kronos](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-kronos.md)\n- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs Laguna M.1](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-laguna-m-1.md)\n- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs Laguna S 2.1](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-laguna-s-2-1.md)\n- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs Laguna XS 2.1](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-laguna-xs-2-1.md)\n- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs LBS-27B](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-lbs-27b.md)\n- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-lfm2-5-2-6b.md)\n- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs 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 LFM3-Audio-2B](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-lfm3-audio-2b.md)\n- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs LFM3-Omni-2B](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-lfm3-omni-2b.md)\n- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs Ling-3.0-tiny](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-ling-3-0-tiny.md)\n- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs Ling-3.1-flash-1019](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-ling-3-1-flash-1019.md)\n- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs LongCat-2.5](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-longcat-2-5.md)\n- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs MiMo-V2.6 Distill Qwen 9B](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-mimo-v2-6-distill-qwen-9b.md)\n- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs MiMo-V2.6-Flash-RL](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-mimo-v2-6-flash-rl.md)\n- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs MiniCPM-o 4.5](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-minicpm-o-4-5.md)\n- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs MiniCPM-V 4.7 1B](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-minicpm-v-4-7-1b.md)\n- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs MiniCPM-V 4.7 35B-A3B](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-minicpm-v-4-7-35b-a3b.md)\n- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs MiniCPM5-2B](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-minicpm5-2b.md)\n- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs MiniMax-M2.5](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-minimax-m2-5.md)\n- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs MiniMax-M2.7](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-minimax-m2-7.md)\n- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs 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