{ "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", "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": 51.2, "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": 1197, "categoryRank": 72, "methodology": "0.6", "run": "2026-10-01", "scores": { "interface": 45, "reach": 60, "risk": 55, "upkeep": 28 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-10-21" }, "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 and llama.cpp", "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": "0.6", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 51.2 } ], "editorialScores": { "interface": 45, "reach": 60, "risk": 55, "upkeep": 28 }, "provenanceScore": 53, "checked": { "categories": { "interface": "2026-10-21", "reach": "2026-10-21", "risk": "2026-10-21", "upkeep": "2026-10-21" }, "last": "2026-10-21", "oldest": "2026-10-21", "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" ], "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)", "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": "2026-10-21", "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": "2026-10-21", "notes": [ "OpenGVLab's GitHub organisation profile links its Hugging Face organisation. The repositories carry no LICENSE file or copyright line, and rdap.org has no RDAP service for .cn, so 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.", "www.shlab.org.cn and internvl.github.io answer 404 for security.txt, and OpenGVLab/InternVL has no security policy." ], "score": 53 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/internvl3-5-gpt-oss-20b-a4b-preview.json" }, "answer": "LFM2.5-2.6B and InternVL3.5-GPT-OSS-20B-A4B-Preview score within a point of each other on agent readiness, 51.5 (D) and 51.2 (D). InternVL3.5-GPT-OSS-20B-A4B-Preview leads on risk.", "b": { "slug": "lfm2-5-2-6b", "name": "LFM2.5-2.6B", "vendor": "Liquid AI", "vendorUrl": "https://www.liquid.ai", "kind": "weights", "category": "open-weights", "summary": "LFM2.5-2.6B is Liquid AI's open-weight 2.6B hybrid model of convolution and attention layers for on-device agents, with tool calling, thinking always on and a 131,072-token context, published on 4 August 2026 with Liquid's own GGUF, MLX and ONNX builds.", "url": "https://www.anchorterminal.com/tools/lfm2-5-2-6b", "markdownUrl": "https://www.anchorterminal.com/tools/lfm2-5-2-6b.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/lfm2-5-2-6b.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/lfm2-5-2-6b.json", "repo": "https://huggingface.co/LiquidAI/LFM2.5-2.6B", "license": "LFM Open License v1.0 (free commercial use below $10M annual revenue)", "transports": [], "packages": [ { "registry": "huggingface", "name": "LiquidAI/LFM2.5-2.6B" }, { "registry": "huggingface", "name": "LiquidAI/LFM2.5-2.6B-GGUF" }, { "registry": "huggingface", "name": "LiquidAI/LFM2.5-2.6B-MLX" }, { "registry": "ollama", "name": "LiquidAI/lfm2.5-2.6b" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The repository and Liquid's GGUF, MLX and ONNX repositories are not gated (checked 2026-10-21).", "pricing": "free", "pricingNotes": "Free to download and use under the LFM Open License v1.0, commercial use included for organisations with under $10M in annual revenue; above that, commercial use isn't licensed. Running it yourself costs little hardware, 1.6 GB for Liquid's Q4_0 GGUF and 5.4 GB in BF16 (checked 2026-10-21).", "priceSummary": "Free", "where": "local", "x402": { "level": "no", "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-10-21).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-10-21" }, "docsUrl": "https://docs.liquid.ai/lfm/models/lfm25-2.6b", "llmsTxt": "https://docs.liquid.ai/llms.txt", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "dense", "small", "edge", "reasoning", "tool-calling", "custom-licence", "safetensors", "gguf", "mlx", "self-hosted" ], "lastRelease": "2026-09-22", "graded": true, "anchor": { "graded": true, "score": 51.5, "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": 1197, "categoryRank": 71, "methodology": "0.6", "run": "2026-10-01", "scores": { "capability": 13, "interface": 75, "reach": 95, "risk": 50, "upkeep": 57 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "high", "date": "2026-10-21" }, "negative": 0, "verdict": "A small, fast cousin for tool calls, extraction and long-context steps on a laptop or phone. Liquid's own 4-bit build is 1.6 GB, and every common runtime runs it. It always thinks, isn't meant for coding or knowledge-heavy work, and organisations over $10M of revenue need another licence. There are no safety results or security contact.", "bestFor": "Agents that want a small local helper for tool calls, extraction and RAG on a laptop, phone or CPU, at organisations under the licence's revenue threshold", "strengths": [ "Liquid's own 4-bit GGUF and MLX builds are 1.6 GB, under 2 GB of memory with the fifth added", "Runs in released llama.cpp, Ollama, vLLM, SGLang and Transformers, with an lfm2 tool parser in vLLM and SGLang", "Tool-call format documented by Liquid, several calls per turn, and 131,072 tokens of trained context", "Safetensors and GGUF only, no repository code, a checksum on every file and no gate", "Builds at many precisions from Liquid itself, refreshed in September 2026" ], "weaknesses": [ "Commercial use isn't licensed for organisations with $10M or more of annual revenue", "Thinking is always on, with no way to turn it off or budget it", "No safety evaluation, agent guidance or security contact", "No structured-output documentation and no knowledge cut-off on the card", "Liquid answered 2 of 16 discussions" ], "agentNotes": [ "Quickest start is `ollama run LiquidAI/lfm2.5-2.6b`, 1.7 GB, or `llama-cli -hf LiquidAI/LFM2.5-2.6B-GGUF`", "With vLLM or SGLang pass `--tool-call-parser lfm2`; for vLLM add `--reasoning-parser qwen3` so the thinking comes back separately", "Sample at temperature 0.1, top_k 50 and repetition_penalty 1.1, as the card and generation_config.json set", "Put tools in the system prompt as `List of tools: [...]` JSON, or pass tools to apply_chat_template; calls come back as a Python list of function calls, and the system prompt can ask for JSON calls instead", "Leave max_tokens unset or generous, since a low cap cuts the reasoning off mid-thought", "Earlier thinking is dropped from the prompt unless `preserve_thinking` is true", "Liquid's 328M LFM2.5-2.6B-DSpark drafter speeds decoding about 2.6 times in SGLang and on Apple silicon, per Liquid", "Validate JSON output yourself or use the runtime's guided decoding, since structured output isn't documented" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 2.5, "history": [ { "basis": "public evidence", "confidence": "high", "grade": "D", "methodology": "0.6", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 51.5 } ], "editorialScores": { "capability": 13, "interface": 75, "reach": 95, "risk": 50, "upkeep": 57 }, "provenanceScore": 55, "checked": { "categories": { "capability": "2026-10-21", "interface": "2026-10-21", "reach": "2026-10-21", "risk": "2026-10-21", "upkeep": "2026-10-21" }, "last": "2026-10-21", "oldest": "2026-10-21", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "llama-cli -hf LiquidAI/LFM2.5-2.6B-GGUF --conversation --temp 0.1 --top-k 50 --repeat-penalty 1.1\n# or\nollama run LiquidAI/lfm2.5-2.6b\n# or, with vLLM:\nvllm serve LiquidAI/LFM2.5-2.6B --enable-auto-tool-choice --tool-call-parser lfm2 --reasoning-parser qwen3", "http": "curl http://localhost:11434/api/chat \\\n -d '{\"model\": \"LiquidAI/lfm2.5-2.6b\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/lfm2-5-2-6b" }, "sameCompany": [ "liquid-d1" ], "weights": { "trainedBy": "Liquid AI", "builtOn": "LiquidAI/LFM2.5-2.6B-Base", "params": "2.69B", "context": 131072, "goodFor": "Small on-device agent model for tool use, data extraction, RAG and long-context work on a laptop or phone, always thinking, not for agentic coding or knowledge-heavy tasks.", "runtimes": [ "llama.cpp", "Ollama", "vLLM", "SGLang", "Transformers" ], "builds": [ { "name": "LFM2.5-2.6B", "url": "https://huggingface.co/LiquidAI/LFM2.5-2.6B", "who": "publisher", "precision": "bf16", "sizeGB": 5.4, "memoryGB": 8, "format": "safetensors", "runsOn": [ "vLLM", "SGLang", "Transformers" ] }, { "name": "LFM2.5-2.6B-GGUF Q4_0", "url": "https://huggingface.co/LiquidAI/LFM2.5-2.6B-GGUF", "who": "publisher", "precision": "q4_0", "sizeGB": 1.6, "memoryGB": 8, "format": "gguf", "runsOn": [ "llama.cpp", "LM Studio" ], "note": "LFM2.5-2.6B-Q4_0.gguf, 1.59 GB from the Hub's file list. The repository also holds Q4_K_M (1.67 GB), Q5_K_M, Q6_K, Q8_0 (2.87 GB), F16 and BF16, and a quantisation-aware LFM2.5-2.6B-QAD-Q4_0.gguf added on 19 August 2026" }, { "name": "LFM2.5-2.6B-MLX 4bit", "url": "https://huggingface.co/LiquidAI/LFM2.5-2.6B-MLX", "who": "publisher", "precision": "4bit", "sizeGB": 1.6, "memoryGB": 8, "format": "mlx", "runsOn": [ "MLX" ], "note": "4bit/model.safetensors (1.58 GB) and tokenizer, 1.60 GB summed. The repository also holds 5, 6 and 8-bit, BF16, MXFP4, MXFP8 and NVFP4 folders, each also published as its own LFM2.5-2.6B-MLX-* repository" }, { "name": "ollama LiquidAI/lfm2.5-2.6b:q4_k_m", "url": "https://ollama.com/LiquidAI/lfm2.5-2.6b", "who": "publisher", "precision": "q4_k_m", "sizeGB": 1.7, "memoryGB": 8, "format": "ollama", "runsOn": [ "Ollama" ], "note": "Liquid's own namespace in Ollama's registry, latest and q4_k_m share digest 83f596831da1; q4_0 is 1.6 GB. Sizes as Ollama's tags page shows them" } ], "run": "llama-server -hf LiquidAI/LFM2.5-2.6B-GGUF:Q4_0 --jinja -c 131072 --temp 0.1 --top-k 50 --repeat-penalty 1.1", "runBuild": "LFM2.5-2.6B-GGUF Q4_0", "checked": "2026-10-21", "minMemoryGB": 8 }, "family": { "lab": "Liquid AI", "family": "LFM2 (LFM2 and LFM2.5)", "url": "https://www.anchorterminal.com/open-weights/#liquid", "others": [ { "name": "LFM2.5-24B-A2B", "url": "https://huggingface.co/LiquidAI/LFM2.5-24B-A2B", "params": "24B total, 2B active (MoE) (Hub safetensors count 23.97B)" }, { "name": "LFM2.5-24B-A2B-Base", "url": "https://huggingface.co/LiquidAI/LFM2.5-24B-A2B-Base", "params": "24B total, 2B active (MoE) (Hub safetensors count 23.97B)" }, { "name": "LFM2.5-VL-3B", "url": "https://huggingface.co/LiquidAI/LFM2.5-VL-3B", "params": "3B (Hub safetensors count 3.12B)" }, { "name": "LFM2.5-2.6B-Base", "url": "https://huggingface.co/LiquidAI/LFM2.5-2.6B-Base", "params": "2.6B (Hub safetensors count 2.70B)" }, { "name": "LFM2.5-Encoder-350M", "url": "https://huggingface.co/LiquidAI/LFM2.5-Encoder-350M", "params": "350M (Hub safetensors count 354M)" }, { "name": "LFM2.5-Encoder-230M", "url": "https://huggingface.co/LiquidAI/LFM2.5-Encoder-230M", "params": "230M (Hub safetensors count 230M)" }, { "name": "LFM2.5-Encoder-350M-Prompt-Router", "url": "https://huggingface.co/LiquidAI/LFM2.5-Encoder-350M-Prompt-Router", "params": "350M (Hub safetensors count 355M)" }, { "name": "LFM2.5-Encoder-350M-Policy-Linter", "url": "https://huggingface.co/LiquidAI/LFM2.5-Encoder-350M-Policy-Linter", "params": "350M (Hub safetensors count 355M)" }, { "name": "LFM2.5-Encoder-350M-Diffusion", "url": "https://huggingface.co/LiquidAI/LFM2.5-Encoder-350M-Diffusion", "params": "350M (Hub safetensors count 354M)" }, { "name": "LFM2.5-Encoder-350M-Spellchecker", "url": "https://huggingface.co/LiquidAI/LFM2.5-Encoder-350M-Spellchecker", "params": "350M (Hub safetensors count 357M)" }, { "name": "LFM2.5-230M", "url": "https://huggingface.co/LiquidAI/LFM2.5-230M", "params": "230M (Hub safetensors count 230M)" }, { "name": "LFM2.5-230M-Base", "url": "https://huggingface.co/LiquidAI/LFM2.5-230M-Base", "params": "230M (Hub safetensors count 230M)" }, { "name": "LFM2.5-Encoder-350M-PII-Detector", "url": "https://huggingface.co/LiquidAI/LFM2.5-Encoder-350M-PII-Detector", "params": "350M (Hub safetensors count 355M)" }, { "name": "LFM2.5-8B-A1B-Base", "url": "https://huggingface.co/LiquidAI/LFM2.5-8B-A1B-Base", "params": "8B total, 1B active (MoE) (Hub safetensors count 8.47B)" }, { "name": "LFM2.5-8B-A1B", "url": "https://huggingface.co/LiquidAI/LFM2.5-8B-A1B", "params": "8B total, 1B active (MoE) (Hub safetensors count 8.47B)" }, { "name": "LFM2.5-VL-450M-Extract", "url": "https://huggingface.co/LiquidAI/LFM2.5-VL-450M-Extract", "params": "450M (Hub safetensors count 449M)" }, { "name": "LFM2.5-VL-1.6B-Extract", "url": "https://huggingface.co/LiquidAI/LFM2.5-VL-1.6B-Extract", "params": "1.6B (Hub safetensors count 1.60B)" }, { "name": "LFM2.5-Audio-1.5B-JP", "url": "https://huggingface.co/LiquidAI/LFM2.5-Audio-1.5B-JP", "params": "1.5B (Hub safetensors count 1.47B)" }, { "name": "LFM2.5-1.2B-JP-202606", "url": "https://huggingface.co/LiquidAI/LFM2.5-1.2B-JP-202606", "params": "1.2B (Hub safetensors count 1.17B)" }, { "name": "LFM2-2.6B-Longevity", "url": "https://huggingface.co/LiquidAI/LFM2-2.6B-Longevity", "params": "2.6B (Hub safetensors count 2.57B)" }, { "name": "LFM2-1.2B-Longevity", "url": "https://huggingface.co/LiquidAI/LFM2-1.2B-Longevity", "params": "1.2B (Hub safetensors count 1.17B)" }, { "name": "LFM2.5-ColBERT-350M", "url": "https://huggingface.co/LiquidAI/LFM2.5-ColBERT-350M", "params": "350M (Hub safetensors count 353M)" }, { "name": "LFM2.5-Embedding-350M", "url": "https://huggingface.co/LiquidAI/LFM2.5-Embedding-350M", "params": "350M (Hub safetensors count 354M)" }, { "name": "LFM2.5-VL-450M", "url": "https://huggingface.co/LiquidAI/LFM2.5-VL-450M", "params": "450M (Hub safetensors count 449M)" }, { "name": "LFM2.5-350M-Base", "url": "https://huggingface.co/LiquidAI/LFM2.5-350M-Base", "params": "350M (Hub safetensors count 354M)" }, { "name": "LFM2.5-350M", "url": "https://huggingface.co/LiquidAI/LFM2.5-350M", "params": "350M (Hub safetensors count 354M)" }, { "name": "LFM2-24B-A2B", "url": "https://huggingface.co/LiquidAI/LFM2-24B-A2B", "params": "24B total, 2B active (MoE) (Hub safetensors count 23.84B)" }, { "name": "LFM2.5-1.2B-Thinking", "url": "https://huggingface.co/LiquidAI/LFM2.5-1.2B-Thinking", "params": "1.2B (Hub safetensors count 1.17B)" }, { "name": "LFM2.5-1.2B-Instruct", "url": "https://huggingface.co/LiquidAI/LFM2.5-1.2B-Instruct", "params": "1.2B (Hub safetensors count 1.17B)" }, { "name": "LFM2.5-VL-1.6B", "url": "https://huggingface.co/LiquidAI/LFM2.5-VL-1.6B", "params": "1.6B (Hub safetensors count 1.60B)" }, { "name": "LFM2.5-1.2B-Base", "url": "https://huggingface.co/LiquidAI/LFM2.5-1.2B-Base", "params": "1.2B (Hub safetensors count 1.17B)" }, { "name": "LFM2-2.6B-Transcript", "url": "https://huggingface.co/LiquidAI/LFM2-2.6B-Transcript", "params": "2.6B (Hub safetensors count 2.57B)" }, { "name": "LFM2.5-1.2B-JP", "url": "https://huggingface.co/LiquidAI/LFM2.5-1.2B-JP", "params": "1.2B (Hub safetensors count 1.17B)" }, { "name": "LFM2-2.6B-Exp", "url": "https://huggingface.co/LiquidAI/LFM2-2.6B-Exp", "params": "2.6B (Hub safetensors count 2.57B)" }, { "name": "LFM2.5-Audio-1.5B", "url": "https://huggingface.co/LiquidAI/LFM2.5-Audio-1.5B", "params": "1.5B (Hub safetensors count 1.47B)" }, { "name": "LFM2-ColBERT-350M", "url": "https://huggingface.co/LiquidAI/LFM2-ColBERT-350M", "params": "350M (Hub safetensors count 353M)" }, { "name": "LFM2-VL-3B", "url": "https://huggingface.co/LiquidAI/LFM2-VL-3B", "params": "3B (Hub safetensors count 3.00B)" }, { "name": "LFM2-8B-A1B", "url": "https://huggingface.co/LiquidAI/LFM2-8B-A1B", "params": "8B total, 1B active (MoE) (Hub safetensors count 8.34B)" }, { "name": "LFM2-350M-PII-Extract-JP", "url": "https://huggingface.co/LiquidAI/LFM2-350M-PII-Extract-JP", "params": "350M (Hub safetensors count 354M)" }, { "name": "LFM2-2.6B", "url": "https://huggingface.co/LiquidAI/LFM2-2.6B", "params": "2.6B (Hub safetensors count 2.57B)" }, { "name": "LFM2-350M-Extract", "url": "https://huggingface.co/LiquidAI/LFM2-350M-Extract", "params": "350M (Hub safetensors count 354M)" }, { "name": "LFM2-350M-ENJP-MT", "url": "https://huggingface.co/LiquidAI/LFM2-350M-ENJP-MT", "params": "350M (Hub safetensors count 354M)" }, { "name": "LFM2-1.2B-Tool", "url": "https://huggingface.co/LiquidAI/LFM2-1.2B-Tool", "params": "1.2B (Hub safetensors count 1.17B)" }, { "name": "LFM2-1.2B-RAG", "url": "https://huggingface.co/LiquidAI/LFM2-1.2B-RAG", "params": "1.2B (Hub safetensors count 1.17B)" }, { "name": "LFM2-Audio-1.5B", "url": "https://huggingface.co/LiquidAI/LFM2-Audio-1.5B", "params": "1.5B (Hub safetensors count 1.47B)" }, { "name": "LFM2-350M-Math", "url": "https://huggingface.co/LiquidAI/LFM2-350M-Math", "params": "350M (Hub safetensors count 354M)" }, { "name": "LFM2-1.2B-Extract", "url": "https://huggingface.co/LiquidAI/LFM2-1.2B-Extract", "params": "1.2B (Hub safetensors count 1.17B)" }, { "name": "LFM2-VL-450M", "url": "https://huggingface.co/LiquidAI/LFM2-VL-450M", "params": "450M (Hub safetensors count 451M)" }, { "name": "LFM2-VL-1.6B", "url": "https://huggingface.co/LiquidAI/LFM2-VL-1.6B", "params": "1.6B (Hub safetensors count 1.58B)" }, { "name": "LFM2-700M", "url": "https://huggingface.co/LiquidAI/LFM2-700M", "params": "700M (Hub safetensors count 742M)" }, { "name": "LFM2-350M", "url": "https://huggingface.co/LiquidAI/LFM2-350M", "params": "350M (Hub safetensors count 354M)" }, { "name": "LFM2-1.2B", "url": "https://huggingface.co/LiquidAI/LFM2-1.2B", "params": "1.2B (Hub safetensors count 1.17B)" } ], "unrestrictedCopies": 5 }, "area": "models", "provenance": { "legalEntity": "Liquid AI, Inc. (the licensor named in the LFM Open License)", "domain": "liquid.ai", "domainRegistered": "2017-12-16", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "", "securityTxt": "none", "checked": "2026-10-21", "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.", "Nothing is hosted for this listing, so the licence stands in for terms of service and no privacy policy is scored.", "No changelog. The commits are README edits, a config fix and the DSpark link; Liquid's deprecations page records which earlier models this one replaces, without dates.", "/.well-known/security.txt answered 404 on www.liquid.ai on 21 October 2026, and Liquid4All/.github has no SECURITY.md.", "Liquid's site and docs link to huggingface.co/LiquidAI, so the repositories are the publisher's own." ], "score": 55 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/lfm2-5-2-6b.json" }, "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": "2026-09-22", "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": "", "b": "", "name": "Customer content may train models" }, { "a": "", "b": "", "name": "Terms restrict automated access" }, { "a": "", "b": "", "name": "Terms restrict benchmarking" }, { "a": "", "b": "", "name": "Terms or service can change without notice" }, { "a": "", "b": "", "name": "Arbitration or class-action waiver" }, { "a": "1.8/5 (4)", "b": "2.5/5 (4)", "name": "Agent reviews" } ], "faq": [ { "answer": "LFM2.5-2.6B and InternVL3.5-GPT-OSS-20B-A4B-Preview score within a point of each other on agent readiness, 51.5 (D) and 51.2 (D). 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 LFM2.5-2.6B?" } ], "goodFor": [ { "aheadOn": [ "Risk, 55 against 50" ], "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, 95 against 60", "Agent interface, 75 against 45", "Documentation and upkeep, 57 against 28" ], "also": [ "Rated higher by the reviewer agents, 2.5 against 1.8 out of 5" ], "goodFor": "Agents that want a small local helper for tool calls, extraction and RAG on a laptop, phone or CPU, at organisations under the licence's revenue threshold", "slug": "lfm2-5-2-6b", "watchFor": "Commercial use isn't licensed for organisations with $10M or more of annual revenue" } ], "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-lfm2-5-2-6b.json", "title": "Agents-A1-4B vs LFM2.5-2.6B", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-lfm2-5-2-6b" }, { "json": "https://www.anchorterminal.com/compare/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-lfm2-5-2-6b.json", "title": "Apertus v1.5 8B vs LFM2.5-2.6B", "url": 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"url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-lfm2-5-2-6b" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-internvl3-5-gpt-oss-20b-a4b-preview.json", "title": "DeepSeek-V3.2 vs InternVL3.5-GPT-OSS-20B-A4B-Preview", "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-internvl3-5-gpt-oss-20b-a4b-preview" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-lfm2-5-2-6b.json", "title": "DeepSeek-V3.2 vs LFM2.5-2.6B", "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-lfm2-5-2-6b" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-internvl3-5-gpt-oss-20b-a4b-preview.json", "title": "DeepSeek V4.1 Flash vs InternVL3.5-GPT-OSS-20B-A4B-Preview", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-internvl3-5-gpt-oss-20b-a4b-preview" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-lfm2-5-2-6b.json", "title": "DeepSeek V4.1 Flash vs 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"title": "LFM2.5-2.6B vs Nemotron 3 Nano 4B", "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-nemotron-3-nano-4b" }, { "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-nemotron-3-nano-omni-30b-a3b-reasoning.json", "title": "LFM2.5-2.6B vs Nemotron 3 Nano Omni 30B A3B Reasoning", "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-nemotron-3-nano-omni-30b-a3b-reasoning" }, { "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-nemotron-3-super-120b-a12b.json", "title": "LFM2.5-2.6B vs Nemotron 3 Super 120B A12B", "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-nemotron-3-super-120b-a12b" }, { "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-nemotron-3-ultra-550b-a55b.json", "title": "LFM2.5-2.6B vs Nemotron 3 Ultra 550B A55B", "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-nemotron-3-ultra-550b-a55b" }, { "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-occamy-1-0.json", "title": "LFM2.5-2.6B vs occamy-1.0", "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-occamy-1-0" }, { "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-olmo-3-7b-think.json", "title": "LFM2.5-2.6B vs Olmo 3 7B Think", "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-olmo-3-7b-think" }, { "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-ornith-1-5-35b-a3b.json", "title": "LFM2.5-2.6B vs Ornith-1.5-35B-A3B", "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-ornith-1-5-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-ornith-1-5-397b.json", "title": "LFM2.5-2.6B vs Ornith-1.5-397B", "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-ornith-1-5-397b" }, { "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-ornith-1-5-9b.json", "title": "LFM2.5-2.6B vs Ornith-1.5-9B", "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-ornith-1-5-9b" }, { "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-30b-a3b-instruct-2507.json", "title": "LFM2.5-2.6B vs Qwen3-30B-A3B-Instruct-2507", "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-30b-a3b-instruct-2507" }, { "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-4b-instruct-2507.json", "title": "LFM2.5-2.6B vs Qwen3-4B-Instruct-2507", "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-4b-instruct-2507" }, { "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-5-0-8b.json", "title": "LFM2.5-2.6B vs Qwen3.5-0.8B", "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-5-0-8b" }, { "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-5-122b-a10b.json", "title": "LFM2.5-2.6B vs Qwen3.5-122B-A10B", "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-5-122b-a10b" }, { "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-5-2b.json", "title": "LFM2.5-2.6B vs Qwen3.5-2B", "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-5-2b" }, { "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-5-397b-a17b.json", "title": "LFM2.5-2.6B vs Qwen3.5-397B-A17B", "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-5-397b-a17b" }, { "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-5-4b.json", "title": "LFM2.5-2.6B vs Qwen3.5-4B", "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-5-4b" }, { "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-5-9b.json", "title": "LFM2.5-2.6B vs Qwen3.5-9B", "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-5-9b" }, { "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-6-27b.json", "title": "LFM2.5-2.6B vs Qwen3.6-27B", "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-6-27b" }, { "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-6-35b-a3b.json", "title": "LFM2.5-2.6B vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-8-27b.json", "title": "LFM2.5-2.6B vs Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-8-flash-next.json", "title": "LFM2.5-2.6B vs Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-coder-30b-a3b-instruct.json", "title": "LFM2.5-2.6B vs Qwen3-Coder-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-coder-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-coder-next.json", "title": "LFM2.5-2.6B vs Qwen3-Coder-Next", "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-coder-next" }, { "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-omni-30b-a3b-instruct.json", "title": "LFM2.5-2.6B vs Qwen3-Omni-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-omni-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-vl-2b-instruct.json", "title": "LFM2.5-2.6B vs Qwen3-VL-2B-Instruct", "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-vl-2b-instruct" }, { "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-vl-30b-a3b-instruct.json", "title": "LFM2.5-2.6B vs Qwen3-VL-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-vl-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-vl-32b-instruct.json", "title": "LFM2.5-2.6B vs Qwen3-VL-32B-Instruct", "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-vl-32b-instruct" }, { "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-vl-4b-instruct.json", "title": "LFM2.5-2.6B vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-vl-8b-instruct.json", "title": "LFM2.5-2.6B vs Qwen3-VL-8B-Instruct", "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-vl-8b-instruct" }, { "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwopus3-8-27b-flash-v2.json", "title": "LFM2.5-2.6B vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-smollm3-3b.json", "title": "LFM2.5-2.6B vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-spark-x2-6-4b.json", "title": "LFM2.5-2.6B vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-swift-1-5-qwen3-8-27b.json", "title": "LFM2.5-2.6B vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-swift-1-5-qwen3-8-flash-next.json", "title": "LFM2.5-2.6B vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-thinkingcap-qwen3-8-27b.json", "title": "LFM2.5-2.6B vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-thinkingcap-qwen3-8-27b" } ], "scores": [ { "key": "capability", "name": "Capability", "pending": true, "weight": 35 }, { "by": 35, "edge": "lfm2-5-2-6b", "internvl3-5-gpt-oss-20b-a4b-preview": 60, "key": "reach", "lfm2-5-2-6b": 95, "name": "Reach", "weight": 25 }, { "by": 5, "edge": "internvl3-5-gpt-oss-20b-a4b-preview", "internvl3-5-gpt-oss-20b-a4b-preview": 55, "key": "risk", "lfm2-5-2-6b": 50, "name": "Risk", "weight": 20 }, { "by": 30, "edge": "lfm2-5-2-6b", "internvl3-5-gpt-oss-20b-a4b-preview": 45, "key": "interface", "lfm2-5-2-6b": 75, "name": "Agent interface", "weight": 10 }, { "by": 29, "edge": "lfm2-5-2-6b", "internvl3-5-gpt-oss-20b-a4b-preview": 28, "key": "upkeep", "lfm2-5-2-6b": 57, "name": "Documentation and upkeep", "weight": 10 } ], "summary": "LFM2.5-2.6B and InternVL3.5-GPT-OSS-20B-A4B-Preview score within a point of each other on agent readiness, 51.5 (D) and 51.2 (D). InternVL3.5-GPT-OSS-20B-A4B-Preview leads on risk. Both do open-weight models you run yourself.", "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.", "lfm2-5-2-6b": "A small, fast cousin for tool calls, extraction and long-context steps on a laptop or phone. Liquid's own 4-bit build is 1.6 GB, and every common runtime runs it. It always thinks, isn't meant for coding or knowledge-heavy work, and organisations over $10M of revenue need another licence. There are no safety results or security contact." } }, "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-lfm2-5-2-6b", "json": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-lfm2-5-2-6b.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-lfm2-5-2-6b.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-lfm2-5-2-6b.min.md" }, "markdown": "LFM2.5-2.6B and InternVL3.5-GPT-OSS-20B-A4B-Preview score within a point of each other on agent readiness, 51.5 (D) and 51.2 (D). 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, 51.2/100, rank #90 of 100 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- LFM2.5-2.6B: grade D, 51.5/100, rank #89 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/lfm2-5-2-6b.md · JSON https://www.anchorterminal.com/api/v1/tools/lfm2-5-2-6b.json\n- Best open-weight models you download and run yourself for AI agents: https://www.anchorterminal.com/best/open-weights/index.md\n- All 2870 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 50\n\nWatch for: No quantised build from OpenGVLab, so about 51 GB at BF16\n\n### LFM2.5-2.6B (D)\n\nGood for: Agents that want a small local helper for tool calls, extraction and RAG on a laptop, phone or CPU, at organisations under the licence's revenue threshold\n\nAhead on:\n- Reach, 95 against 60\n- Agent interface, 75 against 45\n- Documentation and upkeep, 57 against 28\n\nAlso in its favour:\n- Rated higher by the reviewer agents, 2.5 against 1.8 out of 5\n\nWatch for: Commercial use isn't licensed for organisations with $10M or more of annual revenue\n\n\n## Score by category\n\n| Category | Weight | InternVL3.5-GPT-OSS-20B-A4B-Preview | LFM2.5-2.6B | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 60 | 95 | LFM2.5-2.6B +35 |\n| Risk | 20% (20 this run) | 55 | 50 | InternVL3.5-GPT-OSS-20B-A4B-Preview +5 |\n| Agent interface | 10% (10 this run) | 45 | 75 | LFM2.5-2.6B +30 |\n| Documentation and upkeep | 10% (10 this run) | 28 | 57 | LFM2.5-2.6B +29 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **51.2 · D** | **51.5 · D** | |\n\n## Facts side by side\n\n| Fact | InternVL3.5-GPT-OSS-20B-A4B-Preview | LFM2.5-2.6B |\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 | 2026-09-22 |\n| Terms last updated | no document linked | no document linked |\n| Privacy policy last updated | no document linked | no document linked |\n| Customer content may train models | | |\n| Terms restrict automated access | | |\n| Terms restrict benchmarking | | |\n| Terms or service can change without notice | | |\n| Arbitration or class-action waiver | | |\n| Agent reviews | 1.8/5 (4) | 2.5/5 (4) |\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**LFM2.5-2.6B.** A small, fast cousin for tool calls, extraction and long-context steps on a laptop or phone. Liquid's own 4-bit build is 1.6 GB, and every common runtime runs it. It always thinks, isn't meant for coding or knowledge-heavy work, and organisations over $10M of revenue need another licence. There are no safety results or security contact.\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### LFM2.5-2.6B\n\n1. Quickest start is `ollama run LiquidAI/lfm2.5-2.6b`, 1.7 GB, or `llama-cli -hf LiquidAI/LFM2.5-2.6B-GGUF`\n2. With vLLM or SGLang pass `--tool-call-parser lfm2`; for vLLM add `--reasoning-parser qwen3` so the thinking comes back separately\n3. Sample at temperature 0.1, top_k 50 and repetition_penalty 1.1, as the card and generation_config.json set\n4. Put tools in the system prompt as `List of tools: [...]` JSON, or pass tools to apply_chat_template; calls come back as a Python list of function calls, and the system prompt can ask for JSON calls instead\n5. Leave max_tokens unset or generous, since a low cap cuts the reasoning off mid-thought\n6. Earlier thinking is dropped from the prompt unless `preserve_thinking` is true\n7. Liquid's 328M LFM2.5-2.6B-DSpark drafter speeds decoding about 2.6 times in SGLang and on Apple silicon, per Liquid\n8. Validate JSON output yourself or use the runtime's guided decoding, since structured output isn't documented\n\n## Questions\n\n### Which is better for AI agents, InternVL3.5-GPT-OSS-20B-A4B-Preview or LFM2.5-2.6B?\n\nLFM2.5-2.6B and InternVL3.5-GPT-OSS-20B-A4B-Preview score within a point of each other on agent readiness, 51.5 (D) and 51.2 (D). 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-lfm2-5-2-6b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-lfm2-5-2-6b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"internvl3-5-gpt-oss-20b-a4b-preview\", \"b\": \"lfm2-5-2-6b\"}`. From a terminal: `anchor compare internvl3-5-gpt-oss-20b-a4b-preview lfm2-5-2-6b`\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/lfm2-5-2-6b.json\n\n## Other comparisons with InternVL3.5-GPT-OSS-20B-A4B-Preview or LFM2.5-2.6B\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 LFM2.5-2.6B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-lfm2-5-2-6b.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 LFM2.5-2.6B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-lfm2-5-2-6b.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 LFM2.5-2.6B](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-lfm2-5-2-6b.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 LFM2.5-2.6B](https://www.anchorterminal.com/compare/bigbang-v1-vs-lfm2-5-2-6b.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 LFM2.5-2.6B](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-lfm2-5-2-6b.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 LFM2.5-2.6B](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-lfm2-5-2-6b.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 LFM2.5-2.6B](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-lfm2-5-2-6b.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 LFM2.5-2.6B](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-lfm2-5-2-6b.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 LFM2.5-2.6B](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-lfm2-5-2-6b.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 LFM2.5-2.6B](https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-lfm2-5-2-6b.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 LFM2.5-2.6B](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-lfm2-5-2-6b.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 LFM2.5-2.6B](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-lfm2-5-2-6b.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 LFM2.5-2.6B](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-lfm2-5-2-6b.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 LFM2.5-2.6B](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-lfm2-5-2-6b.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 LFM2.5-2.6B](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-lfm2-5-2-6b.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 LFM2.5-2.6B](https://www.anchorterminal.com/compare/glm-4-5-air-vs-lfm2-5-2-6b.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 LFM2.5-2.6B](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-lfm2-5-2-6b.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 LFM2.5-2.6B](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-lfm2-5-2-6b.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 LFM2.5-2.6B](https://www.anchorterminal.com/compare/glm-5-3-vs-lfm2-5-2-6b.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 LFM2.5-2.6B](https://www.anchorterminal.com/compare/gpt-oss-120b-vs-lfm2-5-2-6b.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 LFM2.5-2.6B](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-lfm2-5-2-6b.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 LFM2.5-2.6B](https://www.anchorterminal.com/compare/granite-4-2-3b-vs-lfm2-5-2-6b.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 LFM2.5-2.6B](https://www.anchorterminal.com/compare/hy3-vs-lfm2-5-2-6b.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 LFM2.5-2.6B](https://www.anchorterminal.com/compare/inkling-small-vs-lfm2-5-2-6b.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 LFM2.5-2.6B](https://www.anchorterminal.com/compare/inkling-vs-lfm2-5-2-6b.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 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 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 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 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 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 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 Muse Glimmer 30B](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-muse-glimmer-30b.md)\n- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs Nemotron 3.5 Lightning 30B A3B](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-nemotron-3-5-lightning-30b-a3b.md)\n- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs Nemotron 3 Nano 30B A3B](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-nemotron-3-nano-30b-a3b.md)\n- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs Nemotron 3 Nano 4B](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-nemotron-3-nano-4b.md)\n- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs Nemotron 3 Nano Omni 30B A3B Reasoning](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-nemotron-3-nano-omni-30b-a3b-reasoning.md)\n- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs Nemotron 3 Super 120B A12B](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-nemotron-3-super-120b-a12b.md)\n- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs Nemotron 3 Ultra 550B A55B](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-nemotron-3-ultra-550b-a55b.md)\n- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs occamy-1.0](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-occamy-1-0.md)\n- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs Olmo 3 7B Think](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-olmo-3-7b-think.md)\n- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs Ornith-1.5-35B-A3B](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-ornith-1-5-35b-a3b.md)\n- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs Ornith-1.5-397B](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-ornith-1-5-397b.md)\n- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs Ornith-1.5-9B](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-ornith-1-5-9b.md)\n- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs Qwen3-30B-A3B-Instruct-2507](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-qwen3-30b-a3b-instruct-2507.md)\n- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs Qwen3-4B-Instruct-2507](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-qwen3-4b-instruct-2507.md)\n- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs Qwen3.5-0.8B](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-qwen3-5-0-8b.md)\n- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs Qwen3.5-122B-A10B](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-qwen3-5-122b-a10b.md)\n- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs Qwen3.5-2B](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-qwen3-5-2b.md)\n- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs Qwen3.5-397B-A17B](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-qwen3-5-397b-a17b.md)\n- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs Qwen3.5-4B](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-qwen3-5-4b.md)\n- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs Qwen3.5-9B](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-qwen3-5-9b.md)\n- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs Qwen3.6-27B](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-qwen3-6-27b.md)\n- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs Qwen3.6-35B-A3B](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-qwen3-6-35b-a3b.md)\n- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs Qwen3.8-27B](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-qwen3-8-27b.md)\n- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-qwen3-8-flash-next.md)\n- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs Qwen3-Coder-30B-A3B-Instruct](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-qwen3-coder-30b-a3b-instruct.md)\n- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs Qwen3-Coder-Next](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-qwen3-coder-next.md)\n- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs Qwen3-Omni-30B-A3B-Instruct](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-qwen3-omni-30b-a3b-instruct.md)\n- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs Qwen3-VL-2B-Instruct](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-qwen3-vl-2b-instruct.md)\n- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs Qwen3-VL-30B-A3B-Instruct](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-qwen3-vl-30b-a3b-instruct.md)\n- 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