{ "data": { "a": { "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" }, "answer": "Qwen3.6-35B-A3B scores 63.2 (B) on agent readiness against LFM2.5-2.6B's 51.5 (D), and leads in 2 of 5 scored categories. LFM2.5-2.6B leads on reach, agent interface and documentation and upkeep.", "b": { "slug": "qwen3-6-35b-a3b", "name": "Qwen3.6-35B-A3B", "vendor": "Alibaba (Qwen team)", "vendorUrl": "https://qwen.ai", "kind": "weights", "category": "open-weights", "summary": "Qwen3.6-35B-A3B is Alibaba's open-weight mixture-of-experts vision-language model, 35B parameters with 3B active and a 262,144-token context, published on Hugging Face in BF16 and FP8 under Apache 2.0.", "url": "https://www.anchorterminal.com/tools/qwen3-6-35b-a3b", "markdownUrl": "https://www.anchorterminal.com/tools/qwen3-6-35b-a3b.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/qwen3-6-35b-a3b.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/qwen3-6-35b-a3b.json", "repo": "https://huggingface.co/Qwen/Qwen3.6-35B-A3B", "license": "Apache-2.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "Qwen/Qwen3.6-35B-A3B" }, { "registry": "huggingface", "name": "Qwen/Qwen3.6-35B-A3B-FP8" }, { "registry": "ollama", "name": "qwen3.6:35b" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. Neither the BF16 nor the FP8 repository is gated (checked 2026-10-21).", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0. The card's Qwen-Agent example also names a hosted version on Alibaba Cloud Model Studio, which isn't listed here. Running it yourself costs the hardware, 71.9 GB for the BF16 files, 37.5 GB for FP8 and 23 to 24 GB for Ollama's default 35b build (checked 2026-10-21).", "priceSummary": "Free", "where": "local", "x402": { "level": "no", "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-10-21).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-10-21" }, "docsUrl": "https://huggingface.co/Qwen/Qwen3.6-35B-A3B", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "moe", "multimodal", "reasoning", "tool-calling", "apache-2.0", "safetensors", "fp8", "self-hosted" ], "lastRelease": "2026-04-16", "graded": true, "anchor": { "graded": true, "score": 63.2, "grade": "B", "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": 20, "methodology": "0.6", "run": "2026-10-01", "scores": { "capability": 58, "interface": 70, "reach": 85, "risk": 55, "upkeep": 36 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "high", "date": "2026-10-21" }, "negative": 0, "verdict": "A fast mixture-of-experts cousin with 3B active parameters for coding and tool calling, with image and video input, in every major runtime under plain Apache 2.0. Ollama's 4-bit build needs about 28 GB. The main risk is that nobody at Alibaba maintains it, with no safety results, no security contact and no replies to tool-calling reports.", "bestFor": "Agents with a 32 GB or larger machine that want a quick, permissively licensed coding and tool-calling cousin with vision.", "strengths": [ "Runs in released llama.cpp, Ollama, vLLM, SGLang and Transformers, with vLLM and SGLang recipe pages", "Apache 2.0 with nothing added, files ungated, and three inference providers live on Hugging Face", "Safetensors only with a hash for every file, in the publisher's own organisation", "262,144 tokens native, several tool calls to a turn, and thinking that can be turned off per request" ], "weaknesses": [ "No safety evaluation, no agent-use guidance and no way to report a security flaw", "No reply from a Qwen member in any of its 61 discussions, including reports on tool calling and the template", "No 4-bit or GGUF build from Alibaba, only FP8", "Card leaves out limitations, languages, knowledge cut-off and training data", "Structured output isn't documented, and the files haven't changed since 24 April 2026" ], "agentNotes": [ "Start it with `ollama run qwen3.6:35b` (Q4_K_M, 23 to 24 GB on disk, about 28 GB in memory)", "On the reference machine use `vllm serve Qwen/Qwen3.6-35B-A3B --max-model-len 131072 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder`, BF16, since vLLM doesn't document FP8 compute on gfx1151", "Use the qwen3_coder tool-call parser and the qwen3 reasoning parser in vLLM 0.19.0 or SGLang 0.5.10 or later", "Thinking is on by default and /nothink isn't supported, so pass enable_thinking as false in chat_template_kwargs for short tasks, or preserve_thinking as true to keep earlier reasoning", "Card sampling for thinking is temperature 1.0, top_p 0.95, top_k 20, presence_penalty 1.5, and for non-thinking temperature 0.7, top_p 0.8", "Keep at least 128K of context as the card advises, and turn on YaRN only beyond 262,144 tokens", "Validate JSON output yourself or use the runtime's guided decoding, since the model card documents none" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 2.8, "history": [ { "basis": "public evidence", "confidence": "high", "grade": "B", "methodology": "0.6", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 63.2 } ], "editorialScores": { "capability": 58, "interface": 70, "reach": 85, "risk": 55, "upkeep": 36 }, "provenanceScore": 49, "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": "ollama run qwen3.6:35b\n# or, with the card's vLLM tool-call command, without its 8-GPU tensor parallel flag:\nvllm serve Qwen/Qwen3.6-35B-A3B-FP8 --max-model-len 262144 --reasoning-parser qwen3 \\\n --enable-auto-tool-choice --tool-call-parser qwen3_coder", "http": "curl http://localhost:11434/api/chat \\\n -d '{\"model\": \"qwen3.6:35b\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/qwen3-6-35b-a3b" }, "sameCompany": [ "qwen3-embedding-0-6b", "qwen3-embedding-4b", "qwen3-embedding-8b", "qwen3-vl-embedding-2b", "qwen3-vl-embedding-8b", "qwen3-asr-0-6b", "qwen3-asr-1-7b", "qwen3-tts-1-7b-customvoice", "qwen3-30b-a3b-instruct-2507", "qwen3-4b-instruct-2507", "qwen3-coder-30b-a3b-instruct", "qwen3-coder-next", "qwen3-omni-30b-a3b-instruct", "qwen3-vl-2b-instruct", "qwen3-vl-30b-a3b-instruct", "qwen3-vl-32b-instruct", "qwen3-vl-4b-instruct", "qwen3-vl-8b-instruct", "qwen3-5-0-8b", "qwen3-5-122b-a10b", "qwen3-5-2b", "qwen3-5-397b-a17b", "qwen3-5-4b", "qwen3-5-9b", "qwen3-6-27b", "qwen3-8-27b", "qwen3-8-flash-next" ], "weights": { "trainedBy": "Alibaba (Qwen team)", "params": "35B", "active": "3B", "context": 262144, "goodFor": "Fast mixture-of-experts vision-language model for coding agents and tool calling, text, images and video in, thinking on by default.", "runtimes": [ "llama.cpp", "Ollama", "vLLM", "SGLang", "Transformers" ], "builds": [ { "name": "ollama qwen3.6:35b", "url": "https://ollama.com/library/qwen3.6:35b", "who": "ollama", "precision": "q4_k_m", "sizeGB": 23, "memoryGB": 32, "format": "ollama", "runsOn": [ "Ollama" ], "note": "Q4_K_M with an F16 vision projector (digest a7eb95c53bcf). Size as Ollama's tags page shows it, rounded to whole GB above 10 GB; the Linux build is the llama.cpp variant" }, { "name": "Qwen3.6-35B-A3B-FP8", "url": "https://huggingface.co/Qwen/Qwen3.6-35B-A3B-FP8", "who": "publisher", "precision": "fp8", "sizeGB": 37.5, "memoryGB": 48, "format": "safetensors" }, { "name": "Qwen3.6-35B-A3B", "url": "https://huggingface.co/Qwen/Qwen3.6-35B-A3B", "who": "publisher", "precision": "bf16", "sizeGB": 71.9, "memoryGB": 96, "format": "safetensors", "runsOn": [ "vLLM (ROCm)", "SGLang (ROCm)" ] } ], "run": "vllm serve Qwen/Qwen3.6-35B-A3B-FP8 --max-model-len 262144 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder", "runBuild": "Qwen3.6-35B-A3B-FP8", "checked": "2026-10-21", "minMemoryGB": 32 }, "family": { "lab": "Alibaba (Qwen)", "family": "Qwen3", "url": "https://www.anchorterminal.com/open-weights/#qwen", "others": [ { "name": "Qwen3-0.6B", "url": "https://huggingface.co/Qwen/Qwen3-0.6B", "params": "0.6B" }, { "name": "Qwen3-1.7B", "url": "https://huggingface.co/Qwen/Qwen3-1.7B", "params": "1.7B" }, { "name": "Qwen3-14B", "url": "https://huggingface.co/Qwen/Qwen3-14B", "params": "14B" }, { "name": "Qwen3-235B-A22B", "url": "https://huggingface.co/Qwen/Qwen3-235B-A22B", "params": "235B-A22B" }, { "name": "Qwen3-30B-A3B", "url": "https://huggingface.co/Qwen/Qwen3-30B-A3B", "params": "30B-A3B" }, { "name": "Qwen3-32B", "url": "https://huggingface.co/Qwen/Qwen3-32B", "params": "32B" }, { "name": "Qwen3-4B", "url": "https://huggingface.co/Qwen/Qwen3-4B", "params": "4B" }, { "name": "Qwen3-8B", "url": "https://huggingface.co/Qwen/Qwen3-8B", "params": "8B" }, { "name": "Qwen3-0.6B-Base", "url": "https://huggingface.co/Qwen/Qwen3-0.6B-Base", "params": "0.6B" }, { "name": "Qwen3-1.7B-Base", "url": "https://huggingface.co/Qwen/Qwen3-1.7B-Base", "params": "1.7B" }, { "name": "Qwen3-14B-Base", "url": "https://huggingface.co/Qwen/Qwen3-14B-Base", "params": "14B" }, { "name": "Qwen3-30B-A3B-Base", "url": "https://huggingface.co/Qwen/Qwen3-30B-A3B-Base", "params": "30B-A3B" }, { "name": "Qwen3-4B-Base", "url": "https://huggingface.co/Qwen/Qwen3-4B-Base", "params": "4B" }, { "name": "Qwen3-8B-Base", "url": "https://huggingface.co/Qwen/Qwen3-8B-Base", "params": "8B" }, { "name": "Qwen3-Reranker-0.6B", "url": "https://huggingface.co/Qwen/Qwen3-Reranker-0.6B", "params": "0.6B" }, { "name": "Qwen3-Reranker-8B", "url": "https://huggingface.co/Qwen/Qwen3-Reranker-8B", "params": "8B" }, { "name": "Qwen3-Embedding-0.6B", "url": "https://huggingface.co/Qwen/Qwen3-Embedding-0.6B", "params": "0.6B", "listing": "https://www.anchorterminal.com/tools/qwen3-embedding-0-6b" }, { "name": "Qwen3-Embedding-4B", "url": "https://huggingface.co/Qwen/Qwen3-Embedding-4B", "params": "4B", "listing": "https://www.anchorterminal.com/tools/qwen3-embedding-4b" }, { "name": "Qwen3-Embedding-8B", "url": "https://huggingface.co/Qwen/Qwen3-Embedding-8B", "params": "8B", "listing": "https://www.anchorterminal.com/tools/qwen3-embedding-8b" }, { "name": "Qwen3-Reranker-4B", "url": "https://huggingface.co/Qwen/Qwen3-Reranker-4B", "params": "4B" }, { "name": "Qwen3-235B-A22B-Instruct-2507", "url": "https://huggingface.co/Qwen/Qwen3-235B-A22B-Instruct-2507", "params": "235B-A22B" }, { "name": "Qwen3-Coder-480B-A35B-Instruct", "url": "https://huggingface.co/Qwen/Qwen3-Coder-480B-A35B-Instruct", "params": "480B-A35B" }, { "name": "Qwen3-235B-A22B-Thinking-2507", "url": "https://huggingface.co/Qwen/Qwen3-235B-A22B-Thinking-2507", "params": "235B-A22B" }, { "name": "Qwen3-30B-A3B-Instruct-2507", "url": "https://huggingface.co/Qwen/Qwen3-30B-A3B-Instruct-2507", "params": "30B-A3B", "listing": "https://www.anchorterminal.com/tools/qwen3-30b-a3b-instruct-2507" }, { "name": "Qwen3-30B-A3B-Thinking-2507", "url": "https://huggingface.co/Qwen/Qwen3-30B-A3B-Thinking-2507", "params": "30B-A3B" }, { "name": "Qwen3-Coder-30B-A3B-Instruct", "url": "https://huggingface.co/Qwen/Qwen3-Coder-30B-A3B-Instruct", "params": "30B-A3B", "listing": "https://www.anchorterminal.com/tools/qwen3-coder-30b-a3b-instruct" }, { "name": "Qwen3-4B-Instruct-2507", "url": "https://huggingface.co/Qwen/Qwen3-4B-Instruct-2507", "params": "4B", "listing": "https://www.anchorterminal.com/tools/qwen3-4b-instruct-2507" }, { "name": "Qwen3-4B-Thinking-2507", "url": "https://huggingface.co/Qwen/Qwen3-4B-Thinking-2507", "params": "4B" }, { "name": "Qwen3-Next-80B-A3B-Instruct", "url": "https://huggingface.co/Qwen/Qwen3-Next-80B-A3B-Instruct", "params": "80B-A3B" }, { "name": "Qwen3-Next-80B-A3B-Thinking", "url": "https://huggingface.co/Qwen/Qwen3-Next-80B-A3B-Thinking", "params": "80B-A3B" }, { "name": "Qwen3-Omni-30B-A3B-Captioner", "url": "https://huggingface.co/Qwen/Qwen3-Omni-30B-A3B-Captioner", "params": "30B-A3B" }, { "name": "Qwen3-Omni-30B-A3B-Thinking", "url": "https://huggingface.co/Qwen/Qwen3-Omni-30B-A3B-Thinking", "params": "30B-A3B" }, { "name": "Qwen3-Omni-30B-A3B-Instruct", "url": "https://huggingface.co/Qwen/Qwen3-Omni-30B-A3B-Instruct", "params": "30B-A3B", "listing": "https://www.anchorterminal.com/tools/qwen3-omni-30b-a3b-instruct" }, { "name": "Qwen3-VL-235B-A22B-Instruct", "url": "https://huggingface.co/Qwen/Qwen3-VL-235B-A22B-Instruct", "params": "235B-A22B" }, { "name": "Qwen3-VL-235B-A22B-Thinking", "url": "https://huggingface.co/Qwen/Qwen3-VL-235B-A22B-Thinking", "params": "235B-A22B" }, { "name": "Qwen3Guard-Gen-0.6B", "url": "https://huggingface.co/Qwen/Qwen3Guard-Gen-0.6B", "params": "0.6B" }, { "name": "Qwen3Guard-Gen-4B", "url": "https://huggingface.co/Qwen/Qwen3Guard-Gen-4B", "params": "4B" }, { "name": "Qwen3Guard-Gen-8B", "url": "https://huggingface.co/Qwen/Qwen3Guard-Gen-8B", "params": "8B" }, { "name": "Qwen3Guard-Stream-0.6B", "url": "https://huggingface.co/Qwen/Qwen3Guard-Stream-0.6B", "params": "0.6B" }, { "name": "Qwen3Guard-Stream-4B", "url": "https://huggingface.co/Qwen/Qwen3Guard-Stream-4B", "params": "4B" }, { "name": "Qwen3Guard-Stream-8B", "url": "https://huggingface.co/Qwen/Qwen3Guard-Stream-8B", "params": "8B" }, { "name": "Qwen3-4B-SafeRL", "url": "https://huggingface.co/Qwen/Qwen3-4B-SafeRL", "params": "4B" }, { "name": "Qwen3-VL-30B-A3B-Instruct", "url": "https://huggingface.co/Qwen/Qwen3-VL-30B-A3B-Instruct", "params": "30B-A3B", "listing": "https://www.anchorterminal.com/tools/qwen3-vl-30b-a3b-instruct" }, { "name": "Qwen3-VL-30B-A3B-Thinking", "url": "https://huggingface.co/Qwen/Qwen3-VL-30B-A3B-Thinking", "params": "30B-A3B" }, { "name": "Qwen3-VL-4B-Instruct", "url": "https://huggingface.co/Qwen/Qwen3-VL-4B-Instruct", "params": "4B", "listing": "https://www.anchorterminal.com/tools/qwen3-vl-4b-instruct" }, { "name": "Qwen3-VL-4B-Thinking", "url": "https://huggingface.co/Qwen/Qwen3-VL-4B-Thinking", "params": "4B" }, { "name": "Qwen3-VL-8B-Instruct", "url": "https://huggingface.co/Qwen/Qwen3-VL-8B-Instruct", "params": "8B", "listing": "https://www.anchorterminal.com/tools/qwen3-vl-8b-instruct" }, { "name": "Qwen3-VL-8B-Thinking", "url": "https://huggingface.co/Qwen/Qwen3-VL-8B-Thinking", "params": "8B" }, { "name": "Qwen3-VL-2B-Instruct", "url": "https://huggingface.co/Qwen/Qwen3-VL-2B-Instruct", "params": "2B", "listing": "https://www.anchorterminal.com/tools/qwen3-vl-2b-instruct" }, { "name": "Qwen3-VL-2B-Thinking", "url": "https://huggingface.co/Qwen/Qwen3-VL-2B-Thinking", "params": "2B" }, { "name": "Qwen3-VL-32B-Instruct", "url": "https://huggingface.co/Qwen/Qwen3-VL-32B-Instruct", "params": "32B", "listing": "https://www.anchorterminal.com/tools/qwen3-vl-32b-instruct" }, { "name": "Qwen3-VL-32B-Thinking", "url": "https://huggingface.co/Qwen/Qwen3-VL-32B-Thinking", "params": "32B" }, { "name": "Qwen3-VL-Embedding-2B", "url": "https://huggingface.co/Qwen/Qwen3-VL-Embedding-2B", "params": "2B", "listing": "https://www.anchorterminal.com/tools/qwen3-vl-embedding-2b" }, { "name": "Qwen3-VL-Embedding-8B", "url": "https://huggingface.co/Qwen/Qwen3-VL-Embedding-8B", "params": "8B", "listing": "https://www.anchorterminal.com/tools/qwen3-vl-embedding-8b" }, { "name": "Qwen3-VL-Reranker-2B", "url": "https://huggingface.co/Qwen/Qwen3-VL-Reranker-2B", "params": "2B" }, { "name": "Qwen3-VL-Reranker-8B", "url": "https://huggingface.co/Qwen/Qwen3-VL-Reranker-8B", "params": "8B" }, { "name": "Qwen3-TTS-12Hz-0.6B-Base", "url": "https://huggingface.co/Qwen/Qwen3-TTS-12Hz-0.6B-Base", "params": "0.6B" }, { "name": "Qwen3-TTS-12Hz-0.6B-CustomVoice", "url": "https://huggingface.co/Qwen/Qwen3-TTS-12Hz-0.6B-CustomVoice", "params": "0.6B" }, { "name": "Qwen3-TTS-12Hz-1.7B-Base", "url": "https://huggingface.co/Qwen/Qwen3-TTS-12Hz-1.7B-Base", "params": "1.7B" }, { "name": "Qwen3-TTS-12Hz-1.7B-CustomVoice", "url": "https://huggingface.co/Qwen/Qwen3-TTS-12Hz-1.7B-CustomVoice", "params": "1.7B", "listing": "https://www.anchorterminal.com/tools/qwen3-tts-1-7b-customvoice" }, { "name": "Qwen3-TTS-12Hz-1.7B-VoiceDesign", "url": "https://huggingface.co/Qwen/Qwen3-TTS-12Hz-1.7B-VoiceDesign", "params": "1.7B" }, { "name": "Qwen3-TTS-Tokenizer-12Hz", "url": "https://huggingface.co/Qwen/Qwen3-TTS-Tokenizer-12Hz", "params": "not in name" }, { "name": "Qwen3-ASR-0.6B", "url": "https://huggingface.co/Qwen/Qwen3-ASR-0.6B", "params": "0.6B", "listing": "https://www.anchorterminal.com/tools/qwen3-asr-0-6b" }, { "name": "Qwen3-ASR-1.7B", "url": "https://huggingface.co/Qwen/Qwen3-ASR-1.7B", "params": "1.7B", "listing": "https://www.anchorterminal.com/tools/qwen3-asr-1-7b" }, { "name": "Qwen3-ForcedAligner-0.6B", "url": "https://huggingface.co/Qwen/Qwen3-ForcedAligner-0.6B", "params": "0.6B" }, { "name": "Qwen3-Coder-Next", "url": "https://huggingface.co/Qwen/Qwen3-Coder-Next", "params": "not in name; Hub safetensors total 79.7B", "listing": "https://www.anchorterminal.com/tools/qwen3-coder-next" }, { "name": "Qwen3-Coder-Next-Base", "url": "https://huggingface.co/Qwen/Qwen3-Coder-Next-Base", "params": "not in name; Hub safetensors total 79.7B" }, { "name": "WebWorld-14B", "url": "https://huggingface.co/Qwen/WebWorld-14B", "params": "14B" }, { "name": "WebWorld-32B", "url": "https://huggingface.co/Qwen/WebWorld-32B", "params": "32B" }, { "name": "WebWorld-8B", "url": "https://huggingface.co/Qwen/WebWorld-8B", "params": "8B" }, { "name": "Qwen3.5-397B-A17B", "url": "https://huggingface.co/Qwen/Qwen3.5-397B-A17B", "params": "397B-A17B", "listing": "https://www.anchorterminal.com/tools/qwen3-5-397b-a17b" }, { "name": "Qwen3.5-122B-A10B", "url": "https://huggingface.co/Qwen/Qwen3.5-122B-A10B", "params": "122B-A10B", "listing": "https://www.anchorterminal.com/tools/qwen3-5-122b-a10b" }, { "name": "Qwen3.5-27B", "url": "https://huggingface.co/Qwen/Qwen3.5-27B", "params": "27B" }, { "name": "Qwen3.5-35B-A3B", "url": "https://huggingface.co/Qwen/Qwen3.5-35B-A3B", "params": "35B-A3B" }, { "name": "Qwen3.5-35B-A3B-Base", "url": "https://huggingface.co/Qwen/Qwen3.5-35B-A3B-Base", "params": "35B-A3B" }, { "name": "Qwen3.5-9B-Base", "url": "https://huggingface.co/Qwen/Qwen3.5-9B-Base", "params": "9B" }, { "name": "Qwen3.5-4B", "url": "https://huggingface.co/Qwen/Qwen3.5-4B", "params": "4B", "listing": "https://www.anchorterminal.com/tools/qwen3-5-4b" }, { "name": "Qwen3.5-4B-Base", "url": "https://huggingface.co/Qwen/Qwen3.5-4B-Base", "params": "4B" }, { "name": "Qwen3.5-9B", "url": "https://huggingface.co/Qwen/Qwen3.5-9B", "params": "9B", "listing": "https://www.anchorterminal.com/tools/qwen3-5-9b" }, { "name": "Qwen3.5-0.8B", "url": "https://huggingface.co/Qwen/Qwen3.5-0.8B", "params": "0.8B", "listing": "https://www.anchorterminal.com/tools/qwen3-5-0-8b" }, { "name": "Qwen3.5-0.8B-Base", "url": "https://huggingface.co/Qwen/Qwen3.5-0.8B-Base", "params": "0.8B" }, { "name": "Qwen3.5-2B", "url": "https://huggingface.co/Qwen/Qwen3.5-2B", "params": "2B", "listing": "https://www.anchorterminal.com/tools/qwen3-5-2b" }, { "name": "Qwen3.5-2B-Base", "url": "https://huggingface.co/Qwen/Qwen3.5-2B-Base", "params": "2B" }, { "name": "Qwen3.6-27B", "url": "https://huggingface.co/Qwen/Qwen3.6-27B", "params": "27B", "listing": "https://www.anchorterminal.com/tools/qwen3-6-27b" }, { "name": "Qwen-AgentWorld-35B-A3B", "url": "https://huggingface.co/Qwen/Qwen-AgentWorld-35B-A3B", "params": "35B-A3B" }, { "name": "Qwen3.8-27B", "url": "https://huggingface.co/Qwen/Qwen3.8-27B", "params": "27B", "listing": "https://www.anchorterminal.com/tools/qwen3-8-27b" }, { "name": "Qwen3.8-2.4T-A95B", "url": "https://huggingface.co/Qwen/Qwen3.8-2.4T-A95B", "params": "2.4T-A95B" }, { "name": "Qwen3.8-Flash-Next", "url": "https://huggingface.co/Qwen/Qwen3.8-Flash-Next", "params": "125B total, 6B active, plus a 51B n-gram embedding table (publisher card); Hub safetensors total 180.0B", "listing": "https://www.anchorterminal.com/tools/qwen3-8-flash-next" }, { "name": "Qwen-Drive-1.0-4B", "url": "https://huggingface.co/Qwen/Qwen-Drive-1.0-4B", "params": "4B" } ], "olderCount": 109, "unrestrictedCopies": 5 }, "area": "models", "provenance": { "legalEntity": "Alibaba Cloud (named in the licence's copyright line)", "domain": "qwen.ai", "domainRegistered": "2023-08-27", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "", "securityTxt": "none", "checked": "2026-10-21", "notes": [ "The LICENSE file reads Copyright 2026 Alibaba Cloud. RDAP names Alibaba Cloud (Singapore) Private Limited as the registrant of qwen.ai.", "Nothing is hosted, so the Apache 2.0 licence stands in for terms of service and no privacy policy is scored.", "No changelog. The repository has three commits, the initial release on 15 April 2026, the LICENSE on 22 April and a README upload on 24 April.", "qwen.ai redirects /robots.txt and /.well-known/security.txt to its home page, so there is no security.txt." ], "score": 49 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/qwen3-6-35b-a3b.json" }, "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "Liquid AI", "b": "Alibaba (Qwen team)", "name": "Vendor" }, { "a": "no (local only)", "b": "no (local only)", "name": "Hosted endpoint" }, { "a": "", "b": "", "name": "Transports" }, { "a": "None", "b": "None", "name": "Auth" }, { "a": "Free", "b": "Free", "name": "Pricing" }, { "a": "no", "b": "no", "name": "x402" }, { "a": "LFM Open License v1.0 (free commercial use below $10M annual revenue)", "b": "Apache-2.0", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "yes", "b": "no", "name": "llms.txt" }, { "a": "2026-09-22", "b": "2026-04-16", "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": "2.5/5 (4)", "b": "2.8/5 (4)", "name": "Agent reviews" } ], "faq": [ { "answer": "Qwen3.6-35B-A3B scores 63.2 (B) on agent readiness against LFM2.5-2.6B's 51.5 (D), and leads in 2 of 5 scored categories. LFM2.5-2.6B leads on reach, agent interface and documentation and upkeep.", "question": "Which is better for AI agents, LFM2.5-2.6B or Qwen3.6-35B-A3B?" } ], "goodFor": [ { "aheadOn": [ "Reach, 95 against 85", "Agent interface, 75 against 70", "Documentation and upkeep, 57 against 36" ], "also": null, "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" }, { "aheadOn": [ "Capability, 58 against 13", "Risk, 55 against 50" ], "also": null, "goodFor": "Agents with a 32 GB or larger machine that want a quick, permissively licensed coding and tool-calling cousin with vision.", "slug": "qwen3-6-35b-a3b", "watchFor": "No safety evaluation, no agent-use guidance and no way to report a security flaw" } ], "job": { "capability": "model.open-weights", "name": "Open-weight models you run yourself" }, "others": [ { "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/agents-a1-4b-vs-qwen3-6-35b-a3b.json", "title": "Agents-A1-4B vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-qwen3-6-35b-a3b" }, { "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": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-lfm2-5-2-6b" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-qwen3-6-35b-a3b.json", "title": "Apertus v1.5 8B vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-lfm2-5-2-6b.json", "title": "Apodex-1.1-mini vs LFM2.5-2.6B", "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-lfm2-5-2-6b" }, { "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-qwen3-6-35b-a3b.json", "title": "Apodex-1.1-mini vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-lfm2-5-2-6b.json", "title": "BigBang-v1 vs LFM2.5-2.6B", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-lfm2-5-2-6b" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-qwen3-6-35b-a3b.json", "title": "BigBang-v1 vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-lfm2-5-2-6b.json", "title": "Cosmos-Reason2-2B vs LFM2.5-2.6B", "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-lfm2-5-2-6b" }, { "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-qwen3-6-35b-a3b.json", "title": "Cosmos-Reason2-2B vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-lfm2-5-2-6b.json", "title": "Darwin-36B-Opus vs LFM2.5-2.6B", "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-lfm2-5-2-6b" }, { "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-qwen3-6-35b-a3b.json", "title": "Darwin-36B-Opus vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-qwen3-6-35b-a3b" }, { "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-v3-2-vs-qwen3-6-35b-a3b.json", "title": "DeepSeek-V3.2 vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-lfm2-5-2-6b.json", "title": "DeepSeek V4.1 Flash vs LFM2.5-2.6B", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-lfm2-5-2-6b" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-6-35b-a3b.json", "title": "DeepSeek V4.1 Flash vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-lfm2-5-2-6b.json", "title": "DeepSeek-V4-Flash-0731 vs LFM2.5-2.6B", "url": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-lfm2-5-2-6b" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-qwen3-6-35b-a3b.json", "title": "DeepSeek-V4-Flash-0731 vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-lfm2-5-2-6b.json", "title": "DiffusionGemma 26B A4B IT vs LFM2.5-2.6B", "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-lfm2-5-2-6b" }, { "json": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-qwen3-6-35b-a3b.json", "title": "DiffusionGemma 26B A4B IT vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-lfm2-5-2-6b.json", "title": "Gemma 4 12B IT vs LFM2.5-2.6B", "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-lfm2-5-2-6b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-qwen3-6-35b-a3b.json", "title": "Gemma 4 12B IT vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-lfm2-5-2-6b.json", "title": "Gemma 4 26B A4B IT vs LFM2.5-2.6B", "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-lfm2-5-2-6b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-qwen3-6-35b-a3b.json", "title": "Gemma 4 26B A4B IT vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-lfm2-5-2-6b.json", "title": "Gemma 4 31B IT vs LFM2.5-2.6B", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-lfm2-5-2-6b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-6-35b-a3b.json", "title": "Gemma 4 31B IT vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-lfm2-5-2-6b.json", "title": "Gemma 4 E2B IT vs LFM2.5-2.6B", "url": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-lfm2-5-2-6b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-qwen3-6-35b-a3b.json", "title": "Gemma 4 E2B IT vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-lfm2-5-2-6b.json", "title": "Gemma 4 E4B IT vs LFM2.5-2.6B", "url": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-lfm2-5-2-6b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-qwen3-6-35b-a3b.json", "title": "Gemma 4 E4B IT vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-lfm2-5-2-6b.json", "title": "GLM-4.5-Air vs LFM2.5-2.6B", "url": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-lfm2-5-2-6b" }, { "json": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-qwen3-6-35b-a3b.json", "title": "GLM-4.5-Air vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-lfm2-5-2-6b.json", "title": "GLM-4.7-Flash vs LFM2.5-2.6B", "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-lfm2-5-2-6b" }, { "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwen3-6-35b-a3b.json", "title": "GLM-4.7-Flash vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-lfm2-5-2-6b.json", "title": "GLM-5.3-Flash vs LFM2.5-2.6B", "url": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-lfm2-5-2-6b" }, { "json": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-qwen3-6-35b-a3b.json", "title": "GLM-5.3-Flash vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/glm-5-3-vs-lfm2-5-2-6b.json", "title": "GLM-5.3 vs LFM2.5-2.6B", "url": "https://www.anchorterminal.com/compare/glm-5-3-vs-lfm2-5-2-6b" }, { "json": "https://www.anchorterminal.com/compare/glm-5-3-vs-qwen3-6-35b-a3b.json", "title": "GLM-5.3 vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/glm-5-3-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-lfm2-5-2-6b.json", "title": "gpt-oss-120b vs LFM2.5-2.6B", "url": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-lfm2-5-2-6b" }, { "json": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-qwen3-6-35b-a3b.json", "title": "gpt-oss-120b vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-lfm2-5-2-6b.json", "title": "gpt-oss-20b vs LFM2.5-2.6B", "url": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-lfm2-5-2-6b" }, { "json": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-qwen3-6-35b-a3b.json", "title": "gpt-oss-20b vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-lfm2-5-2-6b.json", "title": "Granite 4.2 3B vs LFM2.5-2.6B", "url": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-lfm2-5-2-6b" }, { "json": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-qwen3-6-35b-a3b.json", "title": "Granite 4.2 3B vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/hy3-vs-lfm2-5-2-6b.json", "title": "Hy3 vs LFM2.5-2.6B", "url": "https://www.anchorterminal.com/compare/hy3-vs-lfm2-5-2-6b" }, { "json": "https://www.anchorterminal.com/compare/hy3-vs-qwen3-6-35b-a3b.json", "title": "Hy3 vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/hy3-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/inkling-small-vs-lfm2-5-2-6b.json", "title": "Inkling-Small vs LFM2.5-2.6B", "url": "https://www.anchorterminal.com/compare/inkling-small-vs-lfm2-5-2-6b" }, { "json": "https://www.anchorterminal.com/compare/inkling-small-vs-qwen3-6-35b-a3b.json", "title": "Inkling-Small vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/inkling-small-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/inkling-vs-lfm2-5-2-6b.json", "title": "Inkling vs LFM2.5-2.6B", "url": "https://www.anchorterminal.com/compare/inkling-vs-lfm2-5-2-6b" }, { "json": "https://www.anchorterminal.com/compare/inkling-vs-qwen3-6-35b-a3b.json", "title": 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"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": 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"https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-qwen3-6-35b-a3b.json", "title": "MiMo-V2.6 Distill Qwen 9B vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-6-35b-a3b.json", "title": "MiniCPM-o 4.5 vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-qwen3-6-35b-a3b.json", "title": "MiniCPM5-2B vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-qwen3-6-35b-a3b.json", "title": "MiniMax-M2.5 vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-6-35b-a3b.json", "title": "MiniMax-M2.7 vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-6-35b-a3b.json", "title": "Muse Glimmer 30B vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-qwen3-6-35b-a3b.json", "title": "Nemotron 3.5 Lightning 30B A3B vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-qwen3-6-35b-a3b.json", "title": "Nemotron 3 Nano 30B A3B vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-qwen3-6-35b-a3b.json", "title": "Nemotron 3 Nano 4B vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-qwen3-6-35b-a3b.json", "title": "Nemotron 3 Nano Omni 30B A3B Reasoning vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-qwen3-6-35b-a3b.json", "title": "Nemotron 3 Super 120B A12B vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-qwen3-6-35b-a3b.json", "title": "Nemotron 3 Ultra 550B A55B vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-6-35b-a3b.json", "title": "occamy-1.0 vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-6-35b-a3b.json", "title": "Olmo 3 7B Think vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-6-35b-a3b.json", "title": "Ornith-1.5-35B-A3B vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-6-35b-a3b.json", "title": "Ornith-1.5-397B vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-6-35b-a3b.json", "title": "Ornith-1.5-9B vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-30b-a3b-instruct-2507-vs-qwen3-6-35b-a3b.json", "title": "Qwen3-30B-A3B-Instruct-2507 vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/qwen3-30b-a3b-instruct-2507-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-4b-instruct-2507-vs-qwen3-6-35b-a3b.json", "title": "Qwen3-4B-Instruct-2507 vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/qwen3-4b-instruct-2507-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-qwen3-6-35b-a3b.json", "title": "Qwen3.5-0.8B vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-qwen3-6-35b-a3b.json", "title": "Qwen3.5-122B-A10B vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-2b-vs-qwen3-6-35b-a3b.json", "title": "Qwen3.5-2B vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/qwen3-5-2b-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-qwen3-6-35b-a3b.json", "title": "Qwen3.5-397B-A17B vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-4b-vs-qwen3-6-35b-a3b.json", "title": "Qwen3.5-4B vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/qwen3-5-4b-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-qwen3-6-35b-a3b.json", "title": "Qwen3.5-9B vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-6-27b-vs-qwen3-6-35b-a3b.json", "title": "Qwen3.6-27B vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/qwen3-6-27b-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-qwen3-8-27b.json", "title": "Qwen3.6-35B-A3B vs Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-qwen3-8-flash-next.json", "title": "Qwen3.6-35B-A3B vs Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-qwen3-coder-30b-a3b-instruct.json", "title": "Qwen3.6-35B-A3B vs Qwen3-Coder-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-qwen3-coder-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-qwen3-coder-next.json", "title": "Qwen3.6-35B-A3B vs Qwen3-Coder-Next", "url": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-qwen3-coder-next" }, { "json": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-qwen3-omni-30b-a3b-instruct.json", "title": "Qwen3.6-35B-A3B vs Qwen3-Omni-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-qwen3-omni-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-qwen3-vl-2b-instruct.json", "title": "Qwen3.6-35B-A3B vs Qwen3-VL-2B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-qwen3-vl-2b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-qwen3-vl-30b-a3b-instruct.json", "title": "Qwen3.6-35B-A3B vs Qwen3-VL-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-qwen3-vl-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-qwen3-vl-32b-instruct.json", "title": "Qwen3.6-35B-A3B vs Qwen3-VL-32B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-qwen3-vl-32b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-qwen3-vl-4b-instruct.json", "title": "Qwen3.6-35B-A3B vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-qwen3-vl-8b-instruct.json", "title": "Qwen3.6-35B-A3B vs Qwen3-VL-8B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-qwen3-vl-8b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-qwopus3-8-27b-flash-v2.json", "title": "Qwen3.6-35B-A3B vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-smollm3-3b.json", "title": "Qwen3.6-35B-A3B vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-spark-x2-6-4b.json", "title": "Qwen3.6-35B-A3B vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-swift-1-5-qwen3-8-27b.json", "title": "Qwen3.6-35B-A3B vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-swift-1-5-qwen3-8-flash-next.json", "title": "Qwen3.6-35B-A3B vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-thinkingcap-qwen3-8-27b.json", "title": "Qwen3.6-35B-A3B vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-thinkingcap-qwen3-8-27b" } ], "scores": [ { "by": 45, "edge": "qwen3-6-35b-a3b", "key": "capability", "lfm2-5-2-6b": 13, "name": "Capability", "qwen3-6-35b-a3b": 58, "weight": 35 }, { "by": 10, "edge": "lfm2-5-2-6b", "key": "reach", "lfm2-5-2-6b": 95, "name": "Reach", "qwen3-6-35b-a3b": 85, "weight": 25 }, { "by": 5, "edge": "qwen3-6-35b-a3b", "key": "risk", "lfm2-5-2-6b": 50, "name": "Risk", "qwen3-6-35b-a3b": 55, "weight": 20 }, { "by": 5, "edge": "lfm2-5-2-6b", "key": "interface", "lfm2-5-2-6b": 75, "name": "Agent interface", "qwen3-6-35b-a3b": 70, "weight": 10 }, { "by": 21, "edge": "lfm2-5-2-6b", "key": "upkeep", "lfm2-5-2-6b": 57, "name": "Documentation and upkeep", "qwen3-6-35b-a3b": 36, "weight": 10 } ], "summary": "Qwen3.6-35B-A3B scores 63.2 (B) on agent readiness against LFM2.5-2.6B's 51.5 (D), and leads in 2 of 5 scored categories. LFM2.5-2.6B leads on reach, agent interface and documentation and upkeep. Both do open-weight models you run yourself.", "tutorials": [], "verdicts": { "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.", "qwen3-6-35b-a3b": "A fast mixture-of-experts cousin with 3B active parameters for coding and tool calling, with image and video input, in every major runtime under plain Apache 2.0. Ollama's 4-bit build needs about 28 GB. The main risk is that nobody at Alibaba maintains it, with no safety results, no security contact and no replies to tool-calling reports." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "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-qwen3-6-35b-a3b.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-6-35b-a3b.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-6-35b-a3b.min.md" }, "markdown": "Qwen3.6-35B-A3B scores 63.2 (B) on agent readiness against LFM2.5-2.6B's 51.5 (D), and leads in 2 of 5 scored categories. LFM2.5-2.6B leads on reach, agent interface and documentation and upkeep. Both do open-weight models you run yourself.\n\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- Qwen3.6-35B-A3B: grade B, 63.2/100, rank #25 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/qwen3-6-35b-a3b.md · JSON https://www.anchorterminal.com/api/v1/tools/qwen3-6-35b-a3b.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### 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 85\n- Agent interface, 75 against 70\n- Documentation and upkeep, 57 against 36\n\nWatch for: Commercial use isn't licensed for organisations with $10M or more of annual revenue\n\n### Qwen3.6-35B-A3B (B)\n\nGood for: Agents with a 32 GB or larger machine that want a quick, permissively licensed coding and tool-calling cousin with vision.\n\nAhead on:\n- Capability, 58 against 13\n- Risk, 55 against 50\n\nWatch for: No safety evaluation, no agent-use guidance and no way to report a security flaw\n\n\n## Score by category\n\n| Category | Weight | LFM2.5-2.6B | Qwen3.6-35B-A3B | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35% (35 this run) | 13 | 58 | Qwen3.6-35B-A3B +45 |\n| Reach | 25% (25 this run) | 95 | 85 | LFM2.5-2.6B +10 |\n| Risk | 20% (20 this run) | 50 | 55 | Qwen3.6-35B-A3B +5 |\n| Agent interface | 10% (10 this run) | 75 | 70 | LFM2.5-2.6B +5 |\n| Documentation and upkeep | 10% (10 this run) | 57 | 36 | LFM2.5-2.6B +21 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **51.5 · D** | **63.2 · B** | |\n\n## Facts side by side\n\n| Fact | LFM2.5-2.6B | Qwen3.6-35B-A3B |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Liquid AI | Alibaba (Qwen team) |\n| Hosted endpoint | no (local only) | no (local only) |\n| Transports | | |\n| Auth | None | None |\n| Pricing | Free | Free |\n| x402 | no | no |\n| Licence | LFM Open License v1.0 (free commercial use below $10M annual revenue) | Apache-2.0 |\n| Read-only variant documented | no | no |\n| llms.txt | yes | no |\n| Last release | 2026-09-22 | 2026-04-16 |\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 | 2.5/5 (4) | 2.8/5 (4) |\n\n## Verdicts\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**Qwen3.6-35B-A3B.** A fast mixture-of-experts cousin with 3B active parameters for coding and tool calling, with image and video input, in every major runtime under plain Apache 2.0. Ollama's 4-bit build needs about 28 GB. The main risk is that nobody at Alibaba maintains it, with no safety results, no security contact and no replies to tool-calling reports.\n\n## Before you call either\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### Qwen3.6-35B-A3B\n\n1. Start it with `ollama run qwen3.6:35b` (Q4_K_M, 23 to 24 GB on disk, about 28 GB in memory)\n2. On the reference machine use `vllm serve Qwen/Qwen3.6-35B-A3B --max-model-len 131072 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder`, BF16, since vLLM doesn't document FP8 compute on gfx1151\n3. Use the qwen3_coder tool-call parser and the qwen3 reasoning parser in vLLM 0.19.0 or SGLang 0.5.10 or later\n4. Thinking is on by default and /nothink isn't supported, so pass enable_thinking as false in chat_template_kwargs for short tasks, or preserve_thinking as true to keep earlier reasoning\n5. Card sampling for thinking is temperature 1.0, top_p 0.95, top_k 20, presence_penalty 1.5, and for non-thinking temperature 0.7, top_p 0.8\n6. Keep at least 128K of context as the card advises, and turn on YaRN only beyond 262,144 tokens\n7. Validate JSON output yourself or use the runtime's guided decoding, since the model card documents none\n\n## Questions\n\n### Which is better for AI agents, LFM2.5-2.6B or Qwen3.6-35B-A3B?\n\nQwen3.6-35B-A3B scores 63.2 (B) on agent readiness against LFM2.5-2.6B's 51.5 (D), and leads in 2 of 5 scored categories. LFM2.5-2.6B leads on reach, agent interface and documentation and upkeep.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-6-35b-a3b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-6-35b-a3b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"lfm2-5-2-6b\", \"b\": \"qwen3-6-35b-a3b\"}`. From a terminal: `anchor compare lfm2-5-2-6b qwen3-6-35b-a3b`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/lfm2-5-2-6b.json and https://www.anchorterminal.com/api/v1/tools/qwen3-6-35b-a3b.json\n\n## Other comparisons with LFM2.5-2.6B or Qwen3.6-35B-A3B\n\n- [Agents-A1-4B vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-lfm2-5-2-6b.md)\n- [Agents-A1-4B vs Qwen3.6-35B-A3B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-qwen3-6-35b-a3b.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- [Apertus v1.5 8B vs Qwen3.6-35B-A3B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-qwen3-6-35b-a3b.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- [Apodex-1.1-mini vs Qwen3.6-35B-A3B](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-qwen3-6-35b-a3b.md)\n- [BigBang-v1 vs 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26B A4B IT vs Qwen3.6-35B-A3B](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-qwen3-6-35b-a3b.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 31B IT vs Qwen3.6-35B-A3B](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-6-35b-a3b.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 E2B IT vs Qwen3.6-35B-A3B](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-qwen3-6-35b-a3b.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- [Gemma 4 E4B IT vs Qwen3.6-35B-A3B](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-qwen3-6-35b-a3b.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-4.5-Air vs Qwen3.6-35B-A3B](https://www.anchorterminal.com/compare/glm-4-5-air-vs-qwen3-6-35b-a3b.md)\n- 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Olmo 3 7B Think](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-olmo-3-7b-think.md)\n- [LFM2.5-2.6B vs Ornith-1.5-35B-A3B](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-ornith-1-5-35b-a3b.md)\n- [LFM2.5-2.6B vs Ornith-1.5-397B](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-ornith-1-5-397b.md)\n- [LFM2.5-2.6B vs Ornith-1.5-9B](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-ornith-1-5-9b.md)\n- [LFM2.5-2.6B vs Qwen3-30B-A3B-Instruct-2507](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-30b-a3b-instruct-2507.md)\n- [LFM2.5-2.6B vs Qwen3-4B-Instruct-2507](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-4b-instruct-2507.md)\n- [LFM2.5-2.6B vs Qwen3.5-0.8B](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-5-0-8b.md)\n- [LFM2.5-2.6B vs Qwen3.5-122B-A10B](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-5-122b-a10b.md)\n- [LFM2.5-2.6B vs Qwen3.5-2B](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-5-2b.md)\n- [LFM2.5-2.6B vs Qwen3.5-397B-A17B](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-5-397b-a17b.md)\n- [LFM2.5-2.6B vs Qwen3.5-4B](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-5-4b.md)\n- [LFM2.5-2.6B vs Qwen3.5-9B](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-5-9b.md)\n- [LFM2.5-2.6B vs Qwen3.6-27B](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-6-27b.md)\n- [LFM2.5-2.6B vs Qwen3.8-27B](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-8-27b.md)\n- [LFM2.5-2.6B vs Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-8-flash-next.md)\n- [LFM2.5-2.6B vs Qwen3-Coder-30B-A3B-Instruct](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-coder-30b-a3b-instruct.md)\n- [LFM2.5-2.6B vs Qwen3-Coder-Next](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-coder-next.md)\n- [LFM2.5-2.6B vs 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[Qwen3.6-35B-A3B vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-thinkingcap-qwen3-8-27b.md)\n", "meta": { "attribution": "Anchor Terminal (https://www.anchorterminal.com)", "attributionRequired": true, "credit": "CC BY 4.0, credit Anchor Terminal and link the page or API address you took it from (https://www.anchorterminal.com/terms/#data)", "docs": "https://www.anchorterminal.com/docs/", "generatedAt": "2026-10-21", "license": "CC-BY-4.0", "method": "https://www.anchorterminal.com/benchmark/", "methodology": "0.6", "openapi": "https://www.anchorterminal.com/openapi.json", "preview": false, "run": "2026-10-01", "runLabel": "October 2026 research run", "terms": "https://www.anchorterminal.com/terms/#data" }, "page": { "breadcrumbs": [ { "name": "Home", "url": "https://www.anchorterminal.com/" }, { "name": "Compare", "url": "https://www.anchorterminal.com/compare/" }, { "name": "LFM2.5-2.6B vs Qwen3.6-35B-A3B", "url": "" } ], "description": "Qwen3.6-35B-A3B scores 63.2 (B) to LFM2.5-2.6B's 51.5 (D) for open-weight models you run yourself. 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