{ "data": { "a": { "slug": "qwen3-5-397b-a17b", "name": "Qwen3.5-397B-A17B", "vendor": "Alibaba (Qwen team)", "vendorUrl": "https://qwen.ai", "kind": "weights", "category": "open-weights", "summary": "Qwen3.5-397B-A17B is Alibaba's largest open-weight Qwen3.5 model, a mixture-of-experts vision-language model of 397B parameters with 17B active, thinking on by default and a 262,144-token context, published on Hugging Face in BF16, FP8 and GPTQ 4-bit under Apache 2.0.", "url": "https://www.anchorterminal.com/tools/qwen3-5-397b-a17b", "markdownUrl": "https://www.anchorterminal.com/tools/qwen3-5-397b-a17b.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/qwen3-5-397b-a17b.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/qwen3-5-397b-a17b.json", "dns": "qwen3-5-397b-a17b.grade.anchorterminal.com", "repo": "https://huggingface.co/Qwen/Qwen3.5-397B-A17B", "license": "Apache-2.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "Qwen/Qwen3.5-397B-A17B" }, { "registry": "huggingface", "name": "Qwen/Qwen3.5-397B-A17B-FP8" }, { "registry": "huggingface", "name": "Qwen/Qwen3.5-397B-A17B-GPTQ-Int4" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. None of the three repositories is gated (checked 2026-10-21).", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0. The card says Qwen3.5-Plus on Alibaba Cloud Model Studio is the hosted version of this model, and several inference providers serve it per token. Running it yourself costs the hardware, 806.8 GB for the BF16 files, 406.2 GB in FP8 and 235.8 GB for Alibaba's GPTQ 4-bit 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/Qwen3.5-397B-A17B", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "moe", "large", "multimodal", "reasoning", "tool-calling", "apache-2.0", "safetensors", "fp8", "gptq", "self-hosted" ], "lastRelease": "2026-02-16", "graded": true, "anchor": { "graded": true, "score": 64.1, "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": 1518, "categoryRank": 17, "methodology": "0.7", "run": "2026-10-01", "scores": { "capability": 72, "interface": 70, "reach": 70, "risk": 55, "upkeep": 34 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "high", "date": "2026-11-02" }, "negative": 0, "verdict": "Alibaba's biggest open Qwen3.5, a cousin for hard coding, agent and vision work. Even at 4 bits it needs about 270 GB, so most agents will call it through Alibaba's Model Studio or one of the many providers that host it. The main risk is silence, with no safety results, no agent guidance and template reports without a reply.", "bestFor": "Agents with access to an 8-GPU server, or calling a hosted endpoint, that want the strongest open Qwen3.5 with vision and a plain licence.", "strengths": [ "Released support in llama.cpp, vLLM, SGLang, Transformers and LM Studio", "Alibaba's own FP8 and GPTQ 4-bit builds", "Plain Apache 2.0 with no gate, no account and no use policy", "Safetensors only, with a checksum on every file in the publisher's own organisation", "262,144 tokens native, several tool calls per turn and thinking that turns off per request", "Hosted by Alibaba as Qwen3.5-Plus and by at least five other providers" ], "weaknesses": [ "No build fits a 256 GB machine, and Ollama has no local build", "No safety results, agent-use guidance or reporting route for flaws", "No reply from Alibaba to any of the 40 most recent discussions, including template bug reports", "Tool format is only in the template and structured output isn't documented", "No limitations, knowledge cut-off or training data sources on the card" ], "agentNotes": [ "The card's server command is `vllm serve Qwen/Qwen3.5-397B-A17B --port 8000 --tensor-parallel-size 8 --max-model-len 262144 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder`, and the FP8 build halves the memory", "It doesn't fit our 128 GB reference machine at any precision, so call a hosted endpoint instead (Model Studio's Qwen3.5-Plus, or a provider serving Qwen/Qwen3.5-397B-A17B)", "On Model Studio pass `\"enable_thinking\": false` directly, while vLLM and SGLang take `\"chat_template_kwargs\": {\"enable_thinking\": false}`", "Sampling per the card is temperature 0.6, top_p 0.95 and top_k 20 when thinking, and temperature 0.7, top_p 0.8, top_k 20 and presence_penalty 1.5 without", "For multi-token prediction in vLLM add `--speculative-config '{\"method\":\"qwen3_next_mtp\",\"num_speculative_tokens\":2}'`", "Keep the context at 128K or more if you rely on thinking, and turn on YaRN only past 262,144 tokens", "Validate any JSON it returns, since structured output isn't documented" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 2.8, "history": [ { "basis": "public evidence", "confidence": "high", "grade": "B", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 64.1 } ], "editorialScores": { "capability": 72, "interface": 70, "reach": 70, "risk": 55, "upkeep": 34 }, "provenanceScore": 54, "checked": { "categories": { "capability": "2026-11-02", "interface": "2026-11-02", "reach": "2026-11-02", "risk": "2026-11-02", "upkeep": "2026-11-02" }, "last": "2026-11-02", "lastMoved": "2026-11-02", "oldest": "2026-11-02", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "# The card's vLLM command, tensor parallel on 8 GPUs:\nvllm serve Qwen/Qwen3.5-397B-A17B-FP8 --port 8000 --tensor-parallel-size 8 --max-model-len 262144 \\\n --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder", "http": "curl http://localhost:8000/v1/chat/completions \\\n -H 'Content-Type: application/json' \\\n -d '{\"model\": \"Qwen/Qwen3.5-397B-A17B-FP8\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/qwen3-5-397b-a17b" }, "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", "qwen-agentworld-35b-a3b", "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", "qwen3-5-4b", "qwen3-5-9b", "qwen3-6-27b", "qwen3-6-35b-a3b", "qwen3-8-27b", "qwen3-8-flash-next" ], "weights": { "trainedBy": "Alibaba (Qwen team)", "params": "397B total, 17B active", "context": 262144, "goodFor": "Alibaba's largest open Qwen3.5, a mixture-of-experts vision-language model for coding, agents and tool calling on a multi-GPU server, thinking on by default.", "runtimes": [ "llama.cpp", "LM Studio", "vLLM", "SGLang", "Transformers" ], "builds": [ { "name": "Qwen3.5-397B-A17B-FP8", "url": "https://huggingface.co/Qwen/Qwen3.5-397B-A17B-FP8", "who": "publisher", "precision": "fp8", "sizeGB": 406.2, "memoryGB": 512, "format": "safetensors" }, { "name": "Qwen3.5-397B-A17B-GPTQ-Int4", "url": "https://huggingface.co/Qwen/Qwen3.5-397B-A17B-GPTQ-Int4", "who": "publisher", "precision": "gptq-int4", "sizeGB": 235.8, "memoryGB": 512, "format": "safetensors", "note": "The card serves it with `--quantization moe_wna16` in vLLM and SGLang" }, { "name": "lmstudio-community Qwen3.5-397B-A17B-MLX-4bit", "url": "https://huggingface.co/lmstudio-community/Qwen3.5-397B-A17B-MLX-4bit", "who": "lmstudio", "precision": "mlx-4bit", "sizeGB": 223.9, "memoryGB": 512, "format": "mlx", "runsOn": [ "LM Studio (Apple silicon)" ], "note": "The smallest build at 4 bits or better from the publisher or a runtime's library, summed from the Hub's file list. LM Studio's catalogue entry states a minimum of 230 GB of system memory" }, { "name": "lmstudio-community Qwen3.5-397B-A17B-GGUF Q4_K_M", "url": "https://huggingface.co/lmstudio-community/Qwen3.5-397B-A17B-GGUF", "who": "lmstudio", "precision": "q4_k_m", "sizeGB": 241.5, "memoryGB": 512, "format": "gguf", "runsOn": [ "llama.cpp", "LM Studio" ], "note": "240.6 GB of Q4_K_M weights plus a 0.9 GB BF16 vision projector, summed from the Hub's file list" }, { "name": "Qwen3.5-397B-A17B", "url": "https://huggingface.co/Qwen/Qwen3.5-397B-A17B", "who": "publisher", "precision": "bf16", "sizeGB": 806.8, "memoryGB": 1024, "format": "safetensors", "runsOn": [ "vLLM (ROCm)", "SGLang (ROCm)" ] } ], "run": "vllm serve Qwen/Qwen3.5-397B-A17B-FP8 --port 8000 --tensor-parallel-size 8 --max-model-len 262144 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder", "runBuild": "Qwen3.5-397B-A17B-FP8", "checked": "2026-10-21", "minMemoryGB": 512 }, "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": "Qwen3.5-122B-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": "Qwen3.6-35B-A3B", "url": "https://huggingface.co/Qwen/Qwen3.6-35B-A3B", "params": "35B-A3B", "listing": "https://www.anchorterminal.com/tools/qwen3-6-35b-a3b" }, { "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. The domain's registration date is per the 20 October 2026 RDAP check for qwen3-8-27b.", "Nothing is hosted, so the Apache 2.0 licence stands in for terms of service and no privacy policy is scored.", "No changelog for the files. The commit history's messages are uploads, README updates and two merged pull requests (a KTransformers guide on 20 February and a corrected medical VQA score on 15 March 2026), the last commit on 24 April 2026. The News list in github.com/QwenLM/Qwen3.5 dates the release to 16 February 2026.", "qwen.ai answers /.well-known/security.txt with a redirect to its home page, so there is no security.txt." ], "score": 54 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/qwen3-5-397b-a17b.json", "live": { "slug": "qwen3-5-397b-a17b", "securityTxt": { "url": "https://qwen.ai/.well-known/security.txt", "state": "none", "checkedAt": "2026-11-10T16:09:18.413964975Z" }, "domain": { "domain": "qwen.ai", "registered": "2023-08-27", "source": "https://rdap.identitydigital.services/rdap/domain/qwen.ai", "checkedAt": "2026-11-08T15:07:12.468678256Z" }, "updatedAt": "2026-11-10T16:09:18.413964975Z" } }, "answer": "Qwen3.5-397B-A17B scores 64.1 (B) on agent readiness against SenseNova-Loop-9B-Preview's 46.8 (D), and leads in 3 of 4 scored categories. SenseNova-Loop-9B-Preview leads on documentation and upkeep.", "b": { "slug": "sensenova-loop-9b-preview", "name": "SenseNova-Loop-9B-Preview", "vendor": "SenseTime (SenseNova)", "vendorUrl": "https://www.sensetime.com/en", "kind": "weights", "category": "open-weights", "summary": "SenseNova-Loop-9B-Preview is SenseTime's open-weight multimodal model of about 9B parameters. It runs 24 of its 32 decoder layers twice, with text and images in, tool calling, a thinking switch and a 256K context. Apache 2.0. No released runtime loads it.", "url": "https://www.anchorterminal.com/tools/sensenova-loop-9b-preview", "markdownUrl": "https://www.anchorterminal.com/tools/sensenova-loop-9b-preview.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/sensenova-loop-9b-preview.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/sensenova-loop-9b-preview.json", "dns": "sensenova-loop-9b-preview.grade.anchorterminal.com", "repo": "https://huggingface.co/sensenova/SenseNova-Loop-9B-Preview", "license": "Apache-2.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "sensenova/SenseNova-Loop-9B-Preview" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The repository is not gated (checked 2026-11-10).", "pricing": "free", "pricingNotes": "Free to download and use under Apache 2.0. Running it yourself costs the hardware: the files are 38.1 GB because most weights are stored in 32-bit floats, and the card's example loads them as BF16 (checked 2026-11-10).", "priceSummary": "Free", "where": "local", "x402": { "level": "no", "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-11-10).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-11-10" }, "docsUrl": "https://github.com/OpenSenseNova/SenseNova-Loop", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "multimodal", "vision", "looped-transformer", "tool-calling", "reasoning", "long-context", "preview", "apache-2.0", "safetensors", "custom-code", "self-hosted" ], "lastRelease": "2026-11-02", "graded": true, "anchor": { "graded": true, "score": 46.8, "grade": "D", "agentReady": false, "rank": 0, "ranked": false, "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools", "rankOf": 1518, "categoryRank": 110, "methodology": "0.7", "run": "2026-10-01", "scores": { "interface": 60, "reach": 40, "risk": 50, "upkeep": 44 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-11-10" }, "negative": 0, "verdict": "An unusual architecture under Apache 2.0, with a 256K training context, a thinking switch and tool calling in the template. It's a preview in practice too: no released runtime runs it, loading it runs the repository's code, the files are twice the size the card implies, and nobody from SenseNova has answered a question yet.", "bestFor": "Researchers who want to study or fine-tune a looped transformer, more than agents that need a model to call today", "strengths": [ "Apache 2.0, ungated", "A 256K training context, stated on the card", "Thinking can be switched per request", "The architecture and parameter counts are documented", "Changed in the last ten days" ], "weaknesses": [ "No released runtime runs it. vLLM and SGLang need unmerged pull requests", "Loading it runs Python from the repository", "The files are 38.1 GB, mostly F32, though the card says BF16", "No quantised build", "No tool-calling documentation beyond the template", "No limitations, safety results or data sources, and the technical report isn't out", "None of 5 questions answered" ], "agentNotes": [ "Read modeling_sensenova_loop.py before you load it. trust_remote_code runs it", "Pin transformers 5.18.0, the version the card checked", "Load with dtype=torch.bfloat16 as the card does, or the 38 GB of F32 weights stay at full size", "Pass enable_thinking=False to apply_chat_template for a direct answer", "Tool calls come back as tool_call blocks with function and parameter tags. Parse them yourself, since no runtime ships a parser", "Wait for the vLLM or SGLang pull request to land in a release before putting it behind an API" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 0, "avgRating": 0, "history": [ { "basis": "public evidence", "confidence": "low", "grade": "D", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 46.8 } ], "editorialScores": { "interface": 60, "reach": 40, "risk": 50, "upkeep": 44 }, "provenanceScore": 51, "checked": { "categories": { "interface": "2026-11-10", "reach": "2026-11-10", "risk": "2026-11-10", "upkeep": "2026-11-10" }, "last": "2026-11-10", "oldest": "2026-11-10", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "pip install \"transformers==5.18.0\" accelerate safetensors\n# then AutoModelForCausalLM.from_pretrained(\"sensenova/SenseNova-Loop-9B-Preview\", trust_remote_code=True, dtype=torch.bfloat16)\n# trust_remote_code runs modeling_sensenova_loop.py from the repository. Read it first", "http": "# No released server runs it. The card's vLLM and SGLang commands build from open pull requests,\n# and its LightLLM example serves an OpenAI-compatible API at http://localhost:8000/v1 on four GPUs" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/sensenova-loop-9b-preview" }, "weights": { "trainedBy": "SenseTime (SenseNova)", "params": "9B", "context": 262144, "goodFor": "Trying a looped transformer for long-context, tool-using work, if you can run the repository's own code on a large GPU.", "runtimes": [], "builds": [ { "name": "SenseNova-Loop-9B-Preview (safetensors, mostly F32)", "url": "https://huggingface.co/sensenova/SenseNova-Loop-9B-Preview/tree/main", "who": "publisher", "precision": "f32", "sizeGB": 38.14, "memoryGB": 48, "format": "safetensors", "note": "Eight shards and the MTP head. Stored mostly as 32-bit floats; the card's example loads it as BF16, about half the size in memory" } ], "run": "python -c \"from transformers import AutoModelForCausalLM; AutoModelForCausalLM.from_pretrained('sensenova/SenseNova-Loop-9B-Preview', trust_remote_code=True)\"", "runBuild": "SenseNova-Loop-9B-Preview (safetensors, mostly F32)", "checked": "2026-11-10", "minMemoryGB": 48 }, "family": { "lab": "SenseTime (SenseNova)", "family": "SenseNova-Loop", "url": "https://www.anchorterminal.com/open-weights/#sensenova" }, "area": "models", "provenance": { "legalEntity": "SenseTime, listed on the Stock Exchange of Hong Kong (00020) per its investor page. SenseNova is its foundation model series, per its site", "domain": "sensetime.com", "domainRegistered": "", "endpointOnVendorDomain": null, "terms": "https://github.com/OpenSenseNova/SenseNova-Loop/blob/main/LICENSE", "privacy": "", "statusPage": "", "changelog": "https://huggingface.co/sensenova/SenseNova-Loop-9B-Preview/commits/main", "securityTxt": "none", "checked": "2026-11-10", "notes": [ "The card names SenseNova only. SenseTime's site presents SenseNova as its foundation model series, and that is the tie between the two; the site wasn't read for a link to this repository.", "The domain's registration date wasn't looked up.", "Nothing is hosted under this listing, so Apache 2.0 stands in for terms of service and no privacy policy is scored.", "www.sensetime.com has no security.txt (404 on 10 November 2026). Its about page gives a security contact.", "The changelog is the commit history, 39 commits from 28 October to 2 November 2026." ], "score": 51 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/sensenova-loop-9b-preview.json" }, "edges": [ { "by": 30, "category": "reach", "leads": "qwen3-5-397b-a17b", "name": "Reach", "reasons": { "a": "Memory scores 0, since even the smallest 4-bit build, LM Studio's MLX 4-bit at 223.9 GB, needs about 269 GB with a fifth added, and Alibaba's own GPTQ-Int4 about 283 GB. Runtimes reach the 25 cap on released llama.cpp, vLLM, SGLang and Transformers and LM Studio's catalogue, though Ollama has no local build. Alibaba's FP8 and GPTQ-Int4 builds give builds 15. Apache 2.0 with nothing added gives terms 15, the ungated repositories give download 10, and Alibaba's Model Studio plus five live providers on Hugging Face give hosted too its 5.", "b": "Memory 15, the only build is the safetensors set, 38.14 GB with its MTP head, since the Hub counts 9.41B of its parameters in F32. Runtimes 0, none of the six runs it in a released version. Builds 0, SenseNova publishes one set of weights and no quantised build. Terms 15, Apache 2.0 in the card's metadata and on the card, which links the LICENSE in the GitHub repository, and nothing added. Download 10, the Hub API reports gated false on 10 November 2026, so the files download with no account and nothing to accept. Hosted 0, Hugging Face maps no inference provider to it, and the card names no API that serves it. 15 + 0 + 0 + 15 + 10 + 0 = 40." }, "scores": { "a": 70, "b": 40 } }, { "by": 10, "category": "interface", "leads": "qwen3-5-397b-a17b", "name": "Agent interface", "reasons": { "a": "The template ships in the repository (20) and renders several tool calls per turn for a model trained for tool use (20). Tool-calling format gets 10, because the card names the qwen3_coder parser but leaves the declaration and call format to the template. Thinking turns off per request and comes back in think tags split by the qwen3 parser (10), and 262,144 tokens is native (10). Structured output scores 0, as nothing documents JSON output for the model.", "b": "Chat template 20, chat_template.jinja in the repository (7,756 characters). Tool-calling format 10, only the template says it: a tools block, and calls written as tool_call blocks with function and parameter tags. Trained tool calling 10, the card says the training recipes include tool trajectories and that the model is for agentic workflows. Structured output 0, Nothing on the card about JSON or schema-constrained output. Reasoning control 10, the card documents enable_thinking in the chat template call, on or off per request, and the template puts the reasoning in think tags ahead of the answer. Context 10, 262,144 tokens in training, per the card's specification table, and config.json gives the language model 262,144 positions. 20 + 10 + 10 + 0 + 10 + 10 = 60." }, "scores": { "a": 70, "b": 60 } }, { "by": 10, "category": "upkeep", "leads": "sensenova-loop-9b-preview", "name": "Documentation and upkeep", "reasons": { "a": "The card covers five of the seven items and misses limitations and a cut-off (14). Training data is a general description without a cut-off (5). The files last changed on 24 April 2026, 192 days before this check, past the 180-day band and within a year (5), and Qwen's GitHub repository holds only a README and licence, so no reference code of Alibaba's changed later. Alibaba answered none of the 40 most recent questions and reports, recounted on 2 November (0), and GitHub issues couldn't be counted. It's a first release at this size (5), and two template reports and a template fix still wait unanswered (5). 14 + 5 + 5 + 0 + 5 + 5 = 34.", "b": "Model card 14, Intended use, the architecture, the parameter counts, the training context and the languages (in the metadata) are on the card. Training data 5, one general sentence on the recipes, with no sources, sizes or cut-off. Last change 20, the last commit, on 2 November 2026, reworked the speculative decoding section of the card, 8 days before this check. Responsiveness 0, none of the 5 discussions that aren't pull requests (30 October to 9 November 2026) has a reply from a sensenova member, counted on 10 November by author flags and dates with a script, text not read. Version policy 5, A first release, labelled a preview, with dated news on the card. File health 0, the files contradict the card on precision. 14 + 5 + 20 + 0 + 5 + 0 = 44." }, "scores": { "a": 34, "b": 44 } }, { "by": 5, "category": "risk", "leads": "qwen3-5-397b-a17b", "name": "Risk", "reasons": { "a": "Safetensors only with no repository code run on load or to build prompts gives file safety 25, and files in the Qwen organisation the publisher's GitHub links to, each hashed on the Hub, give provenance 15. Plain Apache 2.0 keeps licence risk at 15. Safety evaluation, agent guidance and a security programme score 0. The card has no safety results (its SecCodeBench row is a coding capability), no cautions, and no reporting route was found. The Qwen3.5 blog and Alibaba's security response centre couldn't be read, so both are scored as absent and left open.", "b": "File safety 15, safetensors only, no .bin, .pt or .pth files (25). Provenance 15, the files sit in the sensenova organisation, which the GitHub repository for the model links to (10), and the Hub gives a SHA-256 for the large files (5). Safety evaluation 0, no safety results on the card. Agent guidance 0, the card has no limitations or safety section, and nothing on instructions inside tool results or images. Licence risk 15, Apache 2.0 with no use policy, no threshold and no right to change the terms. Security programme 5, SenseTime's about page gives a security contact (5). 15 + 15 + 0 + 0 + 15 + 5 = 50." }, "scores": { "a": 55, "b": 50 } } ], "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "Alibaba (Qwen team)", "b": "SenseTime (SenseNova)", "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", "b": "Apache-2.0", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "no", "b": "no", "name": "llms.txt" }, { "a": "2026-02-16", "b": "2026-11-02", "name": "Last release" }, { "a": "no document linked", "b": "not read yet", "name": "Terms last updated" }, { "a": "no document linked", "b": "no document linked", "name": "Privacy policy last updated" }, { "a": "2.8/5 (4)", "b": "none", "name": "Agent reviews" } ], "faq": [ { "answer": "Qwen3.5-397B-A17B scores 64.1 (B) on agent readiness against SenseNova-Loop-9B-Preview's 46.8 (D), and leads in 3 of 4 scored categories. SenseNova-Loop-9B-Preview leads on documentation and upkeep.", "question": "Which is better for AI agents, Qwen3.5-397B-A17B or SenseNova-Loop-9B-Preview?" } ], "goodFor": [ { "aheadOn": [ "Reach, 70 against 40", "Risk, 55 against 50", "Agent interface, 70 against 60" ], "also": null, "goodFor": "Agents with access to an 8-GPU server, or calling a hosted endpoint, that want the strongest open Qwen3.5 with vision and a plain licence.", "slug": "qwen3-5-397b-a17b", "watchFor": "No build fits a 256 GB machine, and Ollama has no local build" }, { "aheadOn": [ "Documentation and upkeep, 44 against 34" ], "also": null, "goodFor": "Researchers who want to study or fine-tune a looped transformer, more than agents that need a model to call today", "slug": "sensenova-loop-9b-preview", "watchFor": "No released runtime runs it. vLLM and SGLang need unmerged pull requests" } ], "history": { "qwen3-5-397b-a17b": [ { "causes": [ "time" ], "date": "2026-11-02", "delta": -0.5, "from": 64.6, "fromGrade": "B", "to": 64.1, "toGrade": "B" } ], "sensenova-loop-9b-preview": null }, "job": { "capability": "model.open-weights", "name": "Open-weight models you run yourself" }, "others": [ { "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-qwen3-5-397b-a17b.json", "title": "Agents-A1-4B vs Qwen3.5-397B-A17B", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-qwen3-5-397b-a17b" }, { "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-sensenova-loop-9b-preview.json", "title": "Agents-A1-4B vs SenseNova-Loop-9B-Preview", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-sensenova-loop-9b-preview" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-qwen3-5-397b-a17b.json", "title": "Apertus v1.5 8B vs Qwen3.5-397B-A17B", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-qwen3-5-397b-a17b" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-sensenova-loop-9b-preview.json", "title": "Apertus v1.5 8B vs SenseNova-Loop-9B-Preview", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-sensenova-loop-9b-preview" }, { "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-qwen3-5-397b-a17b.json", "title": "Apodex-1.1-mini vs Qwen3.5-397B-A17B", "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-qwen3-5-397b-a17b" }, { "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-sensenova-loop-9b-preview.json", "title": "Apodex-1.1-mini vs SenseNova-Loop-9B-Preview", "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-sensenova-loop-9b-preview" }, { "json": "https://www.anchorterminal.com/compare/arete-27b-vs-qwen3-5-397b-a17b.json", "title": "Arete-27B vs Qwen3.5-397B-A17B", "url": "https://www.anchorterminal.com/compare/arete-27b-vs-qwen3-5-397b-a17b" }, { "json": "https://www.anchorterminal.com/compare/arete-27b-vs-sensenova-loop-9b-preview.json", "title": "Arete-27B vs SenseNova-Loop-9B-Preview", 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SenseNova-Loop-9B-Preview", "url": "https://www.anchorterminal.com/compare/qwen3-8-27b-vs-sensenova-loop-9b-preview" }, { "json": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-sensenova-loop-9b-preview.json", "title": "Qwen3.8-Flash-Next vs SenseNova-Loop-9B-Preview", "url": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-sensenova-loop-9b-preview" }, { "json": "https://www.anchorterminal.com/compare/qwen3-coder-30b-a3b-instruct-vs-sensenova-loop-9b-preview.json", "title": "Qwen3-Coder-30B-A3B-Instruct vs SenseNova-Loop-9B-Preview", "url": "https://www.anchorterminal.com/compare/qwen3-coder-30b-a3b-instruct-vs-sensenova-loop-9b-preview" }, { "json": "https://www.anchorterminal.com/compare/qwen3-coder-next-vs-sensenova-loop-9b-preview.json", "title": "Qwen3-Coder-Next vs SenseNova-Loop-9B-Preview", "url": "https://www.anchorterminal.com/compare/qwen3-coder-next-vs-sensenova-loop-9b-preview" }, { "json": "https://www.anchorterminal.com/compare/qwen3-omni-30b-a3b-instruct-vs-sensenova-loop-9b-preview.json", "title": "Qwen3-Omni-30B-A3B-Instruct vs SenseNova-Loop-9B-Preview", "url": "https://www.anchorterminal.com/compare/qwen3-omni-30b-a3b-instruct-vs-sensenova-loop-9b-preview" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-2b-instruct-vs-sensenova-loop-9b-preview.json", "title": "Qwen3-VL-2B-Instruct vs SenseNova-Loop-9B-Preview", "url": "https://www.anchorterminal.com/compare/qwen3-vl-2b-instruct-vs-sensenova-loop-9b-preview" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-30b-a3b-instruct-vs-sensenova-loop-9b-preview.json", "title": "Qwen3-VL-30B-A3B-Instruct vs SenseNova-Loop-9B-Preview", "url": "https://www.anchorterminal.com/compare/qwen3-vl-30b-a3b-instruct-vs-sensenova-loop-9b-preview" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-32b-instruct-vs-sensenova-loop-9b-preview.json", "title": "Qwen3-VL-32B-Instruct vs SenseNova-Loop-9B-Preview", "url": "https://www.anchorterminal.com/compare/qwen3-vl-32b-instruct-vs-sensenova-loop-9b-preview" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-sensenova-loop-9b-preview.json", "title": "Qwen3-VL-4B-Instruct vs SenseNova-Loop-9B-Preview", "url": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-sensenova-loop-9b-preview" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-8b-instruct-vs-sensenova-loop-9b-preview.json", "title": "Qwen3-VL-8B-Instruct vs SenseNova-Loop-9B-Preview", "url": "https://www.anchorterminal.com/compare/qwen3-vl-8b-instruct-vs-sensenova-loop-9b-preview" }, { "json": "https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-sensenova-loop-9b-preview.json", "title": "Qwopus3.8-27B-Flash-V2 vs SenseNova-Loop-9B-Preview", "url": "https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-sensenova-loop-9b-preview" }, { "json": "https://www.anchorterminal.com/compare/recursive-flint-vs-sensenova-loop-9b-preview.json", "title": "Recursive Flint vs SenseNova-Loop-9B-Preview", "url": "https://www.anchorterminal.com/compare/recursive-flint-vs-sensenova-loop-9b-preview" }, { "json": "https://www.anchorterminal.com/compare/ripple-65b-preview-vs-sensenova-loop-9b-preview.json", "title": "Ripple-65B Preview vs SenseNova-Loop-9B-Preview", "url": "https://www.anchorterminal.com/compare/ripple-65b-preview-vs-sensenova-loop-9b-preview" }, { "json": "https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-smollm3-3b.json", "title": "SenseNova-Loop-9B-Preview vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-spark-x2-6-4b.json", "title": "SenseNova-Loop-9B-Preview vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-step-3-7-flash.json", "title": "SenseNova-Loop-9B-Preview vs Step 3.7 Flash", "url": "https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-step-3-7-flash" }, { "json": "https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-swift-1-5-qwen3-8-27b.json", "title": "SenseNova-Loop-9B-Preview vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-swift-1-5-qwen3-8-flash-next.json", "title": "SenseNova-Loop-9B-Preview vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-thinkingcap-qwen3-8-27b.json", "title": "SenseNova-Loop-9B-Preview vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-unlimited-ocr-2.json", "title": "SenseNova-Loop-9B-Preview vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-wevisone-vl-2b.json", "title": "SenseNova-Loop-9B-Preview vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-wevisone-vl-4b.json", "title": "SenseNova-Loop-9B-Preview vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-zaya1-8b.json", "title": "SenseNova-Loop-9B-Preview vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-zaya1-8b" } ], "panel": null, "scores": [ { "key": "capability", "name": "Capability", "pending": true, "weight": 35 }, { "by": 30, "edge": "qwen3-5-397b-a17b", "key": "reach", "name": "Reach", "qwen3-5-397b-a17b": 70, "sensenova-loop-9b-preview": 40, "weight": 25 }, { "by": 5, "edge": "qwen3-5-397b-a17b", "key": "risk", "name": "Risk", "qwen3-5-397b-a17b": 55, "sensenova-loop-9b-preview": 50, "weight": 20 }, { "by": 10, "edge": "qwen3-5-397b-a17b", "key": "interface", "name": "Agent interface", "qwen3-5-397b-a17b": 70, "sensenova-loop-9b-preview": 60, "weight": 10 }, { "by": 10, "edge": "sensenova-loop-9b-preview", "key": "upkeep", "name": "Documentation and upkeep", "qwen3-5-397b-a17b": 34, "sensenova-loop-9b-preview": 44, "weight": 10 } ], "summary": "Qwen3.5-397B-A17B scores 64.1 (B) on agent readiness against SenseNova-Loop-9B-Preview's 46.8 (D), and leads in 3 of 4 scored categories. SenseNova-Loop-9B-Preview leads on documentation and upkeep. Both do open-weight models you run yourself.", "tasks": null, "tutorials": [], "verdicts": { "qwen3-5-397b-a17b": "Alibaba's biggest open Qwen3.5, a cousin for hard coding, agent and vision work. Even at 4 bits it needs about 270 GB, so most agents will call it through Alibaba's Model Studio or one of the many providers that host it. The main risk is silence, with no safety results, no agent guidance and template reports without a reply.", "sensenova-loop-9b-preview": "An unusual architecture under Apache 2.0, with a 256K training context, a thinking switch and tool calling in the template. It's a preview in practice too: no released runtime runs it, loading it runs the repository's code, the files are twice the size the card implies, and nobody from SenseNova has answered a question yet." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-sensenova-loop-9b-preview", "json": "https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-sensenova-loop-9b-preview.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-sensenova-loop-9b-preview.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-sensenova-loop-9b-preview.min.md" }, "markdown": "Qwen3.5-397B-A17B scores 64.1 (B) on agent readiness against SenseNova-Loop-9B-Preview's 46.8 (D), and leads in 3 of 4 scored categories. SenseNova-Loop-9B-Preview leads on documentation and upkeep. Both do open-weight models you run yourself.\n\n- Qwen3.5-397B-A17B: grade B, 64.1/100, rank #23 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/qwen3-5-397b-a17b.md · JSON https://www.anchorterminal.com/api/v1/tools/qwen3-5-397b-a17b.json\n- SenseNova-Loop-9B-Preview: grade D, 46.8/100, rank #155 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/sensenova-loop-9b-preview.md · JSON https://www.anchorterminal.com/api/v1/tools/sensenova-loop-9b-preview.json\n- Best open-weight models you download and run yourself for AI agents: https://www.anchorterminal.com/best/open-weights/index.md\n- All 6359 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md\n\n## Which one, for what\n\n### Qwen3.5-397B-A17B (B)\n\nGood for: Agents with access to an 8-GPU server, or calling a hosted endpoint, that want the strongest open Qwen3.5 with vision and a plain licence.\n\nAhead on:\n- Reach, 70 against 40\n- Risk, 55 against 50\n- Agent interface, 70 against 60\n\nWatch for: No build fits a 256 GB machine, and Ollama has no local build\n\n### SenseNova-Loop-9B-Preview (D)\n\nGood for: Researchers who want to study or fine-tune a looped transformer, more than agents that need a model to call today\n\nAhead on:\n- Documentation and upkeep, 44 against 34\n\nWatch for: No released runtime runs it. vLLM and SGLang need unmerged pull requests\n\n\n## Why the edge\n\n### Reach: Qwen3.5-397B-A17B +30\n\n- Qwen3.5-397B-A17B (70): Memory scores 0, since even the smallest 4-bit build, LM Studio's MLX 4-bit at 223.9 GB, needs about 269 GB with a fifth added, and Alibaba's own GPTQ-Int4 about 283 GB. Runtimes reach the 25 cap on released llama.cpp, vLLM, SGLang and Transformers and LM Studio's catalogue, though Ollama has no local build. Alibaba's FP8 and GPTQ-Int4 builds give builds 15. Apache 2.0 with nothing added gives terms 15, the ungated repositories give download 10, and Alibaba's Model Studio plus five live providers on Hugging Face give hosted too its 5.\n- SenseNova-Loop-9B-Preview (40): Memory 15, the only build is the safetensors set, 38.14 GB with its MTP head, since the Hub counts 9.41B of its parameters in F32. Runtimes 0, none of the six runs it in a released version. Builds 0, SenseNova publishes one set of weights and no quantised build. Terms 15, Apache 2.0 in the card's metadata and on the card, which links the LICENSE in the GitHub repository, and nothing added. Download 10, the Hub API reports gated false on 10 November 2026, so the files download with no account and nothing to accept. Hosted 0, Hugging Face maps no inference provider to it, and the card names no API that serves it. 15 + 0 + 0 + 15 + 10 + 0 = 40.\n\n### Agent interface: Qwen3.5-397B-A17B +10\n\n- Qwen3.5-397B-A17B (70): The template ships in the repository (20) and renders several tool calls per turn for a model trained for tool use (20). Tool-calling format gets 10, because the card names the qwen3_coder parser but leaves the declaration and call format to the template. Thinking turns off per request and comes back in think tags split by the qwen3 parser (10), and 262,144 tokens is native (10). Structured output scores 0, as nothing documents JSON output for the model.\n- SenseNova-Loop-9B-Preview (60): Chat template 20, chat_template.jinja in the repository (7,756 characters). Tool-calling format 10, only the template says it: a tools block, and calls written as tool_call blocks with function and parameter tags. Trained tool calling 10, the card says the training recipes include tool trajectories and that the model is for agentic workflows. Structured output 0, Nothing on the card about JSON or schema-constrained output. Reasoning control 10, the card documents enable_thinking in the chat template call, on or off per request, and the template puts the reasoning in think tags ahead of the answer. Context 10, 262,144 tokens in training, per the card's specification table, and config.json gives the language model 262,144 positions. 20 + 10 + 10 + 0 + 10 + 10 = 60.\n\n### Documentation and upkeep: SenseNova-Loop-9B-Preview +10\n\n- Qwen3.5-397B-A17B (34): The card covers five of the seven items and misses limitations and a cut-off (14). Training data is a general description without a cut-off (5). The files last changed on 24 April 2026, 192 days before this check, past the 180-day band and within a year (5), and Qwen's GitHub repository holds only a README and licence, so no reference code of Alibaba's changed later. Alibaba answered none of the 40 most recent questions and reports, recounted on 2 November (0), and GitHub issues couldn't be counted. It's a first release at this size (5), and two template reports and a template fix still wait unanswered (5). 14 + 5 + 5 + 0 + 5 + 5 = 34.\n- SenseNova-Loop-9B-Preview (44): Model card 14, Intended use, the architecture, the parameter counts, the training context and the languages (in the metadata) are on the card. Training data 5, one general sentence on the recipes, with no sources, sizes or cut-off. Last change 20, the last commit, on 2 November 2026, reworked the speculative decoding section of the card, 8 days before this check. Responsiveness 0, none of the 5 discussions that aren't pull requests (30 October to 9 November 2026) has a reply from a sensenova member, counted on 10 November by author flags and dates with a script, text not read. Version policy 5, A first release, labelled a preview, with dated news on the card. File health 0, the files contradict the card on precision. 14 + 5 + 20 + 0 + 5 + 0 = 44.\n\n### Risk: Qwen3.5-397B-A17B +5\n\n- Qwen3.5-397B-A17B (55): Safetensors only with no repository code run on load or to build prompts gives file safety 25, and files in the Qwen organisation the publisher's GitHub links to, each hashed on the Hub, give provenance 15. Plain Apache 2.0 keeps licence risk at 15. Safety evaluation, agent guidance and a security programme score 0. The card has no safety results (its SecCodeBench row is a coding capability), no cautions, and no reporting route was found. The Qwen3.5 blog and Alibaba's security response centre couldn't be read, so both are scored as absent and left open.\n- SenseNova-Loop-9B-Preview (50): File safety 15, safetensors only, no .bin, .pt or .pth files (25). Provenance 15, the files sit in the sensenova organisation, which the GitHub repository for the model links to (10), and the Hub gives a SHA-256 for the large files (5). Safety evaluation 0, no safety results on the card. Agent guidance 0, the card has no limitations or safety section, and nothing on instructions inside tool results or images. Licence risk 15, Apache 2.0 with no use policy, no threshold and no right to change the terms. Security programme 5, SenseTime's about page gives a security contact (5). 15 + 15 + 0 + 0 + 15 + 5 = 50.\n\n## Score by category\n\n| Category | Weight | Qwen3.5-397B-A17B | SenseNova-Loop-9B-Preview | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 70 | 40 | Qwen3.5-397B-A17B +30 |\n| Risk | 20% (20 this run) | 55 | 50 | Qwen3.5-397B-A17B +5 |\n| Agent interface | 10% (10 this run) | 70 | 60 | Qwen3.5-397B-A17B +10 |\n| Documentation and upkeep | 10% (10 this run) | 34 | 44 | SenseNova-Loop-9B-Preview +10 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **64.1 · B** | **46.8 · D** | |\n\n## Facts side by side\n\n| Fact | Qwen3.5-397B-A17B | SenseNova-Loop-9B-Preview |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Alibaba (Qwen team) | SenseTime (SenseNova) |\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 | Apache-2.0 |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-02-16 | 2026-11-02 |\n| Terms last updated | no document linked | not read yet |\n| Privacy policy last updated | no document linked | no document linked |\n| Agent reviews | 2.8/5 (4) | none |\n\n## How the scores have moved\n\n### Qwen3.5-397B-A17B\n\n- -0.5 on 2 November 2026: time\n\n### SenseNova-Loop-9B-Preview\n\nNo score change since it was listed.\n\n## Verdicts\n\n**Qwen3.5-397B-A17B.** Alibaba's biggest open Qwen3.5, a cousin for hard coding, agent and vision work. Even at 4 bits it needs about 270 GB, so most agents will call it through Alibaba's Model Studio or one of the many providers that host it. The main risk is silence, with no safety results, no agent guidance and template reports without a reply.\n\n**SenseNova-Loop-9B-Preview.** An unusual architecture under Apache 2.0, with a 256K training context, a thinking switch and tool calling in the template. It's a preview in practice too: no released runtime runs it, loading it runs the repository's code, the files are twice the size the card implies, and nobody from SenseNova has answered a question yet.\n\n## Before you call either\n\n### Qwen3.5-397B-A17B\n\n1. The card's server command is `vllm serve Qwen/Qwen3.5-397B-A17B --port 8000 --tensor-parallel-size 8 --max-model-len 262144 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder`, and the FP8 build halves the memory\n2. It doesn't fit our 128 GB reference machine at any precision, so call a hosted endpoint instead (Model Studio's Qwen3.5-Plus, or a provider serving Qwen/Qwen3.5-397B-A17B)\n3. On Model Studio pass `\"enable_thinking\": false` directly, while vLLM and SGLang take `\"chat_template_kwargs\": {\"enable_thinking\": false}`\n4. Sampling per the card is temperature 0.6, top_p 0.95 and top_k 20 when thinking, and temperature 0.7, top_p 0.8, top_k 20 and presence_penalty 1.5 without\n5. For multi-token prediction in vLLM add `--speculative-config '{\"method\":\"qwen3_next_mtp\",\"num_speculative_tokens\":2}'`\n6. Keep the context at 128K or more if you rely on thinking, and turn on YaRN only past 262,144 tokens\n7. Validate any JSON it returns, since structured output isn't documented\n\n### SenseNova-Loop-9B-Preview\n\n1. Read modeling_sensenova_loop.py before you load it. trust_remote_code runs it\n2. Pin transformers 5.18.0, the version the card checked\n3. Load with dtype=torch.bfloat16 as the card does, or the 38 GB of F32 weights stay at full size\n4. Pass enable_thinking=False to apply_chat_template for a direct answer\n5. Tool calls come back as tool_call blocks with function and parameter tags. Parse them yourself, since no runtime ships a parser\n6. Wait for the vLLM or SGLang pull request to land in a release before putting it behind an API\n\n## Questions\n\n### Which is better for AI agents, Qwen3.5-397B-A17B or SenseNova-Loop-9B-Preview?\n\nQwen3.5-397B-A17B scores 64.1 (B) on agent readiness against SenseNova-Loop-9B-Preview's 46.8 (D), and leads in 3 of 4 scored categories. SenseNova-Loop-9B-Preview leads on documentation and upkeep.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-sensenova-loop-9b-preview.json, and with the fewest tokens: https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-sensenova-loop-9b-preview.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"qwen3-5-397b-a17b\", \"b\": \"sensenova-loop-9b-preview\"}`. From a terminal: `anchor compare qwen3-5-397b-a17b sensenova-loop-9b-preview`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/qwen3-5-397b-a17b.json and https://www.anchorterminal.com/api/v1/tools/sensenova-loop-9b-preview.json\n\n## Other comparisons with Qwen3.5-397B-A17B or SenseNova-Loop-9B-Preview\n\n- [Agents-A1-4B vs Qwen3.5-397B-A17B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-qwen3-5-397b-a17b.md)\n- [Agents-A1-4B vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/agents-a1-4b-vs-sensenova-loop-9b-preview.md)\n- [Apertus v1.5 8B vs Qwen3.5-397B-A17B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-qwen3-5-397b-a17b.md)\n- [Apertus v1.5 8B vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-sensenova-loop-9b-preview.md)\n- [Apodex-1.1-mini vs Qwen3.5-397B-A17B](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-qwen3-5-397b-a17b.md)\n- [Apodex-1.1-mini vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-sensenova-loop-9b-preview.md)\n- [Arete-27B vs Qwen3.5-397B-A17B](https://www.anchorterminal.com/compare/arete-27b-vs-qwen3-5-397b-a17b.md)\n- [Arete-27B vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/arete-27b-vs-sensenova-loop-9b-preview.md)\n- [BigBang-v1 vs Qwen3.5-397B-A17B](https://www.anchorterminal.com/compare/bigbang-v1-vs-qwen3-5-397b-a17b.md)\n- [BigBang-v1 vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/bigbang-v1-vs-sensenova-loop-9b-preview.md)\n- [Carbon-X vs Qwen3.5-397B-A17B](https://www.anchorterminal.com/compare/carbon-x-vs-qwen3-5-397b-a17b.md)\n- [Carbon-X vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/carbon-x-vs-sensenova-loop-9b-preview.md)\n- [Cola2 vs Qwen3.5-397B-A17B](https://www.anchorterminal.com/compare/cola2-vs-qwen3-5-397b-a17b.md)\n- [Cola2 vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/cola2-vs-sensenova-loop-9b-preview.md)\n- [Cosmos-Reason2-2B vs Qwen3.5-397B-A17B](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-qwen3-5-397b-a17b.md)\n- [Cosmos-Reason2-2B vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-sensenova-loop-9b-preview.md)\n- [Darwin-36B-Opus vs Qwen3.5-397B-A17B](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-qwen3-5-397b-a17b.md)\n- [Darwin-36B-Opus vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-sensenova-loop-9b-preview.md)\n- [DeepSeek-V3.2 vs Qwen3.5-397B-A17B](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-qwen3-5-397b-a17b.md)\n- [DeepSeek-V3.2 vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-sensenova-loop-9b-preview.md)\n- [DeepSeek V4.1 Flash vs Qwen3.5-397B-A17B](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-5-397b-a17b.md)\n- [DeepSeek V4.1 Flash vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-sensenova-loop-9b-preview.md)\n- [DeepSeek-V4-Flash-0731 vs Qwen3.5-397B-A17B](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-qwen3-5-397b-a17b.md)\n- [DeepSeek-V4-Flash-0731 vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-sensenova-loop-9b-preview.md)\n- [Devstral Small 2 vs Qwen3.5-397B-A17B](https://www.anchorterminal.com/compare/devstral-small-2-vs-qwen3-5-397b-a17b.md)\n- [Devstral Small 2 vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/devstral-small-2-vs-sensenova-loop-9b-preview.md)\n- [DiffusionGemma 26B A4B IT vs Qwen3.5-397B-A17B](https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-qwen3-5-397b-a17b.md)\n- [DiffusionGemma 26B A4B IT vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-sensenova-loop-9b-preview.md)\n- [EXAONE 4.5 33B vs Qwen3.5-397B-A17B](https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-qwen3-5-397b-a17b.md)\n- [EXAONE 4.5 33B vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-sensenova-loop-9b-preview.md)\n- [Gemma 4 12B IT vs Qwen3.5-397B-A17B](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-qwen3-5-397b-a17b.md)\n- [Gemma 4 12B IT vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-sensenova-loop-9b-preview.md)\n- [Gemma 4 26B A4B IT vs Qwen3.5-397B-A17B](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-qwen3-5-397b-a17b.md)\n- [Gemma 4 26B A4B IT vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-sensenova-loop-9b-preview.md)\n- [Gemma 4 31B IT vs Qwen3.5-397B-A17B](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-5-397b-a17b.md)\n- [Gemma 4 31B IT vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-sensenova-loop-9b-preview.md)\n- [Gemma 4 E2B IT vs Qwen3.5-397B-A17B](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-qwen3-5-397b-a17b.md)\n- [Gemma 4 E2B IT vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-sensenova-loop-9b-preview.md)\n- [Gemma 4 E4B IT vs Qwen3.5-397B-A17B](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-qwen3-5-397b-a17b.md)\n- [Gemma 4 E4B IT vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-sensenova-loop-9b-preview.md)\n- [GLM-4.5-Air vs Qwen3.5-397B-A17B](https://www.anchorterminal.com/compare/glm-4-5-air-vs-qwen3-5-397b-a17b.md)\n- [GLM-4.5-Air vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/glm-4-5-air-vs-sensenova-loop-9b-preview.md)\n- [GLM-4.7-Flash vs Qwen3.5-397B-A17B](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwen3-5-397b-a17b.md)\n- [GLM-4.7-Flash vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-sensenova-loop-9b-preview.md)\n- [GLM-5.3-Flash vs Qwen3.5-397B-A17B](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-qwen3-5-397b-a17b.md)\n- [GLM-5.3-Flash vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-sensenova-loop-9b-preview.md)\n- [GLM-5.3 vs Qwen3.5-397B-A17B](https://www.anchorterminal.com/compare/glm-5-3-vs-qwen3-5-397b-a17b.md)\n- [GLM-5.3 vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/glm-5-3-vs-sensenova-loop-9b-preview.md)\n- [gpt-oss-120b vs Qwen3.5-397B-A17B](https://www.anchorterminal.com/compare/gpt-oss-120b-vs-qwen3-5-397b-a17b.md)\n- [gpt-oss-120b vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/gpt-oss-120b-vs-sensenova-loop-9b-preview.md)\n- [gpt-oss-20b vs Qwen3.5-397B-A17B](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-qwen3-5-397b-a17b.md)\n- [gpt-oss-20b vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-sensenova-loop-9b-preview.md)\n- [Granite 4.2 30B vs Qwen3.5-397B-A17B](https://www.anchorterminal.com/compare/granite-4-2-30b-vs-qwen3-5-397b-a17b.md)\n- [Granite 4.2 30B vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/granite-4-2-30b-vs-sensenova-loop-9b-preview.md)\n- [Granite 4.2 3B vs Qwen3.5-397B-A17B](https://www.anchorterminal.com/compare/granite-4-2-3b-vs-qwen3-5-397b-a17b.md)\n- [Granite 4.2 3B vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/granite-4-2-3b-vs-sensenova-loop-9b-preview.md)\n- [Granite 4.2 8B vs Qwen3.5-397B-A17B](https://www.anchorterminal.com/compare/granite-4-2-8b-vs-qwen3-5-397b-a17b.md)\n- [Granite 4.2 8B vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/granite-4-2-8b-vs-sensenova-loop-9b-preview.md)\n- [Hy-Memory-30B-A3B vs Qwen3.5-397B-A17B](https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-5-397b-a17b.md)\n- [Hy-Memory-30B-A3B vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-sensenova-loop-9b-preview.md)\n- [Hy3 vs Qwen3.5-397B-A17B](https://www.anchorterminal.com/compare/hy3-vs-qwen3-5-397b-a17b.md)\n- [Hy3 vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/hy3-vs-sensenova-loop-9b-preview.md)\n- [Inkling-Small vs Qwen3.5-397B-A17B](https://www.anchorterminal.com/compare/inkling-small-vs-qwen3-5-397b-a17b.md)\n- [Inkling-Small vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/inkling-small-vs-sensenova-loop-9b-preview.md)\n- [Inkling vs Qwen3.5-397B-A17B](https://www.anchorterminal.com/compare/inkling-vs-qwen3-5-397b-a17b.md)\n- [Inkling vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/inkling-vs-sensenova-loop-9b-preview.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 SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-sensenova-loop-9b-preview.md)\n- [Kimi K3.1 Pro vs Qwen3.5-397B-A17B](https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwen3-5-397b-a17b.md)\n- [Kimi K3.1 Pro vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-sensenova-loop-9b-preview.md)\n- [Kimi K3 vs Qwen3.5-397B-A17B](https://www.anchorterminal.com/compare/kimi-k3-vs-qwen3-5-397b-a17b.md)\n- [Kimi K3 vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/kimi-k3-vs-sensenova-loop-9b-preview.md)\n- [Kolibri-1 vs Qwen3.5-397B-A17B](https://www.anchorterminal.com/compare/kolibri-1-vs-qwen3-5-397b-a17b.md)\n- [Kolibri-1 vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/kolibri-1-vs-sensenova-loop-9b-preview.md)\n- [Kronos vs Qwen3.5-397B-A17B](https://www.anchorterminal.com/compare/kronos-vs-qwen3-5-397b-a17b.md)\n- [Kronos vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/kronos-vs-sensenova-loop-9b-preview.md)\n- [Laguna M.1 vs Qwen3.5-397B-A17B](https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-5-397b-a17b.md)\n- [Laguna M.1 vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/laguna-m-1-vs-sensenova-loop-9b-preview.md)\n- [Laguna S 2.1 vs Qwen3.5-397B-A17B](https://www.anchorterminal.com/compare/laguna-s-2-1-vs-qwen3-5-397b-a17b.md)\n- [Laguna S 2.1 vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/laguna-s-2-1-vs-sensenova-loop-9b-preview.md)\n- [Laguna XS 2.1 vs Qwen3.5-397B-A17B](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwen3-5-397b-a17b.md)\n- [Laguna XS 2.1 vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-sensenova-loop-9b-preview.md)\n- [LBS-27B vs Qwen3.5-397B-A17B](https://www.anchorterminal.com/compare/lbs-27b-vs-qwen3-5-397b-a17b.md)\n- [LBS-27B vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/lbs-27b-vs-sensenova-loop-9b-preview.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 SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-sensenova-loop-9b-preview.md)\n- [LFM3-1.7B vs Qwen3.5-397B-A17B](https://www.anchorterminal.com/compare/lfm3-1-7b-vs-qwen3-5-397b-a17b.md)\n- [LFM3-1.7B vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/lfm3-1-7b-vs-sensenova-loop-9b-preview.md)\n- [LFM3-Audio-2B vs Qwen3.5-397B-A17B](https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-qwen3-5-397b-a17b.md)\n- [LFM3-Audio-2B vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-sensenova-loop-9b-preview.md)\n- [LFM3-Omni-2B vs Qwen3.5-397B-A17B](https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-qwen3-5-397b-a17b.md)\n- [LFM3-Omni-2B vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-sensenova-loop-9b-preview.md)\n- [LFM3-VL-2B vs Qwen3.5-397B-A17B](https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-qwen3-5-397b-a17b.md)\n- [LFM3-VL-2B vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-sensenova-loop-9b-preview.md)\n- [Ling-3.0-tiny vs Qwen3.5-397B-A17B](https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwen3-5-397b-a17b.md)\n- [Ling-3.0-tiny vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-sensenova-loop-9b-preview.md)\n- [Ling-3.1-flash-1019 vs Qwen3.5-397B-A17B](https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-qwen3-5-397b-a17b.md)\n- [Ling-3.1-flash-1019 vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-sensenova-loop-9b-preview.md)\n- [LongCat-2.5 vs Qwen3.5-397B-A17B](https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen3-5-397b-a17b.md)\n- [LongCat-2.5 vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/longcat-2-5-vs-sensenova-loop-9b-preview.md)\n- [MiMo-V2.6 Distill Qwen 9B vs Qwen3.5-397B-A17B](https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-qwen3-5-397b-a17b.md)\n- [MiMo-V2.6 Distill Qwen 9B vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-sensenova-loop-9b-preview.md)\n- [MiMo-V2.6-Flash-RL vs Qwen3.5-397B-A17B](https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-qwen3-5-397b-a17b.md)\n- [MiMo-V2.6-Flash-RL vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-sensenova-loop-9b-preview.md)\n- [MiniCPM-o 4.5 vs Qwen3.5-397B-A17B](https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-5-397b-a17b.md)\n- [MiniCPM-o 4.5 vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-sensenova-loop-9b-preview.md)\n- [MiniCPM-V 4.7 1B vs Qwen3.5-397B-A17B](https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-qwen3-5-397b-a17b.md)\n- [MiniCPM-V 4.7 1B vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-sensenova-loop-9b-preview.md)\n- [MiniCPM-V 4.7 35B-A3B vs Qwen3.5-397B-A17B](https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-qwen3-5-397b-a17b.md)\n- [MiniCPM-V 4.7 35B-A3B vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-sensenova-loop-9b-preview.md)\n- [MiniCPM5-2B vs Qwen3.5-397B-A17B](https://www.anchorterminal.com/compare/minicpm5-2b-vs-qwen3-5-397b-a17b.md)\n- [MiniCPM5-2B vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/minicpm5-2b-vs-sensenova-loop-9b-preview.md)\n- [MiniMax-M2.5 vs Qwen3.5-397B-A17B](https://www.anchorterminal.com/compare/minimax-m2-5-vs-qwen3-5-397b-a17b.md)\n- [MiniMax-M2.5 vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/minimax-m2-5-vs-sensenova-loop-9b-preview.md)\n- [MiniMax-M2.7 vs Qwen3.5-397B-A17B](https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-5-397b-a17b.md)\n- [MiniMax-M2.7 vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/minimax-m2-7-vs-sensenova-loop-9b-preview.md)\n- [MiniMax-M3.1-Flash vs Qwen3.5-397B-A17B](https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-qwen3-5-397b-a17b.md)\n- [MiniMax-M3.1-Flash vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-sensenova-loop-9b-preview.md)\n- [Mistral Large 4 vs Qwen3.5-397B-A17B](https://www.anchorterminal.com/compare/mistral-large-4-vs-qwen3-5-397b-a17b.md)\n- [Mistral Large 4 vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/mistral-large-4-vs-sensenova-loop-9b-preview.md)\n- [Muse Glimmer 30B vs Qwen3.5-397B-A17B](https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-5-397b-a17b.md)\n- [Muse Glimmer 30B vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-sensenova-loop-9b-preview.md)\n- [Nanbeige4.2-3B vs Qwen3.5-397B-A17B](https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-qwen3-5-397b-a17b.md)\n- [Nanbeige4.2-3B vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-sensenova-loop-9b-preview.md)\n- [Nanbeige4.3-33B vs Qwen3.5-397B-A17B](https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-qwen3-5-397b-a17b.md)\n- [Nanbeige4.3-33B vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-sensenova-loop-9b-preview.md)\n- [NVIDIA Nemotron 3.5 Bolt E4B 4GB vs Qwen3.5-397B-A17B](https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-qwen3-5-397b-a17b.md)\n- [NVIDIA Nemotron 3.5 Bolt E4B 4GB vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-sensenova-loop-9b-preview.md)\n- [Nemotron 3.5 Lightning 30B A3B vs Qwen3.5-397B-A17B](https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-qwen3-5-397b-a17b.md)\n- [Nemotron 3.5 Lightning 30B A3B vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-sensenova-loop-9b-preview.md)\n- [Nemotron 3 Nano 30B A3B vs Qwen3.5-397B-A17B](https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-qwen3-5-397b-a17b.md)\n- [Nemotron 3 Nano 30B A3B vs 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Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-swift-1-5-qwen3-8-27b.md)\n- [Qwen3.5-397B-A17B vs Swift 1.5 Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-swift-1-5-qwen3-8-flash-next.md)\n- [Qwen3.5-397B-A17B vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-thinkingcap-qwen3-8-27b.md)\n- [Qwen3.5-397B-A17B vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-unlimited-ocr-2.md)\n- [Qwen3.5-397B-A17B vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-wevisone-vl-2b.md)\n- [Qwen3.5-397B-A17B vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-wevisone-vl-4b.md)\n- [Qwen3.5-397B-A17B vs ZAYA1-8B](https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-zaya1-8b.md)\n- [Qwen3.5-4B vs 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Qwen3.8-27B](https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-swift-1-5-qwen3-8-27b.md)\n- [SenseNova-Loop-9B-Preview vs Swift 1.5 Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-swift-1-5-qwen3-8-flash-next.md)\n- [SenseNova-Loop-9B-Preview vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-thinkingcap-qwen3-8-27b.md)\n- [SenseNova-Loop-9B-Preview vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-unlimited-ocr-2.md)\n- [SenseNova-Loop-9B-Preview vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-wevisone-vl-2b.md)\n- [SenseNova-Loop-9B-Preview vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-wevisone-vl-4b.md)\n- [SenseNova-Loop-9B-Preview vs ZAYA1-8B](https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-zaya1-8b.md)\n", "meta": { "attribution": 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