{ "data": { "a": { "slug": "qwen3-5-4b", "name": "Qwen3.5-4B", "vendor": "Alibaba (Qwen team)", "vendorUrl": "https://qwen.ai", "kind": "weights", "category": "open-weights", "summary": "Qwen3.5-4B is Alibaba's small open-weight vision-language model, 4B language parameters with a vision encoder, thinking on by default and a 262,144-token context, published on Hugging Face in BF16 under Apache 2.0.", "url": "https://www.anchorterminal.com/tools/qwen3-5-4b", "markdownUrl": "https://www.anchorterminal.com/tools/qwen3-5-4b.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/qwen3-5-4b.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/qwen3-5-4b.json", "dns": "qwen3-5-4b.grade.anchorterminal.com", "repo": "https://huggingface.co/Qwen/Qwen3.5-4B", "license": "Apache-2.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "Qwen/Qwen3.5-4B" }, { "registry": "ollama", "name": "qwen3.5:4b" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The repository is not gated (checked 2026-10-21).", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0. Running it yourself costs the hardware, 9.3 GB for the BF16 files and 3.3 to 4.0 GB for Ollama's qwen3.5:4b builds (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/Qwen3.5-4B", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "dense", "small", "multimodal", "reasoning", "tool-calling", "apache-2.0", "safetensors", "self-hosted" ], "lastRelease": "2026-03-02", "graded": true, "anchor": { "graded": true, "score": 61.2, "grade": "C", "agentReady": false, "rank": 0, "ranked": false, "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools", "rankOf": 1518, "categoryRank": 28, "methodology": "0.7", "run": "2026-10-01", "scores": { "capability": 53, "interface": 70, "reach": 85, "risk": 55, "upkeep": 34 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "high", "date": "2026-11-02" }, "negative": 0, "verdict": "A small vision-language cousin for tool calls, light agent steps and image or video reading on almost any machine, with switchable thinking. Ollama's 3.3 GB build or the 9.3 GB BF16 files run in every major runtime under plain Apache 2.0. The main risks are no safety results, no structured-output guarantee and no answers from Alibaba since March.", "bestFor": "Agents that want a cheap local helper for tool calls, short reasoning and reading images on a laptop or small GPU", "strengths": [ "Runs in vLLM, Transformers, llama.cpp, Ollama and LM Studio, with a 3.3 GB Ollama build", "Apache 2.0 with nothing added, no account or gate to download", "Safetensors only, no remote code, files hashed in the publisher's organisation", "262,144 tokens native, several tool calls per turn, and thinking that turns off per request", "Also served by featherless-ai and Doubleword for agents without the hardware" ], "weaknesses": [ "No quantised build from Alibaba for this size", "No safety results, agent-use guidance or reporting route for flaws", "No reply from Qwen to any discussion, and a template fix waits since April", "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": [ "Smallest start is `ollama run qwen3.5:4b` (Q4_K_M, about 4 GB with context)", "Full precision `vllm serve Qwen/Qwen3.5-4B --port 8000 --max-model-len 262144 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder`", "Turn thinking off with `\"chat_template_kwargs\": {\"enable_thinking\": false}` in the request body, since it's on by default and `/think` and `/nothink` aren't supported", "There's no generation_config.json, so pass the card's sampling yourself, for example `temperature=0.7, top_p=0.8, top_k=20, presence_penalty=1.5` without thinking", "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": 3, "history": [ { "basis": "public evidence", "confidence": "high", "grade": "C", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 61.2 } ], "editorialScores": { "capability": 53, "interface": 70, "reach": 85, "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", "oldest": "2026-11-02", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "ollama run qwen3.5:4b\n# or, with the card's vLLM command:\nvllm serve Qwen/Qwen3.5-4B --port 8000 --max-model-len 262144 --reasoning-parser qwen3 \\\n --enable-auto-tool-choice --tool-call-parser qwen3_coder\n# or, with llama.cpp and LM Studio's GGUF:\nllama-server -hf lmstudio-community/Qwen3.5-4B-GGUF:Q4_K_M --jinja", "http": "curl http://localhost:11434/api/chat \\\n -d '{\"model\": \"qwen3.5:4b\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/qwen3-5-4b" }, "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", "qwen3-5-397b-a17b", "qwen3-5-9b", "qwen3-6-27b", "qwen3-6-35b-a3b", "qwen3-8-27b", "qwen3-8-flash-next" ], "weights": { "trainedBy": "Alibaba (Qwen team)", "builtOn": "Qwen/Qwen3.5-4B-Base", "params": "4B", "context": 262144, "goodFor": "Small vision-language model for local use: text, images and video in, tool calling, thinking on by default.", "runtimes": [ "llama.cpp", "Ollama", "vLLM", "SGLang", "LM Studio", "Transformers" ], "builds": [ { "name": "ollama qwen3.5:4b", "url": "https://ollama.com/library/qwen3.5:4b", "who": "ollama", "precision": "q4_k_m", "sizeGB": 3.3, "memoryGB": 8, "format": "ollama", "runsOn": [ "Ollama" ], "note": "Q4_K_M with a BF16 vision projector (digest d8b0f5e9760c), also the smallest non-MLX Ollama build. Size as Ollama's tags page shows it, rounded to whole GB above 10 GB; the Linux build is the llama.cpp variant" }, { "name": "lmstudio-community Qwen3.5-4B-GGUF (Q4_K_M)", "url": "https://huggingface.co/lmstudio-community/Qwen3.5-4B-GGUF", "who": "lmstudio", "precision": "q4_k_m", "sizeGB": 3.38, "memoryGB": 8, "format": "gguf", "runsOn": [ "LM Studio", "llama.cpp" ], "note": "From lmstudio-community, LM Studio's organisation on the Hub. 2.71 GB of Q4_K_M weights plus the 0.68 GB BF16 vision projector, from the Hub's model information on 10 November 2026. Recorded after the last grading, so Reach doesn't count it yet" }, { "name": "lmstudio-community Qwen3.5-4B-GGUF (Q6_K)", "url": "https://huggingface.co/lmstudio-community/Qwen3.5-4B-GGUF", "who": "lmstudio", "precision": "q6_k", "sizeGB": 4.14, "memoryGB": 8, "format": "gguf", "runsOn": [ "LM Studio", "llama.cpp" ], "note": "From lmstudio-community, LM Studio's organisation on the Hub. 3.46 GB of Q6_K weights plus the 0.68 GB BF16 vision projector, from the Hub's model information on 10 November 2026. Recorded after the last grading, so Reach doesn't count it yet" }, { "name": "lmstudio-community Qwen3.5-4B-GGUF (Q8_0)", "url": "https://huggingface.co/lmstudio-community/Qwen3.5-4B-GGUF", "who": "lmstudio", "precision": "q8_0", "sizeGB": 5.16, "memoryGB": 8, "format": "gguf", "runsOn": [ "LM Studio", "llama.cpp" ], "note": "From lmstudio-community, LM Studio's organisation on the Hub. 4.48 GB of Q8_0 weights plus the 0.68 GB BF16 vision projector, from the Hub's model information on 10 November 2026. Recorded after the last grading, so Reach doesn't count it yet" }, { "name": "Qwen3.5-4B", "url": "https://huggingface.co/Qwen/Qwen3.5-4B", "who": "publisher", "precision": "bf16", "sizeGB": 9.3, "memoryGB": 12, "format": "safetensors", "runsOn": [ "vLLM (ROCm)", "SGLang (ROCm)" ] } ], "run": "vllm serve Qwen/Qwen3.5-4B --port 8000 --max-model-len 262144 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder", "runBuild": "Qwen3.5-4B", "checked": "2026-10-21", "minMemoryGB": 8 }, "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-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; RDAP didn't answer our reader on 21 October.", "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 five messages are uploads and README updates that don't say what changed. The News list in github.com/QwenLM/Qwen3.5 dates the release to 2 March 2026.", "qwen.ai answers /robots.txt with a redirect to its web app, and the pilot found the same for /.well-known/security.txt, so there is no security.txt." ], "score": 54 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/qwen3-5-4b.json", "live": { "slug": "qwen3-5-4b", "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": "Qwen3.5-4B scores 61.2 (C) on agent readiness against Step 3.7 Flash's 59.8 (C), and leads in 2 of 4 scored categories. Step 3.7 Flash leads on agent interface and documentation and upkeep.", "b": { "slug": "step-3-7-flash", "name": "Step 3.7 Flash", "vendor": "StepFun", "vendorUrl": "https://platform.stepfun.ai", "kind": "weights", "category": "open-weights", "summary": "Step 3.7 Flash is StepFun's open-weight vision-language model, 198B parameters with about 11B active, with tool calling and a 256K context. Apache 2.0. Its 4-bit GGUF is 105 GB, just over what a 128 GB machine holds.", "url": "https://www.anchorterminal.com/tools/step-3-7-flash", "markdownUrl": "https://www.anchorterminal.com/tools/step-3-7-flash.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/step-3-7-flash.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/step-3-7-flash.json", "dns": "step-3-7-flash.grade.anchorterminal.com", "repo": "https://huggingface.co/stepfun-ai/Step-3.7-Flash", "license": "Apache-2.0 (metadata and card; no LICENSE file)", "transports": [], "packages": [ { "registry": "huggingface", "name": "stepfun-ai/Step-3.7-Flash" }, { "registry": "huggingface", "name": "stepfun-ai/Step-3.7-Flash-GGUF" }, { "registry": "huggingface", "name": "stepfun-ai/Step-3.7-Flash-FP8" }, { "registry": "huggingface", "name": "stepfun-ai/Step-3.7-Flash-NVFP4" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. None of the four repositories is gated (checked 2026-11-10).", "pricing": "free", "pricingNotes": "Free to download and use under Apache 2.0. Running it yourself takes 109 GB at IQ4_XS with its projector, 129 GB in NVFP4 or 403 GB in BF16. StepFun's API serves it at $0.20 per million input tokens and $1.15 per million output, per the card, and DeepInfra and Featherless serve it too (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://huggingface.co/stepfun-ai/Step-3.7-Flash", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "moe", "multimodal", "vision", "reasoning", "tool-calling", "agentic", "long-context", "apache-2.0", "safetensors", "gguf", "fp8", "nvfp4", "custom-code", "self-hosted" ], "lastRelease": "2026-06-03", "graded": true, "anchor": { "graded": true, "score": 59.8, "grade": "C", "agentReady": false, "rank": 0, "ranked": false, "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools", "rankOf": 1518, "categoryRank": 73, "methodology": "0.7", "run": "2026-10-01", "scores": { "interface": 85, "reach": 70, "risk": 45, "upkeep": 39 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-11-10" }, "negative": 0, "verdict": "A capable open agent model with a clean licence, every kind of build and four released runtimes. Two things hold it back: it's a little too big for a 128 GB machine at 4 bits, whatever the card says, and StepFun has gone quiet on the repository since June. No safety results, limitations or data description.", "bestFor": "Teams with a GPU server or a provider account that want an open, long-context agent model with vision", "strengths": [ "Apache 2.0, ungated", "GGUF, FP8 and NVFP4 builds from StepFun", "Four released runtimes carry it, with Step parsers in vLLM and SGLang", "A 256K context with images in", "Hosted by StepFun and two providers" ], "weaknesses": [ "About 131 GB to run at 4 bits with images, over a 128 GB machine's share", "Every serve command on the card runs the repository's code", "No reply from StepFun on the repository's discussions", "Not changed since 3 June 2026", "No limitations, safety results or training data on the card" ], "agentNotes": [ "On a 128 GB machine the 4-bit GGUF won't fit with a working context. Use StepFun's API or a provider, or a server", "On vLLM or SGLang pass `--tool-call-parser step3p5 --reasoning-parser step3p5`", "The card's commands pass trust-remote-code. Released Transformers 5.22.2 has the model built in if you'd rather not run the repository's code", "For llama.cpp load the mmproj file from StepFun's GGUF repository for image input", "StepFun's API has two hosts. Use the one where your key was issued, `api.stepfun.ai` or `api.stepfun.com`", "The same model is on our StepFun API listing, with prices and terms" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 0, "avgRating": 0, "history": [ { "basis": "public evidence", "confidence": "low", "grade": "C", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 59.8 } ], "editorialScores": { "interface": 85, "reach": 70, "risk": 45, "upkeep": 39 }, "provenanceScore": 58, "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 \"sglang\u003e=0.5.23\"\nsglang serve --model-path stepfun-ai/Step-3.7-Flash-FP8 --tp 8 --ep 4 --reasoning-parser step3p5 --tool-call-parser step3p5 --enable-multimodal --trust-remote-code", "http": "# SGLang and vLLM serve an OpenAI-compatible API. StepFun's own API is at https://api.stepfun.ai/v1\n# (https://api.stepfun.com/v1 for keys issued in China)" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/step-3-7-flash" }, "sameModel": [ "stepfun-api" ], "weights": { "trainedBy": "StepFun", "params": "198B", "active": "11B", "context": 262144, "goodFor": "Agent work that mixes images, search and code, on a GPU server or through StepFun's API. It's a few GB too big for a 128 GB machine at 4 bits.", "runtimes": [ "llama.cpp", "vLLM", "SGLang", "Transformers" ], "builds": [ { "name": "Step-3.7-Flash-GGUF IQ4_XS", "url": "https://huggingface.co/stepfun-ai/Step-3.7-Flash-GGUF", "who": "publisher", "precision": "iq4_xs", "sizeGB": 108.96, "memoryGB": 192, "format": "gguf", "runsOn": [ "llama.cpp" ], "note": "104.99 GB with the 3.97 GB F16 projector" }, { "name": "Step-3.7-Flash-GGUF Q4_K_S", "url": "https://huggingface.co/stepfun-ai/Step-3.7-Flash-GGUF", "who": "publisher", "precision": "q4_k_s", "sizeGB": 115.47, "memoryGB": 192, "format": "gguf", "runsOn": [ "llama.cpp" ], "note": "111.50 GB with the 3.97 GB F16 projector" }, { "name": "Step-3.7-Flash-NVFP4", "url": "https://huggingface.co/stepfun-ai/Step-3.7-Flash-NVFP4", "who": "publisher", "precision": "nvfp4", "sizeGB": 129.25, "memoryGB": 192, "format": "safetensors", "runsOn": [ "vLLM", "SGLang" ], "note": "StepFun's 4-bit build for NVIDIA GPUs" }, { "name": "Step-3.7-Flash-FP8", "url": "https://huggingface.co/stepfun-ai/Step-3.7-Flash-FP8", "who": "publisher", "precision": "fp8", "sizeGB": 212.53, "memoryGB": 256, "format": "safetensors", "runsOn": [ "vLLM", "SGLang" ], "note": "StepFun's FP8 build" }, { "name": "Step-3.7-Flash-GGUF Q8_0", "url": "https://huggingface.co/stepfun-ai/Step-3.7-Flash-GGUF", "who": "publisher", "precision": "q8_0", "sizeGB": 213.39, "memoryGB": 512, "format": "gguf", "runsOn": [ "llama.cpp" ], "note": "209.42 GB with the 3.97 GB F16 projector" }, { "name": "Step-3.7-Flash (BF16 safetensors)", "url": "https://huggingface.co/stepfun-ai/Step-3.7-Flash", "who": "publisher", "precision": "bf16", "sizeGB": 402.73, "memoryGB": 512, "format": "safetensors", "runsOn": [ "vLLM", "SGLang", "Transformers" ], "note": "26 files" } ], "run": "llama-server -hf stepfun-ai/Step-3.7-Flash-GGUF:IQ4_XS --jinja", "runBuild": "Step-3.7-Flash-GGUF IQ4_XS", "checked": "2026-11-10", "minMemoryGB": 192 }, "family": { "lab": "StepFun", "family": "Step 3", "url": "https://www.anchorterminal.com/open-weights/#stepfun", "others": [ { "name": "Step-3.5-Flash", "url": "https://huggingface.co/stepfun-ai/Step-3.5-Flash" }, { "name": "Step3-VL-10B", "url": "https://huggingface.co/stepfun-ai/Step3-VL-10B" } ] }, "area": "models", "provenance": { "legalEntity": "STEPFUN TECHNOLOGY (HK) LIMITED", "domain": "stepfun.ai", "domainRegistered": "", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://huggingface.co/stepfun-ai/Step-3.7-Flash/commits/main", "securityTxt": "none", "checked": "2026-11-10", "notes": [ "StepFun's legal entity, domain and security.txt are carried from our StepFun API listing.", "Nothing is hosted under this listing, so Apache 2.0 stands in for terms of service and no privacy policy is scored. The StepFun API that serves this model has its own listing.", "The changelog is the commit history, 23 commits from 23 May to 3 June 2026." ], "score": 58 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/step-3-7-flash.json" }, "edges": [ { "by": 15, "category": "reach", "leads": "qwen3-5-4b", "name": "Reach", "reasons": { "a": "Memory earns the full 30 because Ollama's own Q4_K_M build is 3.3 GB, about 4 GB with the fifth added, though Alibaba itself publishes no quantised 4B, so builds scores 0. Runtimes reach the 25 cap on vLLM's stable release, Transformers 5.2.0, llama.cpp, Ollama's library and LM Studio's catalogue. Apache 2.0 with nothing added gives the full 15 for terms, the ungated repository the full 10 for download, and two providers (featherless-ai, live on Hugging Face on 2 November, and Doubleword's own list per the 21 October check) the 5 for hosted too. 30 + 25 + 0 + 15 + 10 + 5 = 85.", "b": "Memory 5, the smallest build at 4 bits is StepFun's IQ4_XS GGUF, 104.99 GB with the 3.97 GB projector, and 108.96 GB × 1.2 = 130.8 GB, over what the reference machine can give a model and within 256 GB. The IQ3 and Q3 builds are below 4 bits and aren't read. Runtimes 20, Released vLLM 0.33.0, SGLang 0.5.23 and Transformers 5.22.2 have the step3p7 model, and llama.cpp b12242 has the architecture, with StepFun's own GGUF. Four at 5 each, and the card documents all four. Builds 15, StepFun publishes GGUF at five quantised precisions, an FP8 build and an NVFP4 build. Terms 15, Apache 2.0 in the card's metadata and its licence section, an OSI licence with nothing added. Download 10, the Hub API reports gated false for all four repositories on 10 November 2026, so the files download with no account and nothing to accept. Hosted 5, StepFun's own API serves it, per the card and our StepFun API listing, and Featherless and DeepInfra are live for it on Hugging Face. 5 + 20 + 15 + 15 + 10 + 5 = 70." }, "scores": { "a": 85, "b": 70 } }, { "by": 15, "category": "interface", "leads": "step-3-7-flash", "name": "Agent interface", "reasons": { "a": "The template ships in the repository (20) and the model was trained for tool use with several calls per turn in the template (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 (5,723 characters). Tool-calling format 25, the card names the parser to use in two runtimes, step3p5 in vLLM (with auto tool choice) and in SGLang, and both ship Step parsers in a release. Trained tool calling 20, the card presents tool orchestration as a main use, with multi-turn results, and the template renders every call in a turn. Structured output 0, Nothing on the card about JSON or schema-constrained output for the weights. Reasoning control 10, the template has a reasoning_effort setting and keeps reasoning_content, and the card names the step3p5 reasoning parser for vLLM and SGLang, which returns the reasoning apart from the answer. Context 10, 256K tokens per the card, and 262,144 positions in config.json. 20 + 25 + 20 + 0 + 10 + 10 = 85." }, "scores": { "a": 70, "b": 85 } }, { "by": 10, "category": "risk", "leads": "qwen3-5-4b", "name": "Risk", "reasons": { "a": "Safetensors only with no remote code gives file safety its 25, and files in the Qwen organisation the publisher's GitHub links to, each with a hash on the Hub, give provenance 15. The Apache 2.0 licence carries none of the deductions, so licence risk keeps 15. Safety evaluation, agent guidance and a security programme score 0, since the card has no safety results or cautions and no reporting route was found. The Qwen3.5 blog and Alibaba's security response centre couldn't be read, so those two are scored as absent and left open.", "b": "File safety 15, safetensors and GGUF only, no .bin, .pt or .pth files (25). Provenance 15, the files sit in the stepfun-ai organisation, which the card's StepFun platform and model page belong to (10). Safety evaluation 0, no safety results on the card. Agent guidance 0, the card has no limitations section and gives no guidance on prompt injection, instruction priority, refusals or safeguards. Licence risk 15, Apache 2.0 has no use policy, threshold or change clause. Security programme 0, no way to report a flaw on the card, and our StepFun API listing found no security.txt, bug bounty or disclosure policy. 15 + 15 + 0 + 0 + 15 + 0 = 45." }, "scores": { "a": 55, "b": 45 } }, { "by": 5, "category": "upkeep", "leads": "step-3-7-flash", "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 2 March 2026, 245 days before 2 November, which is within a year (5). No Qwen member has answered any of the 21 discussions that aren't pull requests, four of them opened since 21 October (0), and the GitHub issues couldn't be counted. Earlier 4B models stay in their own repositories without dated notes (5), and two pull requests fixing the template and adding generation_config.json wait unanswered (5). 14 + 5 + 5 + 0 + 5 + 5 = 34.", "b": "Model card 14, Intended use, the architecture, the parameters, the context and the language (in the metadata) are on the card. Training data 0, Nothing on the card about the training data. Last change 10, the last commit, on 3 June 2026, fixed the modelling code, 160 days before this check and within 180. Responsiveness 0, none of the 11 most recent discussions that aren't pull requests (29 May to 24 June 2026) has a reply from a stepfun-ai member, counted on 10 November by author flags and dates with a script, text not read. Version policy 5, Step-3.5-Flash stays in its own repositories and 3.7 came out in new ones, with no dated note on the older cards that we read. File health 10, Config, template and card agree on the architecture and the context, and June commits fixed the modelling code. 14 + 0 + 10 + 0 + 5 + 10 = 39." }, "scores": { "a": 34, "b": 39 } } ], "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "Alibaba (Qwen team)", "b": "StepFun", "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 (metadata and card; no LICENSE file)", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "no", "b": "no", "name": "llms.txt" }, { "a": "2026-03-02", "b": "2026-06-03", "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": "3/5 (4)", "b": "none", "name": "Agent reviews" } ], "faq": [ { "answer": "Qwen3.5-4B scores 61.2 (C) on agent readiness against Step 3.7 Flash's 59.8 (C), and leads in 2 of 4 scored categories. Step 3.7 Flash leads on agent interface and documentation and upkeep.", "question": "Which is better for AI agents, Qwen3.5-4B or Step 3.7 Flash?" } ], "goodFor": [ { "aheadOn": [ "Reach, 85 against 70", "Risk, 55 against 45" ], "also": null, "goodFor": "Agents that want a cheap local helper for tool calls, short reasoning and reading images on a laptop or small GPU", "slug": "qwen3-5-4b", "watchFor": "No quantised build from Alibaba for this size" }, { "aheadOn": [ "Agent interface, 85 against 70", "Documentation and upkeep, 39 against 34" ], "also": null, "goodFor": "Teams with a GPU server or a provider account that want an open, long-context agent model with vision", "slug": "step-3-7-flash", "watchFor": "About 131 GB to run at 4 bits with images, over a 128 GB machine's share" } ], "history": null, "job": { "capability": "model.open-weights", "name": "Open-weight models you run yourself" }, "others": [ { "json": 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"Spark-X2.6-4B vs Step 3.7 Flash", "url": "https://www.anchorterminal.com/compare/spark-x2-6-4b-vs-step-3-7-flash" }, { "json": "https://www.anchorterminal.com/compare/step-3-7-flash-vs-swift-1-5-qwen3-8-27b.json", "title": "Step 3.7 Flash vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/step-3-7-flash-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/step-3-7-flash-vs-swift-1-5-qwen3-8-flash-next.json", "title": "Step 3.7 Flash vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/step-3-7-flash-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/step-3-7-flash-vs-thinkingcap-qwen3-8-27b.json", "title": "Step 3.7 Flash vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/step-3-7-flash-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/step-3-7-flash-vs-unlimited-ocr-2.json", "title": "Step 3.7 Flash vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/step-3-7-flash-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/step-3-7-flash-vs-wevisone-vl-2b.json", "title": "Step 3.7 Flash vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/step-3-7-flash-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/step-3-7-flash-vs-wevisone-vl-4b.json", "title": "Step 3.7 Flash vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/step-3-7-flash-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/step-3-7-flash-vs-zaya1-8b.json", "title": "Step 3.7 Flash vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/step-3-7-flash-vs-zaya1-8b" } ], "panel": null, "scores": [ { "key": "capability", "name": "Capability", "pending": true, "weight": 35 }, { "by": 15, "edge": "qwen3-5-4b", "key": "reach", "name": "Reach", "qwen3-5-4b": 85, "step-3-7-flash": 70, "weight": 25 }, { "by": 10, "edge": "qwen3-5-4b", "key": "risk", "name": "Risk", "qwen3-5-4b": 55, "step-3-7-flash": 45, "weight": 20 }, { "by": 15, "edge": "step-3-7-flash", "key": "interface", "name": "Agent interface", "qwen3-5-4b": 70, "step-3-7-flash": 85, "weight": 10 }, { "by": 5, "edge": "step-3-7-flash", "key": "upkeep", "name": "Documentation and upkeep", "qwen3-5-4b": 34, "step-3-7-flash": 39, "weight": 10 } ], "summary": "Qwen3.5-4B scores 61.2 (C) on agent readiness against Step 3.7 Flash's 59.8 (C), and leads in 2 of 4 scored categories. Step 3.7 Flash leads on agent interface and documentation and upkeep. Both do open-weight models you run yourself.", "tasks": null, "tutorials": [], "verdicts": { "qwen3-5-4b": "A small vision-language cousin for tool calls, light agent steps and image or video reading on almost any machine, with switchable thinking. Ollama's 3.3 GB build or the 9.3 GB BF16 files run in every major runtime under plain Apache 2.0. The main risks are no safety results, no structured-output guarantee and no answers from Alibaba since March.", "step-3-7-flash": "A capable open agent model with a clean licence, every kind of build and four released runtimes. Two things hold it back: it's a little too big for a 128 GB machine at 4 bits, whatever the card says, and StepFun has gone quiet on the repository since June. No safety results, limitations or data description." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/qwen3-5-4b-vs-step-3-7-flash", "json": "https://www.anchorterminal.com/compare/qwen3-5-4b-vs-step-3-7-flash.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/qwen3-5-4b-vs-step-3-7-flash.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/qwen3-5-4b-vs-step-3-7-flash.min.md" }, "markdown": "Qwen3.5-4B scores 61.2 (C) on agent readiness against Step 3.7 Flash's 59.8 (C), and leads in 2 of 4 scored categories. Step 3.7 Flash leads on agent interface and documentation and upkeep. Both do open-weight models you run yourself.\n\n- Qwen3.5-4B: grade C, 61.2/100, rank #38 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/qwen3-5-4b.md · JSON https://www.anchorterminal.com/api/v1/tools/qwen3-5-4b.json\n- Step 3.7 Flash: grade C, 59.8/100, rank #93 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/step-3-7-flash.md · JSON https://www.anchorterminal.com/api/v1/tools/step-3-7-flash.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### Qwen3.5-4B (C)\n\nGood for: Agents that want a cheap local helper for tool calls, short reasoning and reading images on a laptop or small GPU\n\nAhead on:\n- Reach, 85 against 70\n- Risk, 55 against 45\n\nWatch for: No quantised build from Alibaba for this size\n\n### Step 3.7 Flash (C)\n\nGood for: Teams with a GPU server or a provider account that want an open, long-context agent model with vision\n\nAhead on:\n- Agent interface, 85 against 70\n- Documentation and upkeep, 39 against 34\n\nWatch for: About 131 GB to run at 4 bits with images, over a 128 GB machine's share\n\n\n## Why the edge\n\n### Reach: Qwen3.5-4B +15\n\n- Qwen3.5-4B (85): Memory earns the full 30 because Ollama's own Q4_K_M build is 3.3 GB, about 4 GB with the fifth added, though Alibaba itself publishes no quantised 4B, so builds scores 0. Runtimes reach the 25 cap on vLLM's stable release, Transformers 5.2.0, llama.cpp, Ollama's library and LM Studio's catalogue. Apache 2.0 with nothing added gives the full 15 for terms, the ungated repository the full 10 for download, and two providers (featherless-ai, live on Hugging Face on 2 November, and Doubleword's own list per the 21 October check) the 5 for hosted too. 30 + 25 + 0 + 15 + 10 + 5 = 85.\n- Step 3.7 Flash (70): Memory 5, the smallest build at 4 bits is StepFun's IQ4_XS GGUF, 104.99 GB with the 3.97 GB projector, and 108.96 GB × 1.2 = 130.8 GB, over what the reference machine can give a model and within 256 GB. The IQ3 and Q3 builds are below 4 bits and aren't read. Runtimes 20, Released vLLM 0.33.0, SGLang 0.5.23 and Transformers 5.22.2 have the step3p7 model, and llama.cpp b12242 has the architecture, with StepFun's own GGUF. Four at 5 each, and the card documents all four. Builds 15, StepFun publishes GGUF at five quantised precisions, an FP8 build and an NVFP4 build. Terms 15, Apache 2.0 in the card's metadata and its licence section, an OSI licence with nothing added. Download 10, the Hub API reports gated false for all four repositories on 10 November 2026, so the files download with no account and nothing to accept. Hosted 5, StepFun's own API serves it, per the card and our StepFun API listing, and Featherless and DeepInfra are live for it on Hugging Face. 5 + 20 + 15 + 15 + 10 + 5 = 70.\n\n### Agent interface: Step 3.7 Flash +15\n\n- Qwen3.5-4B (70): The template ships in the repository (20) and the model was trained for tool use with several calls per turn in the template (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- Step 3.7 Flash (85): Chat template 20, chat_template.jinja in the repository (5,723 characters). Tool-calling format 25, the card names the parser to use in two runtimes, step3p5 in vLLM (with auto tool choice) and in SGLang, and both ship Step parsers in a release. Trained tool calling 20, the card presents tool orchestration as a main use, with multi-turn results, and the template renders every call in a turn. Structured output 0, Nothing on the card about JSON or schema-constrained output for the weights. Reasoning control 10, the template has a reasoning_effort setting and keeps reasoning_content, and the card names the step3p5 reasoning parser for vLLM and SGLang, which returns the reasoning apart from the answer. Context 10, 256K tokens per the card, and 262,144 positions in config.json. 20 + 25 + 20 + 0 + 10 + 10 = 85.\n\n### Risk: Qwen3.5-4B +10\n\n- Qwen3.5-4B (55): Safetensors only with no remote code gives file safety its 25, and files in the Qwen organisation the publisher's GitHub links to, each with a hash on the Hub, give provenance 15. The Apache 2.0 licence carries none of the deductions, so licence risk keeps 15. Safety evaluation, agent guidance and a security programme score 0, since the card has no safety results or cautions and no reporting route was found. The Qwen3.5 blog and Alibaba's security response centre couldn't be read, so those two are scored as absent and left open.\n- Step 3.7 Flash (45): File safety 15, safetensors and GGUF only, no .bin, .pt or .pth files (25). Provenance 15, the files sit in the stepfun-ai organisation, which the card's StepFun platform and model page belong to (10). Safety evaluation 0, no safety results on the card. Agent guidance 0, the card has no limitations section and gives no guidance on prompt injection, instruction priority, refusals or safeguards. Licence risk 15, Apache 2.0 has no use policy, threshold or change clause. Security programme 0, no way to report a flaw on the card, and our StepFun API listing found no security.txt, bug bounty or disclosure policy. 15 + 15 + 0 + 0 + 15 + 0 = 45.\n\n### Documentation and upkeep: Step 3.7 Flash +5\n\n- Qwen3.5-4B (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 2 March 2026, 245 days before 2 November, which is within a year (5). No Qwen member has answered any of the 21 discussions that aren't pull requests, four of them opened since 21 October (0), and the GitHub issues couldn't be counted. Earlier 4B models stay in their own repositories without dated notes (5), and two pull requests fixing the template and adding generation_config.json wait unanswered (5). 14 + 5 + 5 + 0 + 5 + 5 = 34.\n- Step 3.7 Flash (39): Model card 14, Intended use, the architecture, the parameters, the context and the language (in the metadata) are on the card. Training data 0, Nothing on the card about the training data. Last change 10, the last commit, on 3 June 2026, fixed the modelling code, 160 days before this check and within 180. Responsiveness 0, none of the 11 most recent discussions that aren't pull requests (29 May to 24 June 2026) has a reply from a stepfun-ai member, counted on 10 November by author flags and dates with a script, text not read. Version policy 5, Step-3.5-Flash stays in its own repositories and 3.7 came out in new ones, with no dated note on the older cards that we read. File health 10, Config, template and card agree on the architecture and the context, and June commits fixed the modelling code. 14 + 0 + 10 + 0 + 5 + 10 = 39.\n\n## Score by category\n\n| Category | Weight | Qwen3.5-4B | Step 3.7 Flash | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 85 | 70 | Qwen3.5-4B +15 |\n| Risk | 20% (20 this run) | 55 | 45 | Qwen3.5-4B +10 |\n| Agent interface | 10% (10 this run) | 70 | 85 | Step 3.7 Flash +15 |\n| Documentation and upkeep | 10% (10 this run) | 34 | 39 | Step 3.7 Flash +5 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **61.2 · C** | **59.8 · C** | |\n\n## Facts side by side\n\n| Fact | Qwen3.5-4B | Step 3.7 Flash |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Alibaba (Qwen team) | StepFun |\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 (metadata and card; no LICENSE file) |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-03-02 | 2026-06-03 |\n| Terms last updated | no document linked | no document linked |\n| Privacy policy last updated | no document linked | no document linked |\n| Agent reviews | 3/5 (4) | none |\n\n## Verdicts\n\n**Qwen3.5-4B.** A small vision-language cousin for tool calls, light agent steps and image or video reading on almost any machine, with switchable thinking. Ollama's 3.3 GB build or the 9.3 GB BF16 files run in every major runtime under plain Apache 2.0. The main risks are no safety results, no structured-output guarantee and no answers from Alibaba since March.\n\n**Step 3.7 Flash.** A capable open agent model with a clean licence, every kind of build and four released runtimes. Two things hold it back: it's a little too big for a 128 GB machine at 4 bits, whatever the card says, and StepFun has gone quiet on the repository since June. No safety results, limitations or data description.\n\n## Before you call either\n\n### Qwen3.5-4B\n\n1. Smallest start is `ollama run qwen3.5:4b` (Q4_K_M, about 4 GB with context)\n2. Full precision `vllm serve Qwen/Qwen3.5-4B --port 8000 --max-model-len 262144 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder`\n3. Turn thinking off with `\"chat_template_kwargs\": {\"enable_thinking\": false}` in the request body, since it's on by default and `/think` and `/nothink` aren't supported\n4. There's no generation_config.json, so pass the card's sampling yourself, for example `temperature=0.7, top_p=0.8, top_k=20, presence_penalty=1.5` without thinking\n5. Keep the context at 128K or more if you rely on thinking, and turn on YaRN only past 262,144 tokens\n6. Validate any JSON it returns, since structured output isn't documented\n\n### Step 3.7 Flash\n\n1. On a 128 GB machine the 4-bit GGUF won't fit with a working context. Use StepFun's API or a provider, or a server\n2. On vLLM or SGLang pass `--tool-call-parser step3p5 --reasoning-parser step3p5`\n3. The card's commands pass trust-remote-code. Released Transformers 5.22.2 has the model built in if you'd rather not run the repository's code\n4. For llama.cpp load the mmproj file from StepFun's GGUF repository for image input\n5. StepFun's API has two hosts. Use the one where your key was issued, `api.stepfun.ai` or `api.stepfun.com`\n6. The same model is on our StepFun API listing, with prices and terms\n\n## Questions\n\n### Which is better for AI agents, Qwen3.5-4B or Step 3.7 Flash?\n\nQwen3.5-4B scores 61.2 (C) on agent readiness against Step 3.7 Flash's 59.8 (C), and leads in 2 of 4 scored categories. Step 3.7 Flash leads on agent interface and documentation and upkeep.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/qwen3-5-4b-vs-step-3-7-flash.json, and with the fewest tokens: https://www.anchorterminal.com/compare/qwen3-5-4b-vs-step-3-7-flash.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"qwen3-5-4b\", \"b\": \"step-3-7-flash\"}`. From a terminal: `anchor compare qwen3-5-4b step-3-7-flash`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/qwen3-5-4b.json and https://www.anchorterminal.com/api/v1/tools/step-3-7-flash.json\n\n## Other comparisons with Qwen3.5-4B or Step 3.7 Flash\n\n- [Agents-A1-4B vs Qwen3.5-4B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-qwen3-5-4b.md)\n- [Agents-A1-4B vs Step 3.7 Flash](https://www.anchorterminal.com/compare/agents-a1-4b-vs-step-3-7-flash.md)\n- [Apertus v1.5 8B vs Qwen3.5-4B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-qwen3-5-4b.md)\n- [Apertus v1.5 8B vs Step 3.7 Flash](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-step-3-7-flash.md)\n- [Apodex-1.1-mini vs Qwen3.5-4B](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-qwen3-5-4b.md)\n- [Apodex-1.1-mini vs Step 3.7 Flash](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-step-3-7-flash.md)\n- [Arete-27B vs 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