{ "data": { "a": { "slug": "qwen3-8-27b", "name": "Qwen3.8-27B", "vendor": "Alibaba (Qwen team)", "vendorUrl": "https://qwen.ai", "kind": "weights", "category": "open-weights", "summary": "Qwen3.8-27B is Alibaba's open-weight dense vision-language model of 27B parameters, with thinking on by default and a 262,144-token context, published on Hugging Face in BF16 and FP8 under Apache 2.0.", "url": "https://www.anchorterminal.com/tools/qwen3-8-27b", "markdownUrl": "https://www.anchorterminal.com/tools/qwen3-8-27b.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/qwen3-8-27b.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/qwen3-8-27b.json", "dns": "qwen3-8-27b.grade.anchorterminal.com", "repo": "https://huggingface.co/Qwen/Qwen3.8-27B", "license": "Apache-2.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "Qwen/Qwen3.8-27B" }, { "registry": "huggingface", "name": "Qwen/Qwen3.8-27B-FP8" }, { "registry": "ollama", "name": "qwen3.8" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The repository is not gated (checked 2026-10-20).", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0. The card says a hosted version on Qwen Cloud is coming soon. Running it yourself costs the hardware, 55.6 GB for the BF16 files and 18 GB for Ollama's default build (checked 2026-10-20).", "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 2026-10-20).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-10-20" }, "docsUrl": "https://huggingface.co/Qwen/Qwen3.8-27B", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "dense", "multimodal", "reasoning", "tool-calling", "apache-2.0", "safetensors", "fp8", "self-hosted" ], "lastRelease": "2026-08-14", "graded": true, "anchor": { "graded": true, "score": 68.6, "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": 8, "methodology": "0.7", "run": "2026-10-01", "scores": { "capability": 70, "interface": 70, "reach": 90, "risk": 55, "upkeep": 36 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "high", "date": "2026-11-01" }, "negative": 0, "verdict": "A dense cousin for coding and agent work with image and video input that runs almost anywhere. Ollama's 4-bit build needs about 22 GB, and seven providers host it for agents without the hardware. The main risk is that nobody at Alibaba answers, there are no safety results or agent guidance, and the tool format is only in the template.", "bestFor": "Agents that want a capable open model with vision on one 24 to 32 GB GPU or Mac, with the widest runtime support and a plain licence.", "strengths": [ "Released support in llama.cpp, Ollama, vLLM, SGLang and Transformers", "Ollama's 4-bit build fits in about 22 GB", "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", "Thinking that turns off or is budgeted per request, several tool calls a turn and 262,144 tokens of native context", "Seven inference providers host it on 1 November 2026" ], "weaknesses": [ "No safety results, prompt-injection guidance or security contact", "Tool calling and structured output aren't documented by Alibaba", "No training data, limitations, languages or knowledge cut-off on the card", "No reply from Qwen to any of the 40 most recent discussions, including template reports", "Only one quantised build from Alibaba, and no change to the files since 14 August 2026" ], "agentNotes": [ "Quickest start is `ollama run qwen3.8`, about 18 GB of files", "On our reference machine use BF16 with `vllm serve Qwen/Qwen3.8-27B --max-model-len 131072 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder`, since FP8 on gfx1151 isn't documented", "Use the qwen3_coder tool parser, SGLang's cookbook says the Hermes parser leaves its calls unparsed", "Thinking is on by default, pass `chat_template_kwargs` with `enable_thinking` false for short tasks, and read the reasoning from `reasoning_content`", "`reasoning_effort` takes xhigh (default), medium or low, and any other value makes the template raise an error", "Sampling per the card is temperature 1.0 and top_p 0.95 when thinking, temperature 0.7, top_p 0.8 and presence_penalty 1.5 without", "Preserved thinking keeps earlier reasoning in the context unless `preserve_thinking` is false, which grows long agent sessions", "Turn on YaRN only beyond 262,144 tokens, the card warns static YaRN can cost quality on short texts", "Validate JSON output yourself or use the runtime's guided decoding, since structured output isn't documented" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 3, "history": [ { "basis": "public evidence", "confidence": "high", "grade": "B", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 68.6 } ], "editorialScores": { "capability": 70, "interface": 70, "reach": 90, "risk": 55, "upkeep": 36 }, "provenanceScore": 58, "checked": { "categories": { "capability": "2026-11-01", "interface": "2026-11-01", "reach": "2026-11-01", "risk": "2026-11-01", "upkeep": "2026-11-01" }, "last": "2026-11-01", "lastMoved": "2026-10-21", "oldest": "2026-11-01", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "ollama run qwen3.8\n# or, with vLLM's recipe:\nvllm serve Qwen/Qwen3.8-27B-FP8 --max-model-len 262144 --reasoning-parser qwen3 \\\n --enable-auto-tool-choice --tool-call-parser qwen3_coder\n# or, with llama.cpp and llama.cpp's own GGUF:\nllama-server -hf ggml-org/Qwen3.8-27B-GGUF:Q4_K_M --jinja", "http": "curl http://localhost:11434/api/chat \\\n -d '{\"model\": \"qwen3.8\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/qwen3-8-27b" }, "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", "qwen3-5-4b", "qwen3-5-9b", "qwen3-6-27b", "qwen3-6-35b-a3b", "qwen3-8-flash-next" ], "weights": { "trainedBy": "Alibaba (Qwen team)", "params": "27B", "context": 262144, "goodFor": "Dense vision-language model for coding and agents, text, images and video in, tool calling and adjustable reasoning effort.", "runtimes": [ "llama.cpp", "Ollama", "vLLM", "SGLang", "Transformers" ], "builds": [ { "name": "ollama qwen3.8:latest", "url": "https://ollama.com/library/qwen3.8:latest", "who": "ollama", "precision": "q4_k_m", "sizeGB": 18, "memoryGB": 24, "format": "ollama", "runsOn": [ "Ollama" ], "note": "Q4_K_M with a BF16 vision projector (digest aaee06c39dcf). Size as Ollama's tags page shows it, rounded to whole GB above 10 GB; the Linux build is the llama.cpp variant" }, { "name": "ggml-org Qwen3.8-27B-GGUF (Q4_K_M)", "url": "https://huggingface.co/ggml-org/Qwen3.8-27B-GGUF", "who": "llama.cpp", "precision": "q4_k_m", "sizeGB": 20.03, "memoryGB": 32, "format": "gguf", "runsOn": [ "llama.cpp" ], "note": "From ggml-org, the llama.cpp project's organisation. 19.40 GB of Q4_K_M weights plus the 0.63 GB Q8_0 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": "Qwen3.8-27B-FP8", "url": "https://huggingface.co/Qwen/Qwen3.8-27B-FP8", "who": "publisher", "precision": "fp8", "sizeGB": 30.9, "memoryGB": 48, "format": "safetensors" }, { "name": "ggml-org Qwen3.8-27B-GGUF (Q8_0)", "url": "https://huggingface.co/ggml-org/Qwen3.8-27B-GGUF", "who": "llama.cpp", "precision": "q8_0", "sizeGB": 29.23, "memoryGB": 48, "format": "gguf", "runsOn": [ "llama.cpp" ], "note": "From ggml-org, the llama.cpp project's organisation. 28.60 GB of Q8_0 weights plus the 0.63 GB Q8_0 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": "Qwen3.8-27B", "url": "https://huggingface.co/Qwen/Qwen3.8-27B", "who": "publisher", "precision": "bf16", "sizeGB": 55.6, "memoryGB": 96, "format": "safetensors", "runsOn": [ "vLLM (ROCm)", "SGLang (ROCm)" ] } ], "run": "vllm serve Qwen/Qwen3.8-27B-FP8 --max-model-len 262144 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder", "runBuild": "Qwen3.8-27B-FP8", "checked": "2026-10-21", "minMemoryGB": 24 }, "family": { "lab": "Alibaba (Qwen)", "family": "Qwen3", "url": "https://www.anchorterminal.com/open-weights/#qwen", "others": [ { "name": "Qwen3-0.6B", "url": "https://huggingface.co/Qwen/Qwen3-0.6B", "params": "0.6B" }, { "name": "Qwen3-1.7B", "url": "https://huggingface.co/Qwen/Qwen3-1.7B", "params": "1.7B" }, { "name": "Qwen3-14B", "url": "https://huggingface.co/Qwen/Qwen3-14B", "params": "14B" }, { "name": "Qwen3-235B-A22B", "url": "https://huggingface.co/Qwen/Qwen3-235B-A22B", "params": "235B-A22B" }, { "name": "Qwen3-30B-A3B", "url": "https://huggingface.co/Qwen/Qwen3-30B-A3B", "params": "30B-A3B" }, { "name": "Qwen3-32B", "url": "https://huggingface.co/Qwen/Qwen3-32B", "params": "32B" }, { "name": "Qwen3-4B", "url": "https://huggingface.co/Qwen/Qwen3-4B", "params": "4B" }, { "name": "Qwen3-8B", "url": "https://huggingface.co/Qwen/Qwen3-8B", "params": "8B" }, { "name": "Qwen3-0.6B-Base", "url": "https://huggingface.co/Qwen/Qwen3-0.6B-Base", "params": "0.6B" }, { "name": "Qwen3-1.7B-Base", "url": "https://huggingface.co/Qwen/Qwen3-1.7B-Base", "params": "1.7B" }, { "name": "Qwen3-14B-Base", "url": "https://huggingface.co/Qwen/Qwen3-14B-Base", "params": "14B" }, { "name": "Qwen3-30B-A3B-Base", "url": "https://huggingface.co/Qwen/Qwen3-30B-A3B-Base", "params": "30B-A3B" }, { "name": "Qwen3-4B-Base", "url": "https://huggingface.co/Qwen/Qwen3-4B-Base", "params": "4B" }, { "name": "Qwen3-8B-Base", "url": "https://huggingface.co/Qwen/Qwen3-8B-Base", "params": "8B" }, { "name": "Qwen3-Reranker-0.6B", "url": "https://huggingface.co/Qwen/Qwen3-Reranker-0.6B", "params": "0.6B" }, { "name": "Qwen3-Reranker-8B", "url": "https://huggingface.co/Qwen/Qwen3-Reranker-8B", "params": "8B" }, { "name": "Qwen3-Embedding-0.6B", "url": "https://huggingface.co/Qwen/Qwen3-Embedding-0.6B", "params": "0.6B", "listing": "https://www.anchorterminal.com/tools/qwen3-embedding-0-6b" }, { "name": "Qwen3-Embedding-4B", "url": "https://huggingface.co/Qwen/Qwen3-Embedding-4B", "params": "4B", "listing": "https://www.anchorterminal.com/tools/qwen3-embedding-4b" }, { "name": "Qwen3-Embedding-8B", "url": "https://huggingface.co/Qwen/Qwen3-Embedding-8B", "params": "8B", "listing": "https://www.anchorterminal.com/tools/qwen3-embedding-8b" }, { "name": "Qwen3-Reranker-4B", "url": "https://huggingface.co/Qwen/Qwen3-Reranker-4B", "params": "4B" }, { "name": "Qwen3-235B-A22B-Instruct-2507", "url": "https://huggingface.co/Qwen/Qwen3-235B-A22B-Instruct-2507", "params": "235B-A22B" }, { "name": "Qwen3-Coder-480B-A35B-Instruct", "url": "https://huggingface.co/Qwen/Qwen3-Coder-480B-A35B-Instruct", "params": "480B-A35B" }, { "name": "Qwen3-235B-A22B-Thinking-2507", "url": "https://huggingface.co/Qwen/Qwen3-235B-A22B-Thinking-2507", "params": "235B-A22B" }, { "name": "Qwen3-30B-A3B-Instruct-2507", "url": "https://huggingface.co/Qwen/Qwen3-30B-A3B-Instruct-2507", "params": "30B-A3B", "listing": "https://www.anchorterminal.com/tools/qwen3-30b-a3b-instruct-2507" }, { "name": "Qwen3-30B-A3B-Thinking-2507", "url": "https://huggingface.co/Qwen/Qwen3-30B-A3B-Thinking-2507", "params": "30B-A3B" }, { "name": "Qwen3-Coder-30B-A3B-Instruct", "url": "https://huggingface.co/Qwen/Qwen3-Coder-30B-A3B-Instruct", "params": "30B-A3B", "listing": "https://www.anchorterminal.com/tools/qwen3-coder-30b-a3b-instruct" }, { "name": "Qwen3-4B-Instruct-2507", "url": "https://huggingface.co/Qwen/Qwen3-4B-Instruct-2507", "params": "4B", "listing": "https://www.anchorterminal.com/tools/qwen3-4b-instruct-2507" }, { "name": "Qwen3-4B-Thinking-2507", "url": "https://huggingface.co/Qwen/Qwen3-4B-Thinking-2507", "params": "4B" }, { "name": "Qwen3-Next-80B-A3B-Instruct", "url": "https://huggingface.co/Qwen/Qwen3-Next-80B-A3B-Instruct", "params": "80B-A3B" }, { "name": "Qwen3-Next-80B-A3B-Thinking", "url": "https://huggingface.co/Qwen/Qwen3-Next-80B-A3B-Thinking", "params": "80B-A3B" }, { "name": "Qwen3-Omni-30B-A3B-Captioner", "url": "https://huggingface.co/Qwen/Qwen3-Omni-30B-A3B-Captioner", "params": "30B-A3B" }, { "name": "Qwen3-Omni-30B-A3B-Thinking", "url": "https://huggingface.co/Qwen/Qwen3-Omni-30B-A3B-Thinking", "params": "30B-A3B" }, { "name": "Qwen3-Omni-30B-A3B-Instruct", "url": "https://huggingface.co/Qwen/Qwen3-Omni-30B-A3B-Instruct", "params": "30B-A3B", "listing": "https://www.anchorterminal.com/tools/qwen3-omni-30b-a3b-instruct" }, { "name": "Qwen3-VL-235B-A22B-Instruct", "url": "https://huggingface.co/Qwen/Qwen3-VL-235B-A22B-Instruct", "params": "235B-A22B" }, { "name": "Qwen3-VL-235B-A22B-Thinking", "url": "https://huggingface.co/Qwen/Qwen3-VL-235B-A22B-Thinking", "params": "235B-A22B" }, { "name": "Qwen3Guard-Gen-0.6B", "url": "https://huggingface.co/Qwen/Qwen3Guard-Gen-0.6B", "params": "0.6B" }, { "name": "Qwen3Guard-Gen-4B", "url": "https://huggingface.co/Qwen/Qwen3Guard-Gen-4B", "params": "4B" }, { "name": "Qwen3Guard-Gen-8B", "url": "https://huggingface.co/Qwen/Qwen3Guard-Gen-8B", "params": "8B" }, { "name": "Qwen3Guard-Stream-0.6B", "url": "https://huggingface.co/Qwen/Qwen3Guard-Stream-0.6B", "params": "0.6B" }, { "name": "Qwen3Guard-Stream-4B", "url": "https://huggingface.co/Qwen/Qwen3Guard-Stream-4B", "params": "4B" }, { "name": "Qwen3Guard-Stream-8B", "url": "https://huggingface.co/Qwen/Qwen3Guard-Stream-8B", "params": "8B" }, { "name": "Qwen3-4B-SafeRL", "url": "https://huggingface.co/Qwen/Qwen3-4B-SafeRL", "params": "4B" }, { "name": "Qwen3-VL-30B-A3B-Instruct", "url": "https://huggingface.co/Qwen/Qwen3-VL-30B-A3B-Instruct", "params": "30B-A3B", "listing": "https://www.anchorterminal.com/tools/qwen3-vl-30b-a3b-instruct" }, { "name": "Qwen3-VL-30B-A3B-Thinking", "url": "https://huggingface.co/Qwen/Qwen3-VL-30B-A3B-Thinking", "params": "30B-A3B" }, { "name": "Qwen3-VL-4B-Instruct", "url": "https://huggingface.co/Qwen/Qwen3-VL-4B-Instruct", "params": "4B", "listing": "https://www.anchorterminal.com/tools/qwen3-vl-4b-instruct" }, { "name": "Qwen3-VL-4B-Thinking", "url": "https://huggingface.co/Qwen/Qwen3-VL-4B-Thinking", "params": "4B" }, { "name": "Qwen3-VL-8B-Instruct", "url": "https://huggingface.co/Qwen/Qwen3-VL-8B-Instruct", "params": "8B", "listing": "https://www.anchorterminal.com/tools/qwen3-vl-8b-instruct" }, { "name": "Qwen3-VL-8B-Thinking", "url": "https://huggingface.co/Qwen/Qwen3-VL-8B-Thinking", "params": "8B" }, { "name": "Qwen3-VL-2B-Instruct", "url": "https://huggingface.co/Qwen/Qwen3-VL-2B-Instruct", "params": "2B", "listing": "https://www.anchorterminal.com/tools/qwen3-vl-2b-instruct" }, { "name": "Qwen3-VL-2B-Thinking", "url": "https://huggingface.co/Qwen/Qwen3-VL-2B-Thinking", "params": "2B" }, { "name": "Qwen3-VL-32B-Instruct", "url": "https://huggingface.co/Qwen/Qwen3-VL-32B-Instruct", "params": "32B", "listing": "https://www.anchorterminal.com/tools/qwen3-vl-32b-instruct" }, { "name": "Qwen3-VL-32B-Thinking", "url": "https://huggingface.co/Qwen/Qwen3-VL-32B-Thinking", "params": "32B" }, { "name": "Qwen3-VL-Embedding-2B", "url": "https://huggingface.co/Qwen/Qwen3-VL-Embedding-2B", "params": "2B", "listing": "https://www.anchorterminal.com/tools/qwen3-vl-embedding-2b" }, { "name": "Qwen3-VL-Embedding-8B", "url": "https://huggingface.co/Qwen/Qwen3-VL-Embedding-8B", "params": "8B", "listing": "https://www.anchorterminal.com/tools/qwen3-vl-embedding-8b" }, { "name": "Qwen3-VL-Reranker-2B", "url": "https://huggingface.co/Qwen/Qwen3-VL-Reranker-2B", "params": "2B" }, { "name": "Qwen3-VL-Reranker-8B", "url": "https://huggingface.co/Qwen/Qwen3-VL-Reranker-8B", "params": "8B" }, { "name": "Qwen3-TTS-12Hz-0.6B-Base", "url": "https://huggingface.co/Qwen/Qwen3-TTS-12Hz-0.6B-Base", "params": "0.6B" }, { "name": "Qwen3-TTS-12Hz-0.6B-CustomVoice", "url": "https://huggingface.co/Qwen/Qwen3-TTS-12Hz-0.6B-CustomVoice", "params": "0.6B" }, { "name": "Qwen3-TTS-12Hz-1.7B-Base", "url": "https://huggingface.co/Qwen/Qwen3-TTS-12Hz-1.7B-Base", "params": "1.7B" }, { "name": "Qwen3-TTS-12Hz-1.7B-CustomVoice", "url": "https://huggingface.co/Qwen/Qwen3-TTS-12Hz-1.7B-CustomVoice", "params": "1.7B", "listing": "https://www.anchorterminal.com/tools/qwen3-tts-1-7b-customvoice" }, { "name": "Qwen3-TTS-12Hz-1.7B-VoiceDesign", "url": "https://huggingface.co/Qwen/Qwen3-TTS-12Hz-1.7B-VoiceDesign", "params": "1.7B" }, { "name": "Qwen3-TTS-Tokenizer-12Hz", "url": "https://huggingface.co/Qwen/Qwen3-TTS-Tokenizer-12Hz", "params": "not in name" }, { "name": "Qwen3-ASR-0.6B", "url": "https://huggingface.co/Qwen/Qwen3-ASR-0.6B", "params": "0.6B", "listing": "https://www.anchorterminal.com/tools/qwen3-asr-0-6b" }, { "name": "Qwen3-ASR-1.7B", "url": "https://huggingface.co/Qwen/Qwen3-ASR-1.7B", "params": "1.7B", "listing": "https://www.anchorterminal.com/tools/qwen3-asr-1-7b" }, { "name": "Qwen3-ForcedAligner-0.6B", "url": "https://huggingface.co/Qwen/Qwen3-ForcedAligner-0.6B", "params": "0.6B" }, { "name": "Qwen3-Coder-Next", "url": "https://huggingface.co/Qwen/Qwen3-Coder-Next", "params": "not in name; Hub safetensors total 79.7B", "listing": "https://www.anchorterminal.com/tools/qwen3-coder-next" }, { "name": "Qwen3-Coder-Next-Base", "url": "https://huggingface.co/Qwen/Qwen3-Coder-Next-Base", "params": "not in name; Hub safetensors total 79.7B" }, { "name": "WebWorld-14B", "url": "https://huggingface.co/Qwen/WebWorld-14B", "params": "14B" }, { "name": "WebWorld-32B", "url": "https://huggingface.co/Qwen/WebWorld-32B", "params": "32B" }, { "name": "WebWorld-8B", "url": "https://huggingface.co/Qwen/WebWorld-8B", "params": "8B" }, { "name": "Qwen3.5-397B-A17B", "url": "https://huggingface.co/Qwen/Qwen3.5-397B-A17B", "params": "397B-A17B", "listing": "https://www.anchorterminal.com/tools/qwen3-5-397b-a17b" }, { "name": "Qwen3.5-122B-A10B", "url": "https://huggingface.co/Qwen/Qwen3.5-122B-A10B", "params": "122B-A10B", "listing": "https://www.anchorterminal.com/tools/qwen3-5-122b-a10b" }, { "name": "Qwen3.5-27B", "url": "https://huggingface.co/Qwen/Qwen3.5-27B", "params": "27B" }, { "name": "Qwen3.5-35B-A3B", "url": "https://huggingface.co/Qwen/Qwen3.5-35B-A3B", "params": "35B-A3B" }, { "name": "Qwen3.5-35B-A3B-Base", "url": "https://huggingface.co/Qwen/Qwen3.5-35B-A3B-Base", "params": "35B-A3B" }, { "name": "Qwen3.5-9B-Base", "url": "https://huggingface.co/Qwen/Qwen3.5-9B-Base", "params": "9B" }, { "name": "Qwen3.5-4B", "url": "https://huggingface.co/Qwen/Qwen3.5-4B", "params": "4B", "listing": "https://www.anchorterminal.com/tools/qwen3-5-4b" }, { "name": "Qwen3.5-4B-Base", "url": "https://huggingface.co/Qwen/Qwen3.5-4B-Base", "params": "4B" }, { "name": "Qwen3.5-9B", "url": "https://huggingface.co/Qwen/Qwen3.5-9B", "params": "9B", "listing": "https://www.anchorterminal.com/tools/qwen3-5-9b" }, { "name": "Qwen3.5-0.8B", "url": "https://huggingface.co/Qwen/Qwen3.5-0.8B", "params": "0.8B", "listing": "https://www.anchorterminal.com/tools/qwen3-5-0-8b" }, { "name": "Qwen3.5-0.8B-Base", "url": "https://huggingface.co/Qwen/Qwen3.5-0.8B-Base", "params": "0.8B" }, { "name": "Qwen3.5-2B", "url": "https://huggingface.co/Qwen/Qwen3.5-2B", "params": "2B", "listing": "https://www.anchorterminal.com/tools/qwen3-5-2b" }, { "name": "Qwen3.5-2B-Base", "url": "https://huggingface.co/Qwen/Qwen3.5-2B-Base", "params": "2B" }, { "name": "Qwen3.6-35B-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": "Qwen3.8-2.4T-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": "2026-10-20", "notes": [ "The LICENSE file reads Copyright 2026 Alibaba Cloud. RDAP names Alibaba Cloud (Singapore) Private Limited as the registrant of qwen.ai.", "Nothing is hosted, so the Apache 2.0 licence stands in for terms of service and no privacy policy is scored.", "No changelog. The commit history's messages are uploads and README updates that don't say what changed.", "qwen.ai answers every path, /.well-known/security.txt and /robots.txt included, with its web app, so there is no security.txt." ], "score": 58 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/qwen3-8-27b.json", "live": { "slug": "qwen3-8-27b", "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": "Qwen3.8-27B scores 68.6 (B) 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.", "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": 20, "category": "reach", "leads": "qwen3-8-27b", "name": "Reach", "reasons": { "a": "Memory 25 of 30, Ollama's smallest 4-bit build is 18 GB, or 21.6 GB with a fifth added, which fits 24 GB. Runtimes 25, released versions of llama.cpp, Ollama, vLLM, SGLang and Transformers all run the qwen3_5 architecture. Builds 10 of 15, Alibaba's only quantised build is FP8. Terms 15, Apache 2.0 with nothing added. Download 10, neither repository is gated. Hosted 5, seven inference providers are live for it on Hugging Face on 1 November, though Qwen Cloud's own version is still described as coming soon.", "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": 90, "b": 70 } }, { "by": 15, "category": "interface", "leads": "step-3-7-flash", "name": "Agent interface", "reasons": { "a": "Chat template 20, a Jinja file in the repository. Tool-calling format 10 of 25, the format lives only in the template and the parser name only in serving commands, Alibaba doesn't describe how tools are declared or returned. Trained tool calling 20, the template renders several calls in one turn. Structured output 0, not documented. Reasoning control 10, thinking turns off per request, reasoning_effort budgets it and the card shows the reasoning coming back in reasoning_content. Context 10, 262,144 tokens native.", "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": "qwen3-8-27b", "name": "Risk", "reasons": { "a": "File safety 25, safetensors only and nothing from the repository runs to load it or build prompts. Provenance 15, the files are in the Qwen organisation that Qwen's GitHub links to, and every file has a checksum. Safety evaluation 0, none published. Agent guidance 0, nothing on prompt injection, refusals or safeguards. Licence risk 15, plain Apache 2.0 that hasn't changed. Security programme 0, no security.txt, disclosure policy or security contact for Qwen was found, and Alibaba's group security centre couldn't be checked, so it's scored as absent.", "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 } } ], "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": "2026-08-14", "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": "Qwen3.8-27B scores 68.6 (B) 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.", "question": "Which is better for AI agents, Qwen3.8-27B or Step 3.7 Flash?" } ], "goodFor": [ { "aheadOn": [ "Reach, 90 against 70", "Risk, 55 against 45" ], "also": null, "goodFor": "Agents that want a capable open model with vision on one 24 to 32 GB GPU or Mac, with the widest runtime support and a plain licence.", "slug": "qwen3-8-27b", "watchFor": "No safety results, prompt-injection guidance or security contact" }, { "aheadOn": [ "Agent interface, 85 against 70" ], "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": { "qwen3-8-27b": [ { "causes": [ "method" ], "date": "2026-10-21", "delta": 4.9, "from": 63.7, "fromGrade": "B", "to": 68.6, "toGrade": "B" } ], "step-3-7-flash": null }, "job": { "capability": "model.open-weights", 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{ "json": "https://www.anchorterminal.com/compare/qwen3-5-2b-vs-qwen3-8-27b.json", "title": "Qwen3.5-2B vs Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/qwen3-5-2b-vs-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-2b-vs-step-3-7-flash.json", "title": "Qwen3.5-2B vs Step 3.7 Flash", "url": "https://www.anchorterminal.com/compare/qwen3-5-2b-vs-step-3-7-flash" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-qwen3-8-27b.json", "title": "Qwen3.5-397B-A17B vs Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-step-3-7-flash.json", "title": "Qwen3.5-397B-A17B vs Step 3.7 Flash", "url": "https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-step-3-7-flash" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-4b-vs-qwen3-8-27b.json", "title": "Qwen3.5-4B vs Qwen3.8-27B", "url": 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}, { "json": "https://www.anchorterminal.com/compare/qwen3-8-27b-vs-qwen3-coder-next.json", "title": "Qwen3.8-27B vs Qwen3-Coder-Next", "url": "https://www.anchorterminal.com/compare/qwen3-8-27b-vs-qwen3-coder-next" }, { "json": "https://www.anchorterminal.com/compare/qwen3-8-27b-vs-qwen3-omni-30b-a3b-instruct.json", "title": "Qwen3.8-27B vs Qwen3-Omni-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-8-27b-vs-qwen3-omni-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-8-27b-vs-qwen3-vl-2b-instruct.json", "title": "Qwen3.8-27B vs Qwen3-VL-2B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-8-27b-vs-qwen3-vl-2b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-8-27b-vs-qwen3-vl-30b-a3b-instruct.json", "title": "Qwen3.8-27B vs Qwen3-VL-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-8-27b-vs-qwen3-vl-30b-a3b-instruct" }, { "json": 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"https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-step-3-7-flash" }, { "json": "https://www.anchorterminal.com/compare/recursive-flint-vs-step-3-7-flash.json", "title": "Recursive Flint vs Step 3.7 Flash", "url": "https://www.anchorterminal.com/compare/recursive-flint-vs-step-3-7-flash" }, { "json": "https://www.anchorterminal.com/compare/ripple-65b-preview-vs-step-3-7-flash.json", "title": "Ripple-65B Preview vs Step 3.7 Flash", "url": "https://www.anchorterminal.com/compare/ripple-65b-preview-vs-step-3-7-flash" }, { "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/smollm3-3b-vs-step-3-7-flash.json", "title": "SmolLM3-3B vs Step 3.7 Flash", "url": "https://www.anchorterminal.com/compare/smollm3-3b-vs-step-3-7-flash" }, { "json": "https://www.anchorterminal.com/compare/spark-x2-6-4b-vs-step-3-7-flash.json", "title": "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": 20, "edge": "qwen3-8-27b", "key": "reach", "name": "Reach", "qwen3-8-27b": 90, "step-3-7-flash": 70, "weight": 25 }, { "by": 10, "edge": "qwen3-8-27b", "key": "risk", "name": "Risk", "qwen3-8-27b": 55, "step-3-7-flash": 45, "weight": 20 }, { "by": 15, "edge": "step-3-7-flash", "key": "interface", "name": "Agent interface", "qwen3-8-27b": 70, "step-3-7-flash": 85, "weight": 10 }, { "by": 3, "edge": "step-3-7-flash", "key": "upkeep", "name": "Documentation and upkeep", "qwen3-8-27b": 36, "step-3-7-flash": 39, "weight": 10 } ], "summary": "Qwen3.8-27B scores 68.6 (B) 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. Both do open-weight models you run yourself.", "tasks": null, "tutorials": [], "verdicts": { "qwen3-8-27b": "A dense cousin for coding and agent work with image and video input that runs almost anywhere. Ollama's 4-bit build needs about 22 GB, and seven providers host it for agents without the hardware. The main risk is that nobody at Alibaba answers, there are no safety results or agent guidance, and the tool format is only in the template.", "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/qwen3-8-27b-vs-step-3-7-flash", "json": "https://www.anchorterminal.com/compare/qwen3-8-27b-vs-step-3-7-flash.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/qwen3-8-27b-vs-step-3-7-flash.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/qwen3-8-27b-vs-step-3-7-flash.min.md" }, "markdown": "Qwen3.8-27B scores 68.6 (B) 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. Both do open-weight models you run yourself.\n\n- Qwen3.8-27B: grade B, 68.6/100, rank #11 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/qwen3-8-27b.md · JSON https://www.anchorterminal.com/api/v1/tools/qwen3-8-27b.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### Qwen3.8-27B (B)\n\nGood for: Agents that want a capable open model with vision on one 24 to 32 GB GPU or Mac, with the widest runtime support and a plain licence.\n\nAhead on:\n- Reach, 90 against 70\n- Risk, 55 against 45\n\nWatch for: No safety results, prompt-injection guidance or security contact\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\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: Qwen3.8-27B +20\n\n- Qwen3.8-27B (90): Memory 25 of 30, Ollama's smallest 4-bit build is 18 GB, or 21.6 GB with a fifth added, which fits 24 GB. Runtimes 25, released versions of llama.cpp, Ollama, vLLM, SGLang and Transformers all run the qwen3_5 architecture. Builds 10 of 15, Alibaba's only quantised build is FP8. Terms 15, Apache 2.0 with nothing added. Download 10, neither repository is gated. Hosted 5, seven inference providers are live for it on Hugging Face on 1 November, though Qwen Cloud's own version is still described as coming soon.\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- Qwen3.8-27B (70): Chat template 20, a Jinja file in the repository. Tool-calling format 10 of 25, the format lives only in the template and the parser name only in serving commands, Alibaba doesn't describe how tools are declared or returned. Trained tool calling 20, the template renders several calls in one turn. Structured output 0, not documented. Reasoning control 10, thinking turns off per request, reasoning_effort budgets it and the card shows the reasoning coming back in reasoning_content. Context 10, 262,144 tokens native.\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: Qwen3.8-27B +10\n\n- Qwen3.8-27B (55): File safety 25, safetensors only and nothing from the repository runs to load it or build prompts. Provenance 15, the files are in the Qwen organisation that Qwen's GitHub links to, and every file has a checksum. Safety evaluation 0, none published. Agent guidance 0, nothing on prompt injection, refusals or safeguards. Licence risk 15, plain Apache 2.0 that hasn't changed. Security programme 0, no security.txt, disclosure policy or security contact for Qwen was found, and Alibaba's group security centre couldn't be checked, so it's scored as absent.\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## Score by category\n\n| Category | Weight | Qwen3.8-27B | Step 3.7 Flash | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 90 | 70 | Qwen3.8-27B +20 |\n| Risk | 20% (20 this run) | 55 | 45 | Qwen3.8-27B +10 |\n| Agent interface | 10% (10 this run) | 70 | 85 | Step 3.7 Flash +15 |\n| Documentation and upkeep | 10% (10 this run) | 36 | 39 | Step 3.7 Flash +3 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **68.6 · B** | **59.8 · C** | |\n\n## Facts side by side\n\n| Fact | Qwen3.8-27B | 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 | 2026-08-14 | 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## How the scores have moved\n\n### Qwen3.8-27B\n\n- +4.9 on 21 October 2026: checklist\n\n### Step 3.7 Flash\n\nNo score change since it was listed.\n\n## Verdicts\n\n**Qwen3.8-27B.** A dense cousin for coding and agent work with image and video input that runs almost anywhere. Ollama's 4-bit build needs about 22 GB, and seven providers host it for agents without the hardware. The main risk is that nobody at Alibaba answers, there are no safety results or agent guidance, and the tool format is only in the template.\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### Qwen3.8-27B\n\n1. Quickest start is `ollama run qwen3.8`, about 18 GB of files\n2. On our reference machine use BF16 with `vllm serve Qwen/Qwen3.8-27B --max-model-len 131072 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder`, since FP8 on gfx1151 isn't documented\n3. Use the qwen3_coder tool parser, SGLang's cookbook says the Hermes parser leaves its calls unparsed\n4. Thinking is on by default, pass `chat_template_kwargs` with `enable_thinking` false for short tasks, and read the reasoning from `reasoning_content`\n5. `reasoning_effort` takes xhigh (default), medium or low, and any other value makes the template raise an error\n6. Sampling per the card is temperature 1.0 and top_p 0.95 when thinking, temperature 0.7, top_p 0.8 and presence_penalty 1.5 without\n7. Preserved thinking keeps earlier reasoning in the context unless `preserve_thinking` is false, which grows long agent sessions\n8. Turn on YaRN only beyond 262,144 tokens, the card warns static YaRN can cost quality on short texts\n9. Validate JSON output yourself or use the runtime's guided decoding, 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, Qwen3.8-27B or Step 3.7 Flash?\n\nQwen3.8-27B scores 68.6 (B) 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.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/qwen3-8-27b-vs-step-3-7-flash.json, and with the fewest tokens: https://www.anchorterminal.com/compare/qwen3-8-27b-vs-step-3-7-flash.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"qwen3-8-27b\", \"b\": \"step-3-7-flash\"}`. From a terminal: `anchor compare qwen3-8-27b step-3-7-flash`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/qwen3-8-27b.json and https://www.anchorterminal.com/api/v1/tools/step-3-7-flash.json\n\n## Other comparisons with Qwen3.8-27B or Step 3.7 Flash\n\n- [Agents-A1-4B vs Qwen3.8-27B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-qwen3-8-27b.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 Qwen3.8-27B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-qwen3-8-27b.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 Qwen3.8-27B](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-qwen3-8-27b.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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1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/step-3-7-flash-vs-swift-1-5-qwen3-8-27b.md)\n- [Step 3.7 Flash vs Swift 1.5 Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/step-3-7-flash-vs-swift-1-5-qwen3-8-flash-next.md)\n- [Step 3.7 Flash vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/step-3-7-flash-vs-thinkingcap-qwen3-8-27b.md)\n- [Step 3.7 Flash vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/step-3-7-flash-vs-unlimited-ocr-2.md)\n- [Step 3.7 Flash vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/step-3-7-flash-vs-wevisone-vl-2b.md)\n- [Step 3.7 Flash vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/step-3-7-flash-vs-wevisone-vl-4b.md)\n- [Step 3.7 Flash vs ZAYA1-8B](https://www.anchorterminal.com/compare/step-3-7-flash-vs-zaya1-8b.md)\n", "meta": { "attribution": "Anchor Terminal (https://www.anchorterminal.com)", "attributionRequired": true, "credit": "CC BY 4.0, credit Anchor Terminal and link the page or API address you took it from (https://www.anchorterminal.com/terms/#data)", "docs": "https://www.anchorterminal.com/docs/", "generatedAt": "2026-11-11", "license": "CC-BY-4.0", "method": "https://www.anchorterminal.com/benchmark/", "methodology": "0.7", "openapi": "https://www.anchorterminal.com/openapi.json", "preview": false, "run": "2026-10-01", "runLabel": "October 2026 research run", "terms": "https://www.anchorterminal.com/terms/#data" }, "page": { "breadcrumbs": [ { "name": "Home", "url": "https://www.anchorterminal.com/" }, { "name": "Compare", "url": "https://www.anchorterminal.com/compare/" }, { "name": "Qwen3.8-27B vs Step 3.7 Flash", "url": "" } ], "description": "Qwen3.8-27B scores 68.6 (B) to Step 3.7 Flash's 59.8 (C) for open-weight models you run yourself. 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