{ "data": { "a": { "slug": "qwen3-5-9b", "name": "Qwen3.5-9B", "vendor": "Alibaba (Qwen team)", "vendorUrl": "https://qwen.ai", "kind": "weights", "category": "open-weights", "summary": "Qwen3.5-9B is Alibaba's open-weight dense vision-language model of 9B parameters, with 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/qwen3-5-9b", "markdownUrl": "https://www.anchorterminal.com/tools/qwen3-5-9b.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/qwen3-5-9b.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/qwen3-5-9b.json", "dns": "qwen3-5-9b.grade.anchorterminal.com", "repo": "https://huggingface.co/Qwen/Qwen3.5-9B", "license": "Apache-2.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "Qwen/Qwen3.5-9B" }, { "registry": "ollama", "name": "qwen3.5:9b" } ], "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, 19.3 GB for the BF16 files and 6.6 GB for Ollama's default build (checked 2026-10-21).", "priceSummary": "Free", "where": "local", "x402": { "level": "no", "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-10-21).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-10-21" }, "docsUrl": "https://huggingface.co/Qwen/Qwen3.5-9B", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "dense", "multimodal", "reasoning", "tool-calling", "apache-2.0", "safetensors", "self-hosted" ], "lastRelease": "2026-03-02", "graded": true, "anchor": { "graded": true, "score": 60.5, "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": 69, "methodology": "0.7", "run": "2026-10-01", "scores": { "capability": 51, "interface": 70, "reach": 85, "risk": 55, "upkeep": 34 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "high", "date": "2026-11-04" }, "negative": 0, "verdict": "A small dense vision-language model with trained tool calling, thinking that turns off per request and a 262,144-token context. Ollama's 4-bit build is 6.6 GB and the BF16 files 19.3 GB, and every major runtime loads it. The files haven't changed since 2 March 2026, Qwen answered none of the 40 latest discussions, and no safety results are published.", "bestFor": "Agents that want a small multimodal tool-calling model on a laptop or a 16 GB GPU, under a plain licence", "strengths": [ "Runs in vLLM, SGLang, llama.cpp, Ollama, LM Studio and released Transformers", "Ollama's 4-bit build is 6.6 GB and the BF16 files 19.3 GB", "Plain Apache 2.0, no gate, no account", "262,144 tokens native, and thinking that turns off per request", "Safetensors only, with a SHA-256 for every shard and a chat template in the repository", "Four inference providers serve it for agents without the hardware" ], "weaknesses": [ "No quantised build from Qwen itself", "No safety evaluation, agent guidance or security contact", "No reply from Qwen in any of the 40 most recent discussions", "No change to the files or card since 2 March 2026", "The tool-call format and structured output aren't documented beyond the template", "No limitations, knowledge cut-off or training data detail on the card" ], "agentNotes": [ "Smallest start is `ollama run qwen3.5:9b` (6.6 GB, q4_k_m)", "On vLLM run `vllm serve Qwen/Qwen3.5-9B --port 8000 --max-model-len 262144 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder`, or tool calls come back as text", "Tool calls use the XML style `\u003ctool_call\u003e\u003cfunction=name\u003e\u003cparameter=x\u003e`, not Hermes JSON, so use the qwen3_coder parser and not hermes", "Thinking is on by default; pass `chat_template_kwargs` with `enable_thinking` false for short tasks, since `/nothink` isn't supported", "There's no generation_config.json, so pass sampling yourself, temperature 1.0, top_p 0.95, top_k 20, presence_penalty 1.5 when thinking, or 0.7, 0.8, 20, 1.5 without", "Keep at least 128K of context when thinking, and turn on YaRN only beyond 262,144 tokens", "Validate JSON output yourself or use the runtime's guided decoding" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 2.5, "history": [ { "basis": "public evidence", "confidence": "high", "grade": "C", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 60.5 } ], "editorialScores": { "capability": 51, "interface": 70, "reach": 85, "risk": 55, "upkeep": 34 }, "provenanceScore": 54, "checked": { "categories": { "capability": "2026-11-04", "interface": "2026-11-04", "reach": "2026-11-04", "risk": "2026-11-04", "upkeep": "2026-11-04" }, "last": "2026-11-04", "oldest": "2026-11-04", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "ollama run qwen3.5:9b\n# or, with the card's vLLM command:\nvllm serve Qwen/Qwen3.5-9B --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/Qwen3.5-9B-GGUF:Q4_K_M --jinja", "http": "curl http://localhost:11434/api/chat \\\n -d '{\"model\": \"qwen3.5:9b\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/qwen3-5-9b" }, "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", "qwen3-6-27b", "qwen3-6-35b-a3b", "qwen3-8-27b", "qwen3-8-flash-next" ], "weights": { "trainedBy": "Alibaba (Qwen team)", "builtOn": "Qwen/Qwen3.5-9B-Base", "params": "9B", "context": 262144, "goodFor": "Dense vision-language model for local agents: text, images and video in, tool calling, thinking on by default.", "runtimes": [ "llama.cpp", "Ollama", "vLLM", "SGLang", "LM Studio", "Transformers" ], "builds": [ { "name": "ollama qwen3.5:9b", "url": "https://ollama.com/library/qwen3.5:9b", "who": "ollama", "precision": "q4_k_m", "sizeGB": 6.6, "memoryGB": 8, "format": "ollama", "runsOn": [ "Ollama" ], "note": "Q4_K_M with a BF16 vision projector (digest 56671c2ab938), 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 Qwen3.5-9B-GGUF (Q4_K_M)", "url": "https://huggingface.co/lmstudio-community/Qwen3.5-9B-GGUF", "who": "lmstudio", "precision": "q4_k_m", "sizeGB": 6.55, "memoryGB": 8, "format": "gguf", "runsOn": [ "LM Studio", "llama.cpp" ], "note": "From lmstudio-community, LM Studio's organisation on the Hub. 5.63 GB of Q4_K_M weights plus the 0.92 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 Qwen3.5-9B-GGUF (Q6_K)", "url": "https://huggingface.co/lmstudio-community/Qwen3.5-9B-GGUF", "who": "lmstudio", "precision": "q6_k", "sizeGB": 8.28, "memoryGB": 12, "format": "gguf", "runsOn": [ "LM Studio", "llama.cpp" ], "note": "From lmstudio-community, LM Studio's organisation on the Hub. 7.36 GB of Q6_K weights plus the 0.92 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 Qwen3.5-9B-GGUF (Q8_0)", "url": "https://huggingface.co/lmstudio-community/Qwen3.5-9B-GGUF", "who": "lmstudio", "precision": "q8_0", "sizeGB": 10.45, "memoryGB": 16, "format": "gguf", "runsOn": [ "LM Studio", "llama.cpp" ], "note": "From lmstudio-community, LM Studio's organisation on the Hub. 9.53 GB of Q8_0 weights plus the 0.92 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": "Qwen3.5-9B", "url": "https://huggingface.co/Qwen/Qwen3.5-9B", "who": "publisher", "precision": "bf16", "sizeGB": 19.3, "memoryGB": 24, "format": "safetensors", "runsOn": [ "vLLM (ROCm)", "SGLang (ROCm)" ] } ], "run": "vllm serve Qwen/Qwen3.5-9B --port 8000 --max-model-len 262144 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder", "runBuild": "Qwen3.5-9B", "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", "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": "Qwen3.5-0.8B", "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 registrant (Alibaba Cloud (Singapore) Private Limited) and registration date of qwen.ai are per the RDAP reading of 20 October 2026; rdap.org didn't answer 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. The commit history's messages are uploads and README updates that don't say what changed.", "qwen.ai redirects /.well-known/security.txt and /robots.txt to its home page, so there is no security.txt." ], "score": 54 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/qwen3-5-9b.json", "live": { "slug": "qwen3-5-9b", "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": "ZAYA1-8B and Qwen3.5-9B score within a point of each other on agent readiness, 61 (C) and 60.5 (C). Qwen3.5-9B leads on reach and agent interface.", "b": { "slug": "zaya1-8b", "name": "ZAYA1-8B", "vendor": "Zyphra", "vendorUrl": "https://www.zyphra.com", "kind": "weights", "category": "open-weights", "summary": "ZAYA1-8B is Zyphra's open-weight reasoning model, a mixture of experts of 8.4B parameters with 760M active, trained from scratch for maths and coding. Apache 2.0. Zyphra's 4-bit build is 5.9 GB. Released SGLang and Transformers run it.", "url": "https://www.anchorterminal.com/tools/zaya1-8b", "markdownUrl": "https://www.anchorterminal.com/tools/zaya1-8b.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/zaya1-8b.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/zaya1-8b.json", "dns": "zaya1-8b.grade.anchorterminal.com", "repo": "https://huggingface.co/Zyphra/ZAYA1-8B", "license": "Apache-2.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "Zyphra/ZAYA1-8B" }, { "registry": "huggingface", "name": "Zyphra/ZAYA1-8B-FP8-Experts" }, { "registry": "huggingface", "name": "Zyphra/ZAYA1-8B-MXFP4-Experts" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. None of the repositories is gated (checked 2026-11-10).", "pricing": "free", "pricingNotes": "Free to download and use under Apache 2.0, per the main repository's metadata. Running it yourself costs little: 5.9 GB with the experts at 4 bits (MXFP4), 9.6 GB in FP8 or 17.7 GB in BF16 (checked 2026-11-10).", "priceSummary": "Free", "where": "local", "x402": { "level": "no", "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-11-10).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-11-10" }, "docsUrl": "https://huggingface.co/Zyphra/ZAYA1-8B", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "moe", "small", "reasoning", "maths", "coding", "tool-calling", "apache-2.0", "safetensors", "fp8", "mxfp4", "self-hosted" ], "lastRelease": "2026-07-02", "graded": true, "anchor": { "graded": true, "score": 61, "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": 67, "methodology": "0.7", "run": "2026-10-01", "scores": { "interface": 60, "reach": 80, "risk": 55, "upkeep": 50 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-11-10" }, "negative": 0, "verdict": "A small reasoning model trained from scratch by one lab, with a clean licence, 4-bit and FP8 builds and a team that answers questions. It's the hardest of the small models to run: its own architecture, a card that points at forks, and tool calling that needs a parser no release has. Little on limits, data or safety.", "bestFor": "People who want a small, fast reasoning model for maths and code and can live without tool calling", "strengths": [ "Apache 2.0, ungated", "5.9 GB at 4 bits, 760M parameters active", "Released Transformers and SGLang carry the architecture", "Zyphra replied in most of the recent discussions", "The original checkpoint is kept beside the reshaped one" ], "weaknesses": [ "Tool calling needs a parser that only Zyphra's vLLM fork has", "Released vLLM and llama.cpp don't run it, and there's no GGUF", "The card's install steps use forks", "No limitations, context length, languages, data or safety results on the card", "Benchmarks on the card are all from Zyphra's own harness" ], "agentNotes": [ "Use it for reasoning, maths and code. Don't count on tool calling outside Zyphra's vLLM fork", "Run it with Transformers 5.22.2 or SGLang 0.5.23, which have the model built in", "Use temperature 1.0 and top_p 0.95 in general, and temperature 0.6 for agent and code work", "Download `Zyphra/ZAYA1-8B`, not the legacy repository, for the released runtimes", "The quantised repositories have no licence tag. The main one is Apache 2.0" ], "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": 61 } ], "editorialScores": { "interface": 60, "reach": 80, "risk": 55, "upkeep": 50 }, "provenanceScore": 56, "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\"\npython -m sglang.launch_server --model-path Zyphra/ZAYA1-8B --reasoning-parser qwen3\n# the card's own route is vLLM from Zyphra's fork, with --tool-call-parser zaya_xml", "http": "# SGLang serves an OpenAI-compatible API at http://localhost:30000/v1. The card recommends temperature 1.0\n# and top_p 0.95 in general, and temperature 0.6 for agent and code work" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/zaya1-8b" }, "weights": { "trainedBy": "Zyphra", "builtOn": "ZAYA1-reasoning-base, Zyphra's own pre-trained model, per the card", "params": "8.4B", "active": "760M", "context": 131072, "goodFor": "Long step-by-step reasoning in maths and code on a small machine, and as the model inside a test-time compute harness.", "runtimes": [ "SGLang", "Transformers" ], "builds": [ { "name": "ZAYA1-8B-MXFP4-Experts", "url": "https://huggingface.co/Zyphra/ZAYA1-8B-MXFP4-Experts", "who": "publisher", "precision": "mxfp4", "sizeGB": 5.85, "memoryGB": 8, "format": "safetensors", "note": "Zyphra's build with the experts at 4 bits. Which released runtime loads it wasn't established" }, { "name": "ZAYA1-8B-FP8-Experts", "url": "https://huggingface.co/Zyphra/ZAYA1-8B-FP8-Experts", "who": "publisher", "precision": "fp8", "sizeGB": 9.64, "memoryGB": 12, "format": "safetensors", "note": "Zyphra's build with the experts in FP8" }, { "name": "ZAYA1-8B (BF16 safetensors)", "url": "https://huggingface.co/Zyphra/ZAYA1-8B", "who": "publisher", "precision": "bf16", "sizeGB": 17.68, "memoryGB": 24, "format": "safetensors", "runsOn": [ "SGLang", "Transformers" ], "note": "Four shards, reshaped for Transformers" } ], "run": "python -m sglang.launch_server --model-path Zyphra/ZAYA1-8B --reasoning-parser qwen3", "runBuild": "ZAYA1-8B (BF16 safetensors)", "checked": "2026-11-10", "minMemoryGB": 8 }, "family": { "lab": "Zyphra", "family": "ZAYA1", "url": "https://www.anchorterminal.com/open-weights/#zyphra", "others": [ { "name": "ZAYA1-VL-8B", "url": "https://huggingface.co/Zyphra/ZAYA1-VL-8B" }, { "name": "ZAYA1-74B-preview", "url": "https://huggingface.co/Zyphra/ZAYA1-74B-preview" } ] }, "area": "models", "provenance": { "legalEntity": "Zyphra, named on the card and its site. The legal entity wasn't looked up", "domain": "zyphra.com", "domainRegistered": "", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://huggingface.co/Zyphra/ZAYA1-8B/commits/main", "securityTxt": "none", "checked": "2026-11-10", "notes": [ "www.zyphra.com's robots.txt allows reading outside /studio and /api. Its about page gives a San Francisco address and names no people in the text read.", "The domain's registration date wasn't looked up. www.zyphra.com has no security.txt (404 on 10 November 2026).", "Nothing is hosted under this listing, so Apache 2.0 stands in for terms of service and no privacy policy is scored. Zyphra's site lists a cloud product for its open models, which wasn't read.", "The changelog is the commit history, 41 commits from 4 May to 26 June 2026." ], "score": 56 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/zaya1-8b.json" }, "edges": [ { "by": 16, "category": "upkeep", "leads": "zaya1-8b", "name": "Documentation and upkeep", "reasons": { "a": "Model card 14, it covers intended use, architecture, parameters, context and 201 languages but gives no limitations and no knowledge cut-off. Training data 5, a general line about trillions of multimodal tokens with no cut-off. Last change 5, the files and card last changed on 2 March 2026 (c202236), 247 days before 4 November, within a year. Responsiveness 0, no Qwen member replied in any of the 40 most recent of the 45 discussions that aren't pull requests (2 March to 29 September 2026). Version policy 5, a first release that was never replaced in place, with no dated notes across generations. File health 5, two reports about the files and card (15 and 51) are still open with no reply. 14 + 5 + 5 + 0 + 5 + 5 = 34. The card's highlights mention sparse Mixture-of-Experts for the series while this 9B is dense, but its own overview matches config.json, so it's not counted as a contradiction.", "b": "Model card 5, Intended use and the parameter counts are on the card. Training data 0, Nothing on the card about the training data. Last change 10, the main repository last changed on 26 June 2026 (the checkpoint reshaped for Transformers), 137 days before this check, and the quantised repositories on 2 July, 131 days. Responsiveness 20, Of the 16 most recent discussions that aren't pull requests (7 May to 14 August 2026), 10 have a reply from a Zyphra member, which is most. Version policy 5, the original checkpoint is kept as ZAYA1-8B-legacy and the card says so at the top, without a date. File health 10, Config, template and card agree on the architecture after the reshape. 5 + 0 + 10 + 20 + 5 + 10 = 50." }, "scores": { "a": 34, "b": 50 } }, { "by": 10, "category": "interface", "leads": "qwen3-5-9b", "name": "Agent interface", "reasons": { "a": "Chat template 20, a Jinja file in the repository. Tool-calling format 10, the card names the qwen3_coder parser for vLLM and SGLang but leaves the declaration and return format to the template, and Qwen's own function-calling guide still describes Qwen3's Hermes format. Trained tool calling 20, the template renders several calls in one turn. Structured output 0, nothing documents asking the weights for JSON. Reasoning control 10, thinking turns off per request with enable_thinking false and the reasoning comes back in think tags that the qwen3 parser splits out. Context 10, 262,144 tokens native.", "b": "Chat template 20, chat_template.jinja in the repository (10,700 characters). Tool-calling format 10, only the template and a fork say it. Trained tool calling 10, the template takes tools and renders calls, and the card recommends settings for agent use, but it doesn't say the model was trained for tool calling or for several calls in a turn, so the lower band. Structured output 0, Nothing on the card about JSON or schema-constrained output. Reasoning control 10, the template has an enable_thinking switch, and the card names the qwen3 reasoning parser, which returns the reasoning apart from the answer. Context 10, 131,072 positions in config.json, for an architecture Zyphra trained end to end, so the config is the model's own. 20 + 10 + 10 + 0 + 10 + 10 = 60." }, "scores": { "a": 70, "b": 60 } }, { "by": 5, "category": "reach", "leads": "qwen3-5-9b", "name": "Reach", "reasons": { "a": "Memory 30, Qwen publishes no quantised 9B, so the smallest 4-bit build from a runtime's own library is Ollama's qwen3.5:9b at 6.6 GB, about 7.9 GB with the fifth added, well under 16 GB. Runtimes 25, the cap, with vLLM listed in its stable docs, Transformers at released tags, llama.cpp, Ollama's library, LM Studio's own catalogue and SGLang all running the architecture. Builds 0, Qwen's FP8 and GPTQ repositories cover only the 27B and larger Qwen3.5 models, so every quantisation of the 9B is someone else's. Terms 15, plain Apache 2.0. Download 10, the repository isn't gated. Hosted 5, Together, Featherless AI, OVHcloud and DeepInfra are live for it on its Hugging Face page. 30 + 25 + 0 + 15 + 10 + 5 = 85.", "b": "Memory 30, the smallest build from Zyphra is the MXFP4-Experts repository, 5.85 GB, and 5.85 GB × 1.2 = 7.0 GB, within 16 GB. Runtimes 10, Released Transformers 5.22.2 has the zaya model and SGLang 0.5.23 has srt/models/zaya.py. Builds 15, Quantised builds from Zyphra at two precisions, FP8 and MXFP4 for the experts, each in its own repository. Terms 15, Apache 2.0 in the card's metadata, an OSI licence with nothing added. Download 10, the Hub API reports gated false for the model and its builds on 10 November 2026, so the files download with no account and nothing to accept. Hosted 0, Hugging Face maps no inference provider to it, and the card names no API. Zyphra's site lists a cloud product for its open models, which wasn't checked for this model. 30 + 10 + 15 + 15 + 10 + 0 = 80." }, "scores": { "a": 85, "b": 80 } } ], "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "Alibaba (Qwen team)", "b": "Zyphra", "name": "Vendor" }, { "a": "no (local only)", "b": "no (local only)", "name": "Hosted endpoint" }, { "a": "", "b": "", "name": "Transports" }, { "a": "None", "b": "None", "name": "Auth" }, { "a": "Free", "b": "Free", "name": "Pricing" }, { "a": "no", "b": "no", "name": "x402" }, { "a": "Apache-2.0", "b": "Apache-2.0", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "no", "b": "no", "name": "llms.txt" }, { "a": "2026-03-02", "b": "2026-07-02", "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": "2.5/5 (4)", "b": "none", "name": "Agent reviews" } ], "faq": [ { "answer": "ZAYA1-8B and Qwen3.5-9B score within a point of each other on agent readiness, 61 (C) and 60.5 (C). Qwen3.5-9B leads on reach and agent interface.", "question": "Which is better for AI agents, Qwen3.5-9B or ZAYA1-8B?" } ], "goodFor": [ { "aheadOn": [ "Reach, 85 against 80", "Agent interface, 70 against 60" ], "also": null, "goodFor": "Agents that want a small multimodal tool-calling model on a laptop or a 16 GB GPU, under a plain licence", "slug": "qwen3-5-9b", "watchFor": "No quantised build from Qwen itself" }, { "aheadOn": [ "Documentation and upkeep, 50 against 34" ], "also": null, "goodFor": "People who want a small, fast reasoning model for maths and code and can live without tool calling", "slug": "zaya1-8b", "watchFor": "Tool calling needs a parser that only Zyphra's vLLM fork has" } ], "history": null, "job": { "capability": "model.open-weights", "name": "Open-weight models you run yourself" }, "others": [ { "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-qwen3-5-9b.json", "title": "Agents-A1-4B vs Qwen3.5-9B", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-qwen3-5-9b" }, { "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-zaya1-8b.json", "title": "Agents-A1-4B vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-zaya1-8b" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-qwen3-5-9b.json", "title": "Apertus v1.5 8B vs Qwen3.5-9B", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-qwen3-5-9b" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-zaya1-8b.json", "title": "Apertus v1.5 8B vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-zaya1-8b" }, { "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-qwen3-5-9b.json", "title": "Apodex-1.1-mini vs Qwen3.5-9B", "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-qwen3-5-9b" }, { "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-zaya1-8b.json", "title": "Apodex-1.1-mini 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"json": "https://www.anchorterminal.com/compare/devstral-small-2-vs-zaya1-8b.json", "title": "Devstral Small 2 vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/devstral-small-2-vs-zaya1-8b" }, { "json": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-qwen3-5-9b.json", "title": "DiffusionGemma 26B A4B IT vs Qwen3.5-9B", "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-qwen3-5-9b" }, { "json": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-zaya1-8b.json", "title": "DiffusionGemma 26B A4B IT vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-zaya1-8b" }, { "json": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-qwen3-5-9b.json", "title": "EXAONE 4.5 33B vs Qwen3.5-9B", "url": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-qwen3-5-9b" }, { "json": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-zaya1-8b.json", "title": "EXAONE 4.5 33B vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-zaya1-8b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-qwen3-5-9b.json", "title": "Gemma 4 12B IT vs Qwen3.5-9B", "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-qwen3-5-9b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-zaya1-8b.json", "title": "Gemma 4 12B IT vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-zaya1-8b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-qwen3-5-9b.json", "title": "Gemma 4 26B A4B IT vs Qwen3.5-9B", "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-qwen3-5-9b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-zaya1-8b.json", "title": "Gemma 4 26B A4B IT vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-zaya1-8b" }, { "json": 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"url": "https://www.anchorterminal.com/compare/inkling-small-vs-zaya1-8b" }, { "json": "https://www.anchorterminal.com/compare/inkling-vs-qwen3-5-9b.json", "title": "Inkling vs Qwen3.5-9B", "url": "https://www.anchorterminal.com/compare/inkling-vs-qwen3-5-9b" }, { "json": "https://www.anchorterminal.com/compare/inkling-vs-zaya1-8b.json", "title": "Inkling vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/inkling-vs-zaya1-8b" }, { "json": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-qwen3-5-9b.json", "title": "InternVL3.5-GPT-OSS-20B-A4B-Preview vs Qwen3.5-9B", "url": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-qwen3-5-9b" }, { "json": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-zaya1-8b.json", "title": "InternVL3.5-GPT-OSS-20B-A4B-Preview vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-zaya1-8b" }, { "json": 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{ "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-5-9b.json", "title": "occamy-1.0 vs Qwen3.5-9B", "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-5-9b" }, { "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-zaya1-8b.json", "title": "occamy-1.0 vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-zaya1-8b" }, { "json": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-5-9b.json", "title": "Olmo 3 7B Think vs Qwen3.5-9B", "url": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-qwen3-5-9b" }, { "json": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-zaya1-8b.json", "title": "Olmo 3 7B Think vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-zaya1-8b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwen3-5-9b.json", "title": "Ornith-1.5-122B vs Qwen3.5-9B", "url": 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"https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-zaya1-8b.json", "title": "Ornith-1.5-397B vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-zaya1-8b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-5-9b.json", "title": "Ornith-1.5-9B vs Qwen3.5-9B", "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-5-9b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-zaya1-8b.json", "title": "Ornith-1.5-9B vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-zaya1-8b" }, { "json": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-qwen3-5-9b.json", "title": "OTel-2.0-LLM-31B-IT vs Qwen3.5-9B", "url": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-qwen3-5-9b" }, { "json": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-zaya1-8b.json", "title": "OTel-2.0-LLM-31B-IT vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-zaya1-8b" }, { "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-5-9b.json", "title": "Qwen-AgentWorld-35B-A3B vs Qwen3.5-9B", "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-5-9b" }, { "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-zaya1-8b.json", "title": "Qwen-AgentWorld-35B-A3B vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-zaya1-8b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-30b-a3b-instruct-2507-vs-qwen3-5-9b.json", "title": "Qwen3-30B-A3B-Instruct-2507 vs Qwen3.5-9B", "url": "https://www.anchorterminal.com/compare/qwen3-30b-a3b-instruct-2507-vs-qwen3-5-9b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-30b-a3b-instruct-2507-vs-zaya1-8b.json", "title": "Qwen3-30B-A3B-Instruct-2507 vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/qwen3-30b-a3b-instruct-2507-vs-zaya1-8b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-4b-instruct-2507-vs-qwen3-5-9b.json", "title": "Qwen3-4B-Instruct-2507 vs Qwen3.5-9B", "url": "https://www.anchorterminal.com/compare/qwen3-4b-instruct-2507-vs-qwen3-5-9b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-4b-instruct-2507-vs-zaya1-8b.json", "title": "Qwen3-4B-Instruct-2507 vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/qwen3-4b-instruct-2507-vs-zaya1-8b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-qwen3-5-9b.json", "title": "Qwen3.5-0.8B vs Qwen3.5-9B", "url": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-qwen3-5-9b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-zaya1-8b.json", "title": "Qwen3.5-0.8B vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-zaya1-8b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-qwen3-5-9b.json", "title": "Qwen3.5-122B-A10B vs Qwen3.5-9B", "url": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-qwen3-5-9b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-zaya1-8b.json", "title": "Qwen3.5-122B-A10B vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-zaya1-8b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-2b-vs-qwen3-5-9b.json", "title": "Qwen3.5-2B vs Qwen3.5-9B", "url": "https://www.anchorterminal.com/compare/qwen3-5-2b-vs-qwen3-5-9b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-2b-vs-zaya1-8b.json", "title": "Qwen3.5-2B vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/qwen3-5-2b-vs-zaya1-8b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-qwen3-5-9b.json", "title": "Qwen3.5-397B-A17B vs Qwen3.5-9B", "url": "https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-qwen3-5-9b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-zaya1-8b.json", "title": "Qwen3.5-397B-A17B vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-zaya1-8b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-4b-vs-qwen3-5-9b.json", "title": "Qwen3.5-4B vs Qwen3.5-9B", "url": "https://www.anchorterminal.com/compare/qwen3-5-4b-vs-qwen3-5-9b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-4b-vs-zaya1-8b.json", "title": "Qwen3.5-4B vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/qwen3-5-4b-vs-zaya1-8b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-qwen3-6-27b.json", "title": "Qwen3.5-9B vs Qwen3.6-27B", "url": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-qwen3-6-27b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-qwen3-6-35b-a3b.json", "title": "Qwen3.5-9B vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-qwen3-8-27b.json", "title": "Qwen3.5-9B vs Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-qwen3-8-flash-next.json", "title": "Qwen3.5-9B vs Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-qwen3-coder-30b-a3b-instruct.json", "title": "Qwen3.5-9B vs Qwen3-Coder-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-qwen3-coder-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-qwen3-coder-next.json", "title": "Qwen3.5-9B vs Qwen3-Coder-Next", "url": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-qwen3-coder-next" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-qwen3-omni-30b-a3b-instruct.json", "title": "Qwen3.5-9B vs Qwen3-Omni-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-qwen3-omni-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-qwen3-vl-2b-instruct.json", "title": "Qwen3.5-9B vs Qwen3-VL-2B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-qwen3-vl-2b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-qwen3-vl-30b-a3b-instruct.json", "title": "Qwen3.5-9B vs Qwen3-VL-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-qwen3-vl-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-qwen3-vl-32b-instruct.json", "title": "Qwen3.5-9B vs Qwen3-VL-32B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-qwen3-vl-32b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-qwen3-vl-4b-instruct.json", "title": "Qwen3.5-9B vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-qwen3-vl-8b-instruct.json", "title": "Qwen3.5-9B vs Qwen3-VL-8B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-qwen3-vl-8b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-qwopus3-8-27b-flash-v2.json", "title": "Qwen3.5-9B vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-recursive-flint.json", "title": "Qwen3.5-9B vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-ripple-65b-preview.json", "title": "Qwen3.5-9B vs Ripple-65B Preview", "url": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-ripple-65b-preview" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-sensenova-loop-9b-preview.json", "title": "Qwen3.5-9B vs SenseNova-Loop-9B-Preview", "url": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-sensenova-loop-9b-preview" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-smollm3-3b.json", "title": "Qwen3.5-9B vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-spark-x2-6-4b.json", "title": "Qwen3.5-9B vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-step-3-7-flash.json", "title": "Qwen3.5-9B vs Step 3.7 Flash", "url": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-step-3-7-flash" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-swift-1-5-qwen3-8-27b.json", "title": "Qwen3.5-9B vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-swift-1-5-qwen3-8-flash-next.json", "title": "Qwen3.5-9B vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-thinkingcap-qwen3-8-27b.json", "title": "Qwen3.5-9B vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-unlimited-ocr-2.json", "title": "Qwen3.5-9B vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-wevisone-vl-2b.json", "title": "Qwen3.5-9B vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-wevisone-vl-4b.json", "title": "Qwen3.5-9B vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-6-27b-vs-zaya1-8b.json", "title": "Qwen3.6-27B vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/qwen3-6-27b-vs-zaya1-8b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-zaya1-8b.json", "title": "Qwen3.6-35B-A3B vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-zaya1-8b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-8-27b-vs-zaya1-8b.json", "title": "Qwen3.8-27B vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/qwen3-8-27b-vs-zaya1-8b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-zaya1-8b.json", "title": "Qwen3.8-Flash-Next vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-zaya1-8b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-coder-30b-a3b-instruct-vs-zaya1-8b.json", "title": "Qwen3-Coder-30B-A3B-Instruct vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/qwen3-coder-30b-a3b-instruct-vs-zaya1-8b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-coder-next-vs-zaya1-8b.json", "title": "Qwen3-Coder-Next vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/qwen3-coder-next-vs-zaya1-8b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-omni-30b-a3b-instruct-vs-zaya1-8b.json", "title": "Qwen3-Omni-30B-A3B-Instruct vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/qwen3-omni-30b-a3b-instruct-vs-zaya1-8b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-2b-instruct-vs-zaya1-8b.json", "title": "Qwen3-VL-2B-Instruct vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/qwen3-vl-2b-instruct-vs-zaya1-8b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-30b-a3b-instruct-vs-zaya1-8b.json", "title": "Qwen3-VL-30B-A3B-Instruct vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/qwen3-vl-30b-a3b-instruct-vs-zaya1-8b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-32b-instruct-vs-zaya1-8b.json", "title": "Qwen3-VL-32B-Instruct vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/qwen3-vl-32b-instruct-vs-zaya1-8b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-zaya1-8b.json", "title": "Qwen3-VL-4B-Instruct vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-zaya1-8b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-8b-instruct-vs-zaya1-8b.json", "title": "Qwen3-VL-8B-Instruct vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/qwen3-vl-8b-instruct-vs-zaya1-8b" }, { "json": "https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-zaya1-8b.json", "title": "Qwopus3.8-27B-Flash-V2 vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-zaya1-8b" }, { "json": "https://www.anchorterminal.com/compare/recursive-flint-vs-zaya1-8b.json", "title": "Recursive Flint vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/recursive-flint-vs-zaya1-8b" }, { "json": "https://www.anchorterminal.com/compare/ripple-65b-preview-vs-zaya1-8b.json", "title": "Ripple-65B Preview vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/ripple-65b-preview-vs-zaya1-8b" }, { "json": "https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-zaya1-8b.json", "title": "SenseNova-Loop-9B-Preview vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-zaya1-8b" }, { "json": "https://www.anchorterminal.com/compare/smollm3-3b-vs-zaya1-8b.json", "title": "SmolLM3-3B vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/smollm3-3b-vs-zaya1-8b" }, { "json": "https://www.anchorterminal.com/compare/spark-x2-6-4b-vs-zaya1-8b.json", "title": "Spark-X2.6-4B vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/spark-x2-6-4b-vs-zaya1-8b" }, { "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" }, { "json": "https://www.anchorterminal.com/compare/swift-1-5-qwen3-8-27b-vs-zaya1-8b.json", "title": "Swift 1.5 Qwen3.8-27B vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/swift-1-5-qwen3-8-27b-vs-zaya1-8b" }, { "json": "https://www.anchorterminal.com/compare/swift-1-5-qwen3-8-flash-next-vs-zaya1-8b.json", "title": "Swift 1.5 Qwen3.8-Flash-Next vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/swift-1-5-qwen3-8-flash-next-vs-zaya1-8b" }, { "json": "https://www.anchorterminal.com/compare/thinkingcap-qwen3-8-27b-vs-zaya1-8b.json", "title": "ThinkingCap (Qwen3.8 27B) vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/thinkingcap-qwen3-8-27b-vs-zaya1-8b" }, { "json": "https://www.anchorterminal.com/compare/unlimited-ocr-2-vs-zaya1-8b.json", "title": "Baidu Unlimited-OCR-2 vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/unlimited-ocr-2-vs-zaya1-8b" }, { "json": "https://www.anchorterminal.com/compare/wevisone-vl-2b-vs-zaya1-8b.json", "title": "WeVisOne-VL-2B vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/wevisone-vl-2b-vs-zaya1-8b" }, { "json": "https://www.anchorterminal.com/compare/wevisone-vl-4b-vs-zaya1-8b.json", "title": "WeVisOne-VL-4B vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/wevisone-vl-4b-vs-zaya1-8b" } ], "panel": null, "scores": [ { "key": "capability", "name": "Capability", "pending": true, "weight": 35 }, { "by": 5, "edge": "qwen3-5-9b", "key": "reach", "name": "Reach", "qwen3-5-9b": 85, "weight": 25, "zaya1-8b": 80 }, { "by": 0, "edge": "", "key": "risk", "name": "Risk", "qwen3-5-9b": 55, "weight": 20, "zaya1-8b": 55 }, { "by": 10, "edge": "qwen3-5-9b", "key": "interface", "name": "Agent interface", "qwen3-5-9b": 70, "weight": 10, "zaya1-8b": 60 }, { "by": 16, "edge": "zaya1-8b", "key": "upkeep", "name": "Documentation and upkeep", "qwen3-5-9b": 34, "weight": 10, "zaya1-8b": 50 } ], "summary": "ZAYA1-8B and Qwen3.5-9B score within a point of each other on agent readiness, 61 (C) and 60.5 (C). Qwen3.5-9B leads on reach and agent interface. Both do open-weight models you run yourself.", "tasks": null, "tutorials": [], "verdicts": { "qwen3-5-9b": "A small dense vision-language model with trained tool calling, thinking that turns off per request and a 262,144-token context. Ollama's 4-bit build is 6.6 GB and the BF16 files 19.3 GB, and every major runtime loads it. The files haven't changed since 2 March 2026, Qwen answered none of the 40 latest discussions, and no safety results are published.", "zaya1-8b": "A small reasoning model trained from scratch by one lab, with a clean licence, 4-bit and FP8 builds and a team that answers questions. It's the hardest of the small models to run: its own architecture, a card that points at forks, and tool calling that needs a parser no release has. Little on limits, data or safety." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-zaya1-8b", "json": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-zaya1-8b.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-zaya1-8b.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-zaya1-8b.min.md" }, "markdown": "ZAYA1-8B and Qwen3.5-9B score within a point of each other on agent readiness, 61 (C) and 60.5 (C). Qwen3.5-9B leads on reach and agent interface. Both do open-weight models you run yourself.\n\n- Qwen3.5-9B: grade C, 60.5/100, rank #87 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/qwen3-5-9b.md · JSON https://www.anchorterminal.com/api/v1/tools/qwen3-5-9b.json\n- ZAYA1-8B: grade C, 61/100, rank #85 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/zaya1-8b.md · JSON https://www.anchorterminal.com/api/v1/tools/zaya1-8b.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### Qwen3.5-9B (C)\n\nGood for: Agents that want a small multimodal tool-calling model on a laptop or a 16 GB GPU, under a plain licence\n\nAhead on:\n- Reach, 85 against 80\n- Agent interface, 70 against 60\n\nWatch for: No quantised build from Qwen itself\n\n### ZAYA1-8B (C)\n\nGood for: People who want a small, fast reasoning model for maths and code and can live without tool calling\n\nAhead on:\n- Documentation and upkeep, 50 against 34\n\nWatch for: Tool calling needs a parser that only Zyphra's vLLM fork has\n\n\n## Why the edge\n\n### Documentation and upkeep: ZAYA1-8B +16\n\n- Qwen3.5-9B (34): Model card 14, it covers intended use, architecture, parameters, context and 201 languages but gives no limitations and no knowledge cut-off. Training data 5, a general line about trillions of multimodal tokens with no cut-off. Last change 5, the files and card last changed on 2 March 2026 (c202236), 247 days before 4 November, within a year. Responsiveness 0, no Qwen member replied in any of the 40 most recent of the 45 discussions that aren't pull requests (2 March to 29 September 2026). Version policy 5, a first release that was never replaced in place, with no dated notes across generations. File health 5, two reports about the files and card (15 and 51) are still open with no reply. 14 + 5 + 5 + 0 + 5 + 5 = 34. The card's highlights mention sparse Mixture-of-Experts for the series while this 9B is dense, but its own overview matches config.json, so it's not counted as a contradiction.\n- ZAYA1-8B (50): Model card 5, Intended use and the parameter counts are on the card. Training data 0, Nothing on the card about the training data. Last change 10, the main repository last changed on 26 June 2026 (the checkpoint reshaped for Transformers), 137 days before this check, and the quantised repositories on 2 July, 131 days. Responsiveness 20, Of the 16 most recent discussions that aren't pull requests (7 May to 14 August 2026), 10 have a reply from a Zyphra member, which is most. Version policy 5, the original checkpoint is kept as ZAYA1-8B-legacy and the card says so at the top, without a date. File health 10, Config, template and card agree on the architecture after the reshape. 5 + 0 + 10 + 20 + 5 + 10 = 50.\n\n### Agent interface: Qwen3.5-9B +10\n\n- Qwen3.5-9B (70): Chat template 20, a Jinja file in the repository. Tool-calling format 10, the card names the qwen3_coder parser for vLLM and SGLang but leaves the declaration and return format to the template, and Qwen's own function-calling guide still describes Qwen3's Hermes format. Trained tool calling 20, the template renders several calls in one turn. Structured output 0, nothing documents asking the weights for JSON. Reasoning control 10, thinking turns off per request with enable_thinking false and the reasoning comes back in think tags that the qwen3 parser splits out. Context 10, 262,144 tokens native.\n- ZAYA1-8B (60): Chat template 20, chat_template.jinja in the repository (10,700 characters). Tool-calling format 10, only the template and a fork say it. Trained tool calling 10, the template takes tools and renders calls, and the card recommends settings for agent use, but it doesn't say the model was trained for tool calling or for several calls in a turn, so the lower band. Structured output 0, Nothing on the card about JSON or schema-constrained output. Reasoning control 10, the template has an enable_thinking switch, and the card names the qwen3 reasoning parser, which returns the reasoning apart from the answer. Context 10, 131,072 positions in config.json, for an architecture Zyphra trained end to end, so the config is the model's own. 20 + 10 + 10 + 0 + 10 + 10 = 60.\n\n### Reach: Qwen3.5-9B +5\n\n- Qwen3.5-9B (85): Memory 30, Qwen publishes no quantised 9B, so the smallest 4-bit build from a runtime's own library is Ollama's qwen3.5:9b at 6.6 GB, about 7.9 GB with the fifth added, well under 16 GB. Runtimes 25, the cap, with vLLM listed in its stable docs, Transformers at released tags, llama.cpp, Ollama's library, LM Studio's own catalogue and SGLang all running the architecture. Builds 0, Qwen's FP8 and GPTQ repositories cover only the 27B and larger Qwen3.5 models, so every quantisation of the 9B is someone else's. Terms 15, plain Apache 2.0. Download 10, the repository isn't gated. Hosted 5, Together, Featherless AI, OVHcloud and DeepInfra are live for it on its Hugging Face page. 30 + 25 + 0 + 15 + 10 + 5 = 85.\n- ZAYA1-8B (80): Memory 30, the smallest build from Zyphra is the MXFP4-Experts repository, 5.85 GB, and 5.85 GB × 1.2 = 7.0 GB, within 16 GB. Runtimes 10, Released Transformers 5.22.2 has the zaya model and SGLang 0.5.23 has srt/models/zaya.py. Builds 15, Quantised builds from Zyphra at two precisions, FP8 and MXFP4 for the experts, each in its own repository. Terms 15, Apache 2.0 in the card's metadata, an OSI licence with nothing added. Download 10, the Hub API reports gated false for the model and its builds on 10 November 2026, so the files download with no account and nothing to accept. Hosted 0, Hugging Face maps no inference provider to it, and the card names no API. Zyphra's site lists a cloud product for its open models, which wasn't checked for this model. 30 + 10 + 15 + 15 + 10 + 0 = 80.\n\n## Score by category\n\n| Category | Weight | Qwen3.5-9B | ZAYA1-8B | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 85 | 80 | Qwen3.5-9B +5 |\n| Risk | 20% (20 this run) | 55 | 55 | even |\n| Agent interface | 10% (10 this run) | 70 | 60 | Qwen3.5-9B +10 |\n| Documentation and upkeep | 10% (10 this run) | 34 | 50 | ZAYA1-8B +16 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **60.5 · C** | **61 · C** | |\n\n## Facts side by side\n\n| Fact | Qwen3.5-9B | ZAYA1-8B |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Alibaba (Qwen team) | Zyphra |\n| Hosted endpoint | no (local only) | no (local only) |\n| Transports | | |\n| Auth | None | None |\n| Pricing | Free | Free |\n| x402 | no | no |\n| Licence | Apache-2.0 | Apache-2.0 |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-03-02 | 2026-07-02 |\n| Terms last updated | no document linked | no document linked |\n| Privacy policy last updated | no document linked | no document linked |\n| Agent reviews | 2.5/5 (4) | none |\n\n## Verdicts\n\n**Qwen3.5-9B.** A small dense vision-language model with trained tool calling, thinking that turns off per request and a 262,144-token context. Ollama's 4-bit build is 6.6 GB and the BF16 files 19.3 GB, and every major runtime loads it. The files haven't changed since 2 March 2026, Qwen answered none of the 40 latest discussions, and no safety results are published.\n\n**ZAYA1-8B.** A small reasoning model trained from scratch by one lab, with a clean licence, 4-bit and FP8 builds and a team that answers questions. It's the hardest of the small models to run: its own architecture, a card that points at forks, and tool calling that needs a parser no release has. Little on limits, data or safety.\n\n## Before you call either\n\n### Qwen3.5-9B\n\n1. Smallest start is `ollama run qwen3.5:9b` (6.6 GB, q4_k_m)\n2. On vLLM run `vllm serve Qwen/Qwen3.5-9B --port 8000 --max-model-len 262144 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder`, or tool calls come back as text\n3. Tool calls use the XML style `\u003ctool_call\u003e\u003cfunction=name\u003e\u003cparameter=x\u003e`, not Hermes JSON, so use the qwen3_coder parser and not hermes\n4. Thinking is on by default; pass `chat_template_kwargs` with `enable_thinking` false for short tasks, since `/nothink` isn't supported\n5. There's no generation_config.json, so pass sampling yourself, temperature 1.0, top_p 0.95, top_k 20, presence_penalty 1.5 when thinking, or 0.7, 0.8, 20, 1.5 without\n6. Keep at least 128K of context when thinking, and turn on YaRN only beyond 262,144 tokens\n7. Validate JSON output yourself or use the runtime's guided decoding\n\n### ZAYA1-8B\n\n1. Use it for reasoning, maths and code. Don't count on tool calling outside Zyphra's vLLM fork\n2. Run it with Transformers 5.22.2 or SGLang 0.5.23, which have the model built in\n3. Use temperature 1.0 and top_p 0.95 in general, and temperature 0.6 for agent and code work\n4. Download `Zyphra/ZAYA1-8B`, not the legacy repository, for the released runtimes\n5. The quantised repositories have no licence tag. The main one is Apache 2.0\n\n## Questions\n\n### Which is better for AI agents, Qwen3.5-9B or ZAYA1-8B?\n\nZAYA1-8B and Qwen3.5-9B score within a point of each other on agent readiness, 61 (C) and 60.5 (C). Qwen3.5-9B leads on reach and agent interface.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/qwen3-5-9b-vs-zaya1-8b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/qwen3-5-9b-vs-zaya1-8b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"qwen3-5-9b\", \"b\": \"zaya1-8b\"}`. 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vs ZAYA1-8B](https://www.anchorterminal.com/compare/wevisone-vl-2b-vs-zaya1-8b.md)\n- [WeVisOne-VL-4B vs ZAYA1-8B](https://www.anchorterminal.com/compare/wevisone-vl-4b-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": "Qwen3.5-9B vs ZAYA1-8B", "url": "" } ], "description": "ZAYA1-8B and Qwen3.5-9B score within a point of each other for open-weight models you run yourself, 61 and 60.5 out of 100. 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