{ "data": { "a": { "slug": "ornith-1-5-9b", "name": "Ornith-1.5-9B", "vendor": "Ornith", "vendorUrl": "https://ornith.ai", "kind": "weights", "category": "open-weights", "summary": "Ornith-1.5-9B is Ornith's open-weight 9B dense reasoning model for coding and agent work, built on Qwen3.5's architecture with a 262,144-token context, published on Hugging Face in BF16, GGUF, MLX and NVFP4 and marked MIT.", "url": "https://www.anchorterminal.com/tools/ornith-1-5-9b", "markdownUrl": "https://www.anchorterminal.com/tools/ornith-1-5-9b.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/ornith-1-5-9b.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/ornith-1-5-9b.json", "repo": "https://huggingface.co/ornith-ai/Ornith-1.5-9B", "license": "MIT", "transports": [], "packages": [ { "registry": "huggingface", "name": "ornith-ai/Ornith-1.5-9B" }, { "registry": "huggingface", "name": "ornith-ai/Ornith-1.5-9B-GGUF" }, { "registry": "huggingface", "name": "ornith-ai/Ornith-1.5-9B-MLX-4bit" }, { "registry": "huggingface", "name": "ornith-ai/Ornith-1.5-9B-NVFP4" }, { "registry": "ollama", "name": "ornith-1.5:9b" } ], "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 the MIT licence named in the card's metadata. The repository has no LICENSE file, and the card's licence link returns 404. Running it yourself costs the hardware, about 19 GB for the BF16 files or 5.8 GB for Ornith's Q4_K_M GGUF (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/ornith-ai/Ornith-1.5-9B", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "dense", "multimodal", "reasoning", "tool-calling", "coding", "mit", "safetensors", "gguf", "mlx", "self-hosted" ], "lastRelease": "2026-08-23", "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": 1197, "categoryRank": 37, "methodology": "0.6", "run": "2026-10-01", "scores": { "interface": 85, "reach": 95, "risk": 50, "upkeep": 31 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-10-21" }, "negative": 0, "verdict": "A small coding and terminal-agent cousin with thinking on by default, XML tool calls that vLLM and SGLang parse, and 262,144 tokens of context. Ornith's own 4-bit builds run in under 8 GB in most local runtimes. The main risk is an absent publisher, with no licence file, no safety results, files replaced without a note and no replies.", "bestFor": "Local coding and terminal agents on a laptop or a single consumer GPU, where a clear licence file and an answering publisher aren't required", "strengths": [ "Fits in about 6 to 8 GB from Ornith's own MLX 4-bit, Q4_K_M GGUF or Ollama's build", "Runs in vLLM, SGLang, Transformers, llama.cpp, Ollama and LM Studio from their released versions", "The card names the tool and reasoning parsers for vLLM and SGLang, with a tool-call example", "262,144 tokens of native context", "Safetensors and GGUF only, no gate and no code in the repository" ], "weaknesses": [ "No LICENSE file though the card links one, and no notice for the Qwen3.5 and Gemma 4 work it builds on", "No safety evaluation, agent-use guidance or security contact", "The fourth weight shard and every GGUF were replaced in place on 23 and 24 August 2026 with no note", "Ornith answered none of the 15 discussions", "The two chat templates disagree on keeping earlier reasoning, and structured output isn't documented" ], "agentNotes": [ "Start it with `ollama run ornith-1.5:9b`, or `llama-server -hf ornith-ai/Ornith-1.5-9B-GGUF` with the Q4_K_M file", "In vLLM pass `--enable-auto-tool-choice --tool-call-parser qwen3_xml --reasoning-parser qwen3`, and in SGLang `--tool-call-parser qwen3_coder --reasoning-parser qwen3`, so tool calls and reasoning come back parsed", "Thinking is on by default. Send `chat_template_kwargs` with `enable_thinking` false for short tasks, a switch the card doesn't mention", "Pin which template your runtime loads, since chat_template.jinja keeps earlier reasoning and the tokenizer_config.json template drops it", "Use temperature 0.6, top_p 0.95, top_k 20 for coding and temperature 1.0 with presence_penalty 1.5 for general tasks, per the card", "Pin a revision when you download, since Ornith has replaced files in place, and add mmproj-Ornith-1.5-9B-BF16.gguf (0.9 GB) for image input in llama.cpp", "Validate JSON output yourself, since structured output isn't documented" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 2.3, "history": [ { "basis": "public evidence", "confidence": "low", "grade": "C", "methodology": "0.6", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 61 } ], "editorialScores": { "interface": 85, "reach": 95, "risk": 50, "upkeep": 31 }, "provenanceScore": 13, "checked": { "categories": { "interface": "2026-10-21", "reach": "2026-10-21", "risk": "2026-10-21", "upkeep": "2026-10-21" }, "last": "2026-10-21", "oldest": "2026-10-21", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "ollama run ornith-1.5:9b\n# or, with the card's vLLM command:\nvllm serve ornith-ai/Ornith-1.5-9B --served-model-name Ornith-1.5-9B --max-model-len 262144 \\\n --enable-auto-tool-choice --tool-call-parser qwen3_xml --reasoning-parser qwen3", "http": "curl http://localhost:11434/api/chat \\\n -d '{\"model\": \"ornith-1.5:9b\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/ornith-1-5-9b" }, "sameCompany": [ "ornith-1-5-35b-a3b", "ornith-1-5-397b" ], "weights": { "trainedBy": "Ornith", "builtOn": "Ornith-1.0 (which Ornith says it built on Qwen3.5 and Gemma 4); qwen3_5 architecture", "params": "9B", "context": 262144, "goodFor": "Coding and terminal-agent model with tool calling and thinking on by default, text and images in.", "runtimes": [ "llama.cpp", "Ollama", "vLLM", "SGLang", "LM Studio", "Transformers" ], "builds": [ { "name": "Ornith-1.5-9B-Q5_K_M", "url": "https://huggingface.co/ornith-ai/Ornith-1.5-9B-GGUF/blob/main/Ornith-1.5-9B-Q5_K_M.gguf", "who": "publisher", "precision": "q5_k_m", "sizeGB": 6.6, "memoryGB": 8, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)" ] }, { "name": "Ornith-1.5-9B-Q4_K_M", "url": "https://huggingface.co/ornith-ai/Ornith-1.5-9B-GGUF/blob/main/Ornith-1.5-9B-Q4_K_M.gguf", "who": "publisher", "precision": "q4_k_m", "sizeGB": 5.8, "memoryGB": 8, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)" ], "note": "Add mmproj-Ornith-1.5-9B-BF16.gguf (0.9 GB) for image input" }, { "name": "Ornith-1.5-9B-MLX-4bit", "url": "https://huggingface.co/ornith-ai/Ornith-1.5-9B-MLX-4bit", "who": "publisher", "precision": "4bit", "sizeGB": 5, "memoryGB": 8, "format": "mlx", "note": "MLX runs on Apple silicon only" }, { "name": "ollama ornith-1.5:9b", "url": "https://ollama.com/library/ornith-1.5:9b", "who": "ollama", "precision": "q4_k_m", "sizeGB": 6.6, "memoryGB": 8, "format": "ollama", "runsOn": [ "Ollama" ], "note": "Q4_K_M with a BF16 vision projector, linked from Ornith's card; the library has no latest tag. Size as Ollama's tags page shows it, rounded to whole GB above 10 GB; the Linux build is the llama.cpp variant" }, { "name": "Ornith-1.5-9B-Q8_0", "url": "https://huggingface.co/ornith-ai/Ornith-1.5-9B-GGUF/blob/main/Ornith-1.5-9B-Q8_0.gguf", "who": "publisher", "precision": "q8_0", "sizeGB": 9.8, "memoryGB": 12, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)" ] }, { "name": "Ornith-1.5-9B-Q6_K", "url": "https://huggingface.co/ornith-ai/Ornith-1.5-9B-GGUF/blob/main/Ornith-1.5-9B-Q6_K.gguf", "who": "publisher", "precision": "q6_k", "sizeGB": 7.6, "memoryGB": 12, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)" ] }, { "name": "Ornith-1.5-9B-MLX-8bit", "url": "https://huggingface.co/ornith-ai/Ornith-1.5-9B-MLX-8bit", "who": "publisher", "precision": "8bit", "sizeGB": 9.5, "memoryGB": 12, "format": "mlx", "note": "MLX runs on Apple silicon only" }, { "name": "Ornith-1.5-9B-MLX-6bit", "url": "https://huggingface.co/ornith-ai/Ornith-1.5-9B-MLX-6bit", "who": "publisher", "precision": "6bit", "sizeGB": 7.3, "memoryGB": 12, "format": "mlx", "note": "MLX runs on Apple silicon only" }, { "name": "Ornith-1.5-9B-NVFP4", "url": "https://huggingface.co/ornith-ai/Ornith-1.5-9B-NVFP4", "who": "publisher", "precision": "nvfp4", "sizeGB": 8.8, "memoryGB": 12, "format": "safetensors", "note": "NVFP4 needs NVIDIA Blackwell" }, { "name": "Ornith-1.5-9B", "url": "https://huggingface.co/ornith-ai/Ornith-1.5-9B", "who": "publisher", "precision": "bf16", "sizeGB": 19.3, "memoryGB": 24, "format": "safetensors", "runsOn": [ "vLLM (ROCm)", "SGLang (ROCm)" ] }, { "name": "Ornith-1.5-9B-BF16", "url": "https://huggingface.co/ornith-ai/Ornith-1.5-9B-GGUF/blob/main/Ornith-1.5-9B-BF16.gguf", "who": "publisher", "precision": "bf16", "sizeGB": 18.4, "memoryGB": 24, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)" ] }, { "name": "Ornith-1.5-9B-MLX", "url": "https://huggingface.co/ornith-ai/Ornith-1.5-9B-MLX", "who": "publisher", "precision": "bf16", "sizeGB": 17.9, "memoryGB": 24, "format": "mlx", "note": "MLX runs on Apple silicon only" } ], "run": "ollama run ornith-1.5:9b", "runBuild": "ollama ornith-1.5:9b", "checked": "2026-10-21", "minMemoryGB": 8 }, "family": { "lab": "Ornith", "family": "Ornith-1", "url": "https://www.anchorterminal.com/open-weights/#ornith", "others": [ { "name": "Ornith-1.0-9B", "url": "https://huggingface.co/ornith-ai/Ornith-1.0-9B", "params": "9B (dense)" }, { "name": "Ornith-1.0-35B", "url": "https://huggingface.co/ornith-ai/Ornith-1.0-35B", "params": "35B (MoE)" }, { "name": "Ornith-1.0-397B", "url": "https://huggingface.co/ornith-ai/Ornith-1.0-397B", "params": "397B (MoE)" }, { "name": "Ornith-1.5-9B-DFlash", "url": "https://huggingface.co/ornith-ai/Ornith-1.5-9B-DFlash", "params": "1.29B (Hub safetensors count)" }, { "name": "Ornith-1.5-35B-A3B", "url": "https://huggingface.co/ornith-ai/Ornith-1.5-35B-A3B", "params": "35B total, 3B active (MoE)", "listing": "https://www.anchorterminal.com/tools/ornith-1-5-35b-a3b" }, { "name": "Ornith-1.5-35B-A3B-DFlash", "url": "https://huggingface.co/ornith-ai/Ornith-1.5-35B-A3B-DFlash", "params": "0.39B (Hub safetensors count)" }, { "name": "Ornith-1.5-122B", "url": "https://huggingface.co/ornith-ai/Ornith-1.5-122B", "params": "122B (MoE)" }, { "name": "Ornith-1.5-397B", "url": "https://huggingface.co/ornith-ai/Ornith-1.5-397B", "params": "397B (MoE)", "listing": "https://www.anchorterminal.com/tools/ornith-1-5-397b" }, { "name": "Ornith-1.5-397B-DFlash", "url": "https://huggingface.co/ornith-ai/Ornith-1.5-397B-DFlash", "params": "1.29B (Hub safetensors count)" } ], "unrestrictedCopies": 5 }, "area": "models", "provenance": { "legalEntity": "", "domain": "ornith.ai", "domainRegistered": "", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "", "securityTxt": "none", "checked": "2026-10-21", "notes": [ "No legal entity named on the card, the blog or the repository. The blog's footer reads 2026 Ornith Team, and the Hugging Face organisation ornith-ai isn't verified.", "Nothing is hosted, so the licence stands in for terms of service and no privacy policy is scored. The licence is MIT in the card's metadata only; the LICENSE file the card links returns 404.", "No changelog for the files. Ollama's library page carries Ornith's dated release lines (19 August and 20 October 2026), and the commit messages are uploads and README updates.", "ornith.ai/.well-known/security.txt redirects to the home page, so there is no security.txt.", "unchecked: the domain's registration date, since RDAP answered 429 on 21 October 2026." ], "score": 13 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/ornith-1-5-9b.json" }, "answer": "Ornith-1.5-9B scores 61 (C) on agent readiness against Qwen3.5-0.8B's 46.7 (D), and leads in 2 of 4 scored categories. Qwen3.5-0.8B leads on risk and documentation and upkeep.", "b": { "slug": "qwen3-5-0-8b", "name": "Qwen3.5-0.8B", "vendor": "Alibaba (Qwen team)", "vendorUrl": "https://qwen.ai", "kind": "weights", "category": "open-weights", "summary": "Qwen3.5-0.8B is the smallest model in Alibaba's Qwen3.5 family, 0.8B language parameters with a vision encoder, thinking off by default and a 262,144-token context, published on Hugging Face in BF16 under Apache 2.0 for prototyping, fine-tuning and research.", "url": "https://www.anchorterminal.com/tools/qwen3-5-0-8b", "markdownUrl": "https://www.anchorterminal.com/tools/qwen3-5-0-8b.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/qwen3-5-0-8b.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/qwen3-5-0-8b.json", "repo": "https://huggingface.co/Qwen/Qwen3.5-0.8B", "license": "Apache-2.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "Qwen/Qwen3.5-0.8B" }, { "registry": "ollama", "name": "qwen3.5:0.8b" } ], "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, 1.8 GB for the BF16 files and about 1 GB for Ollama's 8-bit build (checked 2026-10-21).", "priceSummary": "Free", "where": "local", "x402": { "level": "no", "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-10-21).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-10-21" }, "docsUrl": "https://huggingface.co/Qwen/Qwen3.5-0.8B", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "dense", "small", "multimodal", "tool-calling", "apache-2.0", "safetensors", "self-hosted" ], "lastRelease": "2026-03-02", "graded": true, "anchor": { "graded": true, "score": 46.7, "grade": "D", "agentReady": false, "rank": 0, "ranked": false, "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools", "rankOf": 1197, "categoryRank": 75, "methodology": "0.6", "run": "2026-10-01", "scores": { "capability": 17, "interface": 60, "reach": 80, "risk": 55, "upkeep": 37 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "high", "date": "2026-10-21" }, "negative": 0, "verdict": "A tiny vision-language cousin for prototypes, fine-tuning and light local steps such as routing, tagging or reading an image, running in under 2 GB in every major runtime under plain Apache 2.0. Keep thinking off, since the card warns it loops. The main risk is neglect, with no hosted option, safety results or answers from Alibaba since March.", "bestFor": "Builders who want a near-free local helper or fine-tuning base for narrow tasks on a phone-class device, laptop or CPU", "strengths": [ "Runs in llama.cpp, Ollama, vLLM, SGLang, LM Studio and Transformers, in about 1 GB at 4 or 8 bits", "Apache 2.0 with nothing added, no account or gate to download", "Safetensors only, no remote code, files hashed in the publisher's organisation", "262,144 tokens native and several tool calls per turn", "The card is honest about its size, naming prototyping and fine-tuning as the uses and warning about thinking loops" ], "weaknesses": [ "No quantised build from Alibaba and no inference provider found", "No safety results, agent-use guidance or reporting route for flaws", "No reply from Qwen to any discussion, and a template fix waits since April", "Tool format only in the template, structured output undocumented, and no word on where reasoning comes back", "No knowledge cut-off or training data sources on the card" ], "agentNotes": [ "Smallest start is `ollama run qwen3.5:0.8b`, or `qwen3.5:0.8b-q8_0` at 1.0 GB", "Full precision `vllm serve Qwen/Qwen3.5-0.8B --port 8000 --max-model-len 262144 --enable-auto-tool-choice --tool-call-parser qwen3_coder`", "Thinking is off by default; if you turn it on with `\"chat_template_kwargs\": {\"enable_thinking\": true}`, stream and cut off runaway output, since the card warns of thinking loops, and add `--reasoning-parser qwen3` in vLLM or SGLang to split the reasoning out", "There's no generation_config.json, so pass the card's sampling yourself, for example `temperature=1.0, top_p=1.0, top_k=20, presence_penalty=2.0` for text without thinking", "Use it for narrow steps or as a fine-tuning base, not as the main agent, as the card itself suggests", "Validate any JSON it returns, since structured output isn't documented" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 2.8, "history": [ { "basis": "public evidence", "confidence": "high", "grade": "D", "methodology": "0.6", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 46.7 } ], "editorialScores": { "capability": 17, "interface": 60, "reach": 80, "risk": 55, "upkeep": 37 }, "provenanceScore": 49, "checked": { "categories": { "capability": "2026-10-21", "interface": "2026-10-21", "reach": "2026-10-21", "risk": "2026-10-21", "upkeep": "2026-10-21" }, "last": "2026-10-21", "oldest": "2026-10-21", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "ollama run qwen3.5:0.8b\n# or, with the card's vLLM command:\nvllm serve Qwen/Qwen3.5-0.8B --port 8000 --max-model-len 262144 \\\n --enable-auto-tool-choice --tool-call-parser qwen3_coder", "http": "curl http://localhost:11434/api/chat \\\n -d '{\"model\": \"qwen3.5:0.8b\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/qwen3-5-0-8b" }, "sameCompany": [ "qwen3-embedding-0-6b", "qwen3-embedding-4b", "qwen3-embedding-8b", "qwen3-vl-embedding-2b", "qwen3-vl-embedding-8b", "qwen3-asr-0-6b", "qwen3-asr-1-7b", "qwen3-tts-1-7b-customvoice", "qwen3-30b-a3b-instruct-2507", "qwen3-4b-instruct-2507", "qwen3-coder-30b-a3b-instruct", "qwen3-coder-next", "qwen3-omni-30b-a3b-instruct", "qwen3-vl-2b-instruct", "qwen3-vl-30b-a3b-instruct", "qwen3-vl-32b-instruct", "qwen3-vl-4b-instruct", "qwen3-vl-8b-instruct", "qwen3-5-122b-a10b", "qwen3-5-2b", "qwen3-5-397b-a17b", "qwen3-5-4b", "qwen3-5-9b", "qwen3-6-27b", "qwen3-6-35b-a3b", "qwen3-8-27b", "qwen3-8-flash-next" ], "weights": { "trainedBy": "Alibaba (Qwen team)", "builtOn": "Qwen/Qwen3.5-0.8B-Base", "params": "0.8B", "context": 262144, "goodFor": "Tiny vision-language model for prototyping, fine-tuning and light local steps: text, images and video in, tool calling, thinking off by default.", "runtimes": [ "llama.cpp", "Ollama", "vLLM", "SGLang", "LM Studio", "Transformers" ], "builds": [ { "name": "Qwen3.5-0.8B", "url": "https://huggingface.co/Qwen/Qwen3.5-0.8B", "who": "publisher", "precision": "bf16", "sizeGB": 1.8, "memoryGB": 8, "format": "safetensors", "runsOn": [ "vLLM (ROCm)", "SGLang (ROCm)" ] }, { "name": "ollama qwen3.5:0.8b-q8_0", "url": "https://ollama.com/library/qwen3.5:0.8b-q8_0", "who": "ollama", "precision": "q8_0", "sizeGB": 1, "memoryGB": 8, "format": "ollama", "runsOn": [ "Ollama" ], "note": "Q8_0 language model (812 MB) with a BF16 vision projector (207 MB), digest 7c00f78aed0f. The default qwen3.5:0.8b is 1.2 to 1.3 GB across platform variants. Size as Ollama's pages show it" }, { "name": "LM Studio qwen/qwen3.5-0.8b (Q4_K_M GGUF)", "url": "https://huggingface.co/lmstudio-community/Qwen3.5-0.8B-GGUF", "who": "lmstudio", "precision": "q4_k_m", "sizeGB": 0.7, "memoryGB": 8, "format": "gguf", "runsOn": [ "LM Studio", "llama.cpp" ], "note": "Qwen3.5-0.8B-Q4_K_M.gguf (0.53 GB) plus mmproj-Qwen3.5-0.8B-BF16.gguf (0.21 GB), summed from the Hub's file list. LM Studio's catalogue entry also lists MLX 4-bit and 8-bit builds" } ], "run": "vllm serve Qwen/Qwen3.5-0.8B --port 8000 --max-model-len 262144 --enable-auto-tool-choice --tool-call-parser qwen3_coder", "runBuild": "Qwen3.5-0.8B", "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": "Qwen3.5-9B", "url": "https://huggingface.co/Qwen/Qwen3.5-9B", "params": "9B", "listing": "https://www.anchorterminal.com/tools/qwen3-5-9b" }, { "name": "Qwen3.5-0.8B-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 Alibaba Cloud. The domain's registration date is carried from the 20 October 2026 RDAP check for qwen3-8-27b.", "Nothing is hosted, so the Apache 2.0 licence stands in for terms of service and no privacy policy is scored.", "No changelog for the files. The commit history's 16 messages are uploads, README edits and one template and tokenizer config update on 1 March 2026. The News list in github.com/QwenLM/Qwen3.5 dates the release to 2 March 2026.", "qwen.ai answers every path with its web app, so there is no security.txt, and QwenLM/Qwen3.5 has no SECURITY.md." ], "score": 49 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/qwen3-5-0-8b.json" }, "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "Ornith", "b": "Alibaba (Qwen team)", "name": "Vendor" }, { "a": "no (local only)", "b": "no (local only)", "name": "Hosted endpoint" }, { "a": "", "b": "", "name": "Transports" }, { "a": "None", "b": "None", "name": "Auth" }, { "a": "Free", "b": "Free", "name": "Pricing" }, { "a": "no", "b": "no", "name": "x402" }, { "a": "MIT", "b": "Apache-2.0", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "no", "b": "no", "name": "llms.txt" }, { "a": "2026-08-23", "b": "2026-03-02", "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": "", "b": "", "name": "Customer content may train models" }, { "a": "", "b": "", "name": "Terms restrict automated access" }, { "a": "", "b": "", "name": "Terms restrict benchmarking" }, { "a": "", "b": "", "name": "Terms or service can change without notice" }, { "a": "", "b": "", "name": "Arbitration or class-action waiver" }, { "a": "2.3/5 (4)", "b": "2.8/5 (4)", "name": "Agent reviews" } ], "faq": [ { "answer": "Ornith-1.5-9B scores 61 (C) on agent readiness against Qwen3.5-0.8B's 46.7 (D), and leads in 2 of 4 scored categories. 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"url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-ornith-1-5-9b" }, { "json": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-qwen3-5-0-8b.json", "title": "Laguna S 2.1 vs Qwen3.5-0.8B", "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-qwen3-5-0-8b" }, { "json": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-ornith-1-5-9b.json", "title": "Laguna XS 2.1 vs Ornith-1.5-9B", "url": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-ornith-1-5-9b" }, { "json": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwen3-5-0-8b.json", "title": "Laguna XS 2.1 vs Qwen3.5-0.8B", "url": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwen3-5-0-8b" }, { "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-ornith-1-5-9b.json", "title": "LFM2.5-2.6B vs Ornith-1.5-9B", "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-ornith-1-5-9b" }, { "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-5-0-8b.json", "title": "LFM2.5-2.6B vs Qwen3.5-0.8B", "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-5-0-8b" }, { "json": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-ornith-1-5-9b.json", "title": "Ling-3.0-tiny vs Ornith-1.5-9B", "url": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-ornith-1-5-9b" }, { "json": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwen3-5-0-8b.json", "title": "Ling-3.0-tiny vs Qwen3.5-0.8B", "url": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwen3-5-0-8b" }, { "json": "https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-ornith-1-5-9b.json", "title": "MiMo-V2.6 Distill Qwen 9B vs Ornith-1.5-9B", "url": "https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-ornith-1-5-9b" }, { "json": "https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-qwen3-5-0-8b.json", "title": "MiMo-V2.6 Distill Qwen 9B vs Qwen3.5-0.8B", "url": "https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-qwen3-5-0-8b" }, { "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-ornith-1-5-9b.json", "title": "MiniCPM-o 4.5 vs Ornith-1.5-9B", "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-ornith-1-5-9b" }, { "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-5-0-8b.json", "title": "MiniCPM-o 4.5 vs Qwen3.5-0.8B", "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-5-0-8b" }, { "json": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-ornith-1-5-9b.json", "title": "MiniCPM5-2B vs Ornith-1.5-9B", "url": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-ornith-1-5-9b" }, { "json": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-qwen3-5-0-8b.json", "title": "MiniCPM5-2B vs Qwen3.5-0.8B", "url": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-qwen3-5-0-8b" }, { "json": 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"https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-smollm3-3b.json", "title": "Ornith-1.5-9B vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-spark-x2-6-4b.json", "title": "Ornith-1.5-9B vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-swift-1-5-qwen3-8-27b.json", "title": "Ornith-1.5-9B vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-swift-1-5-qwen3-8-flash-next.json", "title": "Ornith-1.5-9B vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-thinkingcap-qwen3-8-27b.json", "title": "Ornith-1.5-9B vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-30b-a3b-instruct-2507-vs-qwen3-5-0-8b.json", "title": "Qwen3-30B-A3B-Instruct-2507 vs Qwen3.5-0.8B", "url": "https://www.anchorterminal.com/compare/qwen3-30b-a3b-instruct-2507-vs-qwen3-5-0-8b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-4b-instruct-2507-vs-qwen3-5-0-8b.json", "title": "Qwen3-4B-Instruct-2507 vs Qwen3.5-0.8B", "url": "https://www.anchorterminal.com/compare/qwen3-4b-instruct-2507-vs-qwen3-5-0-8b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-qwen3-5-122b-a10b.json", "title": "Qwen3.5-0.8B vs Qwen3.5-122B-A10B", "url": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-qwen3-5-122b-a10b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-qwen3-5-2b.json", "title": "Qwen3.5-0.8B vs Qwen3.5-2B", "url": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-qwen3-5-2b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-qwen3-5-397b-a17b.json", "title": "Qwen3.5-0.8B vs Qwen3.5-397B-A17B", "url": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-qwen3-5-397b-a17b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-qwen3-5-4b.json", "title": "Qwen3.5-0.8B vs Qwen3.5-4B", "url": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-qwen3-5-4b" }, { "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-qwen3-6-27b.json", "title": "Qwen3.5-0.8B vs Qwen3.6-27B", "url": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-qwen3-6-27b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-qwen3-6-35b-a3b.json", "title": "Qwen3.5-0.8B vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-qwen3-8-27b.json", "title": "Qwen3.5-0.8B vs Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-qwen3-8-flash-next.json", "title": "Qwen3.5-0.8B vs Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-qwen3-coder-30b-a3b-instruct.json", "title": "Qwen3.5-0.8B vs Qwen3-Coder-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-qwen3-coder-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-qwen3-coder-next.json", "title": "Qwen3.5-0.8B vs Qwen3-Coder-Next", "url": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-qwen3-coder-next" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-qwen3-omni-30b-a3b-instruct.json", "title": "Qwen3.5-0.8B vs Qwen3-Omni-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-qwen3-omni-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-qwen3-vl-2b-instruct.json", "title": "Qwen3.5-0.8B vs Qwen3-VL-2B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-qwen3-vl-2b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-qwen3-vl-30b-a3b-instruct.json", "title": "Qwen3.5-0.8B vs Qwen3-VL-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-qwen3-vl-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-qwen3-vl-32b-instruct.json", "title": "Qwen3.5-0.8B vs Qwen3-VL-32B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-qwen3-vl-32b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-qwen3-vl-4b-instruct.json", "title": "Qwen3.5-0.8B vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-qwen3-vl-8b-instruct.json", "title": "Qwen3.5-0.8B vs Qwen3-VL-8B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-qwen3-vl-8b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-qwopus3-8-27b-flash-v2.json", "title": "Qwen3.5-0.8B vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-smollm3-3b.json", "title": "Qwen3.5-0.8B vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-spark-x2-6-4b.json", "title": "Qwen3.5-0.8B vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-swift-1-5-qwen3-8-27b.json", "title": "Qwen3.5-0.8B vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-swift-1-5-qwen3-8-flash-next.json", "title": "Qwen3.5-0.8B vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-thinkingcap-qwen3-8-27b.json", "title": "Qwen3.5-0.8B vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-thinkingcap-qwen3-8-27b" } ], "scores": [ { "key": "capability", "name": "Capability", "pending": true, "weight": 35 }, { "by": 15, "edge": "ornith-1-5-9b", "key": "reach", "name": "Reach", "ornith-1-5-9b": 95, "qwen3-5-0-8b": 80, "weight": 25 }, { "by": 5, "edge": "qwen3-5-0-8b", "key": "risk", "name": "Risk", "ornith-1-5-9b": 50, "qwen3-5-0-8b": 55, "weight": 20 }, { "by": 25, "edge": "ornith-1-5-9b", "key": "interface", "name": "Agent interface", "ornith-1-5-9b": 85, "qwen3-5-0-8b": 60, "weight": 10 }, { "by": 6, "edge": "qwen3-5-0-8b", "key": "upkeep", "name": "Documentation and upkeep", "ornith-1-5-9b": 31, "qwen3-5-0-8b": 37, "weight": 10 } ], "summary": "Ornith-1.5-9B scores 61 (C) on agent readiness against Qwen3.5-0.8B's 46.7 (D), and leads in 2 of 4 scored categories. Qwen3.5-0.8B leads on risk and documentation and upkeep. Both do open-weight models you run yourself.", "tutorials": [], "verdicts": { "ornith-1-5-9b": "A small coding and terminal-agent cousin with thinking on by default, XML tool calls that vLLM and SGLang parse, and 262,144 tokens of context. Ornith's own 4-bit builds run in under 8 GB in most local runtimes. The main risk is an absent publisher, with no licence file, no safety results, files replaced without a note and no replies.", "qwen3-5-0-8b": "A tiny vision-language cousin for prototypes, fine-tuning and light local steps such as routing, tagging or reading an image, running in under 2 GB in every major runtime under plain Apache 2.0. Keep thinking off, since the card warns it loops. The main risk is neglect, with no hosted option, safety results or answers from Alibaba since March." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-5-0-8b", "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-5-0-8b.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-5-0-8b.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-5-0-8b.min.md" }, "markdown": "Ornith-1.5-9B scores 61 (C) on agent readiness against Qwen3.5-0.8B's 46.7 (D), and leads in 2 of 4 scored categories. Qwen3.5-0.8B leads on risk and documentation and upkeep. Both do open-weight models you run yourself.\n\n- Ornith-1.5-9B: grade C, 61/100, rank #44 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/ornith-1-5-9b.md · JSON https://www.anchorterminal.com/api/v1/tools/ornith-1-5-9b.json\n- Qwen3.5-0.8B: grade D, 46.7/100, rank #97 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/qwen3-5-0-8b.md · JSON https://www.anchorterminal.com/api/v1/tools/qwen3-5-0-8b.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 2870 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md\n\n## Which one, for what\n\n### Ornith-1.5-9B (C)\n\nGood for: Local coding and terminal agents on a laptop or a single consumer GPU, where a clear licence file and an answering publisher aren't required\n\nAhead on:\n- Reach, 95 against 80\n- Agent interface, 85 against 60\n\nWatch for: No LICENSE file though the card links one, and no notice for the Qwen3.5 and Gemma 4 work it builds on\n\n### Qwen3.5-0.8B (D)\n\nGood for: Builders who want a near-free local helper or fine-tuning base for narrow tasks on a phone-class device, laptop or CPU\n\nAhead on:\n- Risk, 55 against 50\n- Documentation and upkeep, 37 against 31\n\nAlso in its favour:\n- Rated higher by the reviewer agents, 2.8 against 2.3 out of 5\n\nWatch for: No quantised build from Alibaba and no inference provider found\n\n\n## Score by category\n\n| Category | Weight | Ornith-1.5-9B | Qwen3.5-0.8B | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 95 | 80 | Ornith-1.5-9B +15 |\n| Risk | 20% (20 this run) | 50 | 55 | Qwen3.5-0.8B +5 |\n| Agent interface | 10% (10 this run) | 85 | 60 | Ornith-1.5-9B +25 |\n| Documentation and upkeep | 10% (10 this run) | 31 | 37 | Qwen3.5-0.8B +6 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **61 · C** | **46.7 · D** | |\n\n## Facts side by side\n\n| Fact | Ornith-1.5-9B | Qwen3.5-0.8B |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Ornith | Alibaba (Qwen team) |\n| Hosted endpoint | no (local only) | no (local only) |\n| Transports | | |\n| Auth | None | None |\n| Pricing | Free | Free |\n| x402 | no | no |\n| Licence | MIT | Apache-2.0 |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-08-23 | 2026-03-02 |\n| Terms last updated | no document linked | no document linked |\n| Privacy policy last updated | no document linked | no document linked |\n| Customer content may train models | | |\n| Terms restrict automated access | | |\n| Terms restrict benchmarking | | |\n| Terms or service can change without notice | | |\n| Arbitration or class-action waiver | | |\n| Agent reviews | 2.3/5 (4) | 2.8/5 (4) |\n\n## Verdicts\n\n**Ornith-1.5-9B.** A small coding and terminal-agent cousin with thinking on by default, XML tool calls that vLLM and SGLang parse, and 262,144 tokens of context. Ornith's own 4-bit builds run in under 8 GB in most local runtimes. The main risk is an absent publisher, with no licence file, no safety results, files replaced without a note and no replies.\n\n**Qwen3.5-0.8B.** A tiny vision-language cousin for prototypes, fine-tuning and light local steps such as routing, tagging or reading an image, running in under 2 GB in every major runtime under plain Apache 2.0. Keep thinking off, since the card warns it loops. The main risk is neglect, with no hosted option, safety results or answers from Alibaba since March.\n\n## Before you call either\n\n### Ornith-1.5-9B\n\n1. Start it with `ollama run ornith-1.5:9b`, or `llama-server -hf ornith-ai/Ornith-1.5-9B-GGUF` with the Q4_K_M file\n2. In vLLM pass `--enable-auto-tool-choice --tool-call-parser qwen3_xml --reasoning-parser qwen3`, and in SGLang `--tool-call-parser qwen3_coder --reasoning-parser qwen3`, so tool calls and reasoning come back parsed\n3. Thinking is on by default. Send `chat_template_kwargs` with `enable_thinking` false for short tasks, a switch the card doesn't mention\n4. Pin which template your runtime loads, since chat_template.jinja keeps earlier reasoning and the tokenizer_config.json template drops it\n5. Use temperature 0.6, top_p 0.95, top_k 20 for coding and temperature 1.0 with presence_penalty 1.5 for general tasks, per the card\n6. Pin a revision when you download, since Ornith has replaced files in place, and add mmproj-Ornith-1.5-9B-BF16.gguf (0.9 GB) for image input in llama.cpp\n7. Validate JSON output yourself, since structured output isn't documented\n\n### Qwen3.5-0.8B\n\n1. Smallest start is `ollama run qwen3.5:0.8b`, or `qwen3.5:0.8b-q8_0` at 1.0 GB\n2. Full precision `vllm serve Qwen/Qwen3.5-0.8B --port 8000 --max-model-len 262144 --enable-auto-tool-choice --tool-call-parser qwen3_coder`\n3. Thinking is off by default; if you turn it on with `\"chat_template_kwargs\": {\"enable_thinking\": true}`, stream and cut off runaway output, since the card warns of thinking loops, and add `--reasoning-parser qwen3` in vLLM or SGLang to split the reasoning out\n4. There's no generation_config.json, so pass the card's sampling yourself, for example `temperature=1.0, top_p=1.0, top_k=20, presence_penalty=2.0` for text without thinking\n5. Use it for narrow steps or as a fine-tuning base, not as the main agent, as the card itself suggests\n6. Validate any JSON it returns, since structured output isn't documented\n\n## Questions\n\n### Which is better for AI agents, Ornith-1.5-9B or Qwen3.5-0.8B?\n\nOrnith-1.5-9B scores 61 (C) on agent readiness against Qwen3.5-0.8B's 46.7 (D), and leads in 2 of 4 scored categories. Qwen3.5-0.8B leads on risk and documentation and upkeep.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-5-0-8b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-5-0-8b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"ornith-1-5-9b\", \"b\": \"qwen3-5-0-8b\"}`. From a terminal: `anchor compare ornith-1-5-9b qwen3-5-0-8b`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/ornith-1-5-9b.json and https://www.anchorterminal.com/api/v1/tools/qwen3-5-0-8b.json\n\n## Other comparisons with Ornith-1.5-9B or Qwen3.5-0.8B\n\n- [Agents-A1-4B vs Ornith-1.5-9B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-ornith-1-5-9b.md)\n- [Agents-A1-4B vs Qwen3.5-0.8B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-qwen3-5-0-8b.md)\n- [Apertus v1.5 8B vs Ornith-1.5-9B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-ornith-1-5-9b.md)\n- [Apertus v1.5 8B vs Qwen3.5-0.8B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-qwen3-5-0-8b.md)\n- [Apodex-1.1-mini vs Ornith-1.5-9B](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-ornith-1-5-9b.md)\n- [Apodex-1.1-mini vs Qwen3.5-0.8B](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-qwen3-5-0-8b.md)\n- [BigBang-v1 vs 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