{ "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 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 risk.", "b": { "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", "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": 1197, "categoryRank": 46, "methodology": "0.6", "run": "2026-10-01", "scores": { "capability": 51, "interface": 70, "reach": 85, "risk": 55, "upkeep": 34 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "high", "date": "2026-10-21" }, "negative": 0, "verdict": "A small dense vision-language cousin with trained tool calling, switchable thinking and a 262,144-token context, for local agent subtasks. Ollama's 4-bit build needs about 8 GB and the BF16 files about 24 GB, in every major runtime. The main risk is neglect, with no file changes since March, no replies from Qwen to reports and no safety results.", "bestFor": "Agents that want a small multimodal tool-calling cousin 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 fits in about 8 GB, the BF16 files in about 24 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": "0.6", "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": 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 qwen3.5:9b\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", "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", "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": "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": 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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": 49 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/qwen3-5-9b.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": "2.5/5 (4)", "name": "Agent reviews" } ], "faq": [ { "answer": "Ornith-1.5-9B 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 risk.", "question": "Which is better for AI agents, Ornith-1.5-9B or Qwen3.5-9B?" } ], "goodFor": [ { "aheadOn": [ "Reach, 95 against 85", "Agent interface, 85 against 70" ], "also": null, "goodFor": "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", "slug": "ornith-1-5-9b", "watchFor": "No LICENSE file though the card links one, and no notice for the Qwen3.5 and Gemma 4 work it builds on" }, { "aheadOn": [ "Risk, 55 against 50" ], "also": null, "goodFor": "Agents that want a small multimodal tool-calling cousin on a laptop or a 16 GB GPU, under a plain licence", "slug": "qwen3-5-9b", "watchFor": "No quantised build from Qwen itself" } ], "job": { "capability": "model.open-weights", "name": "Open-weight models you run yourself" }, "others": [ { "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-ornith-1-5-9b.json", "title": "Agents-A1-4B vs Ornith-1.5-9B", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-ornith-1-5-9b" }, { "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/apertus-v1-5-8b-vs-ornith-1-5-9b.json", "title": "Apertus v1.5 8B vs Ornith-1.5-9B", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-ornith-1-5-9b" }, { "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/apodex-1-1-mini-vs-ornith-1-5-9b.json", "title": "Apodex-1.1-mini vs Ornith-1.5-9B", "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-ornith-1-5-9b" }, { "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/bigbang-v1-vs-ornith-1-5-9b.json", "title": "BigBang-v1 vs Ornith-1.5-9B", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-ornith-1-5-9b" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-qwen3-5-9b.json", "title": "BigBang-v1 vs Qwen3.5-9B", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-qwen3-5-9b" }, { "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-ornith-1-5-9b.json", "title": "Cosmos-Reason2-2B vs Ornith-1.5-9B", "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-ornith-1-5-9b" }, { "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-qwen3-5-9b.json", "title": "Cosmos-Reason2-2B vs Qwen3.5-9B", "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-qwen3-5-9b" }, { "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-ornith-1-5-9b.json", "title": "Darwin-36B-Opus vs Ornith-1.5-9B", "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-ornith-1-5-9b" }, { "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-qwen3-5-9b.json", "title": "Darwin-36B-Opus vs Qwen3.5-9B", "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-qwen3-5-9b" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-ornith-1-5-9b.json", "title": "DeepSeek-V3.2 vs Ornith-1.5-9B", "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-ornith-1-5-9b" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-qwen3-5-9b.json", "title": "DeepSeek-V3.2 vs Qwen3.5-9B", "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-qwen3-5-9b" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-ornith-1-5-9b.json", "title": "DeepSeek V4.1 Flash vs Ornith-1.5-9B", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-ornith-1-5-9b" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-5-9b.json", "title": "DeepSeek V4.1 Flash vs Qwen3.5-9B", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-5-9b" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-ornith-1-5-9b.json", "title": "DeepSeek-V4-Flash-0731 vs Ornith-1.5-9B", "url": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-ornith-1-5-9b" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-qwen3-5-9b.json", "title": "DeepSeek-V4-Flash-0731 vs Qwen3.5-9B", "url": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-qwen3-5-9b" }, { "json": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-ornith-1-5-9b.json", "title": "DiffusionGemma 26B A4B IT vs Ornith-1.5-9B", "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-ornith-1-5-9b" }, { "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/gemma-4-12b-it-vs-ornith-1-5-9b.json", "title": "Gemma 4 12B IT vs Ornith-1.5-9B", "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-ornith-1-5-9b" }, { "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-4-26b-a4b-it-vs-ornith-1-5-9b.json", "title": "Gemma 4 26B A4B IT vs Ornith-1.5-9B", "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-ornith-1-5-9b" }, { "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-4-31b-it-vs-ornith-1-5-9b.json", "title": "Gemma 4 31B IT vs Ornith-1.5-9B", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-ornith-1-5-9b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-5-9b.json", "title": "Gemma 4 31B IT vs Qwen3.5-9B", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-5-9b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-ornith-1-5-9b.json", "title": "Gemma 4 E2B IT vs Ornith-1.5-9B", "url": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-ornith-1-5-9b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-qwen3-5-9b.json", "title": "Gemma 4 E2B IT vs Qwen3.5-9B", "url": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-qwen3-5-9b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-ornith-1-5-9b.json", "title": "Gemma 4 E4B IT vs Ornith-1.5-9B", "url": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-ornith-1-5-9b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-qwen3-5-9b.json", "title": "Gemma 4 E4B IT vs Qwen3.5-9B", "url": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-qwen3-5-9b" }, { "json": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-ornith-1-5-9b.json", "title": "GLM-4.5-Air vs Ornith-1.5-9B", "url": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-ornith-1-5-9b" }, { "json": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-qwen3-5-9b.json", "title": "GLM-4.5-Air vs Qwen3.5-9B", "url": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-qwen3-5-9b" }, { "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-ornith-1-5-9b.json", "title": "GLM-4.7-Flash vs Ornith-1.5-9B", "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-ornith-1-5-9b" }, { "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwen3-5-9b.json", "title": "GLM-4.7-Flash vs Qwen3.5-9B", "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwen3-5-9b" }, { "json": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-ornith-1-5-9b.json", "title": "GLM-5.3-Flash vs Ornith-1.5-9B", "url": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-ornith-1-5-9b" }, { "json": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-qwen3-5-9b.json", "title": "GLM-5.3-Flash vs Qwen3.5-9B", "url": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-qwen3-5-9b" }, { "json": "https://www.anchorterminal.com/compare/glm-5-3-vs-ornith-1-5-9b.json", "title": "GLM-5.3 vs Ornith-1.5-9B", "url": "https://www.anchorterminal.com/compare/glm-5-3-vs-ornith-1-5-9b" }, { "json": "https://www.anchorterminal.com/compare/glm-5-3-vs-qwen3-5-9b.json", "title": "GLM-5.3 vs Qwen3.5-9B", "url": "https://www.anchorterminal.com/compare/glm-5-3-vs-qwen3-5-9b" }, { "json": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-ornith-1-5-9b.json", "title": "gpt-oss-120b vs Ornith-1.5-9B", "url": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-ornith-1-5-9b" }, { "json": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-qwen3-5-9b.json", "title": "gpt-oss-120b vs Qwen3.5-9B", "url": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-qwen3-5-9b" }, { "json": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-ornith-1-5-9b.json", "title": "gpt-oss-20b vs Ornith-1.5-9B", "url": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-ornith-1-5-9b" }, { "json": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-qwen3-5-9b.json", "title": "gpt-oss-20b vs Qwen3.5-9B", "url": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-qwen3-5-9b" }, { "json": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-ornith-1-5-9b.json", "title": "Granite 4.2 3B vs Ornith-1.5-9B", "url": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-ornith-1-5-9b" }, { "json": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-qwen3-5-9b.json", "title": "Granite 4.2 3B vs Qwen3.5-9B", "url": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-qwen3-5-9b" }, { "json": "https://www.anchorterminal.com/compare/hy3-vs-ornith-1-5-9b.json", "title": "Hy3 vs Ornith-1.5-9B", "url": "https://www.anchorterminal.com/compare/hy3-vs-ornith-1-5-9b" }, { "json": "https://www.anchorterminal.com/compare/hy3-vs-qwen3-5-9b.json", "title": "Hy3 vs Qwen3.5-9B", "url": "https://www.anchorterminal.com/compare/hy3-vs-qwen3-5-9b" }, { "json": "https://www.anchorterminal.com/compare/inkling-small-vs-ornith-1-5-9b.json", "title": "Inkling-Small vs Ornith-1.5-9B", "url": "https://www.anchorterminal.com/compare/inkling-small-vs-ornith-1-5-9b" }, { "json": "https://www.anchorterminal.com/compare/inkling-small-vs-qwen3-5-9b.json", "title": "Inkling-Small vs Qwen3.5-9B", "url": "https://www.anchorterminal.com/compare/inkling-small-vs-qwen3-5-9b" }, { "json": "https://www.anchorterminal.com/compare/inkling-vs-ornith-1-5-9b.json", "title": "Inkling vs Ornith-1.5-9B", "url": "https://www.anchorterminal.com/compare/inkling-vs-ornith-1-5-9b" }, { "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/internvl3-5-gpt-oss-20b-a4b-preview-vs-ornith-1-5-9b.json", "title": "InternVL3.5-GPT-OSS-20B-A4B-Preview vs Ornith-1.5-9B", "url": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-ornith-1-5-9b" }, { "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/kimi-k3-vs-ornith-1-5-9b.json", "title": "Kimi K3 vs Ornith-1.5-9B", "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-ornith-1-5-9b" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-qwen3-5-9b.json", "title": "Kimi K3 vs Qwen3.5-9B", "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-qwen3-5-9b" }, { "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-ornith-1-5-9b.json", "title": "Kolibri-1 vs Ornith-1.5-9B", "url": "https://www.anchorterminal.com/compare/kolibri-1-vs-ornith-1-5-9b" }, { "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-qwen3-5-9b.json", "title": "Kolibri-1 vs Qwen3.5-9B", "url": "https://www.anchorterminal.com/compare/kolibri-1-vs-qwen3-5-9b" }, { "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-ornith-1-5-9b.json", "title": "Laguna M.1 vs Ornith-1.5-9B", "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-ornith-1-5-9b" }, { "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-5-9b.json", "title": "Laguna M.1 vs Qwen3.5-9B", "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-5-9b" }, { "json": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-ornith-1-5-9b.json", "title": "Laguna S 2.1 vs Ornith-1.5-9B", "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-qwen3-5-9b.json", "title": "Laguna S 2.1 vs Qwen3.5-9B", "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-qwen3-5-9b" }, { "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-qwen3-5-9b.json", "title": "Laguna XS 2.1 vs Qwen3.5-9B", "url": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwen3-5-9b" }, { "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-qwen3-5-9b.json", "title": "LFM2.5-2.6B vs Qwen3.5-9B", "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-5-9b" }, { "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-qwen3-5-9b.json", "title": "Ling-3.0-tiny vs Qwen3.5-9B", "url": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwen3-5-9b" }, { "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-qwen3-5-9b.json", "title": "MiMo-V2.6 Distill Qwen 9B vs Qwen3.5-9B", "url": "https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-qwen3-5-9b" }, { "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-qwen3-5-9b.json", "title": "MiniCPM-o 4.5 vs Qwen3.5-9B", "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen3-5-9b" }, { "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-qwen3-5-9b.json", "title": "MiniCPM5-2B vs Qwen3.5-9B", "url": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-qwen3-5-9b" }, { "json": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-ornith-1-5-9b.json", "title": "MiniMax-M2.5 vs Ornith-1.5-9B", "url": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-ornith-1-5-9b" }, { "json": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-qwen3-5-9b.json", "title": "MiniMax-M2.5 vs Qwen3.5-9B", "url": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-qwen3-5-9b" }, { "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-ornith-1-5-9b.json", "title": "MiniMax-M2.7 vs Ornith-1.5-9B", "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-ornith-1-5-9b" }, { "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-5-9b.json", "title": "MiniMax-M2.7 vs Qwen3.5-9B", "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-5-9b" }, { "json": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-ornith-1-5-9b.json", "title": "Muse Glimmer 30B vs Ornith-1.5-9B", "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-ornith-1-5-9b" }, { "json": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-5-9b.json", "title": "Muse Glimmer 30B vs Qwen3.5-9B", "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-5-9b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-ornith-1-5-9b.json", "title": "Nemotron 3.5 Lightning 30B A3B vs Ornith-1.5-9B", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-ornith-1-5-9b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-qwen3-5-9b.json", "title": "Nemotron 3.5 Lightning 30B A3B vs Qwen3.5-9B", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-qwen3-5-9b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-ornith-1-5-9b.json", "title": "Nemotron 3 Nano 30B A3B vs Ornith-1.5-9B", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-ornith-1-5-9b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-qwen3-5-9b.json", "title": "Nemotron 3 Nano 30B A3B vs Qwen3.5-9B", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-qwen3-5-9b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-ornith-1-5-9b.json", "title": "Nemotron 3 Nano 4B vs Ornith-1.5-9B", "url": 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"json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-5-4b.json", "title": "Ornith-1.5-9B vs Qwen3.5-4B", "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-5-4b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-6-27b.json", "title": "Ornith-1.5-9B vs Qwen3.6-27B", "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-6-27b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-6-35b-a3b.json", "title": "Ornith-1.5-9B vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-8-27b.json", "title": "Ornith-1.5-9B vs Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-8-flash-next.json", "title": "Ornith-1.5-9B vs Qwen3.8-Flash-Next", "url": 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"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/qwen3-5-122b-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/qwen3-5-2b-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/qwen3-5-397b-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/qwen3-5-4b-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/qwen3-5-9b-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-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-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" } ], "scores": [ { "key": "capability", "name": "Capability", "pending": true, "weight": 35 }, { "by": 10, "edge": "ornith-1-5-9b", "key": "reach", "name": "Reach", "ornith-1-5-9b": 95, "qwen3-5-9b": 85, "weight": 25 }, { "by": 5, "edge": "qwen3-5-9b", "key": "risk", "name": "Risk", "ornith-1-5-9b": 50, "qwen3-5-9b": 55, "weight": 20 }, { "by": 15, "edge": "ornith-1-5-9b", "key": "interface", "name": "Agent interface", "ornith-1-5-9b": 85, "qwen3-5-9b": 70, "weight": 10 }, { "by": 3, "edge": "qwen3-5-9b", "key": "upkeep", "name": "Documentation and upkeep", "ornith-1-5-9b": 31, "qwen3-5-9b": 34, "weight": 10 } ], "summary": "Ornith-1.5-9B 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 risk. 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.", "qwen3-5-9b": "A small dense vision-language cousin with trained tool calling, switchable thinking and a 262,144-token context, for local agent subtasks. Ollama's 4-bit build needs about 8 GB and the BF16 files about 24 GB, in every major runtime. The main risk is neglect, with no file changes since March, no replies from Qwen to reports and no safety results." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-5-9b", "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-5-9b.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-5-9b.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-5-9b.min.md" }, "markdown": "Ornith-1.5-9B 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 risk. 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- Qwen3.5-9B: grade C, 60.5/100, rank #53 of 100 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- 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 85\n- Agent interface, 85 against 70\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### Qwen3.5-9B (C)\n\nGood for: Agents that want a small multimodal tool-calling cousin on a laptop or a 16 GB GPU, under a plain licence\n\nAhead on:\n- Risk, 55 against 50\n\nWatch for: No quantised build from Qwen itself\n\n\n## Score by category\n\n| Category | Weight | Ornith-1.5-9B | Qwen3.5-9B | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 95 | 85 | Ornith-1.5-9B +10 |\n| Risk | 20% (20 this run) | 50 | 55 | Qwen3.5-9B +5 |\n| Agent interface | 10% (10 this run) | 85 | 70 | Ornith-1.5-9B +15 |\n| Documentation and upkeep | 10% (10 this run) | 31 | 34 | Qwen3.5-9B +3 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **61 · C** | **60.5 · C** | |\n\n## Facts side by side\n\n| Fact | Ornith-1.5-9B | Qwen3.5-9B |\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) | 2.5/5 (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**Qwen3.5-9B.** A small dense vision-language cousin with trained tool calling, switchable thinking and a 262,144-token context, for local agent subtasks. Ollama's 4-bit build needs about 8 GB and the BF16 files about 24 GB, in every major runtime. The main risk is neglect, with no file changes since March, no replies from Qwen to reports and no safety results.\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### Qwen3.5-9B\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## Questions\n\n### Which is better for AI agents, Ornith-1.5-9B or Qwen3.5-9B?\n\nOrnith-1.5-9B 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 risk.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-5-9b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-5-9b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"ornith-1-5-9b\", \"b\": \"qwen3-5-9b\"}`. 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(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": "2026-10-21", "license": "CC-BY-4.0", "method": "https://www.anchorterminal.com/benchmark/", "methodology": "0.6", "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": "Ornith-1.5-9B vs Qwen3.5-9B", "url": "" } ], "description": "Ornith-1.5-9B 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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