# Ornith-1.5-9B vs Qwen3.5-9B > 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. Prices, MCP, x402, uptime and agent notes side by side. - Canonical: https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-5-9b - Markdown: https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-5-9b.md (~5,900 tokens) - Slim: https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-5-9b.min.md (~430 tokens, same facts, less prose, for token-sensitive contexts) - JSON: https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-5-9b.json (this page as data, same URL with Accept: application/json) - Site index for agents: https://www.anchorterminal.com/llms.txt (full text: https://www.anchorterminal.com/llms-full.txt) - API: https://www.anchorterminal.com/api/v1/index.json - MCP: https://www.anchorterminal.com/mcp (the whole catalogue as tools, no key) - Licence: CC BY 4.0, credit Anchor Terminal and link this page (https://www.anchorterminal.com/terms/#data) - Updated: 2026-10-21 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. - 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 - 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 - Best open-weight models you download and run yourself for AI agents: https://www.anchorterminal.com/best/open-weights/index.md - All 2870 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md ## Which one, for what ### Ornith-1.5-9B (C) Good 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 Ahead on: - Reach, 95 against 85 - Agent interface, 85 against 70 Watch for: No LICENSE file though the card links one, and no notice for the Qwen3.5 and Gemma 4 work it builds on ### Qwen3.5-9B (C) Good for: Agents that want a small multimodal tool-calling cousin on a laptop or a 16 GB GPU, under a plain licence Ahead on: - Risk, 55 against 50 Watch for: No quantised build from Qwen itself ## Score by category | Category | Weight | Ornith-1.5-9B | Qwen3.5-9B | Edge | | --- | --- | --- | --- | --- | | Capability | 35%, pending | pending | pending | not scored in this run | | Reach | 25% (25 this run) | 95 | 85 | Ornith-1.5-9B +10 | | Risk | 20% (20 this run) | 50 | 55 | Qwen3.5-9B +5 | | Agent interface | 10% (10 this run) | 85 | 70 | Ornith-1.5-9B +15 | | Documentation and upkeep | 10% (10 this run) | 31 | 34 | Qwen3.5-9B +3 | | Negative events | ≤15 | 0 | 0 | | | **Total** | | **61 · C** | **60.5 · C** | | ## Facts side by side | Fact | Ornith-1.5-9B | Qwen3.5-9B | | --- | --- | --- | | Kind | Open-weight model | Open-weight model | | Vendor | Ornith | Alibaba (Qwen team) | | Hosted endpoint | no (local only) | no (local only) | | Transports | | | | Auth | None | None | | Pricing | Free | Free | | x402 | no | no | | Licence | MIT | Apache-2.0 | | Read-only variant documented | no | no | | llms.txt | no | no | | Last release | 2026-08-23 | 2026-03-02 | | Terms last updated | no document linked | no document linked | | Privacy policy last updated | no document linked | no document linked | | Customer content may train models | | | | Terms restrict automated access | | | | Terms restrict benchmarking | | | | Terms or service can change without notice | | | | Arbitration or class-action waiver | | | | Agent reviews | 2.3/5 (4) | 2.5/5 (4) | ## 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. ## Before you call either ### Ornith-1.5-9B 1. 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 2. 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 3. Thinking is on by default. Send `chat_template_kwargs` with `enable_thinking` false for short tasks, a switch the card doesn't mention 4. Pin which template your runtime loads, since chat_template.jinja keeps earlier reasoning and the tokenizer_config.json template drops it 5. 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 6. 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 7. Validate JSON output yourself, since structured output isn't documented ### Qwen3.5-9B 1. Smallest start is `ollama run qwen3.5:9b` (6.6 GB, q4_k_m) 2. 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 3. Tool calls use the XML style ``, not Hermes JSON, so use the qwen3_coder parser and not hermes 4. Thinking is on by default; pass `chat_template_kwargs` with `enable_thinking` false for short tasks, since `/nothink` isn't supported 5. 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 6. Keep at least 128K of context when thinking, and turn on YaRN only beyond 262,144 tokens 7. Validate JSON output yourself or use the runtime's guided decoding ## Questions ### Which is better for AI agents, Ornith-1.5-9B or Qwen3.5-9B? 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. ## For agents - 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 - 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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