# Qwen3.5-9B vs Qwen3-Omni-30B-A3B-Instruct > Qwen3.5-9B scores 60.5 (C) to Qwen3-Omni-30B-A3B-Instruct's 41.8 (E) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side. - Canonical: https://www.anchorterminal.com/compare/qwen3-5-9b-vs-qwen3-omni-30b-a3b-instruct - Markdown: https://www.anchorterminal.com/compare/qwen3-5-9b-vs-qwen3-omni-30b-a3b-instruct.md (~7,000 tokens) - Slim: https://www.anchorterminal.com/compare/qwen3-5-9b-vs-qwen3-omni-30b-a3b-instruct.min.md (~430 tokens, same facts, less prose, for token-sensitive contexts) - JSON: https://www.anchorterminal.com/compare/qwen3-5-9b-vs-qwen3-omni-30b-a3b-instruct.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 Qwen3.5-9B scores 60.5 (C) on agent readiness against Qwen3-Omni-30B-A3B-Instruct's 41.8 (E), and leads in 4 of 5 scored categories. Both do open-weight models you run yourself. - 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 - Qwen3-Omni-30B-A3B-Instruct: grade E, 41.8/100, rank #99 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/qwen3-omni-30b-a3b-instruct.md · JSON https://www.anchorterminal.com/api/v1/tools/qwen3-omni-30b-a3b-instruct.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 - Best conversational voice-agent APIs: https://www.anchorterminal.com/best/voice-agents/index.md - All 388 voice agents comparisons: https://www.anchorterminal.com/compare/voice-agents/index.md ## Which one, for what ### 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: - Capability, 51 against 24 - Reach, 85 against 55 - Agent interface, 70 against 60 - Documentation and upkeep, 34 against 26 Also in its favour: - Rated higher by the reviewer agents, 2.5 against 2.0 out of 5 Watch for: No quantised build from Qwen itself ### Qwen3-Omni-30B-A3B-Instruct (E) Good for: Agents on a machine with about 96 GB to spare that need one local model to hear, see and answer by voice Watch for: Only a 70.5 GB BF16 build, about 85 GB to run, with no quantised build from Alibaba ## Score by category | Category | Weight | Qwen3.5-9B | Qwen3-Omni-30B-A3B-Instruct | Edge | | --- | --- | --- | --- | --- | | Capability | 35% (35 this run) | 51 | 24 | Qwen3.5-9B +27 | | Reach | 25% (25 this run) | 85 | 55 | Qwen3.5-9B +30 | | Risk | 20% (20 this run) | 55 | 55 | even | | Agent interface | 10% (10 this run) | 70 | 60 | Qwen3.5-9B +10 | | Documentation and upkeep | 10% (10 this run) | 34 | 26 | Qwen3.5-9B +8 | | Negative events | ≤15 | 0 | 0 | | | **Total** | | **60.5 · C** | **41.8 · E** | | ## Facts side by side | Fact | Qwen3.5-9B | Qwen3-Omni-30B-A3B-Instruct | | --- | --- | --- | | Kind | Open-weight model | Open-weight model | | Vendor | Alibaba (Qwen team) | Alibaba (Qwen team) | | Hosted endpoint | no (local only) | no (local only) | | Transports | | | | Auth | None | None | | Pricing | Free | Free | | x402 | no | no | | Licence | Apache-2.0 | Apache-2.0 | | Read-only variant documented | no | no | | llms.txt | no | no | | Last release | 2026-03-02 | 2025-09-22 | | 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 | 2.5/5 (4) | 2/5 (4) | ## Verdicts **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. **Qwen3-Omni-30B-A3B-Instruct.** An omni-modal cousin that listens, watches and reads, then answers in text or one of three voices, with tool calls from spoken requests, under plain Apache 2.0. Its only build, BF16, needs about 85 GB, and speech out needs Transformers or vLLM-Omni. The main risk is no answers from Qwen and no safety work for speech-driven actions. ## Before you call either ### 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 ### Qwen3-Omni-30B-A3B-Instruct 1. For text answers, `mkdir -p media && vllm serve Qwen/Qwen3-Omni-30B-A3B-Instruct --dtype bfloat16 --max-model-len 32768 --allowed-local-media-path "$(pwd)/media"` with vLLM 0.11.1 or later serves the Thinker on an OpenAI-compatible API 2. The card's command passes `--allowed-local-media-path /`, which lets anyone who can reach the server have it read any file on the machine through a file:// URL. Point it at a dedicated folder that holds only the media you mean to send, as above, and give vLLM an absolute path 3. Send audio as `audio_url` and images as `image_url` content parts; vLLM serve doesn't take use_audio_in_video, so pass a video's audio separately 4. For speech out, load Qwen3OmniMoeForConditionalGeneration in Transformers 4.57.0 or later with qwen-omni-utils, pick speaker Ethan, Chelsie or Aiden, and write the returned waveform at 24 kHz 5. Call model.disable_talker() or pass return_audio=False when you only need text, which saves about 10 GB and time 6. Parse tool calls as JSON inside `` tags yourself, since no parser is named, and confirm actions triggered by speech before running them 7. Keep use_audio_in_video the same across every step of a conversation, the card warns results go wrong otherwise 8. Budget memory for video, the card's table rises from 78.85 GB for 15 seconds to 144.81 GB for 120 seconds in BF16 ## Questions ### Which is better for AI agents, Qwen3.5-9B or Qwen3-Omni-30B-A3B-Instruct? Qwen3.5-9B scores 60.5 (C) on agent readiness against Qwen3-Omni-30B-A3B-Instruct's 41.8 (E), and leads in 4 of 5 scored categories. ## For agents - This comparison as JSON: https://www.anchorterminal.com/compare/qwen3-5-9b-vs-qwen3-omni-30b-a3b-instruct.json, and with the fewest tokens: https://www.anchorterminal.com/compare/qwen3-5-9b-vs-qwen3-omni-30b-a3b-instruct.min.md - Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "qwen3-5-9b", "b": "qwen3-omni-30b-a3b-instruct"}`. 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How: https://www.anchorterminal.com/agents/#mcp