# GLM-5.3-Flash vs Qwen3.5-9B > GLM-5.3-Flash scores 65.1 (B) to Qwen3.5-9B's 60.5 (C) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side. - Canonical: https://www.anchorterminal.com/compare/glm-5-3-flash-vs-qwen3-5-9b - Markdown: https://www.anchorterminal.com/compare/glm-5-3-flash-vs-qwen3-5-9b.md (~5,900 tokens) - Slim: https://www.anchorterminal.com/compare/glm-5-3-flash-vs-qwen3-5-9b.min.md (~430 tokens, same facts, less prose, for token-sensitive contexts) - JSON: https://www.anchorterminal.com/compare/glm-5-3-flash-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 GLM-5.3-Flash scores 65.1 (B) on agent readiness against Qwen3.5-9B's 60.5 (C), and leads in 2 of 5 scored categories. Qwen3.5-9B leads on reach. Both do open-weight models you run yourself. - GLM-5.3-Flash: grade B, 65.1/100, rank #16 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/glm-5-3-flash.md · JSON https://www.anchorterminal.com/api/v1/tools/glm-5-3-flash.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 ### GLM-5.3-Flash (B) Good for: Teams with a multi-GPU server who want an MIT-licensed multimodal agent model with a 1M-token context, or agents that call it through a hosted provider Ahead on: - Capability, 75 against 51 - Documentation and upkeep, 58 against 34 Watch for: Z.ai's smallest build is 328.4 GB in FP8, and no Ollama or LM Studio local build exists ### 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: - Reach, 85 against 60 Watch for: No quantised build from Qwen itself ## Score by category | Category | Weight | GLM-5.3-Flash | Qwen3.5-9B | Edge | | --- | --- | --- | --- | --- | | Capability | 35% (35 this run) | 75 | 51 | GLM-5.3-Flash +24 | | Reach | 25% (25 this run) | 60 | 85 | Qwen3.5-9B +25 | | Risk | 20% (20 this run) | 55 | 55 | even | | Agent interface | 10% (10 this run) | 70 | 70 | even | | Documentation and upkeep | 10% (10 this run) | 58 | 34 | GLM-5.3-Flash +24 | | Negative events | ≤15 | 0 | 0 | | | **Total** | | **65.1 · B** | **60.5 · C** | | ## Facts side by side | Fact | GLM-5.3-Flash | Qwen3.5-9B | | --- | --- | --- | | Kind | Open-weight model | Open-weight model | | Vendor | Z.ai | 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 | 2026-08-26 | 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 | 2.8/5 (4) | 2.5/5 (4) | ## Verdicts **GLM-5.3-Flash.** An MIT-licensed multimodal agent and coding model with a 1M-token context and several tool calls per turn, served by vLLM, SGLang, llama.cpp and Transformers. Z.ai's smallest build is 328 GB in FP8, so it needs a multi-GPU server. The main risk is that nothing on safety or prompt injection is published and there is no way to report a flaw. **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 ### GLM-5.3-Flash 1. Start it with `vllm serve zai-org/GLM-5.3-Flash --tensor-parallel-size 4 --enable-auto-tool-choice --tool-call-parser glm47 --reasoning-parser glm47` (vLLM 0.32.0 or later, about 386 GB of GPU memory per vLLM's recipe) 2. Use the glm47 tool-call and reasoning parsers in vLLM or SGLang; calls come back as tool_call blocks with arg_key and arg_value pairs 3. Pass `chat_template_kwargs: {"reasoning_effort": "low"}` for short tasks; anything other than low or high falls back to max 4. Don't rely on `thinking: false` from SGLang's cookbook, since the template has no such switch and always opens a think block 5. Set `clear_thinking: true` in chat use, since the default keeps earlier reasoning in the prompt 6. On a 128 GB machine only third-party low-bit builds come near fitting, none of them from Z.ai ### 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, GLM-5.3-Flash or Qwen3.5-9B? GLM-5.3-Flash scores 65.1 (B) on agent readiness against Qwen3.5-9B's 60.5 (C), and leads in 2 of 5 scored categories. Qwen3.5-9B leads on reach. ## For agents - This comparison as JSON: https://www.anchorterminal.com/compare/glm-5-3-flash-vs-qwen3-5-9b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/glm-5-3-flash-vs-qwen3-5-9b.min.md - Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "glm-5-3-flash", "b": "qwen3-5-9b"}`. 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[Qwen3.5-9B vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/qwen3-5-9b-vs-thinkingcap-qwen3-8-27b.md) --- Query this catalogue directly over MCP: https://www.anchorterminal.com/mcp (Streamable HTTP, no key). How: https://www.anchorterminal.com/agents/#mcp