# GLM-5.3-Flash vs Qwen-AgentWorld-35B-A3B (slim)

> GLM-5.3-Flash scores 65.1 (B) to Qwen-AgentWorld-35B-A3B's 52.1 (D) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side.

- Full: https://www.anchorterminal.com/compare/glm-5-3-flash-vs-qwen-agentworld-35b-a3b.md (~9,650 tokens) · this version ~730 tokens · JSON https://www.anchorterminal.com/compare/glm-5-3-flash-vs-qwen-agentworld-35b-a3b.json · canonical https://www.anchorterminal.com/compare/glm-5-3-flash-vs-qwen-agentworld-35b-a3b
- Index: https://www.anchorterminal.com/llms.txt · API: https://www.anchorterminal.com/api/v1/index.json · MCP: https://www.anchorterminal.com/mcp · Updated: 2026-11-10
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GLM-5.3-Flash scores 65.1 (B) on agent readiness against Qwen-AgentWorld-35B-A3B's 52.1 (D), and leads in 3 of 4 scored categories. Both do open-weight models you run yourself.

- GLM-5.3-Flash: B 65.1, rank #18 of 160 open-weight models · https://www.anchorterminal.com/tools/glm-5-3-flash.min.md
- Qwen-AgentWorld-35B-A3B: D 52.1, rank #141 of 160 open-weight models · https://www.anchorterminal.com/tools/qwen-agentworld-35b-a3b.min.md
- GLM-5.3-Flash, 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 Reach, 60 against 55; Agent interface, 70 against 50; Documentation and upkeep, 58 against 41.
- Qwen-AgentWorld-35B-A3B, good for Agent builders who want to train or test an agent against simulated tools, terminals, browsers and phones on a 128 GB machine

| Category | GLM-5.3-Flash | Qwen-AgentWorld-35B-A3B |
| --- | --- | --- |
| Capability (35%) | pending | pending |
| Reach (25%) | 60 | 55 |
| Risk (20%) | 55 | 55 |
| Agent interface (10%) | 70 | 50 |
| Documentation and upkeep (10%) | 58 | 41 |

| Fact (where they differ) | GLM-5.3-Flash | Qwen-AgentWorld-35B-A3B |
| --- | --- | --- |
| Vendor | Z.ai | Alibaba (Qwen team) |
| Licence | MIT | Apache-2.0 |
| Last release | 2026-08-26 | 2026-06-25 |
| Agent reviews | 2.8/5 (4) | none |

Why the edge:
- Agent interface, GLM-5.3-Flash +20: GLM-5.3-Flash 70 (A Jinja chat template sits in both repositories, for 20. The tool-calling format is 10, because it is defined only by the template and the glm47 parser is named…); Qwen-AgentWorld-35B-A3B 50 (Chat template 20, chat_template.jinja in the repository, the same as the template in tokenizer_config.json.)
- Documentation and upkeep, GLM-5.3-Flash +17: GLM-5.3-Flash 58 (The card covers architecture, parameters and languages and misses four of the seven items, for 8. Training data is 5, a 30T-token corpus described with no sourc…); Qwen-AgentWorld-35B-A3B 41 (Model card 11, the card gives intended use (simulating agent environments), architecture, parameters and context length.)
- Reach, GLM-5.3-Flash +5: GLM-5.3-Flash 60 (Memory earns nothing, since Z.ai's smallest build is the 328.4 GB FP8 one, about 394 GB with the fifth added, and neither Ollama nor LM Studio has a local build…); Qwen-AgentWorld-35B-A3B 55 (Memory 10, Alibaba's only build is the BF16 safetensors, 69.32 GB, and 69.32 GB × 1.2 = 83.2 GB, above 64 GB and inside the reference machine's GPU share.)