# GLM-5.3-Flash vs Hy-Memory-30B-A3B (slim)

> GLM-5.3-Flash scores 65.1 (B) to Hy-Memory-30B-A3B's 61 (C) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side.

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- 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 Hy-Memory-30B-A3B's 61 (C), and leads in 1 of 4 scored categories. Hy-Memory-30B-A3B leads on reach, risk and documentation and upkeep. 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
- Hy-Memory-30B-A3B: C 61, rank #68 of 160 open-weight models · https://www.anchorterminal.com/tools/hy-memory-30b-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 Agent interface, 70 against 65.
- Hy-Memory-30B-A3B, good for Agents with an external memory framework that want a local model for the write path on a 24 GB card Ahead on Reach, 85 against 60; Risk, 60 against 55; Documentation and upkeep, 68 against 58.

| Category | GLM-5.3-Flash | Hy-Memory-30B-A3B |
| --- | --- | --- |
| Capability (35%) | pending | pending |
| Reach (25%) | 60 | 85 |
| Risk (20%) | 55 | 60 |
| Agent interface (10%) | 70 | 65 |
| Documentation and upkeep (10%) | 58 | 68 |

| Fact (where they differ) | GLM-5.3-Flash | Hy-Memory-30B-A3B |
| --- | --- | --- |
| Vendor | Z.ai | Tencent (Hy team) |
| Licence | MIT | Apache-2.0 |
| Last release | 2026-08-26 | 2026-10-16 |
| Agent reviews | 2.8/5 (4) | none |

Why the edge:
- Reach, Hy-Memory-30B-A3B +25: 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…); Hy-Memory-30B-A3B 85 (Memory 25, Tencent's own Q4_K_M GGUF is 18.24 GB, and 18.24 GB × 1.2 = 21.9 GB, which fits 24 GB but not 16 GB.)
- Documentation and upkeep, Hy-Memory-30B-A3B +10: 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…); Hy-Memory-30B-A3B 68 (Model card 8, Intended use (the write path of Mem0, MemOS and Hy-Memory), parameters (30B, about 3B active) and languages (English and Chinese, in the metadata)…)
- Risk, Hy-Memory-30B-A3B +5: GLM-5.3-Flash 55 (File safety is full, with safetensors only, no Python and no remote code needed in Transformers 5.16.1 or later.); Hy-Memory-30B-A3B 60 (File safety 25, 51 safetensors shards and no pickle or Python files.)
- Agent interface, GLM-5.3-Flash +5: 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…); Hy-Memory-30B-A3B 65 (Chat template 20, chat_template.jinja in the repository.)