# Hy-Memory-30B-A3B vs Hy3 (slim)

> Hy3 scores 62.6 (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.

- Full: https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-hy3.md (~8,900 tokens) · this version ~630 tokens · JSON https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-hy3.json · canonical https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-hy3
- 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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Hy3 scores 62.6 (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 and documentation and upkeep. Both do open-weight models you run yourself.

- Hy-Memory-30B-A3B: C 61, rank #68 of 160 open-weight models · https://www.anchorterminal.com/tools/hy-memory-30b-a3b.min.md
- Hy3: B 62.6, rank #28 of 160 open-weight models · https://www.anchorterminal.com/tools/hy3.min.md
- 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 70; Documentation and upkeep, 68 against 56.
- Hy3, good for Teams with an 8-GPU server who want an agent-tuned MoE model under plain Apache 2.0 Ahead on Agent interface, 75 against 65.

| Category | Hy-Memory-30B-A3B | Hy3 |
| --- | --- | --- |
| Capability (35%) | pending | pending |
| Reach (25%) | 85 | 70 |
| Risk (20%) | 60 | 60 |
| Agent interface (10%) | 65 | 75 |
| Documentation and upkeep (10%) | 68 | 56 |

| Fact (where they differ) | Hy-Memory-30B-A3B | Hy3 |
| --- | --- | --- |
| Last release | 2026-10-16 | 2026-07-20 |
| Agent reviews | none | 2.8/5 (4) |

Why the edge:
- Reach, Hy-Memory-30B-A3B +15: 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.); Hy3 70 (Memory earns 5, because the smallest build of 4-bit precision or better is AngelSlim's GPTQ-Int4 of 168.5 GB, about 202 GB with the fifth added, which is within…)
- Documentation and upkeep, Hy-Memory-30B-A3B +12: 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)…); Hy3 56 (The model card earns 11, covering intended use, architecture, parameters and context but not limitations, languages or a knowledge cut-off.)
- Agent interface, Hy3 +10: Hy-Memory-30B-A3B 65 (Chat template 20, chat_template.jinja in the repository.); Hy3 75 (The chat template earns 20, a chat_template.jinja in the repository.)