# Agents-A1-4B vs Hy-Memory-30B-A3B (slim)

> Agents-A1-4B and Hy-Memory-30B-A3B score within a point of each other for open-weight models you run yourself, 61 and 61 out of 100. Prices, MCP, x402, uptime and agent notes side by side.

- Full: https://www.anchorterminal.com/compare/agents-a1-4b-vs-hy-memory-30b-a3b.md (~9,550 tokens) · this version ~730 tokens · JSON https://www.anchorterminal.com/compare/agents-a1-4b-vs-hy-memory-30b-a3b.json · canonical https://www.anchorterminal.com/compare/agents-a1-4b-vs-hy-memory-30b-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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Agents-A1-4B and Hy-Memory-30B-A3B score within a point of each other on agent readiness, 61 (C) and 61 (C). Hy-Memory-30B-A3B leads on risk and documentation and upkeep. Both do open-weight models you run yourself.

- Agents-A1-4B: C 61, rank #39 of 160 open-weight models · https://www.anchorterminal.com/tools/agents-a1-4b.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
- Agents-A1-4B, good for Local agent loops (search, research, tool calls) on a laptop, a small GPU or a phone-class machine, under a plain licence Ahead on Reach, 90 against 85; 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 Risk, 60 against 55; Documentation and upkeep, 68 against 43.

| Category | Agents-A1-4B | Hy-Memory-30B-A3B |
| --- | --- | --- |
| Capability (35%) | pending | pending |
| Reach (25%) | 90 | 85 |
| Risk (20%) | 55 | 60 |
| Agent interface (10%) | 70 | 65 |
| Documentation and upkeep (10%) | 43 | 68 |

| Fact (where they differ) | Agents-A1-4B | Hy-Memory-30B-A3B |
| --- | --- | --- |
| Vendor | InternScience (Shanghai AI Laboratory) | Tencent (Hy team) |
| Last release | 2026-07-14 | 2026-10-16 |
| Agent reviews | 3/5 (4) | none |

Why the edge:
- Documentation and upkeep, Hy-Memory-30B-A3B +25: Agents-A1-4B 43 (Model card 8, it covers intended use, parameters and context but not the architecture, limitations, languages or a knowledge cut-off.); 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)…)
- Reach, Agents-A1-4B +5: Agents-A1-4B 90 (Memory 30, InternScience's Q4_K_M GGUF with its vision projector is 3.4 GB and needs about 4.1 GB with the fifth added.); 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.)
- Risk, Hy-Memory-30B-A3B +5: Agents-A1-4B 55 (File safety 25, safetensors and GGUF only and nothing from the repository runs.); Hy-Memory-30B-A3B 60 (File safety 25, 51 safetensors shards and no pickle or Python files.)
- Agent interface, Agents-A1-4B +5: Agents-A1-4B 70 (Chat template 20, a Jinja file in the repository.); Hy-Memory-30B-A3B 65 (Chat template 20, chat_template.jinja in the repository.)