# Hy-Memory-30B-A3B vs Qwen3.8-27B (slim)

> Qwen3.8-27B scores 68.6 (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-qwen3-8-27b.md (~9,700 tokens) · this version ~730 tokens · JSON https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-8-27b.json · canonical https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-8-27b
- 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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Qwen3.8-27B scores 68.6 (B) on agent readiness against Hy-Memory-30B-A3B's 61 (C), and leads in 2 of 4 scored categories. Hy-Memory-30B-A3B leads on risk 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
- Qwen3.8-27B: B 68.6, rank #11 of 160 open-weight models · https://www.anchorterminal.com/tools/qwen3-8-27b.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 Risk, 60 against 55; Documentation and upkeep, 68 against 36.
- Qwen3.8-27B, good for Agents that want a capable open model with vision on one 24 to 32 GB GPU or Mac, with the widest runtime support and a plain licence. Ahead on Reach, 90 against 85; Agent interface, 70 against 65.

| Category | Hy-Memory-30B-A3B | Qwen3.8-27B |
| --- | --- | --- |
| Capability (35%) | pending | pending |
| Reach (25%) | 85 | 90 |
| Risk (20%) | 60 | 55 |
| Agent interface (10%) | 65 | 70 |
| Documentation and upkeep (10%) | 68 | 36 |

| Fact (where they differ) | Hy-Memory-30B-A3B | Qwen3.8-27B |
| --- | --- | --- |
| Vendor | Tencent (Hy team) | Alibaba (Qwen team) |
| Last release | 2026-10-16 | 2026-08-14 |
| Agent reviews | none | 3/5 (4) |

Why the edge:
- Documentation and upkeep, Hy-Memory-30B-A3B +32: 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)…); Qwen3.8-27B 36 (Model card 11 of 20, intended use, architecture, parameters and context are there, but limitations, languages and a knowledge cut-off aren't.)
- Reach, Qwen3.8-27B +5: 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.); Qwen3.8-27B 90 (Memory 25 of 30, Ollama's smallest 4-bit build is 18 GB, or 21.6 GB with a fifth added, which fits 24 GB.)
- Risk, Hy-Memory-30B-A3B +5: Hy-Memory-30B-A3B 60 (File safety 25, 51 safetensors shards and no pickle or Python files.); Qwen3.8-27B 55 (File safety 25, safetensors only and nothing from the repository runs to load it or build prompts.)
- Agent interface, Qwen3.8-27B +5: Hy-Memory-30B-A3B 65 (Chat template 20, chat_template.jinja in the repository.); Qwen3.8-27B 70 (Chat template 20, a Jinja file in the repository.)