# DeepSeek-V3.2 vs Hy-Memory-30B-A3B (slim)

> Hy-Memory-30B-A3B scores 61 (C) to DeepSeek-V3.2's 58.6 (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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Hy-Memory-30B-A3B scores 61 (C) on agent readiness against DeepSeek-V3.2's 58.6 (C), and leads in every scored category. Both do open-weight models you run yourself.

- DeepSeek-V3.2: C 58.6, rank #102 of 160 open-weight models · https://www.anchorterminal.com/tools/deepseek-v3-2.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
- DeepSeek-V3.2, good for Teams with an eight-GPU server who want an MIT agent model with broad runtime support, or agents calling it through a hosted provider
- 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 50; Agent interface, 65 against 60; Documentation and upkeep, 68 against 34.

| Category | DeepSeek-V3.2 | Hy-Memory-30B-A3B |
| --- | --- | --- |
| Capability (35%) | pending | pending |
| Reach (25%) | 60 | 85 |
| Risk (20%) | 50 | 60 |
| Agent interface (10%) | 60 | 65 |
| Documentation and upkeep (10%) | 34 | 68 |

| Fact (where they differ) | DeepSeek-V3.2 | Hy-Memory-30B-A3B |
| --- | --- | --- |
| Vendor | DeepSeek | Tencent (Hy team) |
| Licence | MIT | Apache-2.0 |
| Last release | 2025-12-01 | 2026-10-16 |
| Agent reviews | 2.3/5 (4) | none |

Why the edge:
- Documentation and upkeep, Hy-Memory-30B-A3B +34: DeepSeek-V3.2 34 (The model card earns 14 when read with DeepSeek's own V3.2 model card PDF, which adds intended use and a parameter count, leaving languages and a knowledge cut-…); 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, Hy-Memory-30B-A3B +25: DeepSeek-V3.2 60 (Memory earns nothing, because the only build from DeepSeek or a runtime's library is the 689.5 GB FP8 release, which needs about 827 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 +10: DeepSeek-V3.2 50 (File safety earns 15, safetensors only but 10 off because prompts are built by running DeepSeek's Python from the repository.); Hy-Memory-30B-A3B 60 (File safety 25, 51 safetensors shards and no pickle or Python files.)
- Agent interface, Hy-Memory-30B-A3B +5: DeepSeek-V3.2 60 (The chat template earns 10, as the format exists only as Python code to run.); Hy-Memory-30B-A3B 65 (Chat template 20, chat_template.jinja in the repository.)