# DeepSeek V4.1 Flash vs Hy-Memory-30B-A3B (slim)

> DeepSeek V4.1 Flash and Hy-Memory-30B-A3B score within a point of each other for open-weight models you run yourself, 61.5 and 61 out of 100. Prices, MCP, x402, uptime and agent notes side by side.

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DeepSeek V4.1 Flash and Hy-Memory-30B-A3B score within a point of each other on agent readiness, 61.5 (C) and 61 (C). Hy-Memory-30B-A3B leads on reach, risk and documentation and upkeep. Both do open-weight models you run yourself.

- DeepSeek V4.1 Flash: C 61.5, rank #35 of 160 open-weight models · https://www.anchorterminal.com/tools/deepseek-v4-1-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
- DeepSeek V4.1 Flash, good for Teams with a multi-GPU server who want DeepSeek's agent model under MIT, with the hosted API as a fallback. Ahead on Agent interface, 75 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 50; Documentation and upkeep, 68 against 48.

| Category | DeepSeek V4.1 Flash | Hy-Memory-30B-A3B |
| --- | --- | --- |
| Capability (35%) | pending | pending |
| Reach (25%) | 60 | 85 |
| Risk (20%) | 50 | 60 |
| Agent interface (10%) | 75 | 65 |
| Documentation and upkeep (10%) | 48 | 68 |

| Fact (where they differ) | DeepSeek V4.1 Flash | Hy-Memory-30B-A3B |
| --- | --- | --- |
| Vendor | DeepSeek | Tencent (Hy team) |
| Licence | MIT | Apache-2.0 |
| Last release | 2026-10-17 | 2026-10-16 |
| Agent reviews | 2.8/5 (4) | none |

Why the edge:
- Reach, Hy-Memory-30B-A3B +25: DeepSeek V4.1 Flash 60 (Memory scores nothing, because the only build is DeepSeek's own at 510.3 GB, about 612 GB with the fifth added, and nobody's runtime library holds a smaller one…); 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 +20: DeepSeek V4.1 Flash 48 (The cards cover architecture, parameters and context but not intended use, limitations, languages or a cut-off, so 8. Training data is a general description wit…); 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 +10: DeepSeek V4.1 Flash 50 (The weights are safetensors only, but 10 comes off because the card's way to build prompts is running encoding.py from the repository, since there's no Jinja te…); Hy-Memory-30B-A3B 60 (File safety 25, 51 safetensors shards and no pickle or Python files.)
- Agent interface, DeepSeek V4.1 Flash +10: DeepSeek V4.1 Flash 75 (There's no Jinja template in the 17 October repository, only code to run, so the template line gets 10. DeepSeek documents the DSML tool format both ways and sh…); Hy-Memory-30B-A3B 65 (Chat template 20, chat_template.jinja in the repository.)