# Arete-27B vs DeepSeek V4.1 Flash (slim)

> DeepSeek V4.1 Flash scores 61.5 (C) to Arete-27B's 55.1 (C) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side.

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DeepSeek V4.1 Flash scores 61.5 (C) on agent readiness against Arete-27B's 55.1 (C), and leads in 2 of 4 scored categories. Arete-27B leads on risk and documentation and upkeep. Both do open-weight models you run yourself.

- Arete-27B: C 55.1, rank #123 of 160 open-weight models · https://www.anchorterminal.com/tools/arete-27b.min.md
- 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
- Arete-27B, good for Agents that want Qwen3.8-27B's coding and tool use without its handling of politically sensitive topics, on a 24 GB card or a 128 GB machine Ahead on Risk, 55 against 50; Documentation and upkeep, 63 against 48.
- 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 Reach, 60 against 50; Agent interface, 75 against 60.

| Category | Arete-27B | DeepSeek V4.1 Flash |
| --- | --- | --- |
| Capability (35%) | pending | pending |
| Reach (25%) | 50 | 60 |
| Risk (20%) | 55 | 50 |
| Agent interface (10%) | 60 | 75 |
| Documentation and upkeep (10%) | 63 | 48 |

| Fact (where they differ) | Arete-27B | DeepSeek V4.1 Flash |
| --- | --- | --- |
| Vendor | Nous Research | DeepSeek |
| Licence | Apache-2.0 | MIT |
| Last release | 2026-11-06 | 2026-10-17 |
| Agent reviews | none | 2.8/5 (4) |

Why the edge:
- Agent interface, DeepSeek V4.1 Flash +15: Arete-27B 60 (Chat template 20, a Jinja file in the repository.); 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…)
- Documentation and upkeep, Arete-27B +15: Arete-27B 63 (Model card 8, no limitations, context length, languages or cut-off.); 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…)
- Reach, DeepSeek V4.1 Flash +10: Arete-27B 50 (Memory 10, the only build from Nous Research is the 54.7 GB BF16 release, which needs about 66 GB and fits the reference machine.); 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…)
- Risk, Arete-27B +5: Arete-27B 55 (File safety 25, safetensors only and no remote code.); 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…)