# Arete-27B vs Ling-3.0-tiny (slim)

> Ling-3.0-tiny scores 56.2 (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.

- Full: https://www.anchorterminal.com/compare/arete-27b-vs-ling-3-0-tiny.md (~8,650 tokens) · this version ~630 tokens · JSON https://www.anchorterminal.com/compare/arete-27b-vs-ling-3-0-tiny.json · canonical https://www.anchorterminal.com/compare/arete-27b-vs-ling-3-0-tiny
- 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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Ling-3.0-tiny scores 56.2 (C) on agent readiness against Arete-27B's 55.1 (C), and leads in 1 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
- Ling-3.0-tiny: C 56.2, rank #117 of 160 open-weight models · https://www.anchorterminal.com/tools/ling-3-0-tiny.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 56.
- Ling-3.0-tiny, good for Agents on a laptop or small GPU that want a fast local model for tool calls and short reasoning, under MIT Ahead on Reach, 95 against 50.

| Category | Arete-27B | Ling-3.0-tiny |
| --- | --- | --- |
| Capability (35%) | pending | pending |
| Reach (25%) | 50 | 95 |
| Risk (20%) | 55 | 50 |
| Agent interface (10%) | 60 | 60 |
| Documentation and upkeep (10%) | 63 | 56 |

| Fact (where they differ) | Arete-27B | Ling-3.0-tiny |
| --- | --- | --- |
| Vendor | Nous Research | inclusionAI (Ant Group) |
| Licence | Apache-2.0 | MIT |
| Last release | 2026-11-06 | 2026-08-31 |
| Agent reviews | none | 2.8/5 (4) |

Why the edge:
- Reach, Ling-3.0-tiny +45: 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.); Ling-3.0-tiny 95 (Memory earns the full 30, since inclusionAI's own Q4_K_M GGUF is 4.8 GB, about 6 GB with a fifth added.)
- Documentation and upkeep, Arete-27B +7: Arete-27B 63 (Model card 8, no limitations, context length, languages or cut-off.); Ling-3.0-tiny 56 (Model card 11, intended use, architecture, parameters and context are there, limitations, languages and a cut-off aren't.)
- Risk, Arete-27B +5: Arete-27B 55 (File safety 25, safetensors only and no remote code.); Ling-3.0-tiny 50 (File safety gets 15, safetensors and GGUF only, but the card's vLLM command runs the repository's config code with --trust-remote-code.)