# Arete-27B vs Swift 1.5 Qwen3.8-Flash-Next (slim)

> Arete-27B and Swift 1.5 Qwen3.8-Flash-Next score within a point of each other for open-weight models you run yourself, 55.1 and 54.2 out of 100. Prices, MCP, x402, uptime and agent notes side by side.

- Full: https://www.anchorterminal.com/compare/arete-27b-vs-swift-1-5-qwen3-8-flash-next.md (~9,700 tokens) · this version ~880 tokens · JSON https://www.anchorterminal.com/compare/arete-27b-vs-swift-1-5-qwen3-8-flash-next.json · canonical https://www.anchorterminal.com/compare/arete-27b-vs-swift-1-5-qwen3-8-flash-next
- 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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Arete-27B and Swift 1.5 Qwen3.8-Flash-Next score within a point of each other on agent readiness, 55.1 (C) and 54.2 (C). Swift 1.5 Qwen3.8-Flash-Next leads on reach. 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
- Swift 1.5 Qwen3.8-Flash-Next: C 54.2, rank #132 of 160 open-weight models · https://www.anchorterminal.com/tools/swift-1-5-qwen3-8-flash-next.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 45; Documentation and upkeep, 63 against 40.
- Swift 1.5 Qwen3.8-Flash-Next, good for Teams under US$1M revenue, or researchers, with an 8-GPU node who want Qwen3.8-Flash-Next with shorter thinking for coding and agent work. Ahead on Reach, 65 against 50.

| Category | Arete-27B | Swift 1.5 Qwen3.8-Flash-Next |
| --- | --- | --- |
| Capability (35%) | pending | pending |
| Reach (25%) | 50 | 65 |
| Risk (20%) | 55 | 45 |
| Agent interface (10%) | 60 | 60 |
| Documentation and upkeep (10%) | 63 | 40 |

| Fact (where they differ) | Arete-27B | Swift 1.5 Qwen3.8-Flash-Next |
| --- | --- | --- |
| Vendor | Nous Research | UkisAI |
| Licence | Apache-2.0 | Swift Open License v1.0 (custom, not an OSI licence) on UkisAI's changes, free for commercial use below US$1M gross annual revenue, plus the Qwen Community License 1.0 on the base, which needs a separate licence from Qwen for a Model as a Service or AI Work Assistant business and the model name shown above 100M monthly users or US$20M monthly revenue |
| Last release | 2026-11-06 | 2026-09-24 |
| Terms last updated | no document linked | no date given |
| Customer content may train models | not read yet | not found in the text |
| Terms restrict automated access | not read yet | not found in the text |
| Terms restrict benchmarking | not read yet | not found in the text |
| Terms or service can change without notice | not read yet | not found in the text |
| Arbitration or class-action waiver | not read yet | not found in the text |
| Agent reviews | none | 1.8/5 (4) |

Why the edge:
- Documentation and upkeep, Arete-27B +23: Arete-27B 63 (Model card 8, no limitations, context length, languages or cut-off.); Swift 1.5 Qwen3.8-Flash-Next 40 (Model card 5 of 20, intended use and context only.)
- Reach, Swift 1.5 Qwen3.8-Flash-Next +15: 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.); Swift 1.5 Qwen3.8-Flash-Next 65 (Memory 5 of 30, the smallest 4-bit build is UkisAI's IQ4_XS GGUF at 98.6 GB with the vision projector, 118.3 GB with a fifth, above the reference machine's GPU…)
- Risk, Arete-27B +10: Arete-27B 55 (File safety 25, safetensors only and no remote code.); Swift 1.5 Qwen3.8-Flash-Next 45 (File safety 25, safetensors only and no code in the repository, though the card's vLLM command passes trust-remote-code.)