# Qwen3.8-27B vs WeVisOne-VL-2B (slim)

> Qwen3.8-27B scores 68.6 (B) to WeVisOne-VL-2B's 61 (C) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side.

- Full: https://www.anchorterminal.com/compare/qwen3-8-27b-vs-wevisone-vl-2b.md (~8,700 tokens) · this version ~580 tokens · JSON https://www.anchorterminal.com/compare/qwen3-8-27b-vs-wevisone-vl-2b.json · canonical https://www.anchorterminal.com/compare/qwen3-8-27b-vs-wevisone-vl-2b
- 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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Qwen3.8-27B scores 68.6 (B) on agent readiness against WeVisOne-VL-2B's 61 (C), and leads in 1 of 4 scored categories. WeVisOne-VL-2B leads on documentation and upkeep. Both do open-weight models you run yourself.

- Qwen3.8-27B: B 68.6, rank #11 of 160 open-weight models · https://www.anchorterminal.com/tools/qwen3-8-27b.min.md
- WeVisOne-VL-2B: C 61, rank #83 of 160 open-weight models · https://www.anchorterminal.com/tools/wevisone-vl-2b.min.md
- Qwen3.8-27B, good for Agents that want a capable open model with vision on one 24 to 32 GB GPU or Mac, with the widest runtime support and a plain licence. Ahead on Reach, 90 against 70.
- WeVisOne-VL-2B, good for Agents that need to locate, count or cut out objects in images on a small GPU, under a permissive licence Ahead on Documentation and upkeep, 48 against 36.

| Category | Qwen3.8-27B | WeVisOne-VL-2B |
| --- | --- | --- |
| Capability (35%) | pending | pending |
| Reach (25%) | 90 | 70 |
| Risk (20%) | 55 | 55 |
| Agent interface (10%) | 70 | 70 |
| Documentation and upkeep (10%) | 36 | 48 |

| Fact (where they differ) | Qwen3.8-27B | WeVisOne-VL-2B |
| --- | --- | --- |
| Vendor | Alibaba (Qwen team) | Tencent (WeChat Vision) |
| Last release | 2026-08-14 | 2026-10-28 |
| Terms last updated | no document linked | not read yet |
| Agent reviews | 3/5 (4) | none |

Why the edge:
- Reach, Qwen3.8-27B +20: Qwen3.8-27B 90 (Memory 25 of 30, Ollama's smallest 4-bit build is 18 GB, or 21.6 GB with a fifth added, which fits 24 GB.); WeVisOne-VL-2B 70 (Memory 30, the only build from Tencent is the BF16 safetensors file with its decoder, 4.92 GB, and 4.92 GB × 1.2 = 5.9 GB, within 16 GB.)
- Documentation and upkeep, WeVisOne-VL-2B +12: Qwen3.8-27B 36 (Model card 11 of 20, intended use, architecture, parameters and context are there, but limitations, languages and a knowledge cut-off aren't.); WeVisOne-VL-2B 48 (Model card 8, Intended use, limitations and the architecture are on the card.)