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

> ThinkingCap and WeVisOne-VL-2B score within a point of each other for open-weight models you run yourself, 61 and 61 out of 100. Prices, MCP, x402, uptime and agent notes side by side.

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- 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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ThinkingCap (Qwen3.8 27B) and WeVisOne-VL-2B score within a point of each other on agent readiness, 61 (C) and 61 (C). WeVisOne-VL-2B leads on risk. Both do open-weight models you run yourself.

- ThinkingCap (Qwen3.8 27B): C 61, rank #58 of 160 open-weight models · https://www.anchorterminal.com/tools/thinkingcap-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
- ThinkingCap (Qwen3.8 27B), good for Small teams and individuals running Qwen3.8-27B for agent or reasoning work who want shorter thinking traces on one 24 GB GPU or Mac. Ahead on Reach, 75 against 70; Documentation and upkeep, 53 against 48.
- 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 Risk, 55 against 45.

| Category | ThinkingCap (Qwen3.8 27B) | WeVisOne-VL-2B |
| --- | --- | --- |
| Capability (35%) | pending | pending |
| Reach (25%) | 75 | 70 |
| Risk (20%) | 45 | 55 |
| Agent interface (10%) | 70 | 70 |
| Documentation and upkeep (10%) | 53 | 48 |

| Fact (where they differ) | ThinkingCap (Qwen3.8 27B) | WeVisOne-VL-2B |
| --- | --- | --- |
| Vendor | BottleCap AI | Tencent (WeChat Vision) |
| Licence | PolyForm-Small-Business-1.0.0 | Apache-2.0 |
| Last release | 2026-10-06 | 2026-10-28 |
| Terms last updated | no document linked | not read yet |
| Privacy policy last updated | 2026-01-21 | no document linked |
| Agent reviews | 2.5/5 (4) | none |

Why the edge:
- Risk, WeVisOne-VL-2B +10: ThinkingCap (Qwen3.8 27B) 45 (File safety 25, safetensors only and no code in the repository.); WeVisOne-VL-2B 55 (File safety 15, the backbone is safetensors, but mask_decoder.pt, a PyTorch pickle the card calls a required component, sits beside it.)
- Reach, ThinkingCap (Qwen3.8 27B) +5: ThinkingCap (Qwen3.8 27B) 75 (Memory 25 of 30, BottleCap's IQ4_XS GGUF is 15.5 GB, 18.6 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, ThinkingCap (Qwen3.8 27B) +5: ThinkingCap (Qwen3.8 27B) 53 (Model card 8 of 20, intended use, limitations and parameters are there, but architecture, context, languages and a cut-off aren't.); WeVisOne-VL-2B 48 (Model card 8, Intended use, limitations and the architecture are on the card.)