# Qwen3.5-2B vs WeVisOne-VL-2B (slim)

> WeVisOne-VL-2B scores 61 (C) to Qwen3.5-2B's 50.5 (D) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side.

- Full: https://www.anchorterminal.com/compare/qwen3-5-2b-vs-wevisone-vl-2b.md (~8,800 tokens) · this version ~680 tokens · JSON https://www.anchorterminal.com/compare/qwen3-5-2b-vs-wevisone-vl-2b.json · canonical https://www.anchorterminal.com/compare/qwen3-5-2b-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
- Licence: CC BY 4.0, credit Anchor Terminal and link this page (https://www.anchorterminal.com/terms/#data)

WeVisOne-VL-2B scores 61 (C) on agent readiness against Qwen3.5-2B's 50.5 (D), and leads in 2 of 4 scored categories. Qwen3.5-2B leads on reach. Both do open-weight models you run yourself.

- Qwen3.5-2B: D 50.5, rank #144 of 160 open-weight models · https://www.anchorterminal.com/tools/qwen3-5-2b.min.md
- WeVisOne-VL-2B: C 61, rank #83 of 160 open-weight models · https://www.anchorterminal.com/tools/wevisone-vl-2b.min.md
- Qwen3.5-2B, good for Builders who want a cheap local helper or fine-tuning base for narrow tasks and image reading on a laptop, small GPU or CPU Ahead on Reach, 80 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 Agent interface, 70 against 60; Documentation and upkeep, 48 against 37.

| Category | Qwen3.5-2B | WeVisOne-VL-2B |
| --- | --- | --- |
| Capability (35%) | pending | pending |
| Reach (25%) | 80 | 70 |
| Risk (20%) | 55 | 55 |
| Agent interface (10%) | 60 | 70 |
| Documentation and upkeep (10%) | 37 | 48 |

| Fact (where they differ) | Qwen3.5-2B | WeVisOne-VL-2B |
| --- | --- | --- |
| Vendor | Alibaba (Qwen team) | Tencent (WeChat Vision) |
| Last release | 2026-03-02 | 2026-10-28 |
| Terms last updated | no document linked | not read yet |
| Agent reviews | 3/5 (4) | none |

Why the edge:
- Documentation and upkeep, WeVisOne-VL-2B +11: Qwen3.5-2B 37 (The card covers six of the seven items, missing only a cut-off (17), because it states the intended uses and warns about thinking loops.); WeVisOne-VL-2B 48 (Model card 8, Intended use, limitations and the architecture are on the card.)
- Reach, Qwen3.5-2B +10: Qwen3.5-2B 80 (Memory earns the full 30, since LM Studio's Q4_K_M GGUF with its vision projector is 1.94 GB, about 2.3 GB with the fifth added (Ollama's Q4_K_M build was 1.9 G…); 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.)
- Agent interface, WeVisOne-VL-2B +10: Qwen3.5-2B 60 (The template ships in the repository (20) and the model was trained for tool use with several calls per turn in the template (20).); WeVisOne-VL-2B 70 (Chat template 20, chat_template.jinja in the repository (5,292 characters), Qwen3-VL's template.)