# Qwen3.5-0.8B vs WeVisOne-VL-4B (slim)

> WeVisOne-VL-4B scores 61 (C) to Qwen3.5-0.8B's 46.7 (D) for open-weight models you run yourself. 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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WeVisOne-VL-4B scores 61 (C) on agent readiness against Qwen3.5-0.8B's 46.7 (D), and leads in 2 of 4 scored categories. Qwen3.5-0.8B leads on reach. Both do open-weight models you run yourself.

- Qwen3.5-0.8B: D 46.7, rank #156 of 160 open-weight models · https://www.anchorterminal.com/tools/qwen3-5-0-8b.min.md
- WeVisOne-VL-4B: C 61, rank #84 of 160 open-weight models · https://www.anchorterminal.com/tools/wevisone-vl-4b.min.md
- Qwen3.5-0.8B, good for Builders who want a near-free local helper or fine-tuning base for narrow tasks on a phone-class device, laptop or CPU Ahead on Reach, 80 against 70.
- WeVisOne-VL-4B, 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 | Qwen3.5-0.8B | WeVisOne-VL-4B |
| --- | --- | --- |
| 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) | Qwen3.5-0.8B | WeVisOne-VL-4B |
| --- | --- | --- |
| Vendor | Alibaba (Qwen team) | Tencent (WeChat Vision) |
| Last release | 2026-03-02 | 2026-10-27 |
| Terms last updated | no document linked | not read yet |
| Agent reviews | 2.8/5 (4) | none |

Why the edge:
- Documentation and upkeep, WeVisOne-VL-4B +11: Qwen3.5-0.8B 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-4B 48 (Model card 8, Intended use, limitations and the architecture are on the card.)
- Reach, Qwen3.5-0.8B +10: Qwen3.5-0.8B 80 (Memory earns the full 30, since LM Studio's Q4_K_M build with its projector is 0.73 GB (0.88 GB with the fifth added) and Ollama's Q8_0 build was 1.0 GB, both f…); WeVisOne-VL-4B 70 (Memory 30, the only build from Tencent is the BF16 safetensors file with its decoder, 9.7 GB, and 9.7 GB × 1.2 = 11.6 GB, within 16 GB.)
- Agent interface, WeVisOne-VL-4B +10: Qwen3.5-0.8B 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-4B 70 (Chat template 20, chat_template.jinja in the repository (5,292 characters), Qwen3-VL's template.)