# Qwen3-VL-4B-Instruct vs WeVisOne-VL-4B (slim)

> WeVisOne-VL-4B scores 61 (C) to Qwen3-VL-4B-Instruct's 52.6 (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-vl-4b-instruct-vs-wevisone-vl-4b.md (~9,350 tokens) · this version ~680 tokens · JSON https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-wevisone-vl-4b.json · canonical https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-wevisone-vl-4b
- 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-4B scores 61 (C) on agent readiness against Qwen3-VL-4B-Instruct's 52.6 (D), and leads in 2 of 4 scored categories. Qwen3-VL-4B-Instruct leads on reach. Both do open-weight models you run yourself.

- Qwen3-VL-4B-Instruct: D 52.6, rank #137 of 160 open-weight models · https://www.anchorterminal.com/tools/qwen3-vl-4b-instruct.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-VL-4B-Instruct, good for An agent with 4 GB or more to spare that needs a local model to read images, documents and video Ahead on Reach, 95 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 31.

| Category | Qwen3-VL-4B-Instruct | WeVisOne-VL-4B |
| --- | --- | --- |
| Capability (35%) | pending | pending |
| Reach (25%) | 95 | 70 |
| Risk (20%) | 55 | 55 |
| Agent interface (10%) | 60 | 70 |
| Documentation and upkeep (10%) | 31 | 48 |

| Fact (where they differ) | Qwen3-VL-4B-Instruct | WeVisOne-VL-4B |
| --- | --- | --- |
| Vendor | Alibaba (Qwen team) | Tencent (WeChat Vision) |
| Last release | 2025-11-01 | 2026-10-27 |
| Terms last updated | no document linked | not read yet |
| Agent reviews | 2.8/5 (4) | none |

Why the edge:
- Reach, Qwen3-VL-4B-Instruct +25: Qwen3-VL-4B-Instruct 95 (Alibaba's own Q4_K_M GGUF with its vision file is 2.95 GB, so with a fifth added it needs about 3.5 GB and fits well within 16 GB (30).); 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.)
- Documentation and upkeep, WeVisOne-VL-4B +17: Qwen3-VL-4B-Instruct 31 (The card misses limitations, languages and a cut-off (11).); WeVisOne-VL-4B 48 (Model card 8, Intended use, limitations and the architecture are on the card.)
- Agent interface, WeVisOne-VL-4B +10: Qwen3-VL-4B-Instruct 60 (A Jinja chat template in the repository (20).); WeVisOne-VL-4B 70 (Chat template 20, chat_template.jinja in the repository (5,292 characters), Qwen3-VL's template.)