# Qwen3-VL-32B-Instruct vs WeVisOne-VL-2B (slim)

> WeVisOne-VL-2B scores 61 (C) to Qwen3-VL-32B-Instruct's 56.4 (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-vl-32b-instruct-vs-wevisone-vl-2b.md (~9,550 tokens) · this version ~680 tokens · JSON https://www.anchorterminal.com/compare/qwen3-vl-32b-instruct-vs-wevisone-vl-2b.json · canonical https://www.anchorterminal.com/compare/qwen3-vl-32b-instruct-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-VL-32B-Instruct's 56.4 (C), and leads in 2 of 4 scored categories. Qwen3-VL-32B-Instruct leads on reach. Both do open-weight models you run yourself.

- Qwen3-VL-32B-Instruct: C 56.4, rank #115 of 160 open-weight models · https://www.anchorterminal.com/tools/qwen3-vl-32b-instruct.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-VL-32B-Instruct, good for An agent with a 32 GB GPU or more that wants the largest dense Qwen3-VL model for images, documents and video Ahead on Reach, 85 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 31.

| Category | Qwen3-VL-32B-Instruct | WeVisOne-VL-2B |
| --- | --- | --- |
| Capability (35%) | pending | pending |
| Reach (25%) | 85 | 70 |
| Risk (20%) | 55 | 55 |
| Agent interface (10%) | 60 | 70 |
| Documentation and upkeep (10%) | 31 | 48 |

| Fact (where they differ) | Qwen3-VL-32B-Instruct | WeVisOne-VL-2B |
| --- | --- | --- |
| Vendor | Alibaba (Qwen team) | Tencent (WeChat Vision) |
| Last release | 2025-11-01 | 2026-10-28 |
| Terms last updated | no document linked | not read yet |
| Agent reviews | 2.5/5 (4) | none |

Why the edge:
- Documentation and upkeep, WeVisOne-VL-2B +17: Qwen3-VL-32B-Instruct 31 (The card misses limitations, languages and a cut-off (11).); WeVisOne-VL-2B 48 (Model card 8, Intended use, limitations and the architecture are on the card.)
- Reach, Qwen3-VL-32B-Instruct +15: Qwen3-VL-32B-Instruct 85 (Alibaba's own Q4_K_M GGUF is 19.76 GB and 20.53 GB with the vision file it needs for images, so with a fifth added it needs about 25 GB and the 32 GB band (20).); 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-VL-32B-Instruct 60 (A Jinja chat template in the repository (20).); WeVisOne-VL-2B 70 (Chat template 20, chat_template.jinja in the repository (5,292 characters), Qwen3-VL's template.)