# Qwen3.5-122B-A10B vs WeVisOne-VL-2B (slim)

> Qwen3.5-122B-A10B scores 63.1 (B) to WeVisOne-VL-2B's 61 (C) 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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Qwen3.5-122B-A10B scores 63.1 (B) on agent readiness against WeVisOne-VL-2B's 61 (C), and leads in 1 of 4 scored categories. WeVisOne-VL-2B leads on documentation and upkeep. Both do open-weight models you run yourself.

- Qwen3.5-122B-A10B: B 63.1, rank #25 of 160 open-weight models · https://www.anchorterminal.com/tools/qwen3-5-122b-a10b.min.md
- WeVisOne-VL-2B: C 61, rank #83 of 160 open-weight models · https://www.anchorterminal.com/tools/wevisone-vl-2b.min.md
- Qwen3.5-122B-A10B, good for Agents with a 128 GB unified-memory machine or two large GPUs that want a capable vision-language model with a plain licence. 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 Documentation and upkeep, 48 against 34.

| Category | Qwen3.5-122B-A10B | WeVisOne-VL-2B |
| --- | --- | --- |
| Capability (35%) | pending | pending |
| Reach (25%) | 80 | 70 |
| Risk (20%) | 55 | 55 |
| Agent interface (10%) | 70 | 70 |
| Documentation and upkeep (10%) | 34 | 48 |

| Fact (where they differ) | Qwen3.5-122B-A10B | WeVisOne-VL-2B |
| --- | --- | --- |
| Vendor | Alibaba (Qwen team) | Tencent (WeChat Vision) |
| Last release | 2026-02-24 | 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 +14: Qwen3.5-122B-A10B 34 (The card covers five of the seven items and misses limitations and a cut-off (14).); WeVisOne-VL-2B 48 (Model card 8, Intended use, limitations and the architecture are on the card.)
- Reach, Qwen3.5-122B-A10B +10: Qwen3.5-122B-A10B 80 (Memory earns 10 of 30, since Alibaba's GPTQ 4-bit build is 78.9 GB, about 95 GB with a fifth added, which fits our reference machine but no smaller band.); 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.)