# SenseNova-Loop-9B-Preview vs WeVisOne-VL-2B (slim)

> WeVisOne-VL-2B scores 61 (C) to SenseNova-Loop-9B-Preview's 46.8 (D) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side.

- Full: https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-wevisone-vl-2b.md (~9,700 tokens) · this version ~680 tokens · JSON https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-wevisone-vl-2b.json · canonical https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-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 SenseNova-Loop-9B-Preview's 46.8 (D), and leads in every scored category. Both do open-weight models you run yourself.

- SenseNova-Loop-9B-Preview: D 46.8, rank #155 of 160 open-weight models · https://www.anchorterminal.com/tools/sensenova-loop-9b-preview.min.md
- WeVisOne-VL-2B: C 61, rank #83 of 160 open-weight models · https://www.anchorterminal.com/tools/wevisone-vl-2b.min.md
- SenseNova-Loop-9B-Preview, good for Researchers who want to study or fine-tune a looped transformer, more than agents that need a model to call today
- 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 Reach, 70 against 40; Risk, 55 against 50; Agent interface, 70 against 60.

| Category | SenseNova-Loop-9B-Preview | WeVisOne-VL-2B |
| --- | --- | --- |
| Capability (35%) | pending | pending |
| Reach (25%) | 40 | 70 |
| Risk (20%) | 50 | 55 |
| Agent interface (10%) | 60 | 70 |
| Documentation and upkeep (10%) | 44 | 48 |

| Fact (where they differ) | SenseNova-Loop-9B-Preview | WeVisOne-VL-2B |
| --- | --- | --- |
| Vendor | SenseTime (SenseNova) | Tencent (WeChat Vision) |
| Last release | 2026-11-02 | 2026-10-28 |

Why the edge:
- Reach, WeVisOne-VL-2B +30: SenseNova-Loop-9B-Preview 40 (Memory 15, the only build is the safetensors set, 38.14 GB with its MTP head, since the Hub counts 9.41B of its parameters in F32. Runtimes 0, none of the six r…); 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: SenseNova-Loop-9B-Preview 60 (Chat template 20, chat_template.jinja in the repository (7,756 characters).); WeVisOne-VL-2B 70 (Chat template 20, chat_template.jinja in the repository (5,292 characters), Qwen3-VL's template.)
- Risk, WeVisOne-VL-2B +5: SenseNova-Loop-9B-Preview 50 (File safety 15, safetensors only, no .bin, .pt or .pth files (25).); WeVisOne-VL-2B 55 (File safety 15, the backbone is safetensors, but mask_decoder.pt, a PyTorch pickle the card calls a required component, sits beside it.)