# LBS-27B vs WeVisOne-VL-2B (slim)

> WeVisOne-VL-2B scores 61 (C) to LBS-27B's 55.8 (C) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side.

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WeVisOne-VL-2B scores 61 (C) on agent readiness against LBS-27B's 55.8 (C), and leads in 3 of 4 scored categories. Both do open-weight models you run yourself.

- LBS-27B: C 55.8, rank #118 of 160 open-weight models · https://www.anchorterminal.com/tools/lbs-27b.min.md
- WeVisOne-VL-2B: C 61, rank #83 of 160 open-weight models · https://www.anchorterminal.com/tools/wevisone-vl-2b.min.md
- LBS-27B, good for Agents with about 96 GB of GPU memory that need to find click targets on dense professional screenshots, or read fine detail in large images, and can run a small crop loop and wait for independent results.
- 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 60; Risk, 55 against 50; Documentation and upkeep, 48 against 43.

| Category | LBS-27B | WeVisOne-VL-2B |
| --- | --- | --- |
| Capability (35%) | pending | pending |
| Reach (25%) | 60 | 70 |
| Risk (20%) | 50 | 55 |
| Agent interface (10%) | 70 | 70 |
| Documentation and upkeep (10%) | 43 | 48 |

| Fact (where they differ) | LBS-27B | WeVisOne-VL-2B |
| --- | --- | --- |
| Vendor | inclusionAI (Ant Group) | Tencent (WeChat Vision) |
| Licence | Apache-2.0 (the card's metadata and licence section; the LICENSE file sits in the GitHub repository inclusionAI/UI-Venus on its LBS branch, not in the Hugging Face repository) | Apache-2.0 |
| Last release | 2026-10-26 | 2026-10-28 |
| Terms last updated | no document linked | not read yet |

Why the edge:
- Reach, WeVisOne-VL-2B +10: LBS-27B 60 (Memory earns 10, since the only build is 54.73 GB of BF16, about 66 GB with a fifth added, which fits the reference machine but not 64 GB.); 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.)
- Risk, WeVisOne-VL-2B +5: LBS-27B 50 (File safety earns 15, safetensors only but with the documented route running the publisher's scripts to serve it and to write its system prompt.); 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.)
- Documentation and upkeep, WeVisOne-VL-2B +5: LBS-27B 43 (The model card earns 8, with intended use, one limitation and parameters, and no architecture, context, languages or cut-off.); WeVisOne-VL-2B 48 (Model card 8, Intended use, limitations and the architecture are on the card.)