# Cola2 vs WeVisOne-VL-2B (slim)

> WeVisOne-VL-2B scores 61 (C) to Cola2's 33.9 (F) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side.

- Full: https://www.anchorterminal.com/compare/cola2-vs-wevisone-vl-2b.md (~8,850 tokens) · this version ~730 tokens · JSON https://www.anchorterminal.com/compare/cola2-vs-wevisone-vl-2b.json · canonical https://www.anchorterminal.com/compare/cola2-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
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WeVisOne-VL-2B scores 61 (C) on agent readiness against Cola2's 33.9 (F), and leads in 3 of 4 scored categories. Cola2 leads on documentation and upkeep. Both do open-weight models you run yourself.

- Cola2: F 33.9, rank #160 of 160 open-weight models · https://www.anchorterminal.com/tools/cola2.min.md
- WeVisOne-VL-2B: C 61, rank #83 of 160 open-weight models · https://www.anchorterminal.com/tools/wevisone-vl-2b.min.md
- Cola2, good for Researchers working on latent or diffusion language models who want a checkpoint to study Ahead on Documentation and upkeep, 58 against 48.
- 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 25; Risk, 55 against 40; Agent interface, 70 against 20.

| Category | Cola2 | WeVisOne-VL-2B |
| --- | --- | --- |
| Capability (35%) | pending | pending |
| Reach (25%) | 25 | 70 |
| Risk (20%) | 40 | 55 |
| Agent interface (10%) | 20 | 70 |
| Documentation and upkeep (10%) | 58 | 48 |

| Fact (where they differ) | Cola2 | WeVisOne-VL-2B |
| --- | --- | --- |
| Vendor | ByteDance (Seed) | Tencent (WeChat Vision) |
| Licence | None stated for the weights (the code is Apache 2.0) | Apache-2.0 |
| Last release | 2026-11-06 | 2026-10-28 |
| Terms last updated | no document linked | not read yet |

Why the edge:
- Agent interface, WeVisOne-VL-2B +50: Cola2 20 (Chat template 20, A chat template in tokenizer_config.json (4,116 characters), which came with the Qwen3 tokenizer.); WeVisOne-VL-2B 70 (Chat template 20, chat_template.jinja in the repository (5,292 characters), Qwen3-VL's template.)
- Reach, WeVisOne-VL-2B +45: Cola2 25 (Memory 15, the only build is the F32 safetensors set, 35.07 GB, and 35.07 GB × 1.2 = 42.1 GB, within 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 +15: Cola2 40 (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.)
- Documentation and upkeep, Cola2 +10: Cola2 58 (Model card 8, Intended use and limitations (the out-of-scope list, and the unconverged latent model) and the architecture are on the card and the GitHub model c…); WeVisOne-VL-2B 48 (Model card 8, Intended use, limitations and the architecture are on the card.)