# Inkling-Small vs WeVisOne-VL-2B (slim)

> Inkling-Small scores 67 (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.

- Full: https://www.anchorterminal.com/compare/inkling-small-vs-wevisone-vl-2b.md (~9,500 tokens) · this version ~730 tokens · JSON https://www.anchorterminal.com/compare/inkling-small-vs-wevisone-vl-2b.json · canonical https://www.anchorterminal.com/compare/inkling-small-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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Inkling-Small scores 67 (B) on agent readiness against WeVisOne-VL-2B's 61 (C), and leads in 3 of 4 scored categories. WeVisOne-VL-2B leads on reach. Both do open-weight models you run yourself.

- Inkling-Small: B 67, rank #13 of 160 open-weight models · https://www.anchorterminal.com/tools/inkling-small.min.md
- WeVisOne-VL-2B: C 61, rank #83 of 160 open-weight models · https://www.anchorterminal.com/tools/wevisone-vl-2b.min.md
- Inkling-Small, good for Teams with 256 GB or more of datacentre GPU memory who want an open multimodal agent model with audio input and adjustable thinking. Ahead on Risk, 70 against 55; Agent interface, 85 against 70; Documentation and upkeep, 87 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 55.

| Category | Inkling-Small | WeVisOne-VL-2B |
| --- | --- | --- |
| Capability (35%) | pending | pending |
| Reach (25%) | 55 | 70 |
| Risk (20%) | 70 | 55 |
| Agent interface (10%) | 85 | 70 |
| Documentation and upkeep (10%) | 87 | 48 |

| Fact (where they differ) | Inkling-Small | WeVisOne-VL-2B |
| --- | --- | --- |
| Vendor | Thinking Machines Lab | Tencent (WeChat Vision) |
| Last release | 2026-07-31 | 2026-10-28 |
| Terms last updated | no document linked | not read yet |
| Agent reviews | 3.5/5 (4) | none |

Why the edge:
- Documentation and upkeep, Inkling-Small +39: Inkling-Small 87 (Model card 17 of 20, everything but a dated knowledge cut-off, counting Thinking Machines' own model card page.); WeVisOne-VL-2B 48 (Model card 8, Intended use, limitations and the architecture are on the card.)
- Reach, WeVisOne-VL-2B +15: Inkling-Small 55 (Memory 5 of 30, the smallest 4-bit build is Thinking Machines' NVFP4 at 170.8 GB, about 205 GB with a fifth added, so it needs 256 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, Inkling-Small +15: Inkling-Small 70 (File safety 25, safetensors only and no repository code to run.); 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.)
- Agent interface, Inkling-Small +15: Inkling-Small 85 (Chat template 20, a Jinja file in the repository.); WeVisOne-VL-2B 70 (Chat template 20, chat_template.jinja in the repository (5,292 characters), Qwen3-VL's template.)