# Inkling 2 Small vs WeVisOne-VL-4B (slim)

> WeVisOne-VL-4B scores 61 (C) to Inkling 2 Small's 58.8 (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: 2026-11-12
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WeVisOne-VL-4B scores 61 (C) on agent readiness against Inkling 2 Small's 58.8 (C), and leads in 1 of 4 scored categories. Inkling 2 Small leads on risk, agent interface and documentation and upkeep. Both do open-weight models you run yourself.

- Inkling 2 Small: C 58.8, rank #102 of 161 open-weight models · https://www.anchorterminal.com/tools/inkling-2-small.min.md
- WeVisOne-VL-4B: C 61, rank #84 of 161 open-weight models · https://www.anchorterminal.com/tools/wevisone-vl-4b.min.md
- Inkling 2 Small, good for Teams with 512 GB or more of datacentre GPU memory who want Thinking Machines' newest open multimodal agent model and can run a runtime's pre-release image. Ahead on Risk, 65 against 55; Agent interface, 85 against 70; Documentation and upkeep, 67 against 48.
- WeVisOne-VL-4B, 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.

| Category | Inkling 2 Small | WeVisOne-VL-4B |
| --- | --- | --- |
| Capability (35%) | pending | pending |
| Reach (25%) | 40 | 70 |
| Risk (20%) | 65 | 55 |
| Agent interface (10%) | 85 | 70 |
| Documentation and upkeep (10%) | 67 | 48 |

| Fact (where they differ) | Inkling 2 Small | WeVisOne-VL-4B |
| --- | --- | --- |
| Vendor | Thinking Machines Lab | Tencent (WeChat Vision) |
| Last release | 2026-11-12 | 2026-10-27 |
| Terms last updated | no document linked | not read yet |

Why the edge:
- Reach, WeVisOne-VL-4B +30: Inkling 2 Small 40 (Memory 0 of 30, the smallest build is Thinking Machines' NVFP4 checkpoint at 393.5 GB, about 472 GB with a fifth added, above the 256 GB line.); WeVisOne-VL-4B 70 (Memory 30, the only build from Tencent is the BF16 safetensors file with its decoder, 9.7 GB, and 9.7 GB × 1.2 = 11.6 GB, within 16 GB.)
- Documentation and upkeep, Inkling 2 Small +19: Inkling 2 Small 67 (Model card 17 of 20, everything but a dated knowledge cut-off.); WeVisOne-VL-4B 48 (Model card 8, Intended use, limitations and the architecture are on the card.)
- Agent interface, Inkling 2 Small +15: Inkling 2 Small 85 (Chat template 20, a Jinja file in the repository.); WeVisOne-VL-4B 70 (Chat template 20, chat_template.jinja in the repository (5,292 characters), Qwen3-VL's template.)
- Risk, Inkling 2 Small +10: Inkling 2 Small 65 (File safety 25, safetensors only and no repository code to run.); WeVisOne-VL-4B 55 (File safety 15, the backbone is safetensors, but mask_decoder.pt, a PyTorch pickle the card calls a required component, sits beside it.)