# Hy3 vs WeVisOne-VL-4B (slim)

> Hy3 scores 62.6 (B) to WeVisOne-VL-4B'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/hy3-vs-wevisone-vl-4b.md (~8,600 tokens) · this version ~630 tokens · JSON https://www.anchorterminal.com/compare/hy3-vs-wevisone-vl-4b.json · canonical https://www.anchorterminal.com/compare/hy3-vs-wevisone-vl-4b
- 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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Hy3 scores 62.6 (B) on agent readiness against WeVisOne-VL-4B's 61 (C), and leads in 3 of 4 scored categories. Both do open-weight models you run yourself.

- Hy3: B 62.6, rank #28 of 160 open-weight models · https://www.anchorterminal.com/tools/hy3.min.md
- WeVisOne-VL-4B: C 61, rank #84 of 160 open-weight models · https://www.anchorterminal.com/tools/wevisone-vl-4b.min.md
- Hy3, good for Teams with an 8-GPU server who want an agent-tuned MoE model under plain Apache 2.0 Ahead on Risk, 60 against 55; Agent interface, 75 against 70; Documentation and upkeep, 56 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

| Category | Hy3 | WeVisOne-VL-4B |
| --- | --- | --- |
| Capability (35%) | pending | pending |
| Reach (25%) | 70 | 70 |
| Risk (20%) | 60 | 55 |
| Agent interface (10%) | 75 | 70 |
| Documentation and upkeep (10%) | 56 | 48 |

| Fact (where they differ) | Hy3 | WeVisOne-VL-4B |
| --- | --- | --- |
| Vendor | Tencent (Hy team) | Tencent (WeChat Vision) |
| Last release | 2026-07-20 | 2026-10-27 |
| Terms last updated | no document linked | not read yet |
| Agent reviews | 2.8/5 (4) | none |

Why the edge:
- Documentation and upkeep, Hy3 +8: Hy3 56 (The model card earns 11, covering intended use, architecture, parameters and context but not limitations, languages or a knowledge cut-off.); WeVisOne-VL-4B 48 (Model card 8, Intended use, limitations and the architecture are on the card.)
- Risk, Hy3 +5: Hy3 60 (File safety earns the full 25, safetensors only with no pickle files, and the documented route runs vLLM or SGLang from their own code with a Jinja template, so…); 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.)
- Agent interface, Hy3 +5: Hy3 75 (The chat template earns 20, a chat_template.jinja in the repository.); WeVisOne-VL-4B 70 (Chat template 20, chat_template.jinja in the repository (5,292 characters), Qwen3-VL's template.)