# DeepSeek-V3.2 vs WeVisOne-VL-4B (slim)

> WeVisOne-VL-4B scores 61 (C) to DeepSeek-V3.2's 58.6 (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: 2026-11-10
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WeVisOne-VL-4B scores 61 (C) on agent readiness against DeepSeek-V3.2's 58.6 (C), and leads in every scored category. Both do open-weight models you run yourself.

- DeepSeek-V3.2: C 58.6, rank #102 of 160 open-weight models · https://www.anchorterminal.com/tools/deepseek-v3-2.min.md
- WeVisOne-VL-4B: C 61, rank #84 of 160 open-weight models · https://www.anchorterminal.com/tools/wevisone-vl-4b.min.md
- DeepSeek-V3.2, good for Teams with an eight-GPU server who want an MIT agent model with broad runtime support, or agents calling it through a hosted provider
- 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 60; Risk, 55 against 50; Agent interface, 70 against 60; Documentation and upkeep, 48 against 34.

| Category | DeepSeek-V3.2 | WeVisOne-VL-4B |
| --- | --- | --- |
| Capability (35%) | pending | pending |
| Reach (25%) | 60 | 70 |
| Risk (20%) | 50 | 55 |
| Agent interface (10%) | 60 | 70 |
| Documentation and upkeep (10%) | 34 | 48 |

| Fact (where they differ) | DeepSeek-V3.2 | WeVisOne-VL-4B |
| --- | --- | --- |
| Vendor | DeepSeek | Tencent (WeChat Vision) |
| Licence | MIT | Apache-2.0 |
| Last release | 2025-12-01 | 2026-10-27 |
| Terms last updated | no document linked | not read yet |
| Agent reviews | 2.3/5 (4) | none |

Why the edge:
- Documentation and upkeep, WeVisOne-VL-4B +14: DeepSeek-V3.2 34 (The model card earns 14 when read with DeepSeek's own V3.2 model card PDF, which adds intended use and a parameter count, leaving languages and a knowledge cut-…); WeVisOne-VL-4B 48 (Model card 8, Intended use, limitations and the architecture are on the card.)
- Reach, WeVisOne-VL-4B +10: DeepSeek-V3.2 60 (Memory earns nothing, because the only build from DeepSeek or a runtime's library is the 689.5 GB FP8 release, which needs about 827 GB with the fifth added.); 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.)
- Agent interface, WeVisOne-VL-4B +10: DeepSeek-V3.2 60 (The chat template earns 10, as the format exists only as Python code to run.); WeVisOne-VL-4B 70 (Chat template 20, chat_template.jinja in the repository (5,292 characters), Qwen3-VL's template.)
- Risk, WeVisOne-VL-4B +5: DeepSeek-V3.2 50 (File safety earns 15, safetensors only but 10 off because prompts are built by running DeepSeek's Python from the repository.); 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.)