# DeepSeek-V4-Flash-0731 vs WeVisOne-VL-4B (slim)

> DeepSeek-V4-Flash-0731 and WeVisOne-VL-4B score within a point of each other for open-weight models you run yourself, 61.9 and 61 out of 100. 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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DeepSeek-V4-Flash-0731 and WeVisOne-VL-4B score within a point of each other on agent readiness, 61.9 (C) and 61 (C). WeVisOne-VL-4B leads on risk and documentation and upkeep. Both do open-weight models you run yourself.

- DeepSeek-V4-Flash-0731: C 61.9, rank #33 of 160 open-weight models · https://www.anchorterminal.com/tools/deepseek-v4-flash-0731.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-V4-Flash-0731, good for Agents or teams with a multi-GPU server, or a provider account, who want DeepSeek's V4 agent model under MIT with a 1M context Ahead on Agent interface, 75 against 70.
- 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 Risk, 55 against 50; Documentation and upkeep, 48 against 38.

| Category | DeepSeek-V4-Flash-0731 | WeVisOne-VL-4B |
| --- | --- | --- |
| Capability (35%) | pending | pending |
| Reach (25%) | 70 | 70 |
| Risk (20%) | 50 | 55 |
| Agent interface (10%) | 75 | 70 |
| Documentation and upkeep (10%) | 38 | 48 |

| Fact (where they differ) | DeepSeek-V4-Flash-0731 | WeVisOne-VL-4B |
| --- | --- | --- |
| Vendor | DeepSeek | Tencent (WeChat Vision) |
| Licence | MIT | Apache-2.0 |
| Last release | 2026-07-31 | 2026-10-27 |
| Terms last updated | no document linked | not read yet |
| Agent reviews | 2.5/5 (4) | none |

Why the edge:
- Documentation and upkeep, WeVisOne-VL-4B +10: DeepSeek-V4-Flash-0731 38 (Model card 8, the card and the DSpark card it names give architecture, parameters and context, with intended use, limitations, languages and cut-off missing.); WeVisOne-VL-4B 48 (Model card 8, Intended use, limitations and the architecture are on the card.)
- Risk, WeVisOne-VL-4B +5: DeepSeek-V4-Flash-0731 50 (File safety 15, safetensors only, less 10 because prompts are built by running DeepSeek's encoding_dsv4.py from the repository and the card's serving commands p…); 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, DeepSeek-V4-Flash-0731 +5: DeepSeek-V4-Flash-0731 75 (Chat template 10, there is no Jinja template and the format is given as Python to run.); WeVisOne-VL-4B 70 (Chat template 20, chat_template.jinja in the repository (5,292 characters), Qwen3-VL's template.)