# DeepSeek V4.1 Flash vs WeVisOne-VL-2B (slim)

> DeepSeek V4.1 Flash and WeVisOne-VL-2B score within a point of each other for open-weight models you run yourself, 61.5 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 V4.1 Flash and WeVisOne-VL-2B score within a point of each other on agent readiness, 61.5 (C) and 61 (C). WeVisOne-VL-2B leads on reach and risk. Both do open-weight models you run yourself.

- DeepSeek V4.1 Flash: C 61.5, rank #35 of 160 open-weight models · https://www.anchorterminal.com/tools/deepseek-v4-1-flash.min.md
- WeVisOne-VL-2B: C 61, rank #83 of 160 open-weight models · https://www.anchorterminal.com/tools/wevisone-vl-2b.min.md
- DeepSeek V4.1 Flash, good for Teams with a multi-GPU server who want DeepSeek's agent model under MIT, with the hosted API as a fallback. Ahead on Agent interface, 75 against 70.
- 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 60; Risk, 55 against 50.

| Category | DeepSeek V4.1 Flash | WeVisOne-VL-2B |
| --- | --- | --- |
| Capability (35%) | pending | pending |
| Reach (25%) | 60 | 70 |
| Risk (20%) | 50 | 55 |
| Agent interface (10%) | 75 | 70 |
| Documentation and upkeep (10%) | 48 | 48 |

| Fact (where they differ) | DeepSeek V4.1 Flash | WeVisOne-VL-2B |
| --- | --- | --- |
| Vendor | DeepSeek | Tencent (WeChat Vision) |
| Licence | MIT | Apache-2.0 |
| Last release | 2026-10-17 | 2026-10-28 |
| Terms last updated | no document linked | not read yet |
| Agent reviews | 2.8/5 (4) | none |

Why the edge:
- Reach, WeVisOne-VL-2B +10: DeepSeek V4.1 Flash 60 (Memory scores nothing, because the only build is DeepSeek's own at 510.3 GB, about 612 GB with the fifth added, and nobody's runtime library holds a smaller one…); 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, WeVisOne-VL-2B +5: DeepSeek V4.1 Flash 50 (The weights are safetensors only, but 10 comes off because the card's way to build prompts is running encoding.py from the repository, since there's no Jinja te…); 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, DeepSeek V4.1 Flash +5: DeepSeek V4.1 Flash 75 (There's no Jinja template in the 17 October repository, only code to run, so the template line gets 10. DeepSeek documents the DSML tool format both ways and sh…); WeVisOne-VL-2B 70 (Chat template 20, chat_template.jinja in the repository (5,292 characters), Qwen3-VL's template.)