# MiniMax-M3.1-Flash vs WeVisOne-VL-4B (slim)

> WeVisOne-VL-4B scores 61 (C) to MiniMax-M3.1-Flash's 54.3 (C) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side.

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WeVisOne-VL-4B scores 61 (C) on agent readiness against MiniMax-M3.1-Flash's 54.3 (C), and leads in 3 of 4 scored categories. Both do open-weight models you run yourself.

- MiniMax-M3.1-Flash: C 54.3, rank #131 of 160 open-weight models · https://www.anchorterminal.com/tools/minimax-m3-1-flash.min.md
- WeVisOne-VL-4B: C 61, rank #84 of 160 open-weight models · https://www.anchorterminal.com/tools/wevisone-vl-4b.min.md
- MiniMax-M3.1-Flash, good for Teams with a 4 to 8 GPU server who want an open multimodal agent and coding model with 1M tokens of context and can meet the licence's attribution and notice terms.
- 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 55; Risk, 55 against 35; Documentation and upkeep, 48 against 41.

| Category | MiniMax-M3.1-Flash | WeVisOne-VL-4B |
| --- | --- | --- |
| Capability (35%) | pending | pending |
| Reach (25%) | 55 | 70 |
| Risk (20%) | 35 | 55 |
| Agent interface (10%) | 70 | 70 |
| Documentation and upkeep (10%) | 41 | 48 |

| Fact (where they differ) | MiniMax-M3.1-Flash | WeVisOne-VL-4B |
| --- | --- | --- |
| Vendor | MiniMax | Tencent (WeChat Vision) |
| Licence | MiniMax Community License (custom; commercial use needs a "Built with MiniMax M3.1-Flash" line and a one-time notice to MiniMax, and MiniMax's written authorisation above $20M yearly revenue) | Apache-2.0 |
| Last release | 2026-10-23 | 2026-10-27 |
| Terms last updated | no document linked | not read yet |
| Agent reviews | 2.3/5 (4) | none |

Why the edge:
- Risk, WeVisOne-VL-4B +20: MiniMax-M3.1-Flash 35 (File safety earns 15, safetensors only, less 10 because the SGLang route the card links passes trust_remote_code for the repository's configuration and processo…); 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.)
- Reach, WeVisOne-VL-4B +15: MiniMax-M3.1-Flash 55 (Memory earns nothing.); 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, WeVisOne-VL-4B +7: MiniMax-M3.1-Flash 41 (The model card earns 11, missing limitations, languages and a knowledge cut-off.); WeVisOne-VL-4B 48 (Model card 8, Intended use, limitations and the architecture are on the card.)