# MiniMax-M2.5 vs WeVisOne-VL-4B (slim)

> WeVisOne-VL-4B scores 61 (C) to MiniMax-M2.5's 59 (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-M2.5's 59 (C), and leads in 3 of 4 scored categories. MiniMax-M2.5 leads on agent interface. Both do open-weight models you run yourself.

- MiniMax-M2.5: C 59, rank #99 of 160 open-weight models · https://www.anchorterminal.com/tools/minimax-m2-5.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-M2.5, good for Teams with a 4 to 8 GPU server who want an open agent and coding model with parallel tool calls and can live with its use policy 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 Reach, 70 against 65; Risk, 55 against 30; Documentation and upkeep, 48 against 37.

| Category | MiniMax-M2.5 | WeVisOne-VL-4B |
| --- | --- | --- |
| Capability (35%) | pending | pending |
| Reach (25%) | 65 | 70 |
| Risk (20%) | 30 | 55 |
| Agent interface (10%) | 75 | 70 |
| Documentation and upkeep (10%) | 37 | 48 |

| Fact (where they differ) | MiniMax-M2.5 | WeVisOne-VL-4B |
| --- | --- | --- |
| Vendor | MiniMax | Tencent (WeChat Vision) |
| Licence | MiniMax Model License (model) and modified MIT (code) | Apache-2.0 |
| Last release | 2026-02-12 | 2026-10-27 |
| Terms last updated | no document linked | not read yet |
| Agent reviews | 1.8/5 (4) | none |

Why the edge:
- Risk, WeVisOne-VL-4B +25: MiniMax-M2.5 30 (File safety earns 15, safetensors only with no pickle files, less 10 because every documented loading route passes trust_remote_code for the repository's Python…); 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.)
- Documentation and upkeep, WeVisOne-VL-4B +11: MiniMax-M2.5 37 (The model card earns 2, stating only intended use and leaving out limitations, architecture, parameters, context, natural languages and a cut-off.); WeVisOne-VL-4B 48 (Model card 8, Intended use, limitations and the architecture are on the card.)
- Reach, WeVisOne-VL-4B +5: MiniMax-M2.5 65 (Memory earns 5. MiniMax only publishes its 230 GB FP8 release, and the smallest build of 4-bit or better from a runtime's own library is LM Studio's MLX 4-bit a…); 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, MiniMax-M2.5 +5: MiniMax-M2.5 75 (The chat template earns 20 for 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.)