# MiniCPM-o 4.5 vs WeVisOne-VL-4B (slim)

> MiniCPM-o 4.5 and WeVisOne-VL-4B score within a point of each other for open-weight models you run yourself, 61 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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MiniCPM-o 4.5 and WeVisOne-VL-4B score within a point of each other on agent readiness, 61 (C) and 61 (C). WeVisOne-VL-4B leads on risk and agent interface. Both do open-weight models you run yourself.

- MiniCPM-o 4.5: C 61, rank #48 of 160 open-weight models · https://www.anchorterminal.com/tools/minicpm-o-4-5.min.md
- WeVisOne-VL-4B: C 61, rank #84 of 160 open-weight models · https://www.anchorterminal.com/tools/wevisone-vl-4b.min.md
- MiniCPM-o 4.5, good for Agents that need a local model to see, hear and speak on a laptop or a single small GPU, with a plain licence. Ahead on Reach, 90 against 70; Documentation and upkeep, 66 against 48.
- 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 40; Agent interface, 70 against 35.

| Category | MiniCPM-o 4.5 | WeVisOne-VL-4B |
| --- | --- | --- |
| Capability (35%) | pending | pending |
| Reach (25%) | 90 | 70 |
| Risk (20%) | 40 | 55 |
| Agent interface (10%) | 35 | 70 |
| Documentation and upkeep (10%) | 66 | 48 |

| Fact (where they differ) | MiniCPM-o 4.5 | WeVisOne-VL-4B |
| --- | --- | --- |
| Vendor | OpenBMB | Tencent (WeChat Vision) |
| Licence | Apache-2.0; built from Qwen3-8B, SigLIP2, Whisper-medium and CosyVoice2, all Apache 2.0 on their Hub cards | Apache-2.0 |
| Last release | 2026-10-24 | 2026-10-27 |
| Terms last updated | no document linked | not read yet |
| Agent reviews | 2.8/5 (4) | none |

Why the edge:
- Agent interface, WeVisOne-VL-4B +35: MiniCPM-o 4.5 35 (Chat template 20, a Jinja template in the tokenizer config.); WeVisOne-VL-4B 70 (Chat template 20, chat_template.jinja in the repository (5,292 characters), Qwen3-VL's template.)
- Reach, MiniCPM-o 4.5 +20: MiniCPM-o 4.5 90 (Memory 30, OpenBMB's 4-bit GGUF with its vision file is 5.9 GB, about 7 GB with a 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.)
- Documentation and upkeep, MiniCPM-o 4.5 +18: MiniCPM-o 4.5 66 (Model card 11 of 20, limitations, context length and a knowledge cut-off are missing.); WeVisOne-VL-4B 48 (Model card 8, Intended use, limitations and the architecture are on the card.)
- Risk, WeVisOne-VL-4B +15: MiniCPM-o 4.5 40 (File safety 5 of 25, two pickle files sit in the speech stage and the documented load runs repository code.); 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.)