# Nemotron 3 Nano 4B vs WeVisOne-VL-4B (slim)

> WeVisOne-VL-4B scores 61 (C) to Nemotron 3 Nano 4B's 56.8 (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 Nemotron 3 Nano 4B's 56.8 (C), and leads in 0 of 4 scored categories. Nemotron 3 Nano 4B leads on reach, risk and documentation and upkeep. Both do open-weight models you run yourself.

- Nemotron 3 Nano 4B: C 56.8, rank #110 of 160 open-weight models · https://www.anchorterminal.com/tools/nemotron-3-nano-4b.min.md
- WeVisOne-VL-4B: C 61, rank #84 of 160 open-weight models · https://www.anchorterminal.com/tools/wevisone-vl-4b.min.md
- Nemotron 3 Nano 4B, good for Agents on laptops, edge boards and small GPUs that need a cheap local tool-calling helper in English Ahead on Reach, 90 against 70; Risk, 65 against 55; Documentation and upkeep, 55 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

| Category | Nemotron 3 Nano 4B | WeVisOne-VL-4B |
| --- | --- | --- |
| Capability (35%) | pending | pending |
| Reach (25%) | 90 | 70 |
| Risk (20%) | 65 | 55 |
| Agent interface (10%) | 70 | 70 |
| Documentation and upkeep (10%) | 55 | 48 |

| Fact (where they differ) | Nemotron 3 Nano 4B | WeVisOne-VL-4B |
| --- | --- | --- |
| Vendor | NVIDIA | Tencent (WeChat Vision) |
| Licence | NVIDIA Nemotron Open Model License | Apache-2.0 |
| Last release | 2026-03-16 | 2026-10-27 |
| Terms last updated | no document linked | not read yet |
| Agent reviews | 3.3/5 (4) | none |

Why the edge:
- Reach, Nemotron 3 Nano 4B +20: Nemotron 3 Nano 4B 90 (Memory earns the full 30, since NVIDIA's own 2.84 GB GGUF needs under 4 GB with the 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.)
- Risk, Nemotron 3 Nano 4B +10: Nemotron 3 Nano 4B 65 (File safety earns 15, safetensors and GGUF only but with 10 off because the documented commands pass trust_remote_code and load a parser plugin from the reposit…); 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, Nemotron 3 Nano 4B +7: Nemotron 3 Nano 4B 55 (The model card earns 20, all seven items with limitations in the linked subcard.); WeVisOne-VL-4B 48 (Model card 8, Intended use, limitations and the architecture are on the card.)