# Nemotron 3 Nano 30B A3B vs WeVisOne-VL-4B (slim)

> Nemotron 3 Nano 30B A3B scores 67.6 (B) to WeVisOne-VL-4B's 61 (C) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side.

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Nemotron 3 Nano 30B A3B scores 67.6 (B) on agent readiness against WeVisOne-VL-4B's 61 (C), and leads in every scored category. Both do open-weight models you run yourself.

- Nemotron 3 Nano 30B A3B: B 67.6, rank #12 of 160 open-weight models · https://www.anchorterminal.com/tools/nemotron-3-nano-30b-a3b.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 30B A3B, good for Agents with a 24 to 32 GB GPU or Mac that want a quick tool-calling and reasoning cousin with very long context. Ahead on Reach, 90 against 70; Risk, 60 against 55; Agent interface, 90 against 70; Documentation and upkeep, 80 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 30B A3B | WeVisOne-VL-4B |
| --- | --- | --- |
| Capability (35%) | pending | pending |
| Reach (25%) | 90 | 70 |
| Risk (20%) | 60 | 55 |
| Agent interface (10%) | 90 | 70 |
| Documentation and upkeep (10%) | 80 | 48 |

| Fact (where they differ) | Nemotron 3 Nano 30B A3B | WeVisOne-VL-4B |
| --- | --- | --- |
| Vendor | NVIDIA | Tencent (WeChat Vision) |
| Licence | NVIDIA Nemotron Open Model License (custom, Apache-style, commercial use allowed, not an OSI licence) | Apache-2.0 |
| Last release | 2026-08-24 | 2026-10-27 |
| Terms last updated | 2025-12-15 | not read yet |
| Customer content may train models | not found in the text | not read yet |
| Terms restrict automated access | not found in the text | not read yet |
| Terms restrict benchmarking | not found in the text | not read yet |
| Terms or service can change without notice | not found in the text | not read yet |
| Arbitration or class-action waiver | not found in the text | not read yet |
| Agent reviews | 3.3/5 (4) | none |

Why the edge:
- Documentation and upkeep, Nemotron 3 Nano 30B A3B +32: Nemotron 3 Nano 30B A3B 80 (The model card earns 20, with all seven items on the card and its subcards.); WeVisOne-VL-4B 48 (Model card 8, Intended use, limitations and the architecture are on the card.)
- Reach, Nemotron 3 Nano 30B A3B +20: Nemotron 3 Nano 30B A3B 90 (Memory earns 25 because the smallest 4-bit build is Ollama's NVFP4 MLX build at 18 GB, 21.6 GB with the fifth added, and NVIDIA's own NVFP4 build lands in the s…); 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, Nemotron 3 Nano 30B A3B +20: Nemotron 3 Nano 30B A3B 90 (The chat template earns 20 as a Jinja file in the repository.); WeVisOne-VL-4B 70 (Chat template 20, chat_template.jinja in the repository (5,292 characters), Qwen3-VL's template.)
- Risk, Nemotron 3 Nano 30B A3B +5: Nemotron 3 Nano 30B A3B 60 (File safety earns 15, safetensors only but with the documented serving path passing trust-remote-code and loading a reasoning parser file from the repository.); 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.)