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

> Nemotron 3 Nano Omni 30B A3B Reasoning and WeVisOne-VL-4B score within a point of each other for open-weight models you run yourself, 61.2 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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Nemotron 3 Nano Omni 30B A3B Reasoning and WeVisOne-VL-4B score within a point of each other on agent readiness, 61.2 (C) and 61 (C). Both do open-weight models you run yourself.

- Nemotron 3 Nano Omni 30B A3B Reasoning: C 61.2, rank #36 of 160 open-weight models · https://www.anchorterminal.com/tools/nemotron-3-nano-omni-30b-a3b-reasoning.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 Omni 30B A3B Reasoning, good for Agents with a 32 to 48 GB NVIDIA GPU that need one local model to read video, audio, documents and screens and call tools Ahead on Reach, 85 against 70; Risk, 60 against 55; Agent interface, 90 against 70; Documentation and upkeep, 70 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 Omni 30B A3B Reasoning | WeVisOne-VL-4B |
| --- | --- | --- |
| Capability (35%) | pending | pending |
| Reach (25%) | 85 | 70 |
| Risk (20%) | 60 | 55 |
| Agent interface (10%) | 90 | 70 |
| Documentation and upkeep (10%) | 70 | 48 |

| Fact (where they differ) | Nemotron 3 Nano Omni 30B A3B Reasoning | WeVisOne-VL-4B |
| --- | --- | --- |
| Vendor | NVIDIA | Tencent (WeChat Vision) |
| Licence | NVIDIA Open Model Agreement (custom, commercial use allowed, not an OSI licence) | Apache-2.0 |
| Last release | 2026-08-24 | 2026-10-27 |
| Terms last updated | no date given | 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 | 3/5 (4) | none |

Why the edge:
- Documentation and upkeep, Nemotron 3 Nano Omni 30B A3B Reasoning +22: Nemotron 3 Nano Omni 30B A3B Reasoning 70 (The card covers all seven fields, reading the 2019 to 2025 data collection period as the cut-off (20).); WeVisOne-VL-4B 48 (Model card 8, Intended use, limitations and the architecture are on the card.)
- Agent interface, Nemotron 3 Nano Omni 30B A3B Reasoning +20: Nemotron 3 Nano Omni 30B A3B Reasoning 90 (A Jinja template ships in the repository (20).); WeVisOne-VL-4B 70 (Chat template 20, chat_template.jinja in the repository (5,292 characters), Qwen3-VL's template.)
- Reach, Nemotron 3 Nano Omni 30B A3B Reasoning +15: Nemotron 3 Nano Omni 30B A3B Reasoning 85 (Memory earns 20 because the smallest 4-bit builds, Ollama's NVFP4 at 21 GB and NVIDIA's NVFP4 at 22.4 GB, need about 25 to 27 GB with the fifth added, which fit…); 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 Omni 30B A3B Reasoning +5: Nemotron 3 Nano Omni 30B A3B Reasoning 60 (File safety gets 15, safetensors only but loaded with trust_remote_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.)