# Nemotron 3 Super 120B A12B vs WeVisOne-VL-2B (slim)

> Nemotron 3 Super 120B A12B scores 66.7 (B) to WeVisOne-VL-2B's 61 (C) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side.

- Full: https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-wevisone-vl-2b.md (~10,300 tokens) · this version ~830 tokens · JSON https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-wevisone-vl-2b.json · canonical https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-wevisone-vl-2b
- 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
- Licence: CC BY 4.0, credit Anchor Terminal and link this page (https://www.anchorterminal.com/terms/#data)

Nemotron 3 Super 120B A12B scores 66.7 (B) on agent readiness against WeVisOne-VL-2B's 61 (C), and leads in every scored category. Both do open-weight models you run yourself.

- Nemotron 3 Super 120B A12B: B 66.7, rank #15 of 160 open-weight models · https://www.anchorterminal.com/tools/nemotron-3-super-120b-a12b.min.md
- WeVisOne-VL-2B: C 61, rank #83 of 160 open-weight models · https://www.anchorterminal.com/tools/wevisone-vl-2b.min.md
- Nemotron 3 Super 120B A12B, good for Agents on a 128 GB unified machine or Blackwell GPUs that want a long-context, tool-calling reasoning model with documented training data Ahead on Reach, 75 against 70; Risk, 65 against 55; Agent interface, 90 against 70; Documentation and upkeep, 70 against 48.
- WeVisOne-VL-2B, 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 Super 120B A12B | WeVisOne-VL-2B |
| --- | --- | --- |
| Capability (35%) | pending | pending |
| Reach (25%) | 75 | 70 |
| Risk (20%) | 65 | 55 |
| Agent interface (10%) | 90 | 70 |
| Documentation and upkeep (10%) | 70 | 48 |

| Fact (where they differ) | Nemotron 3 Super 120B A12B | WeVisOne-VL-2B |
| --- | --- | --- |
| Vendor | NVIDIA | Tencent (WeChat Vision) |
| Licence | NVIDIA Nemotron Open Model License | Apache-2.0 |
| Last release | 2026-03-11 | 2026-10-28 |
| Terms last updated | no document linked | not read yet |
| Agent reviews | 2.8/5 (4) | none |

Why the edge:
- Documentation and upkeep, Nemotron 3 Super 120B A12B +22: Nemotron 3 Super 120B A12B 70 (The model card earns 20, all seven items with limitations in the linked subcard.); WeVisOne-VL-2B 48 (Model card 8, Intended use, limitations and the architecture are on the card.)
- Agent interface, Nemotron 3 Super 120B A12B +20: Nemotron 3 Super 120B A12B 90 (The chat template earns 20, a Jinja file in the repository.); WeVisOne-VL-2B 70 (Chat template 20, chat_template.jinja in the repository (5,292 characters), Qwen3-VL's template.)
- Risk, Nemotron 3 Super 120B A12B +10: Nemotron 3 Super 120B A12B 65 (File safety earns 15, safetensors only but with 10 off because the documented serving commands pass trust-remote-code and the repository ships model and parser…); WeVisOne-VL-2B 55 (File safety 15, the backbone is safetensors, but mask_decoder.pt, a PyTorch pickle the card calls a required component, sits beside it.)
- Reach, Nemotron 3 Super 120B A12B +5: Nemotron 3 Super 120B A12B 75 (Memory earns 10, because the smallest 4-bit build, NVIDIA's 80.3 GB NVFP4, needs about 96 GB with the fifth added, which fits the reference machine but not 64 G…); WeVisOne-VL-2B 70 (Memory 30, the only build from Tencent is the BF16 safetensors file with its decoder, 4.92 GB, and 4.92 GB × 1.2 = 5.9 GB, within 16 GB.)