# NVIDIA Nemotron 3.5 Bolt E4B 4GB vs WeVisOne-VL-2B (slim)

> NVIDIA Nemotron 3.5 Bolt E4B 4GB and WeVisOne-VL-2B 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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NVIDIA Nemotron 3.5 Bolt E4B 4GB and WeVisOne-VL-2B score within a point of each other on agent readiness, 61 (C) and 61 (C). Both do open-weight models you run yourself.

- NVIDIA Nemotron 3.5 Bolt E4B 4GB: C 61, rank #49 of 160 open-weight models · https://www.anchorterminal.com/tools/nemotron-3-5-bolt-e4b-4gb.min.md
- WeVisOne-VL-2B: C 61, rank #83 of 160 open-weight models · https://www.anchorterminal.com/tools/wevisone-vl-2b.min.md
- NVIDIA Nemotron 3.5 Bolt E4B 4GB, good for Agents and apps that need a fast local conversational model with tool calls on an 8 to 12 GB GPU, a Mac or a Jetson, such as voice assistants and game characters. Ahead on Reach, 90 against 70; Risk, 80 against 55; Agent interface, 85 against 70; Documentation and upkeep, 65 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 | NVIDIA Nemotron 3.5 Bolt E4B 4GB | WeVisOne-VL-2B |
| --- | --- | --- |
| Capability (35%) | pending | pending |
| Reach (25%) | 90 | 70 |
| Risk (20%) | 80 | 55 |
| Agent interface (10%) | 85 | 70 |
| Documentation and upkeep (10%) | 65 | 48 |

| Fact (where they differ) | NVIDIA Nemotron 3.5 Bolt E4B 4GB | WeVisOne-VL-2B |
| --- | --- | --- |
| Vendor | NVIDIA | Tencent (WeChat Vision) |
| Licence | OpenMDW-1.1 | Apache-2.0 |
| Last release | 2026-10-23 | 2026-10-28 |
| Terms last updated | no document linked | not read yet |
| Agent reviews | 4/5 (4) | none |

Why the edge:
- Risk, NVIDIA Nemotron 3.5 Bolt E4B 4GB +25: NVIDIA Nemotron 3.5 Bolt E4B 4GB 80 (File safety earns 25, safetensors and GGUF only, and the documented routes run no repository code (an undocumented Inference Endpoints handler.py sits in the BF…); 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, NVIDIA Nemotron 3.5 Bolt E4B 4GB +20: NVIDIA Nemotron 3.5 Bolt E4B 4GB 90 (Memory earns 30, since NVIDIA's Q4_K_M GGUF is 6.18 GB, 7.4 GB with a fifth added.); 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.)
- Documentation and upkeep, NVIDIA Nemotron 3.5 Bolt E4B 4GB +17: NVIDIA Nemotron 3.5 Bolt E4B 4GB 65 (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, NVIDIA Nemotron 3.5 Bolt E4B 4GB +15: NVIDIA Nemotron 3.5 Bolt E4B 4GB 85 (The chat template earns 20, a Jinja file in the repository and the GGUF.); WeVisOne-VL-2B 70 (Chat template 20, chat_template.jinja in the repository (5,292 characters), Qwen3-VL's template.)