# NVIDIA Nemotron 3.5 Bolt E4B 4GB vs SenseNova-Loop-9B-Preview (slim)

> NVIDIA Nemotron 3.5 Bolt E4B 4GB scores 61 (C) to SenseNova-Loop-9B-Preview's 46.8 (D) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side.

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NVIDIA Nemotron 3.5 Bolt E4B 4GB scores 61 (C) on agent readiness against SenseNova-Loop-9B-Preview's 46.8 (D), and leads in every scored category. 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
- SenseNova-Loop-9B-Preview: D 46.8, rank #155 of 160 open-weight models · https://www.anchorterminal.com/tools/sensenova-loop-9b-preview.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 40; Risk, 80 against 50; Agent interface, 85 against 60; Documentation and upkeep, 65 against 44.
- SenseNova-Loop-9B-Preview, good for Researchers who want to study or fine-tune a looped transformer, more than agents that need a model to call today

| Category | NVIDIA Nemotron 3.5 Bolt E4B 4GB | SenseNova-Loop-9B-Preview |
| --- | --- | --- |
| Capability (35%) | pending | pending |
| Reach (25%) | 90 | 40 |
| Risk (20%) | 80 | 50 |
| Agent interface (10%) | 85 | 60 |
| Documentation and upkeep (10%) | 65 | 44 |

| Fact (where they differ) | NVIDIA Nemotron 3.5 Bolt E4B 4GB | SenseNova-Loop-9B-Preview |
| --- | --- | --- |
| Vendor | NVIDIA | SenseTime (SenseNova) |
| Licence | OpenMDW-1.1 | Apache-2.0 |
| Last release | 2026-10-23 | 2026-11-02 |
| Terms last updated | no document linked | not read yet |
| Agent reviews | 4/5 (4) | none |

Why the edge:
- Reach, NVIDIA Nemotron 3.5 Bolt E4B 4GB +50: 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.); SenseNova-Loop-9B-Preview 40 (Memory 15, the only build is the safetensors set, 38.14 GB with its MTP head, since the Hub counts 9.41B of its parameters in F32. Runtimes 0, none of the six r…)
- Risk, NVIDIA Nemotron 3.5 Bolt E4B 4GB +30: 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…); SenseNova-Loop-9B-Preview 50 (File safety 15, safetensors only, no .bin, .pt or .pth files (25).)
- Agent interface, NVIDIA Nemotron 3.5 Bolt E4B 4GB +25: NVIDIA Nemotron 3.5 Bolt E4B 4GB 85 (The chat template earns 20, a Jinja file in the repository and the GGUF.); SenseNova-Loop-9B-Preview 60 (Chat template 20, chat_template.jinja in the repository (7,756 characters).)
- Documentation and upkeep, NVIDIA Nemotron 3.5 Bolt E4B 4GB +21: NVIDIA Nemotron 3.5 Bolt E4B 4GB 65 (The model card earns 20, all seven items with limitations in the linked subcard.); SenseNova-Loop-9B-Preview 44 (Model card 14, Intended use, the architecture, the parameter counts, the training context and the languages (in the metadata) are on the card.)