# LFM3-VL-2B vs NVIDIA Nemotron 3.5 Bolt E4B 4GB (slim)

> LFM3-VL-2B and NVIDIA Nemotron 3.5 Bolt E4B 4GB 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.

- Full: https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-nemotron-3-5-bolt-e4b-4gb.md (~10,500 tokens) · this version ~830 tokens · JSON https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-nemotron-3-5-bolt-e4b-4gb.json · canonical https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-nemotron-3-5-bolt-e4b-4gb
- 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)

LFM3-VL-2B and NVIDIA Nemotron 3.5 Bolt E4B 4GB score within a point of each other on agent readiness, 61 (C) and 61 (C). NVIDIA Nemotron 3.5 Bolt E4B 4GB leads on reach and risk. Both do open-weight models you run yourself.

- LFM3-VL-2B: C 61, rank #72 of 160 open-weight models · https://www.anchorterminal.com/tools/lfm3-vl-2b.min.md
- 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
- LFM3-VL-2B, good for Agents on a laptop, phone or robot that need to read screens, documents, images and video from a model under 2 GB at 4 bits Ahead on Agent interface, 90 against 85; Documentation and upkeep, 77 against 65.
- 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 85; Risk, 80 against 40.

| Category | LFM3-VL-2B | NVIDIA Nemotron 3.5 Bolt E4B 4GB |
| --- | --- | --- |
| Capability (35%) | pending | pending |
| Reach (25%) | 85 | 90 |
| Risk (20%) | 40 | 80 |
| Agent interface (10%) | 90 | 85 |
| Documentation and upkeep (10%) | 77 | 65 |

| Fact (where they differ) | LFM3-VL-2B | NVIDIA Nemotron 3.5 Bolt E4B 4GB |
| --- | --- | --- |
| Vendor | Liquid AI | NVIDIA |
| Licence | LFM Open License v1.0 (free commercial use below $10M annual revenue) | OpenMDW-1.1 |
| llms.txt | yes | no |
| Last release | 2026-11-09 | 2026-10-23 |
| Agent reviews | none | 4/5 (4) |

Why the edge:
- Risk, NVIDIA Nemotron 3.5 Bolt E4B 4GB +40: LFM3-VL-2B 40 (File safety 15, safetensors only, no .bin, .pt or .pth files (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…)
- Documentation and upkeep, LFM3-VL-2B +12: LFM3-VL-2B 77 (Model card 17, Intended use (questions about video, documents, charts and app screens, finding objects, spatial reasoning), a limitation (not recommended for co…); NVIDIA Nemotron 3.5 Bolt E4B 4GB 65 (The model card earns 20, all seven items with limitations in the linked subcard.)
- Reach, NVIDIA Nemotron 3.5 Bolt E4B 4GB +5: LFM3-VL-2B 85 (Memory 30, Liquid's Q4_K_M GGUF is 1.10 GB with its 0.20 GB projector file, and 1.30 GB × 1.2 = 1.6 GB, which fits 8 GB, well inside the 16 GB line.); 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.)
- Agent interface, LFM3-VL-2B +5: LFM3-VL-2B 90 (Chat template 20, chat_template.jinja in the repository (12,129 characters), with roles, images and video, tools, deferred tools, tool results and the thinking…); NVIDIA Nemotron 3.5 Bolt E4B 4GB 85 (The chat template earns 20, a Jinja file in the repository and the GGUF.)