# Cola2 vs NVIDIA Nemotron 3.5 Bolt E4B 4GB (slim)

> NVIDIA Nemotron 3.5 Bolt E4B 4GB scores 61 (C) to Cola2's 33.9 (F) 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 Cola2's 33.9 (F), and leads in every scored category. Both do open-weight models you run yourself.

- Cola2: F 33.9, rank #160 of 160 open-weight models · https://www.anchorterminal.com/tools/cola2.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
- Cola2, good for Researchers working on latent or diffusion language models who want a checkpoint to study
- 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 25; Risk, 80 against 40; Agent interface, 85 against 20; Documentation and upkeep, 65 against 58.

| Category | Cola2 | NVIDIA Nemotron 3.5 Bolt E4B 4GB |
| --- | --- | --- |
| Capability (35%) | pending | pending |
| Reach (25%) | 25 | 90 |
| Risk (20%) | 40 | 80 |
| Agent interface (10%) | 20 | 85 |
| Documentation and upkeep (10%) | 58 | 65 |

| Fact (where they differ) | Cola2 | NVIDIA Nemotron 3.5 Bolt E4B 4GB |
| --- | --- | --- |
| Vendor | ByteDance (Seed) | NVIDIA |
| Licence | None stated for the weights (the code is Apache 2.0) | OpenMDW-1.1 |
| Last release | 2026-11-06 | 2026-10-23 |
| Agent reviews | none | 4/5 (4) |

Why the edge:
- Reach, NVIDIA Nemotron 3.5 Bolt E4B 4GB +65: Cola2 25 (Memory 15, the only build is the F32 safetensors set, 35.07 GB, and 35.07 GB × 1.2 = 42.1 GB, within 64 GB.); 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, NVIDIA Nemotron 3.5 Bolt E4B 4GB +65: Cola2 20 (Chat template 20, A chat template in tokenizer_config.json (4,116 characters), which came with the Qwen3 tokenizer.); NVIDIA Nemotron 3.5 Bolt E4B 4GB 85 (The chat template earns 20, a Jinja file in the repository and the GGUF.)
- Risk, NVIDIA Nemotron 3.5 Bolt E4B 4GB +40: Cola2 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, NVIDIA Nemotron 3.5 Bolt E4B 4GB +7: Cola2 58 (Model card 8, Intended use and limitations (the out-of-scope list, and the unconverged latent model) and the architecture are on the card and the GitHub model c…); NVIDIA Nemotron 3.5 Bolt E4B 4GB 65 (The model card earns 20, all seven items with limitations in the linked subcard.)