# DeepSeek R1 0528 NVFP4 vs Gemma 3n E4B Instruct

Gemma 3n E4B Instruct is not currently in the Allocate serving catalog, so this page lists no prices for it: every price on this site comes from the live catalog.

## Specifications

| | DeepSeek R1 0528 NVFP4 | Gemma 3n E4B Instruct |
| --- | --- | --- |
| Lab | Deepseek | Google |
| Access | Open weights | Not served on Allocate |
| Context window | 160K tokens | n/a |
| List price, input | $3 / M tokens | Not served |
| List price, output | $7 / M tokens | Not served |
| Cached input | n/a | n/a |
| License | MIT | Not listed |
| Fine-tunable | Yes | Yes |

Specifications and provider list prices from the Allocate catalog, checked 2026-09-12.

## Choose DeepSeek R1 0528 NVFP4 for

- Fine-tuning under a permissive license (MIT)

## Choose Gemma 3n E4B Instruct for

- Cheap classification
- On-device and edge deployments
- High-volume short prompts

## Common questions

### Which has the bigger context window?

DeepSeek R1 0528 NVFP4: 163,840 tokens (160K) against an unlisted window for Gemma 3n E4B Instruct.

### Can I fine-tune DeepSeek R1 0528 NVFP4 or Gemma 3n E4B Instruct?

Both publish open weights (DeepSeek R1 0528 NVFP4: MIT; Gemma 3n E4B Instruct: Not listed), so both can be fine-tuned. On Allocate the trained weights stay inside your boundary and belong to you.

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[HTML page](https://allocate.network/compare/deepseek-deepseek-r1-0528-vs-google-gemma-3n-e4b-it) · [DeepSeek R1 0528 NVFP4](https://allocate.network/models/deepseek-deepseek-r1-0528.md) · [Gemma 3n E4B Instruct](https://allocate.network/models/google-gemma-3n-e4b-it.md) · [Machine-readable catalog](https://allocate.network/catalog.json)