# Gemma 3n E4B Instruct vs Meta Llama 3.3 70B Instruct Turbo

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

| | Gemma 3n E4B Instruct | Meta Llama 3.3 70B Instruct Turbo |
| --- | --- | --- |
| Lab | Google | Meta |
| Access | Not served on Allocate | Open weights |
| Context window | n/a | 128K tokens |
| List price, input | Not served | $1.04 / M tokens |
| List price, output | Not served | $1.04 / M tokens |
| Cached input | n/a | n/a |
| License | Not listed | Llama community |
| Fine-tunable | Yes | Yes |

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

## Choose Gemma 3n E4B Instruct for

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

## Choose Meta Llama 3.3 70B Instruct Turbo for

- Training toward a model you own

## Common questions

### Which has the bigger context window?

Meta Llama 3.3 70B Instruct Turbo: 131,072 tokens (128K) against an unlisted window for Gemma 3n E4B Instruct.

### Can I fine-tune Gemma 3n E4B Instruct or Meta Llama 3.3 70B Instruct Turbo?

Both publish open weights (Gemma 3n E4B Instruct: Not listed; Meta Llama 3.3 70B Instruct Turbo: Llama community), 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/google-gemma-3n-e4b-it-vs-meta-llama-3-3-70b-instruct-turbo) · [Gemma 3n E4B Instruct](https://allocate.network/models/google-gemma-3n-e4b-it.md) · [Meta Llama 3.3 70B Instruct Turbo](https://allocate.network/models/meta-llama-3-3-70b-instruct-turbo.md) · [Machine-readable catalog](https://allocate.network/catalog.json)