# Meta Llama 3.3 70B Instruct Turbo vs Pearl Gemma 4 31B Instruct

Pearl Gemma 4 31B 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

| | Meta Llama 3.3 70B Instruct Turbo | Pearl Gemma 4 31B Instruct |
| --- | --- | --- |
| Lab | Meta | Pearl AI |
| Access | Open weights | Not served on Allocate |
| Context window | 128K tokens | n/a |
| List price, input | $1.04 / M tokens | Not served |
| List price, output | $1.04 / M tokens | Not served |
| Cached input | n/a | n/a |
| License | Llama community | Not listed |
| Fine-tunable | Yes | Yes |

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

## Choose Meta Llama 3.3 70B Instruct Turbo for

- Training toward a model you own

## Choose Pearl Gemma 4 31B Instruct for

- Long-document reasoning
- Open-weight fine-tuning
- Mid-size general work

## Common questions

### Which has the bigger context window?

Meta Llama 3.3 70B Instruct Turbo: 131,072 tokens (128K) against an unlisted window for Pearl Gemma 4 31B Instruct.

### Can I fine-tune Meta Llama 3.3 70B Instruct Turbo or Pearl Gemma 4 31B Instruct?

Both publish open weights (Meta Llama 3.3 70B Instruct Turbo: Llama community; Pearl Gemma 4 31B 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/meta-llama-3-3-70b-instruct-turbo-vs-pearl-gemma-4-31b-it) · [Meta Llama 3.3 70B Instruct Turbo](https://allocate.network/models/meta-llama-3-3-70b-instruct-turbo.md) · [Pearl Gemma 4 31B Instruct](https://allocate.network/models/pearl-gemma-4-31b-it.md) · [Machine-readable catalog](https://allocate.network/catalog.json)