# DeepSeek R1 0528 NVFP4 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

| | DeepSeek R1 0528 NVFP4 | Pearl Gemma 4 31B Instruct |
| --- | --- | --- |
| Lab | Deepseek | Pearl AI |
| 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 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?

DeepSeek R1 0528 NVFP4: 163,840 tokens (160K) against an unlisted window for Pearl Gemma 4 31B Instruct.

### Can I fine-tune DeepSeek R1 0528 NVFP4 or Pearl Gemma 4 31B Instruct?

Both publish open weights (DeepSeek R1 0528 NVFP4: MIT; 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.

---

[HTML page](https://allocate.network/compare/deepseek-deepseek-r1-0528-vs-pearl-gemma-4-31b-it) · [DeepSeek R1 0528 NVFP4](https://allocate.network/models/deepseek-deepseek-r1-0528.md) · [Pearl Gemma 4 31B Instruct](https://allocate.network/models/pearl-gemma-4-31b-it.md) · [Machine-readable catalog](https://allocate.network/catalog.json)