# Pearl Gemma 4 31B Instruct vs GLM 4.7 FP8

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

| | Pearl Gemma 4 31B Instruct | GLM 4.7 FP8 |
| --- | --- | --- |
| Lab | Pearl AI | Z.ai |
| Access | Not served on Allocate | Open weights |
| Context window | n/a | 198K tokens |
| List price, input | Not served | $0.45 / M tokens |
| List price, output | Not served | $2 / M tokens |
| Cached input | n/a | n/a |
| License | Not listed | MIT |
| Fine-tunable | Yes | Yes |

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

## Choose Pearl Gemma 4 31B Instruct for

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

## Choose GLM 4.7 FP8 for

- Fine-tuning under a permissive license (MIT)

## Common questions

### Which has the bigger context window?

GLM 4.7 FP8: 202,752 tokens (198K) against an unlisted window for Pearl Gemma 4 31B Instruct.

### Can I fine-tune Pearl Gemma 4 31B Instruct or GLM 4.7 FP8?

Both publish open weights (Pearl Gemma 4 31B Instruct: Not listed; GLM 4.7 FP8: MIT), 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/pearl-gemma-4-31b-it-vs-z-ai-glm-4-7) · [Pearl Gemma 4 31B Instruct](https://allocate.network/models/pearl-gemma-4-31b-it.md) · [GLM 4.7 FP8](https://allocate.network/models/z-ai-glm-4-7.md) · [Machine-readable catalog](https://allocate.network/catalog.json)