# Mixtral-8x7B Instruct v0.1 vs Pearl-ai Gemma-4-31B-it-pearl

On provider list prices, Pearl-ai Gemma-4-31B-it-pearl costs $0.28 per million input tokens against $0.60 for Mixtral-8x7B Instruct v0.1: 2.1x apart. Output is $0.86 against $0.60. On Allocate both bill at list plus the 7% transaction fee.

## Specifications

| | Mixtral-8x7B Instruct v0.1 | Pearl-ai Gemma-4-31B-it-pearl |
| --- | --- | --- |
| Lab | mistralai | pearl.ai |
| Access | Open weights | Open weights |
| Context window | 32K tokens | 256K tokens |
| List price, input | $0.60 / M tokens | $0.28 / M tokens |
| List price, output | $0.60 / M tokens | $0.86 / M tokens |
| Cached input | n/a | n/a |
| License | Apache 2.0 | Not listed |
| Fine-tunable | Yes | Yes |

Specifications and provider list prices from the Allocate catalog, checked 2026-07-08. Billed price is list plus the 7% transaction fee.

## What the numbers say

Take 1,000,000 requests a month at 1,200 input and 350 output tokens each. That workload costs $637 a month on Pearl-ai Gemma-4-31B-it-pearl and $930 on Mixtral-8x7B Instruct v0.1 at list: a gap of $293, or 1.5x.

Pearl-ai Gemma-4-31B-it-pearl reads 256K tokens per request against 32K for Mixtral-8x7B Instruct v0.1, 8.0x the window. That decides which one can take whole documents without splitting them.

## Choose Mixtral-8x7B Instruct v0.1 for

- Fine-tuning under a permissive license (Apache 2.0)

## Choose Pearl-ai Gemma-4-31B-it-pearl for

- The lower list price ($0.28 in / $0.86 out per M tokens)
- The longer context window (256K vs 32K tokens)

## Common questions

### Which is cheaper, Mixtral-8x7B Instruct v0.1 or Pearl-ai Gemma-4-31B-it-pearl?

Pearl-ai Gemma-4-31B-it-pearl, on this workload shape. At list prices it is $0.28/$0.86 per million tokens in and out against $0.60/$0.60 for Mixtral-8x7B Instruct v0.1. Billed on Allocate: $0.30/$0.92 against $0.64/$0.64, list plus 7%.

### Which has the bigger context window?

Pearl-ai Gemma-4-31B-it-pearl: 262,144 tokens (256K) against 32,768 (32K) for Mixtral-8x7B Instruct v0.1.

### Can I fine-tune Mixtral-8x7B Instruct v0.1 or Pearl-ai Gemma-4-31B-it-pearl?

Both publish open weights (Mixtral-8x7B Instruct v0.1: Apache 2.0; Pearl-ai Gemma-4-31B-it-pearl: 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/mistral-mixtral-8x7b-instruct-v0-1-vs-pearl-gemma-4-31b-it) · [Mixtral-8x7B Instruct v0.1](https://allocate.network/models/mistral-mixtral-8x7b-instruct-v0-1.md) · [Pearl-ai Gemma-4-31B-it-pearl](https://allocate.network/models/pearl-gemma-4-31b-it.md) · [Machine-readable catalog](https://allocate.network/catalog.json)