AllocateMixtral-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.
Mixtral-8x7B Instruct v0.1P Pearl-ai Gemma-4-31B-it-pearlSpecifications 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.
Related comparisons
Run the numbers on your workload
Or don’t choose. On Allocate a route name is the contract: point yours at one model today, swap to the other tomorrow, and compare them on your live traffic with per-token metering.