AllocateKimi K2.5 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.
Kimi K2.5P Pearl Gemma 4 31B InstructLabTogethercomputerPearl AI
AccessOpen weightsNot served on Allocate
Context window256K tokensn/a
List price, input$0.5 / M tokensNot served
List price, output$2.8 / M tokensNot served
Cached inputn/an/a
LicenseNot listedNot listed
Fine-tunableYesYes
Specifications and provider list prices from the Allocate catalog, checked 2026-09-12.
Choose Kimi K2.5 for
- Whole-document reasoning
- Long-context retrieval
- Open-weight fine-tuning
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?
Kimi K2.5: 262,144 tokens (256K) against an unlisted window for Pearl Gemma 4 31B Instruct.
Can I fine-tune Kimi K2.5 or Pearl Gemma 4 31B Instruct?
Both publish open weights (Kimi K2.5: Not listed; 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.
Related comparisons
Kimi K2.5 vs MiniMax M3Pearl Gemma 4 31B Instruct vs MiniMax M3Kimi K2.5 vs Meta Llama 3.3 70B Instruct TurboPearl Gemma 4 31B Instruct vs Meta Llama 3.3 70B Instruct TurboKimi K2.5 vs Deepseek V3.1 NVFP4Pearl Gemma 4 31B Instruct vs Deepseek V3.1 NVFP4
Run the numbers on your workload
Or do not 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.