AllocatePearl-ai Gemma-4-31B-it-pearl vs GLM 4.5 Air
On provider list prices, GLM 4.5 Air costs $0.13 per million input tokens against $0.28 for Pearl-ai Gemma-4-31B-it-pearl: 2.2x apart. Output is $0.85 against $0.86.
Specifications and provider list prices from the Allocate catalog, checked 2026-07-21.
What the numbers say
Take 1,000,000 requests a month at 1,200 input and 350 output tokens each. That workload costs $453.50 a month on GLM 4.5 Air and $637 on Pearl-ai Gemma-4-31B-it-pearl at list: a gap of $183.50, or 1.4x.
Pearl-ai Gemma-4-31B-it-pearl reads 256K tokens per request against 128K for GLM 4.5 Air, 2.0x the window. That decides which one can take whole documents without splitting them.
Choose Pearl-ai Gemma-4-31B-it-pearl for
- The longer context window (256K vs 128K tokens)
Choose GLM 4.5 Air for
- The lower list price ($0.13 in / $0.85 out per M tokens)
- Fine-tuning under a permissive license (MIT)
- Published cached-input pricing ($0.025 per M tokens)
Common questions
Which is cheaper, Pearl-ai Gemma-4-31B-it-pearl or GLM 4.5 Air?
GLM 4.5 Air, on this workload shape. At list prices it is $0.13/$0.85 per million tokens in and out against $0.28/$0.86 for Pearl-ai Gemma-4-31B-it-pearl. Billed on Allocate: $0.14/$0.91 against $0.30/$0.92.
Which has the bigger context window?
Pearl-ai Gemma-4-31B-it-pearl: 262,144 tokens (256K) against 131,072 (128K) for GLM 4.5 Air.
Can I fine-tune Pearl-ai Gemma-4-31B-it-pearl or GLM 4.5 Air?
Both publish open weights (Pearl-ai Gemma-4-31B-it-pearl: Not listed; GLM 4.5 Air: MIT), 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 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.