AllocateGemma 4 31B-it FP8 vs GPT-5.6 Luna
On provider list prices, Gemma 4 31B-it FP8 costs $0.39 per million input tokens against $0.20 for GPT-5.6 Luna: effectively level. Output is $0.97 against $1.20 (1.2x).
Gemma 4 31B-it FP8
GPT-5.6 LunaSpecifications 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 $660 a month on GPT-5.6 Luna and $807.50 on Gemma 4 31B-it FP8 at list: a gap of $147.50, or 1.2x.
GPT-5.6 Luna reads 1M tokens per request against 256K for Gemma 4 31B-it FP8, 3.8x the window. That decides which one can take whole documents without splitting them.
Choose Gemma 4 31B-it FP8 for
- Open weights you can fine-tune and own
- Fine-tuning under a permissive license (Apache 2.0)
Choose GPT-5.6 Luna for
- The lower list price ($0.20 in / $1.20 out per M tokens)
- The longer context window (1M vs 256K tokens)
- Published cached-input pricing ($0.02 per M tokens)
Common questions
Which is cheaper, Gemma 4 31B-it FP8 or GPT-5.6 Luna?
GPT-5.6 Luna, on this workload shape. At list prices it is $0.20/$1.20 per million tokens in and out against $0.39/$0.97 for Gemma 4 31B-it FP8. Billed on Allocate: $0.21/$1.28 against $0.42/$1.04.
Which has the bigger context window?
GPT-5.6 Luna: 1,000,000 tokens (1M) against 262,144 (256K) for Gemma 4 31B-it FP8.
Can I fine-tune Gemma 4 31B-it FP8 or GPT-5.6 Luna?
Gemma 4 31B-it FP8 publishes open weights (Apache 2.0) and can be fine-tuned on your own data. GPT-5.6 Luna is a closed model served over API; its weights are not available.
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.