AllocateGPT-5.6 Luna vs Glm 4.5 Air Fp8
On provider list prices, Glm 4.5 Air Fp8 costs $0.20 per million input tokens against $0.20 for GPT-5.6 Luna: effectively level. Output is $1.10 against $1.20 (1.1x).
GPT-5.6 LunaG Glm 4.5 Air Fp8Specifications 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 $625 a month on Glm 4.5 Air Fp8 and $660 on GPT-5.6 Luna at list: a gap of $35.
GPT-5.6 Luna reads 1M tokens per request against 128K for Glm 4.5 Air Fp8, 7.6x the window. That decides which one can take whole documents without splitting them.
Choose GPT-5.6 Luna for
- The longer context window (1M vs 128K tokens)
- Published cached-input pricing ($0.02 per M tokens)
Choose Glm 4.5 Air Fp8 for
- Open weights you can fine-tune and own
- Fine-tuning under a permissive license (MIT)
Common questions
Which is cheaper, GPT-5.6 Luna or Glm 4.5 Air Fp8?
Glm 4.5 Air Fp8, on this workload shape. At list prices it is $0.20/$1.10 per million tokens in and out against $0.20/$1.20 for GPT-5.6 Luna. Billed on Allocate: $0.21/$1.18 against $0.21/$1.28.
Which has the bigger context window?
GPT-5.6 Luna: 1,000,000 tokens (1M) against 131,072 (128K) for Glm 4.5 Air Fp8.
Can I fine-tune GPT-5.6 Luna or Glm 4.5 Air Fp8?
Glm 4.5 Air Fp8 publishes open weights (MIT) 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.