AllocateGPT-5.5 vs GLM 4.7 FP8
On provider list prices, GLM 4.7 FP8 costs $0.45 per million input tokens against $5 for GPT-5.5: 11.1x apart. Output is $2 against $30 (15.0x). On Allocate both bill at list plus the 7% transaction fee.
GPT-5.5G GLM 4.7 FP8Specifications 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 $1,240 a month on GLM 4.7 FP8 and $16,500 on GPT-5.5 at list: a gap of $15,260, or 13.3x.
GPT-5.5 reads 400K tokens per request against 198K for GLM 4.7 FP8, 2.0x the window. That decides which one can take whole documents without splitting them.
Choose GLM 4.7 FP8 for
- The lower list price ($0.45 in / $2 out per M tokens)
- Open weights you can fine-tune and own
- Fine-tuning under a permissive license (MIT)
Common questions
Which is cheaper, GPT-5.5 or GLM 4.7 FP8?
GLM 4.7 FP8, on this workload shape. At list prices it is $0.45/$2 per million tokens in and out against $5/$30 for GPT-5.5. Billed on Allocate: $0.48/$2.14 against $5.35/$32.10, list plus 7%.
Which has the bigger context window?
GPT-5.5: 400,000 tokens (400K) against 202,752 (198K) for GLM 4.7 FP8.
Can I fine-tune GPT-5.5 or GLM 4.7 FP8?
GLM 4.7 FP8 publishes open weights (MIT) and can be fine-tuned on your own data. GPT-5.5 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.