AllocateOpenAI GPT-OSS 120B vs GLM 4.7 FP8
On provider list prices, OpenAI GPT-OSS 120B costs $0.15 per million input tokens against $0.45 for GLM 4.7 FP8: 3.0x apart. Output is $0.60 against $2 (3.3x). On Allocate both bill at list plus the 7% transaction fee.
OpenAI GPT-OSS 120BG 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 $390 a month on OpenAI GPT-OSS 120B and $1,240 on GLM 4.7 FP8 at list: a gap of $850, or 3.2x.
GLM 4.7 FP8 reads 198K tokens per request against 128K for OpenAI GPT-OSS 120B, 1.5x the window. That decides which one can take whole documents without splitting them.
Choose OpenAI GPT-OSS 120B for
- The lower list price ($0.15 in / $0.60 out per M tokens)
Choose GLM 4.7 FP8 for
- The longer context window (198K vs 128K tokens)
- Fine-tuning under a permissive license (MIT)
Common questions
Which is cheaper, OpenAI GPT-OSS 120B or GLM 4.7 FP8?
OpenAI GPT-OSS 120B, on this workload shape. At list prices it is $0.15/$0.60 per million tokens in and out against $0.45/$2 for GLM 4.7 FP8. Billed on Allocate: $0.16/$0.64 against $0.48/$2.14, list plus 7%.
Which has the bigger context window?
GLM 4.7 FP8: 202,752 tokens (198K) against 131,072 (128K) for OpenAI GPT-OSS 120B.
Can I fine-tune OpenAI GPT-OSS 120B or GLM 4.7 FP8?
Both publish open weights (OpenAI GPT-OSS 120B: Custom license; GLM 4.7 FP8: 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 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.