AllocateInkling vs GLM 4.7 FP8
On provider list prices, GLM 4.7 FP8 costs $0.45 per million input tokens against $1.87 for Inkling: 4.2x apart. Output is $2 against $4.68 (2.3x). On Allocate both bill at list plus the 7% transaction fee.
InklingG 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 $3,882 on Inkling at list: a gap of $2,642, or 3.1x.
Inkling reads 1M tokens per request against 198K for GLM 4.7 FP8, 4.9x the window. That decides which one can take whole documents without splitting them.
Choose Inkling for
- The longer context window (1M vs 198K tokens)
- Fine-tuning under a permissive license (Apache 2.0)
- Published cached-input pricing ($0.37 per M tokens)
Choose GLM 4.7 FP8 for
- The lower list price ($0.45 in / $2 out per M tokens)
- Fine-tuning under a permissive license (MIT)
Common questions
Which is cheaper, Inkling 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 $1.87/$4.68 for Inkling. Billed on Allocate: $0.48/$2.14 against $2.00/$5.01, list plus 7%.
Which has the bigger context window?
Inkling: 1,000,000 tokens (1M) against 202,752 (198K) for GLM 4.7 FP8.
Can I fine-tune Inkling or GLM 4.7 FP8?
Both publish open weights (Inkling: Apache 2.0; 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.