AllocateKimi K2.5 vs GLM 4.7 FP8
On provider list prices, GLM 4.7 FP8 costs $0.45 per million input tokens against $0.50 for Kimi K2.5: 1.1x apart. Output is $2 against $2.80 (1.4x). On Allocate both bill at list plus the 7% transaction fee.
Kimi K2.5G 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 $1,580 on Kimi K2.5 at list: a gap of $340, or 1.3x.
Kimi K2.5 reads 256K tokens per request against 198K for GLM 4.7 FP8, 1.3x the window. That decides which one can take whole documents without splitting them.
Choose Kimi K2.5 for
- Whole-document reasoning
- Long-context retrieval
- Open-weight fine-tuning
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, Kimi K2.5 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 $0.50/$2.80 for Kimi K2.5. Billed on Allocate: $0.48/$2.14 against $0.54/$3.00, list plus 7%.
Which has the bigger context window?
Kimi K2.5: 262,144 tokens (256K) against 202,752 (198K) for GLM 4.7 FP8.
Can I fine-tune Kimi K2.5 or GLM 4.7 FP8?
Both publish open weights (Kimi K2.5: Not listed; 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.