Kimi K2.5 vs GLM 5.2
On provider list prices, Kimi K2.5 costs $0.50 per million input tokens against $1.40 for GLM 5.2: 2.8x apart. Output is $2.80 against $4.40 (1.6x). On Allocate both bill at list plus the 7% transaction fee.
Specifications 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 $1,580 a month on Kimi K2.5 and $3,220 on GLM 5.2 at list: a gap of $1,640, or 2.0x.
Choose Kimi K2.5 for
- Whole-document reasoning
- Long-context retrieval
- Open-weight fine-tuning
Choose GLM 5.2 for
- Agents on open weights
- Code and structured outputs
- Fine-tuning toward an owned model
Common questions
Which is cheaper, Kimi K2.5 or GLM 5.2?
Kimi K2.5, on this workload shape. At list prices it is $0.50/$2.80 per million tokens in and out against $1.40/$4.40 for GLM 5.2. Billed on Allocate: $0.54/$3.00 against $1.50/$4.71, list plus 7%.
Which has the bigger context window?
They match: both read 262,144 tokens (256K) per request.
Can I fine-tune Kimi K2.5 or GLM 5.2?
Both publish open weights (Kimi K2.5: Not listed; GLM 5.2: Not listed), 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.