AllocateDeepseek V3.1 NVFP4 vs Kimi K2.5
On provider list prices, Deepseek V3.1 NVFP4 costs $0.60 per million input tokens against $0.50 for Kimi K2.5: effectively level. Output is $1.70 against $2.80 (1.6x). On Allocate both bill at list plus the 7% transaction fee.
Deepseek V3.1 NVFP4
Kimi K2.5Specifications 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 $1,315 a month on Deepseek V3.1 NVFP4 and $1,580 on Kimi K2.5 at list: a gap of $265, or 1.2x.
Kimi K2.5 reads 256K tokens per request against 128K for Deepseek V3.1 NVFP4, 2.0x the window. That decides which one can take whole documents without splitting them.
Choose Deepseek V3.1 NVFP4 for
- Fine-tuning under a permissive license (MIT)
Choose Kimi K2.5 for
- Whole-document reasoning
- Long-context retrieval
- Open-weight fine-tuning
Common questions
Which is cheaper, Deepseek V3.1 NVFP4 or Kimi K2.5?
Deepseek V3.1 NVFP4, on this workload shape. At list prices it is $0.60/$1.70 per million tokens in and out against $0.50/$2.80 for Kimi K2.5. Billed on Allocate: $0.64/$1.82 against $0.54/$3.00, list plus 7%.
Which has the bigger context window?
Kimi K2.5: 262,144 tokens (256K) against 131,072 (128K) for Deepseek V3.1 NVFP4.
Can I fine-tune Deepseek V3.1 NVFP4 or Kimi K2.5?
Both publish open weights (Deepseek V3.1 NVFP4: MIT; Kimi K2.5: 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.