AllocateKimi K2.5 vs Meta Llama 3.1 405B Instruct
On provider list prices, Kimi K2.5 costs $0.50 per million input tokens against $3.50 for Meta Llama 3.1 405B Instruct: 7.0x apart. Output is $2.80 against $3.50 (1.3x). On Allocate both bill at list plus the 7% transaction fee.
Kimi K2.5
Meta Llama 3.1 405B InstructSpecifications 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 $5,425 on Meta Llama 3.1 405B Instruct at list: a gap of $3,845, or 3.4x.
Kimi K2.5 reads 256K tokens per request against 4K for Meta Llama 3.1 405B Instruct, 64.0x 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 Meta Llama 3.1 405B Instruct for
- Training toward a model you own
Common questions
Which is cheaper, Kimi K2.5 or Meta Llama 3.1 405B Instruct?
Kimi K2.5, on this workload shape. At list prices it is $0.50/$2.80 per million tokens in and out against $3.50/$3.50 for Meta Llama 3.1 405B Instruct. Billed on Allocate: $0.54/$3.00 against $3.75/$3.75, list plus 7%.
Which has the bigger context window?
Kimi K2.5: 262,144 tokens (256K) against 4,096 (4K) for Meta Llama 3.1 405B Instruct.
Can I fine-tune Kimi K2.5 or Meta Llama 3.1 405B Instruct?
Both publish open weights (Kimi K2.5: Not listed; Meta Llama 3.1 405B Instruct: Llama community), 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.