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Comparisons

Kimi K2.5 vs Meta Llama 3.3 70B Instruct Turbo

On provider list prices, Meta Llama 3.3 70B Instruct Turbo costs $1.04 per million input tokens against $0.50 for Kimi K2.5: effectively level. Output is $1.04 against $2.80 (2.7x). On Allocate both bill at list plus the 7% transaction fee.

Kimi K2.5 Meta Llama 3.3 70B Instruct Turbo
LabTogethercomputerMeta
AccessOpen weightsOpen weights
Context window256K tokens128K tokens
List price, input$0.5 / M tokens$1.04 / M tokens
List price, output$2.8 / M tokens$1.04 / M tokens
Cached inputn/an/a
LicenseNot listedLlama community
Fine-tunableYesYes

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 $1,612 on Meta Llama 3.3 70B Instruct Turbo at list: a gap of $32.

Kimi K2.5 reads 256K tokens per request against 128K for Meta Llama 3.3 70B Instruct Turbo, 2.0x the window. That decides which one can take whole documents without splitting them.

Kimi K2.5$0.50$2.80
Meta Llama 3.3 70B Instruct Turbo$1.04$1.04
InputOutput

Choose Kimi K2.5 for

  • Whole-document reasoning
  • Long-context retrieval
  • Open-weight fine-tuning
Kimi K2.5 details

Choose Meta Llama 3.3 70B Instruct Turbo for

  • Training toward a model you own
Meta Llama 3.3 70B Instruct Turbo details

Common questions

Which is cheaper, Kimi K2.5 or Meta Llama 3.3 70B Instruct Turbo?

Kimi K2.5, on this workload shape. At list prices it is $0.50/$2.80 per million tokens in and out against $1.04/$1.04 for Meta Llama 3.3 70B Instruct Turbo. Billed on Allocate: $0.54/$3.00 against $1.11/$1.11, list plus 7%.

Which has the bigger context window?

Kimi K2.5: 262,144 tokens (256K) against 131,072 (128K) for Meta Llama 3.3 70B Instruct Turbo.

Can I fine-tune Kimi K2.5 or Meta Llama 3.3 70B Instruct Turbo?

Both publish open weights (Kimi K2.5: Not listed; Meta Llama 3.3 70B Instruct Turbo: Llama community), so both can be fine-tuned. On Allocate the trained weights stay inside your boundary and belong to you.

Related comparisons

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Run the numbers on your workload

LLM price comparisonAgent cost calculatorKimi K2.5Meta Llama 3.3 70B Instruct Turbo

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.

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