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Comparisons

Meta Llama 3.1 405B Instruct vs Kimi K2.7 Code

On provider list prices, Kimi K2.7 Code costs $0.95 per million input tokens against $3.50 for Meta Llama 3.1 405B Instruct: 3.7x apart. Output is $4 against $3.50. On Allocate both bill at list plus the 7% transaction fee.

Meta Llama 3.1 405B Instruct Kimi K2.7 Code
LabMetaMoonshot AI
AccessOpen weightsOpen weights
Context window4K tokens256K tokens
List price, input$3.5 / M tokens$0.95 / M tokens
List price, output$3.5 / M tokens$4 / M tokens
Cached inputn/a$0.19 / M tokens
LicenseLlama communityNot listed
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 $2,540 a month on Kimi K2.7 Code and $5,425 on Meta Llama 3.1 405B Instruct at list: a gap of $2,885, or 2.1x.

Kimi K2.7 Code 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.

Kimi K2.7 Code$0.95$4
Meta Llama 3.1 405B Instruct$3.50$3.50
InputOutput

Choose Meta Llama 3.1 405B Instruct for

  • Training toward a model you own
Meta Llama 3.1 405B Instruct details

Choose Kimi K2.7 Code for

  • The lower list price ($0.95 in / $4 out per M tokens)
  • The longer context window (256K vs 4K tokens)
  • Published cached-input pricing ($0.19 per M tokens)
Kimi K2.7 Code details

Common questions

Which is cheaper, Meta Llama 3.1 405B Instruct or Kimi K2.7 Code?

Kimi K2.7 Code, on this workload shape. At list prices it is $0.95/$4 per million tokens in and out against $3.50/$3.50 for Meta Llama 3.1 405B Instruct. Billed on Allocate: $1.02/$4.28 against $3.75/$3.75, list plus 7%.

Which has the bigger context window?

Kimi K2.7 Code: 262,144 tokens (256K) against 4,096 (4K) for Meta Llama 3.1 405B Instruct.

Can I fine-tune Meta Llama 3.1 405B Instruct or Kimi K2.7 Code?

Both publish open weights (Meta Llama 3.1 405B Instruct: Llama community; Kimi K2.7 Code: Not listed), so both can be fine-tuned. On Allocate the trained weights stay inside your boundary and belong to you.

Related comparisons

Meta Llama 3.1 405B Instruct vs Meta Llama 3.3 70B Instruct TurboKimi K2.7 Code vs Meta Llama 3.3 70B Instruct TurboMeta Llama 3.1 405B Instruct vs Llama 4 Scout Instruct (17Bx16E)Kimi K2.7 Code vs Llama 4 Scout Instruct (17Bx16E)Meta Llama 3.1 405B Instruct vs GLM 5.2Kimi K2.7 Code vs GLM 5.2

Run the numbers on your workload

LLM price comparisonAgent cost calculatorMeta Llama 3.1 405B InstructKimi K2.7 Code

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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