Comparisons

Kimi K2.7 Code vs OpenAI GPT-OSS 120B

On provider list prices, OpenAI GPT-OSS 120B costs $0.15 per million input tokens against $0.95 for Kimi K2.7 Code: 6.3x apart. Output is $0.60 against $4 (6.7x). On Allocate both bill at list plus the 7% transaction fee.

Kimi K2.7 Code OpenAI GPT-OSS 120B
LabMoonshot AIOpenAI
AccessOpen weightsOpen weights
Context window256K tokens128K tokens
List price, input$0.95 / M tokens$0.15 / M tokens
List price, output$4 / M tokens$0.6 / M tokens
Cached input$0.19 / M tokensn/a
LicenseNot listedCustom license
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 $390 a month on OpenAI GPT-OSS 120B and $2,540 on Kimi K2.7 Code at list: a gap of $2,150, or 6.5x.

Kimi K2.7 Code reads 256K tokens per request against 128K for OpenAI GPT-OSS 120B, 2.0x the window. That decides which one can take whole documents without splitting them.

OpenAI GPT-OSS 120B$0.15$0.60
Kimi K2.7 Code$0.95$4
InputOutput

Choose Kimi K2.7 Code for

  • The longer context window (256K vs 128K tokens)
  • Published cached-input pricing ($0.19 per M tokens)
Kimi K2.7 Code details

Choose OpenAI GPT-OSS 120B for

  • The lower list price ($0.15 in / $0.60 out per M tokens)
OpenAI GPT-OSS 120B details

Common questions

Which is cheaper, Kimi K2.7 Code or OpenAI GPT-OSS 120B?

OpenAI GPT-OSS 120B, on this workload shape. At list prices it is $0.15/$0.60 per million tokens in and out against $0.95/$4 for Kimi K2.7 Code. Billed on Allocate: $0.16/$0.64 against $1.02/$4.28, list plus 7%.

Which has the bigger context window?

Kimi K2.7 Code: 262,144 tokens (256K) against 131,072 (128K) for OpenAI GPT-OSS 120B.

Can I fine-tune Kimi K2.7 Code or OpenAI GPT-OSS 120B?

Both publish open weights (Kimi K2.7 Code: Not listed; OpenAI GPT-OSS 120B: Custom license), 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.