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Comparisons

DeepSeek R1 0528 NVFP4 vs Kimi K2.7 Code

On provider list prices, Kimi K2.7 Code costs $0.95 per million input tokens against $3 for DeepSeek R1 0528 NVFP4: 3.2x apart. Output is $4 against $7 (1.8x). On Allocate both bill at list plus the 7% transaction fee.

DeepSeek R1 0528 NVFP4 Kimi K2.7 Code
LabDeepseekMoonshot AI
AccessOpen weightsOpen weights
Context window160K tokens256K tokens
List price, input$3 / M tokens$0.95 / M tokens
List price, output$7 / M tokens$4 / M tokens
Cached inputn/a$0.19 / M tokens
LicenseMITNot 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 $6,050 on DeepSeek R1 0528 NVFP4 at list: a gap of $3,510, or 2.4x.

Kimi K2.7 Code reads 256K tokens per request against 160K for DeepSeek R1 0528 NVFP4, 1.6x the window. That decides which one can take whole documents without splitting them.

Kimi K2.7 Code$0.95$4
DeepSeek R1 0528 NVFP4$3$7
InputOutput

Choose DeepSeek R1 0528 NVFP4 for

  • Fine-tuning under a permissive license (MIT)
DeepSeek R1 0528 NVFP4 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 160K tokens)
  • Published cached-input pricing ($0.19 per M tokens)
Kimi K2.7 Code details

Common questions

Which is cheaper, DeepSeek R1 0528 NVFP4 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/$7 for DeepSeek R1 0528 NVFP4. Billed on Allocate: $1.02/$4.28 against $3.21/$7.49, list plus 7%.

Which has the bigger context window?

Kimi K2.7 Code: 262,144 tokens (256K) against 163,840 (160K) for DeepSeek R1 0528 NVFP4.

Can I fine-tune DeepSeek R1 0528 NVFP4 or Kimi K2.7 Code?

Both publish open weights (DeepSeek R1 0528 NVFP4: MIT; 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

DeepSeek R1 0528 NVFP4 vs Deepseek V4 ProKimi K2.7 Code vs Deepseek V4 ProDeepSeek R1 0528 NVFP4 vs Meta Llama 3.1 405B InstructKimi K2.7 Code vs Meta Llama 3.1 405B InstructDeepSeek R1 0528 NVFP4 vs GLM 5.2Kimi K2.7 Code vs GLM 5.2

Run the numbers on your workload

LLM price comparisonAgent cost calculatorDeepSeek R1 0528 NVFP4Kimi 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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