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Kimi K2.5 vs GPT-4.1

Detailed pricing comparison and cost analysis.

Updated September 2026

Cost Simulator

Kimi K2.5 Cost
$1.20
GPT-4.1 Cost
$3.60
Kimi K2.5 is 67% cheaper
FeatureKimi K2.5GPT-4.1
ProviderMoonshot AIOpenAI
Input Price (1M)$0.60$2.00
Output Price (1M)$3.00$8.00
Context Window262,1441,000,000

Verdict

Kimi K2.5 costs $0.60 per 1M input tokens and $3.00 per 1M output tokens. GPT-4.1 costs $2.00 per 1M input tokens and $8.00 per 1M output tokens. Kimi K2.5 is 70% cheaper on input tokens than GPT-4.1. For output tokens, Kimi K2.5 is the more affordable option at $3.00/1M vs $8.00.

On context window, GPT-4.1 supports 1,000,000 tokens — meaning it can fit more conversation history, documents, or code in a single request. This matters for RAG pipelines, long document analysis, and agentic workflows where context builds up over many turns.

When to choose Kimi K2.5

  • ✓ You need the lowest input token cost ($ 0.60/1M)
  • ✓ Your workload is output-heavy — Kimi K2.5 generates text cheaper
  • ✓ You are already integrated with Moonshot AI

When to choose GPT-4.1

  • ✓ You need a larger context window (1,000,000 tokens)
  • ✓ You are already integrated with OpenAI

Use the calculator above to simulate your specific workload and find the exact break-even point. For most applications, the cheapest model is the one that minimises your total monthly bill given your input-to-output token ratio.

Frequently Asked Questions

Is Kimi K2.5 cheaper than GPT-4.1?

Kimi K2.5 is cheaper on input tokens at $0.60/1M vs $2.00/1M for GPT-4.1 — a 70% saving.

What is the context window of Kimi K2.5 vs GPT-4.1?

Kimi K2.5 has a 262,144-token context window. GPT-4.1 has a 1,000,000-token context window. GPT-4.1 supports the larger context, suitable for longer documents and agentic workflows.

Which model is better: Kimi K2.5 or GPT-4.1?

The best choice depends on your use case. For cost efficiency on input tokens, Kimi K2.5 is the cheaper option. For maximum context length, GPT-4.1 supports 1,000,000 tokens. Use the comparison table above to find the right fit for your workload.