Last verified 2026-03-19 (left) · 2026-03-12 (right)

GPT-5.4 mini vs Kimi K2.5 — Pricing & Capability Comparison

GPT-5.4 mini charges $0.75 per million input tokens and $4.50 per million output tokens. Kimi K2.5 comes in at $0.60 / $3.00. Context windows span 400K vs 262K tokens respectively.

TL;DR — Quick Comparison

✓Kimi K2.5 is cheaper overall: $3.60 per 1M tokens (in+out) vs $5.25 for GPT-5.4 mini — saves $1.65 per 1M tokens
✓Input pricing: GPT-5.4 mini $0.75/1M vs Kimi K2.5 $0.60/1M
✓Output pricing: GPT-5.4 mini $4.50/1M vs Kimi K2.5 $3.00/1M
✓Context window: GPT-5.4 mini offers more (400K vs 262K)
✓Use our calculator below to estimate costs for your specific usage pattern

Input price (per 1M)

GPT-5.4 mini

$0.75

Kimi K2.5

$0.60

Kimi K2.5 leads here

Output price (per 1M)

GPT-5.4 mini

$4.50

Kimi K2.5

$3.00

Kimi K2.5 leads here

Context window

GPT-5.4 mini

400,000 tokens

Kimi K2.5

262,144 tokens

GPT-5.4 mini leads here

Cached input

GPT-5.4 mini

$0.075

Kimi K2.5

$0.100

GPT-5.4 mini leads here

Which one should you choose?

Skip the spreadsheet if you just need the practical takeaway. Use these rules when deciding between GPT-5.4 mini and Kimi K2.5.

Choose Kimi K2.5 if input tokens dominate your bill

Kimi K2.5 has the lower input rate, which usually matters most for chat, RAG, classification, and long-prompt workflows where prompt volume stays much larger than generated output.

Choose Kimi K2.5 if you generate long answers

Kimi K2.5 is cheaper on output tokens, so it tends to win for report generation, coding assistance, reasoning traces, and any workflow where completions are long.

Choose GPT-5.4 mini if context size is the blocker

GPT-5.4 mini offers the larger published context window, which is more important than small pricing differences when you need to fit large files, long chats, or multi-document prompts into one request.

Cost comparison for 10K-token workloads

Side-by-side pricing for identical workloads (10,000 total tokens per request) across different distributions.

Scenario	GPT-5.4 mini	Kimi K2.5	GPT-5.4 mini cached	Kimi K2.5 cached
Balanced conversation 50% input · 50% output	$0.0262	$0.0180	$0.0229	$0.0155
Input-heavy workflow 80% input · 20% output	$0.0150	$0.0108	$0.0096	$0.0068
Generation heavy 30% input · 70% output	$0.0338	$0.0228	$0.0317	$0.0213
Cached system prompt 90% cached input · 10% fresh output	$0.0112	$0.0084	$0.0052	$0.0039

Frequently asked questions

Which is cheaper: GPT-5.4 mini or Kimi K2.5?

Kimi K2.5 is cheaper for input tokens at $0.60 per 1M tokens compared to $0.75. For output, Kimi K2.5 costs $3.00 per 1M tokens versus $4.50 for GPT-5.4 mini.

What is the cost per 1M tokens for GPT-5.4 mini?

GPT-5.4 mini pricing: $0.75 per 1M input tokens and $4.50 per 1M output tokens. Context window: 400,000 tokens.

What is the cost per 1M tokens for Kimi K2.5?

Kimi K2.5 pricing: $0.60 per 1M input tokens and $3.00 per 1M output tokens. Context window: 262,144 tokens.

How much does it cost per 1K tokens?

Per 1K tokens: GPT-5.4 mini costs $0.0008 input / $0.0045 output. Kimi K2.5 costs $0.0006 input / $0.0030 output. This is useful for calculating small-scale usage costs.

Which model supports a larger context window?

GPT-5.4 mini offers 400,000 tokens (400K) versus 262K for Kimi K2.5.

What is the estimated monthly cost for typical usage?

For a typical workload of 10M input + 2M output tokens per month: GPT-5.4 mini would cost approximately $16.50, while Kimi K2.5 would cost $12.00. Kimi K2.5 is more economical for this usage pattern.

Do these models support prompt caching?

GPT-5.4 mini supports prompt caching at $0.075 per 1M cached tokens, reducing costs for repeated context by up to 90%. Kimi K2.5 supports caching at $0.100 per 1M tokens, saving up to 83%.

Which model is best for my use case?

Choose Kimi K2.5 for cost-sensitive applications with high input volume. Choose GPT-5.4 mini if you need 400K context for long documents or conversations. Consider prompt caching if you have repeated context. Use our token calculator to model your specific usage pattern.