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

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

GPT-5.4 nano charges $0.20 per million input tokens and $1.25 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

✓GPT-5.4 nano is cheaper overall: $1.45 per 1M tokens (in+out) vs $3.60 for Kimi K2.5 — saves $2.15 per 1M tokens
✓Input pricing: GPT-5.4 nano $0.20/1M vs Kimi K2.5 $0.60/1M
✓Output pricing: GPT-5.4 nano $1.25/1M vs Kimi K2.5 $3.00/1M
✓Context window: GPT-5.4 nano offers more (400K vs 262K)
✓Use our calculator below to estimate costs for your specific usage pattern

Input price (per 1M)

GPT-5.4 nano

$0.20

Kimi K2.5

$0.60

GPT-5.4 nano leads here

Output price (per 1M)

GPT-5.4 nano

$1.25

Kimi K2.5

$3.00

GPT-5.4 nano leads here

Context window

GPT-5.4 nano

400,000 tokens

Kimi K2.5

262,144 tokens

GPT-5.4 nano leads here

Cached input

GPT-5.4 nano

$0.020

Kimi K2.5

$0.100

GPT-5.4 nano 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 nano and Kimi K2.5.

Choose GPT-5.4 nano if input tokens dominate your bill

GPT-5.4 nano 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 GPT-5.4 nano if you generate long answers

GPT-5.4 nano 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 nano if context size is the blocker

GPT-5.4 nano 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 nano	Kimi K2.5	GPT-5.4 nano cached	Kimi K2.5 cached
Balanced conversation 50% input · 50% output	$0.0073	$0.0180	$0.0064	$0.0155
Input-heavy workflow 80% input · 20% output	$0.0041	$0.0108	$0.0027	$0.0068
Generation heavy 30% input · 70% output	$0.0094	$0.0228	$0.0088	$0.0213
Cached system prompt 90% cached input · 10% fresh output	$0.0030	$0.0084	$0.0014	$0.0039

Frequently asked questions

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

GPT-5.4 nano is cheaper for input tokens at $0.20 per 1M tokens compared to $0.60. For output, GPT-5.4 nano costs $1.25 per 1M tokens versus $3.00 for Kimi K2.5.

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

GPT-5.4 nano pricing: $0.20 per 1M input tokens and $1.25 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 nano costs $0.0002 input / $0.0013 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 nano 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 nano would cost approximately $4.50, while Kimi K2.5 would cost $12.00. GPT-5.4 nano is more economical for this usage pattern.

Do these models support prompt caching?

GPT-5.4 nano supports prompt caching at $0.020 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 GPT-5.4 nano for cost-sensitive applications with high input volume. Choose GPT-5.4 nano 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.