Last verified 2025-11-26 (left) · 2026-03-19 (right)

GPT-5.1 nano vs GPT-5.4 mini — Pricing & Capability Comparison

GPT-5.1 nano charges $0.05 per million input tokens and $0.40 per million output tokens. GPT-5.4 mini comes in at $0.75 / $4.50. Context windows span 200K vs 400K tokens respectively.

TL;DR — Quick Comparison

✓GPT-5.1 nano is cheaper overall: $0.45 per 1M tokens (in+out) vs $5.25 for GPT-5.4 mini — saves $4.80 per 1M tokens
✓Input pricing: GPT-5.1 nano $0.05/1M vs GPT-5.4 mini $0.75/1M
✓Output pricing: GPT-5.1 nano $0.40/1M vs GPT-5.4 mini $4.50/1M
✓Context window: GPT-5.4 mini offers more (400K vs 200K)
✓Use our calculator below to estimate costs for your specific usage pattern

Input price (per 1M)

GPT-5.1 nano

$0.05

GPT-5.4 mini

$0.75

GPT-5.1 nano leads here

Output price (per 1M)

GPT-5.1 nano

$0.40

GPT-5.4 mini

$4.50

GPT-5.1 nano leads here

Context window

GPT-5.1 nano

200,000 tokens

GPT-5.4 mini

400,000 tokens

GPT-5.4 mini leads here

Cached input

GPT-5.1 nano

$0.005

GPT-5.4 mini

$0.075

GPT-5.1 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-5.1 nano and GPT-5.4 mini.

Choose GPT-5.1 nano if input tokens dominate your bill

GPT-5.1 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-5.1 nano if you generate long answers

GPT-5.1 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 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-5.1 nano	GPT-5.4 mini	GPT-5.1 nano cached	GPT-5.4 mini cached
Balanced conversation 50% input · 50% output	$0.0023	$0.0262	$0.0020	$0.0229
Input-heavy workflow 80% input · 20% output	$0.0012	$0.0150	$0.0008	$0.0096
Generation heavy 30% input · 70% output	$0.0030	$0.0338	$0.0028	$0.0317
Cached system prompt 90% cached input · 10% fresh output	$0.0009	$0.0112	$0.0004	$0.0052

Frequently asked questions

Which is cheaper: GPT-5.1 nano or GPT-5.4 mini?

GPT-5.1 nano is cheaper for input tokens at $0.05 per 1M tokens compared to $0.75. For output, GPT-5.1 nano costs $0.40 per 1M tokens versus $4.50 for GPT-5.4 mini.

What is the cost per 1M tokens for GPT-5.1 nano?

GPT-5.1 nano pricing: $0.05 per 1M input tokens and $0.40 per 1M output tokens. Context window: 200,000 tokens.

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.

How much does it cost per 1K tokens?

Per 1K tokens: GPT-5.1 nano costs $0.0001 input / $0.0004 output. GPT-5.4 mini costs $0.0008 input / $0.0045 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 200K for GPT-5.1 nano.

What is the estimated monthly cost for typical usage?

For a typical workload of 10M input + 2M output tokens per month: GPT-5.1 nano would cost approximately $1.30, while GPT-5.4 mini would cost $16.50. GPT-5.1 nano is more economical for this usage pattern.

Do these models support prompt caching?

GPT-5.1 nano supports prompt caching at $0.005 per 1M cached tokens, reducing costs for repeated context by up to 90%. GPT-5.4 mini supports caching at $0.075 per 1M tokens, saving up to 90%.

Which model is best for my use case?

Choose GPT-5.1 nano 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.