Last verified 2025-12-18 (left) · 2026-03-19 (right)

Gemini 3 Flash vs GPT-5.4 nano — Pricing & Capability Comparison

Gemini 3 Flash charges $0.50 per million input tokens and $3.00 per million output tokens. GPT-5.4 nano comes in at $0.20 / $1.25. Context windows span 1M vs 400K tokens respectively.

TL;DR — Quick Comparison

✓GPT-5.4 nano is cheaper overall: $1.45 per 1M tokens (in+out) vs $3.50 for Gemini 3 Flash — saves $2.05 per 1M tokens
✓Input pricing: Gemini 3 Flash $0.50/1M vs GPT-5.4 nano $0.20/1M
✓Output pricing: Gemini 3 Flash $3.00/1M vs GPT-5.4 nano $1.25/1M
✓Context window: Gemini 3 Flash offers more (1M vs 400K)
✓Use our calculator below to estimate costs for your specific usage pattern

Input price (per 1M)

Gemini 3 Flash

$0.50

GPT-5.4 nano

$0.20

GPT-5.4 nano leads here

Output price (per 1M)

Gemini 3 Flash

$3.00

GPT-5.4 nano

$1.25

GPT-5.4 nano leads here

Context window

Gemini 3 Flash

1,000,000 tokens

GPT-5.4 nano

400,000 tokens

Gemini 3 Flash leads here

Cached input

Gemini 3 Flash

$0.050

GPT-5.4 nano

$0.020

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 Gemini 3 Flash and GPT-5.4 nano.

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 Gemini 3 Flash if context size is the blocker

Gemini 3 Flash 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	Gemini 3 Flash	GPT-5.4 nano	Gemini 3 Flash cached	GPT-5.4 nano cached
Balanced conversation 50% input · 50% output	$0.0175	$0.0073	$0.0152	$0.0064
Input-heavy workflow 80% input · 20% output	$0.0100	$0.0041	$0.0064	$0.0027
Generation heavy 30% input · 70% output	$0.0225	$0.0094	$0.0212	$0.0088
Cached system prompt 90% cached input · 10% fresh output	$0.0075	$0.0030	$0.0034	$0.0014

Frequently asked questions

Which is cheaper: Gemini 3 Flash or GPT-5.4 nano?

GPT-5.4 nano is cheaper for input tokens at $0.20 per 1M tokens compared to $0.50. For output, GPT-5.4 nano costs $1.25 per 1M tokens versus $3.00 for Gemini 3 Flash.

What is the cost per 1M tokens for Gemini 3 Flash?

Gemini 3 Flash pricing: $0.50 per 1M input tokens and $3.00 per 1M output tokens. Context window: 1,000,000 tokens.

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.

How much does it cost per 1K tokens?

Per 1K tokens: Gemini 3 Flash costs $0.0005 input / $0.0030 output. GPT-5.4 nano costs $0.0002 input / $0.0013 output. This is useful for calculating small-scale usage costs.

Which model supports a larger context window?

Gemini 3 Flash offers 1,000,000 tokens (1M) versus 400K for GPT-5.4 nano.

What is the estimated monthly cost for typical usage?

For a typical workload of 10M input + 2M output tokens per month: Gemini 3 Flash would cost approximately $11.00, while GPT-5.4 nano would cost $4.50. GPT-5.4 nano is more economical for this usage pattern.

Do these models support prompt caching?

Gemini 3 Flash supports prompt caching at $0.050 per 1M cached tokens, reducing costs for repeated context by up to 90%. GPT-5.4 nano supports caching at $0.020 per 1M tokens, saving up to 90%.

Which model is best for my use case?

Choose GPT-5.4 nano for cost-sensitive applications with high input volume. Choose Gemini 3 Flash if you need 1M context for long documents or conversations. Consider prompt caching if you have repeated context. Use our token calculator to model your specific usage pattern.