Is Gemini 2.5 Pro or GPT-5 Nano cheaper?

For a typical request (10,000 input + 2,000 output tokens), GPT-5 Nano is cheaper — about 96% less, or roughly $3120 saved per 100,000 requests. Gemini 2.5 Pro runs $1.25/$10 per 1M input/output tokens; GPT-5 Nano runs $0.05/$0.4.

Which has the larger context window?

Gemini 2.5 Pro, at 1,000,000 tokens versus 400,000.

How accurate are these token counts?

Gemini 2.5 Pro: Approximated with o200k_base; drift typically ~3% on English and code. GPT-5 Nano: Exact tokenization via the canonical OpenAI vocab (o200k_base). The dollar math itself is exact once the token count is known.

Gemini 2.5 Pro vs GPT-5 Nano: pricing & cost comparison

On input tokens, GPT-5 Nano is the cheaper of the two — 96% less per million ($1.25 vs $0.05). On output, GPT-5 Nano is 96% cheaper ($10 vs $0.4) — and since output is usually the dominant cost driver, that gap matters more than it looks.

Side by side

	Gemini 2.5 Pro	GPT-5 Nano
Input / 1M tokens	$1.25	$0.05
Output / 1M tokens	$10	$0.4
Context window	1,000,000	400,000
Token-count accuracy	±3%	exact
Cost — 10,000 input + 2,000 output tokens	$0.0325	$0.0013

What a real request costs

Take a representative turn — 10,000 input + 2,000 output tokens. Gemini 2.5 Pro comes to $0.0325, GPT-5 Nano to $0.0013. Across 100,000 requests that's a $3120 swing in favour of GPT-5 Nano. To run the numbers on your actual prompt, paste it into the calculator and toggle Compare across all models.

Which should you pick?

These are different vendors, so a switch means a different API and a slightly different tokenizer — budget a small calibration buffer. GPT-5 Nano give exact counts; the others land within a few percent. See the full breakdown on the dedicated pages for Gemini 2.5 Pro and GPT-5 Nano.

FAQ

Is Gemini 2.5 Pro or GPT-5 Nano cheaper?: For a typical request (10,000 input + 2,000 output tokens), GPT-5 Nano is cheaper — about 96% less, or roughly $3120 saved per 100,000 requests. Gemini 2.5 Pro runs $1.25/$10 per 1M input/output tokens; GPT-5 Nano runs $0.05/$0.4.
Which has the larger context window?: Gemini 2.5 Pro, at 1,000,000 tokens versus 400,000.
How accurate are these token counts?: Gemini 2.5 Pro: Approximated with o200k_base; drift typically ~3% on English and code. GPT-5 Nano: Exact tokenization via the canonical OpenAI vocab (o200k_base). The dollar math itself is exact once the token count is known.