Gemini 2.5 Pro vs GPT-4.1 Mini: pricing & cost comparison
On input tokens, GPT-4.1 Mini is the cheaper of the two — 68% less per million ($1.25 vs $0.4). On output, GPT-4.1 Mini is 84% cheaper ($10 vs $1.6) — and since output is usually the dominant cost driver, that gap matters more than it looks.
Side by side
| Gemini 2.5 Pro | GPT-4.1 Mini | |
|---|---|---|
| Input / 1M tokens | $1.25 | $0.4 |
| Output / 1M tokens | $10 | $1.6 |
| Context window | 1,000,000 | 1,047,576 |
| Token-count accuracy | ±3% | exact |
| Cost — 10,000 input + 2,000 output tokens | $0.0325 | $0.0072 |
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-4.1 Mini to $0.0072. Across 100,000 requests that's a $2530 swing in favour of GPT-4.1 Mini. 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-4.1 Mini 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-4.1 Mini.
FAQ
- Is Gemini 2.5 Pro or GPT-4.1 Mini cheaper?
- For a typical request (10,000 input + 2,000 output tokens), GPT-4.1 Mini is cheaper — about 78% less, or roughly $2530 saved per 100,000 requests. Gemini 2.5 Pro runs $1.25/$10 per 1M input/output tokens; GPT-4.1 Mini runs $0.4/$1.6.
- Which has the larger context window?
- GPT-4.1 Mini, at 1,047,576 tokens versus 1,000,000.
- How accurate are these token counts?
- Gemini 2.5 Pro: Approximated with o200k_base; drift typically ~3% on English and code. GPT-4.1 Mini: Exact tokenization via the canonical OpenAI vocab (o200k_base). The dollar math itself is exact once the token count is known.