Gemini 2.5 Pro vs GPT-4.1: pricing & cost comparison
On input tokens, Gemini 2.5 Pro is the cheaper of the two — 38% less per million ($1.25 vs $2). On output, GPT-4.1 is 20% cheaper ($10 vs $8) — 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 | |
|---|---|---|
| Input / 1M tokens | $1.25 | $2 |
| Output / 1M tokens | $10 | $8 |
| Context window | 1,000,000 | 1,047,576 |
| Token-count accuracy | ±3% | exact |
| Cost — 10,000 input + 2,000 output tokens | $0.0325 | $0.036 |
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 to $0.036. Across 100,000 requests that's a $350 swing in favour of Gemini 2.5 Pro. 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 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.
FAQ
- Is Gemini 2.5 Pro or GPT-4.1 cheaper?
- For a typical request (10,000 input + 2,000 output tokens), Gemini 2.5 Pro is cheaper — about 10% less, or roughly $350 saved per 100,000 requests. Gemini 2.5 Pro runs $1.25/$10 per 1M input/output tokens; GPT-4.1 runs $2/$8.
- Which has the larger context window?
- GPT-4.1, 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: Exact tokenization via the canonical OpenAI vocab (o200k_base). The dollar math itself is exact once the token count is known.