Published rates rank differently once prompt caching, batch discounts and your own input-to-output ratio are applied. Run both against your numbers.
GPT-5 vs Gemini 2.5 Pro
Two frontier models with near-identical headline pricing.
OpenAI retires GPT-5 on 11 Dec 2026; the named replacement is GPT-5.6 Sol.
Monthly cost by workload shape
The same volume costs very different amounts depending on the shape of the traffic. No caching or batch applied.
| Workload | GPT-5 | Gemini 2.5 Pro | Difference | |
|---|---|---|---|---|
Support chat 200/day · 1500 in / 300 out | $29.68 | $29.68 | Same cost | Model it → |
RAG / search answers 20K/day · 3000 in / 400 out | $4,718.20 | $4,718.20 | Same cost | Model it → |
Agent / tool-use loop 8.0K/day · 2500 in / 600 out | $2,222.12 | $2,222.12 | Same cost | Model it → |
Batch summarization 100K/day · 5000 in / 500 out | $34.2K | $34.2K | Same cost | Model it → |
Code assistant 15K/day · 2000 in / 800 out | $4,794.30 | $4,794.30 | Same cost | Model it → |
Frequently asked questions
Which is cheaper, GPT-5 or Gemini 2.5 Pro?
Neither, on list price: both charge $1.25 input and $10.00 output per 1M tokens, so they cost the same at every input:output ratio. Caching, batch and long-context terms decide the bill.
What is the price difference between GPT-5 and Gemini 2.5 Pro?
GPT-5: $1.25 input, $10.00 output per 1M tokens. Gemini 2.5 Pro: $1.25 input, $10.00 output. That is 1.0x on input and 1.0x on output.
Does prompt caching change the answer?
GPT-5 caches input at $0.125/1M and Gemini 2.5 Pro caches at $0.125/1M. For input-heavy workloads with repeated context, caching can matter more than the base rate difference, model both in the calculator rather than comparing rate cards.
This page compares published prices only. It makes no claim about which model performs better on any task; that depends entirely on your evaluations.
Rate cards decide nothing on their own.
Most teams run several models at once. Finitizer Tokenomics shows what each one actually costs you, read from your own bills and exports, which workloads could test a smaller model, what they would cost on an open-weight model, and, for models you serve yourself, the cost per token beside the API price. A routing decision starts from your spend rather than a price page.
See Finitizer Tokenomics