Comparison page
Gemini 2.5 Flash vs GPT-5 nano
Compare two ultra-efficient tiers for routing, extraction, and other high-volume production tasks.
Side-by-side pricing
The table uses the same example request on both models so you can compare ultra-low-cost routing and extraction without changing the prompt shape.
Input 1,000 / Output 250
| Model | Input / 1M | Cached input / 1M | Output / 1M | Example request | Source |
|---|---|---|---|---|---|
Gemini 2.5 Flash Fast Google model for balanced cost, latency, and multimodal capability. | $0.540 | No public rate | $4.50 | $0.0017 | Google Gemini pricing |
GPT-5 nano Lowest-cost OpenAI model for routing, classification, and ultra-high-volume requests. | $0.0500 | No public rate | $0.400 | $0.0002 | OpenAI pricing |
When to choose each model
Gemini 2.5 Flash
Choose Gemini 2.5 Flash when you want the economics or capability profile of Google Gemini and you can justify the published rate.
GPT-5 nano
Choose GPT-5 nano when you need the second option in this comparison and want to test whether it lowers spend without hurting the workflow.
Use cases
- • High-volume tagging, routing, and classification.
- • Cost-sensitive assistant backends with strict budgets.
- • Evaluating the cheapest acceptable default model.
If the prompt is noisy or repetitive, run it through the prompt optimizer first.
If the brief is too loose, use the context engineer to tighten the instructions before you compare models again.
For the broader pricing strategy, read the LLM cost optimization pillar and the cost-per-million-tokens cheat sheet.
FAQ
Which model is cheaper?
GPT-5 nano is the lower-cost input-rate option, while Gemini 2.5 Flash remains a strong low-cost multimodal option.
When would Gemini be preferred?
Use Gemini when the Google ecosystem or multimodal behavior matters more than absolute token cost.
Is this a good default comparison?
Yes. These are the kinds of models teams use when they want to reduce spend on routine work without giving up reliability.