Gemini Cost Calculator
Estimate Google Gemini API spend for long-context assistants, multimodal apps, and AI product planning.
Estimate Google Gemini API costs from input tokens, output tokens, and request volume. Calculate monthly AI spend for Gemini apps. Everything runs as a static browser tool with no login, database, or AI API call.
Gemini planning
Estimate Gemini API spend
Model Google Gemini costs for long-context assistants, multimodal workflows, and AI apps before production usage grows.
Estimated Google spend
Gemini 2.5 Flash
Input $0.30 / 1M tokens · Output $2.50 / 1M tokens
Useful for high-volume app flows where speed and cost matter.
Per day
$3.92
Per month
$118
Per year
$1,431
Per 1k requests
$4.90
Compare Google model costs
Based on your token and request assumptions above. Verify current provider pricing before production budgeting.
| Model | Input price | Output price | Monthly estimate |
|---|---|---|---|
| Gemini 2.5 Pro | $1.25 / 1M | $10.00 / 1M | $480 |
| Gemini 2.5 Flash | $0.30 / 1M | $2.50 / 1M | $118 |
Estimate Gemini API costs for Pro and Flash-style models
Plan long-context AI app budgets from token volume
Compare monthly spend without uploading prompts or calling Google APIs
Use cases
Best practices
FAQ
What is a Gemini cost calculator?
A Gemini cost calculator estimates Google AI API spend from model price, token usage, and request volume.
How are Gemini API costs calculated?
Gemini API estimates combine input token cost and output token cost, then multiply by the number of requests in your workload.
What Gemini models are included?
This calculator includes reference Gemini Pro and Flash-style pricing rows for planning comparisons.
How does long context affect Gemini cost?
Long prompts, retrieved documents, and large chat history increase input tokens and can raise monthly spend quickly.
Does this tool call the Gemini API?
No. It is a static browser calculator and does not call Google APIs or upload your inputs.
How can I reduce Gemini API costs?
Trim context, summarize previous turns, use smaller models for simple tasks, and set strict output token limits.