Which is cheaper for input tokens: Google: Gemini 2.5 Pro Preview 05-06 or Google: Gemini 2.5 Pro?
Google: Gemini 2.5 Pro Preview 05-06 is cheaper or equal on input token cost by $0.00 per 1M tokens.
Head-to-Head Pricing Benchmark
Side-by-side pricing and context window comparison for production model selection.
Default Recommendation (120M input + 60M output)
Google: Gemini 2.5 Pro Preview 05-06 is lower-cost for the default monthly workload scenario.
Adjust the workload in the calculator below to see a live recommendation for your usage.
| Metric | Google: Gemini 2.5 Pro Preview 05-06 | Google: Gemini 2.5 Pro |
|---|---|---|
| Developer | ||
| Context Window | 1,048,576 | 1,048,576 |
| Input Cost / 1M Tokens | $1.25 | $1.25 |
| Output Cost / 1M Tokens | $10.00 | $10.00 |
| Projected Monthly Cost | $750 | $750 |
| Vision | ✅ Yes | ✅ Yes |
| Tool Calling | ✅ Yes | ✅ Yes |
| Structured Output | ✅ Yes | ✅ Yes |
| Reasoning | ✅ Yes | ✅ Yes |
| MMLU Score | 83.7 | 89.5 |
| GPQA | 82.2 | 88.7 |
Price History
Current Input / 1M
$1.25
Current Output / 1M
$10.00
Price History
Current Input / 1M
$1.25
Current Output / 1M
$10.00
Adjust your workload to see projected monthly costs.
Google: Gemini 2.5 Pro Preview 05-06
$750
per month
Lower costGoogle: Gemini 2.5 Pro
$750
per month
Live Recommendation
Google: Gemini 2.5 Pro Preview 05-06 is lower-cost at 120M input + 60M output tokens/month.
Continue evaluation with more “A vs B pricing” decision pages.
Quick Compare
Select two models to see a head-to-head pricing breakdown.
Common questions for Google: Gemini 2.5 Pro Preview 05-06 vs Google: Gemini 2.5 Pro pricing decisions.
Google: Gemini 2.5 Pro Preview 05-06 is cheaper or equal on input token cost by $0.00 per 1M tokens.
Google: Gemini 2.5 Pro Preview 05-06 is cheaper or equal on output token cost by $0.00 per 1M tokens.
$0.00 difference for the default scenario (120M input + 60M output tokens/month).
Use this page to compare context window and token pricing, then open each model page to evaluate additional alternatives and monthly workload fit.