Gemini 1.5 Pro API Pricing
Gemini 1.5 Pro costs $1.25 per million input tokens and $5.00 per million output tokens at list price. Here is the cost at volume and how it ranks against 6 other models (4 of 7 by blended price).
alternatives
Gemini 1.5 Pro vs every other model on price
| model | blended / 1M | vs Gemini 1.5 Pro |
|---|---|---|
| Gemini 1.5 FlashGoogle (Vertex) | $0.13 | 94% cheaper |
| GPT-4o miniOpenAI | $0.26 | 88% cheaper |
| Claude HaikuAnthropic | $1.60 | 27% cheaper |
| Claude OpusAnthropic | $30.00 | 14× the price |
| Claude SonnetAnthropic | $6.00 | 2.7× the price |
| GPT-4oOpenAI | $4.38 | 2.0× the price |
cheaper than Gemini 1.5 Pro · public list prices as of 2026-06 · estimates, not quotes
when_it_fits
When Gemini 1.5 Pro is the right call
Google's balanced mid-tier model on Vertex.
On blended price, Gemini 1.5 Pro is mid-pack: 3 compared here are cheaper and 3 cost more. Whether its rate is worth paying comes down to fit. A model that resolves a task in fewer attempts or shorter prompts can come out ahead of a cheaper one that needs retries, so price it on your own evaluation set and token mix rather than the per-million rate alone.
faq
Questions & answers
- How much does Gemini 1.5 Pro cost?
- Gemini 1.5 Pro is $1.25 per million input tokens and $5.00 per million output tokens, at public list price (2026-06). At a typical 3:1 input-to-output mix that blends to about $2.19 per million tokens.
- What is a cheaper alternative to Gemini 1.5 Pro?
- The cheapest option compared here is Gemini 1.5 Flash at about $0.13 per million blended, roughly 94% less than Gemini 1.5 Pro. Whether it fits depends on your task: a cheaper model that needs retries or longer prompts can cost more in practice.
- How much does Gemini 1.5 Pro cost to run an AI agent per month?
- On a representative agent workload (2,000 requests a day, 3 tool calls, RAG on), Gemini 1.5 Pro comes to about $2,267 a month. That includes the agentic context tax, where every tool call re-sends the conversation and you pay for it again. Your real figure depends on volume and tokens, so run it in the calculator.
A model's list price is not its bill.
Prompt caching, context trimming, and the right tier per task usually move an LLM bill more than the model choice does. Book a call, or leave your email and I'll reach out.
Prefer proof first? See how this plays out in real case studies →