GPT-4o vs Gemini 1.5 Pro
On a typical agent workload, Gemini 1.5 Pro runs about 50% cheaper than GPT-4o at list price ($2,267 against $4,532 a month). Here is the full cost breakdown.
| metric | GPT-4oOpenAI · Mid tier | Gemini 1.5 ProGoogle (Vertex) · Mid tier |
|---|---|---|
| Input priceper 1M tokens | $2.50 | $1.25 |
| Output priceper 1M tokens | $10.00 | $5.00 |
| Blended price3:1 input:output mix, per 1M | $4.38 | $2.19 |
| One chat request1.5k in / 600 out, no tools | $0.0097 | $0.0049 |
| Agent workload / month2,000 req/day, 3 tool calls, RAG on | $4,532 | $2,267 |
cheaper · public list prices as of 2026-06 · estimates, not quotes
which_to_choose
Which one should you pick?
On price alone, Gemini 1.5 Pro wins. It comes in around 50% cheaper than GPT-4o on the same agent workload ($2,267 against $4,532 a month at 2,000 requests a day), and the gap widens as volume and tool calls grow, because every tool call re-sends the context and you pay for it at each model's rate.
The case for GPT-4o comes down to fit. If it resolves a task in fewer attempts or shorter prompts on your workload, the higher per-token rate can still come out ahead of a cheaper model that needs retries. Price the two on your own evaluation set and your actual token mix before you commit, because the list price rarely decides it alone.
GPT-4o: OpenAI's flagship general-purpose model. Gemini 1.5 Pro: Google's balanced mid-tier model on Vertex.
faq
Questions & answers
- Is GPT-4o or Gemini 1.5 Pro cheaper?
- Gemini 1.5 Pro is cheaper at list price. It runs $1.25 per million input tokens and $5.00 per million output tokens, against $2.50 and $10.00 for GPT-4o. On a typical agent workload that works out to about 50% less per month.
- What is the price difference between GPT-4o and Gemini 1.5 Pro?
- GPT-4o is $2.50 in and $10.00 out per million tokens; Gemini 1.5 Pro is $1.25 in and $5.00 out. Output tokens cost several times more than input on both, so the gap that matters most depends on how much your workload generates versus reads.
- Should I switch from GPT-4o to Gemini 1.5 Pro to cut cost?
- Possibly. Gemini 1.5 Pro is about 50% cheaper on the same workload, and the saving grows with volume and tool calls because each tool call re-sends the context. But a cheaper model that needs retries or longer prompts can cost more in practice, so price both on your own evaluation set and your actual token mix before you switch.
Picking a model is the easy part. Making it cheap in production is the work.
Prompt caching, context trimming, and the right tier per task usually cut an LLM bill by more than switching models. Book a call, or leave your email and I'll reach out.
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