GPT-5 mini vs Gemini 2.5 Flash

The two most common high-volume production choices.

Prices verified 4 Aug 2026
Input / 1M$0.250
Output / 1M$2.00
Cached input / 1M$0.025
Batch50% off
Context400K tokens
Blended (3:1)$0.688
Input / 1M$0.300
Output / 1M$2.50
Cached input / 1M$0.030
Batch50% off
Context1.0M tokens
Blended (3:1)$0.850

Monthly cost by workload shape

The same volume costs very different amounts depending on the shape of the traffic. No caching or batch applied.

WorkloadGPT-5 miniGemini 2.5 FlashDifference
Customer support chatbot
50K/day · 800 in / 300 out
$1,217.60$1,506.78GPT-5 minisaves $289.18Model it →
RAG / search answers
20K/day · 3000 in / 400 out
$943.64$1,156.72GPT-5 minisaves $213.08Model it →
Agent / tool-use loop
8.0K/day · 2500 in / 600 out
$444.42$547.92GPT-5 minisaves $103.50Model it →
Batch summarization
100K/day · 5000 in / 500 out
$6,849.00$8,371.00GPT-5 minisaves $1,522.00Model it →
Code assistant
15K/day · 2000 in / 800 out
$958.86$1,187.16GPT-5 minisaves $228.30Model it →

Frequently asked questions

Which is cheaper, GPT-5 mini or Gemini 2.5 Flash?

At a typical 3:1 input-to-output ratio, GPT-5 mini is cheaper: $0.688 blended per 1M tokens against $0.850. That holds at every input:output ratio — one model is cheaper on both rates.

What is the price difference between GPT-5 mini and Gemini 2.5 Flash?

GPT-5 mini: $0.250 input, $2.00 output per 1M tokens. Gemini 2.5 Flash: $0.300 input, $2.50 output. That is 1.2x on input and 1.3x on output.

Does prompt caching change the answer?

GPT-5 mini caches input at $0.025/1M and Gemini 2.5 Flash caches at $0.030/1M. For input-heavy workloads with repeated context, caching can matter more than the base rate difference — model both in the calculator rather than comparing rate cards.

This page compares published prices only. It makes no claim about which model performs better on any task — that depends entirely on your evaluations.

Rate cards decide nothing on their own.

Most teams run several models at once. Finitizer TokenOps shows what each one actually costs you in production, by team and feature, so a routing decision is made on data rather than on a price page.