Which one costs less
On a representative request of 1,000 input and 500 output tokens, GPT-5 mini comes to $0.001250 against $0.006000 — a factor of 4.8.
Context and token counting
Context windows are comparable here, so the decision comes down to price and output quality rather than capacity. Check your real context size in the context window calculator.
One practical asymmetry: token counts for GPT-5 mini can be computed exactly in a browser, while Gemini 3.5 Flash has no public browser-capable tokenizer, so any pre-flight count for it is an estimate. That matters for cost forecasting and for anything that has to sit close to a context limit. The methodology page sets out what the estimate is based on.
The same two models at a million requests a month
One sample request settles nothing, because the two providers price input and output on different ratios. These are four representative workload shapes, each at a million requests a month, computed from the rates in the table.
GPT-5 mini is cheaper on all four shapes, by between 4.7× and 5.6×. The gap is widest on document summary and narrowest on code generation, so the saving is largest exactly where the volume usually is.
The absolute figures matter as much as the ratios. On document summary alone the difference is $30,600.00 a month — the kind of number that justifies an afternoon of measurement before the decision rather than after it.
What this comparison deliberately does not say
It does not say which one is better. Published benchmark scores are run on public sets that models may have seen, they are reported by parties with an interest in the result, and none of them are measured on your data. Repeating them here would add confident-sounding numbers with nothing behind them.
Price, context window and tokenizer availability are facts, with sources and dates attached. Quality is a measurement you have to take yourself — build an evaluation set with the eval dataset builder and run both models against your own cases. Thirty real examples will tell you more than any leaderboard.
If a migration between these two is what you are weighing, the model migration checklist covers what changes beyond price: tool calling formats, stop reasons, refusal behaviour and the re-tuning that prompts usually need.
Price your own workload
The table above uses representative shapes, not yours. Put your real request volume, prompt size and output length into the cost calculator, and measure a real prompt in the token counter first so the inputs are counted rather than guessed. Prices above were read from each provider’s documentation on August 3, 2026 and August 3, 2026; follow the source links to confirm before committing spend.
Questions
Is GPT-5 mini always cheaper than Gemini 3.5 Flash?
On every workload shape priced above, yes — GPT-5 mini is cheaper, by between 4.7× and 5.6× depending on how output-heavy the work is. Price is not the only criterion, but on this pair it does not reverse.
Can I count tokens for Gemini 3.5 Flash and GPT-5 mini before sending?
Only GPT-5 mini. It has a published tokenizer that runs in a browser, so counts are exact. Gemini 3.5 Flash does not, so any pre-flight figure for it is an estimate — which matters most when a prompt sits close to the context limit.
How current are these prices?
Gemini 3.5 Flash was read from Google's own pricing page on August 3, 2026 and GPT-5 mini from OpenAI's on August 3, 2026. Both source links are in the table above. Model prices fall without notice, so confirm at the source before committing to a volume decision.
Does the cheaper model produce worse answers?
Not necessarily, and this page does not claim to know. Published benchmark scores are not reproducible on your data, so the only honest measurement is your own evaluation set on your own cases. Price is the part that can be stated as fact; quality is the part you have to measure.
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