gemini-2.5-flash vs gpt-5.4-nano for classification
As of September 7, 2026, gemini-2.5-flash measures 0.963 on classification vs 0.800 for gpt-5.4-nano (+0.162), at 1.3× the price ($0.0391 vs $0.0301 per 1K requests).
same suite, same items, same scoring — frontier v8, measured 2026-09-07 · method
| model | vendor | measured quality | $ / 1K requests | p95 latency |
|---|---|---|---|---|
| gemini-2.5-flash | 0.963 | $0.0391 | 575 ms | |
| gpt-5.4-nano | openai | 0.800 | $0.0301 | 1237 ms |
Classification — routing tickets, labeling intents, applying rules to text — is the workload where model choice matters most and is judged least. The task has a right answer, so quality is measurable exactly, and the measured gap between the cheapest adequate model and a frontier model is routinely the widest of any workload. Paying frontier prices here is the most common form of silent AI overspend.
These two are part of a larger measured frontier — what is the best model for text classification shows every measured option for this workload, and other workloads rank these models differently: a model that wins here can lose on another kind of work, which is the whole argument for routing per workload rather than picking one model for everything.
Potion routes each request to the cheapest option measured at your quality bar — including picks this public page does not name. Get an API key or read the docs.