Claude vs Gemini: API Pricing Compared

Unlike the other comparisons on this site, there is no crossover point here. On the same workload, the cheapest Claude model costs about ten times the cheapest Gemini model, and the most expensive Claude model costs about five times the most expensive Gemini one. Gemini is cheaper everywhere.

That makes this a capability question rather than a pricing question. Nobody picks Claude on this table, so the only honest way to run the comparison is to work out whether the output difference on your actual task is worth a five to tenfold premium. For a lot of extraction, classification and summarisation work it plainly is not. For work where a wrong answer is expensive to catch, it often is.

The useful discipline is to measure cost per successfully completed task rather than cost per million tokens. A cheaper model that needs two attempts, a longer prompt and a human check is not cheaper. A cheaper model that gets it right first time on your workload makes the premium indefensible.

Long agentic runs are the clearest case. When a model executes many steps against tools, small differences in instruction-following compound, and a failure at step twelve wastes everything before it. Cost per completed run can favour the more expensive model even at a large per-token premium.

The reverse case is high-volume, low-stakes, well-specified work. Tagging support tickets, extracting fields from documents, routing requests. The task is easy to verify, a failure is cheap, and volume is exactly where a tenfold rate difference turns into a real number on the invoice.

Prompt caching narrows the gap but does not close it. Both providers discount cached input to roughly a tenth of the standard rate, so a high hit rate scales both bills down together and leaves the ratio between them broadly intact.