When a public LLM benchmark says one model is better than another, it is testing broad, difficult reasoning and, more importantly, opinionated tasks.
Your use case might not need that. For example, if you are summarizing tickets, classifying intent, or extracting fields from structured text, a smaller and cheaper model can match the output of a frontier model.
General benchmarks tell us which model wins on average across a wide mix of tasks. They do not tell us which model wins on your specific, narrow, repeatable task.
The only way to know for sure is to build your own benchmark.
One simple way is to take real examples from your workload, run them across your candidate models, and score the outputs against what you actually need. It is laborious work, but yes, your favorite LLM can help you build that :)
A model ranked lower on a general benchmark can be the better, cheaper choice for your use case, simply because your task was never complex enough to need the extra capability in the first place.
In a gist, always try to answer: Which model is better for your task? Not which model is better in general.