Measure the task that matters
For a decision workflow, track at least four outcomes: label quality on representative examples, end-to-end latency, total input and output cost, and the rate at which uncertain cases reach a safe fallback. A single speed figure does not establish accuracy; agreement with another model is not the same as correctness against a trusted label.
Make comparisons fair
- Use the same examples, prompt criteria, and expected outputs for each system.
- Enable structured output or schema modes when the comparison model supports them.
- Report median and tail latency, with network and provider path stated.
- Use repeated runs where model settings are stochastic; fix settings consistently when repeatability is the question.
- Keep ambiguous examples separate rather than forcing a possibly subjective label.
- Publish sample size, date, code and known limitations with any result.
Tradeoffs to consider
A decision-only system gives up free-form generation. It may be a poor fit for tasks needing explanation, summarization, long reasoning, multimodal input or an answer outside a predefined outcome set. Constrained output can make integration easier, but it does not guarantee that the chosen option is correct.
Treat confidence as a signal
Test calibration on your own data. Group predictions into confidence ranges and compare each range with observed correctness. Decide what to do when confidence is low, and do not use a confidence number as the only control for high-impact or security-sensitive actions.
Source policy
This site does not present vendor marketing figures as independent measurements. When we publish a result, it should include the original source, method, date and caveats. Until then, run a small benchmark on your own representative set.