Evaluating the GPT-5 Series on Custom Benchmarks
OpenAI’s GPT-5 models just dropped, and there’s a lot of buzz. The demos look exciting and reported benchmark results show improvements across general intelligence, coding tasks, reasoning, and hallucinations. But do you need to upgrade? Should you use GPT-5, GPT-5-mini, or GPT-5-nano? And most importantly, how do you evaluate model performance in your own application?
With new models coming out all the time, these are questions we get frequently at HumanSignal. The details change, but the answer is always to build confidence in AI quality by testing on representative data. In this post, we’ll walk though the process of building out a custom benchmark, outline the evaluation method, and share some early findings on the newest OpenAI models.
We hope you’ll find this example a helpful guide to building your own benchmarks!(See also: our post on Why Benchmarks Matter)
