Nice question! The paper acknowledges that LLMs generate mutations slower and are more costly than traditional methods like PIT and Major, which are traditional testing tools. They did include metrics like cost per 1K mutations. However, the researchers focused on the effectiveness and high quality of the mutations generated by LLMs. For instance, GPT-3.5 achieves a 96.7% real bug detectability rate compared to Major’s 91.6% (not to mention GPT-4 outperformed all of them). All in all, LLMs produced fewer equivalent mutants, mutants with higher fault detection potential, as well as higher coupling and semantic similarity with real faults.