Long-term and large-scale acoustic surveys of bats have become possible with the increased availability of recording hardware and advances in battery and memory storage technologies. The volume of data generated in surveys necessitates automated call detection, either in real time via a triggering function or offline, yet researchers are hesitant to replace traditional hand analysis without a thorough understanding of the accuracy and costs of automated detection. We compared detection accuracy and computational cost of the underlying algorithms used in commercial detectors (a zero-crossing detector, a spectral peak detector, and a high-band energy detector) with a model-based analysis method called the links detector. We predicted that the links detector would be more accurate than the other detectors, producing a larger effective detection range, because the links detector uses more information to make detection decisions. We also predicted that the links detector would be the most computationally expensive algorithm because of the processing needed for the extra information. We quantified the performance of the detectors using a synthetic recording environment, which provided an absolute ground truth for the experiments and allowed us to measure the effective detection range of each algorithm. The zero-crossing and high-band energy detectors, the fastest, were about 40 times faster than the links detector. Most of the computational cost was attributed to the filter used to remove low-frequency noise. The links detector, the most accurate, increased effective detection range by 6–12 m compared to the other detectors depending on species. The results will allow bat researchers to better understand the costs and benefits of automated detection methods.