Balancing the speed-accuracy tradeoff (SAT) is necessary for successful behavior. Using a visual search task with interleaved cues emphasizing speed or accuracy, we recently reported diverse contributions of frontal eye field (FEF) neurons instantiating salience evidence and response preparation. Here, we report replication of visual search SAT performance in two macaque monkeys, new information about variation of saccade dynamics with SAT, extension of the neurophysiological investigation to describe processes in the superior colliculus (SC), and a description of the origin of search errors in this task. Saccade vigor varied idiosyncratically across SAT conditions and monkeys but tended to decrease with response time. As observed in the FEF, speed-accuracy tradeoff was accomplished through several distinct adjustments in the superior colliculus. In “Accurate” relative to “Fast” trials, visually responsive neurons in SC as in FEF had lower baseline firing rates and later target selection. The magnitude of these adjustments in SC was indistinguishable from that in FEF. Search errors occurred when visual salience neurons in the FEF and the SC treated distractors as targets, even in the Accurate condition. Unlike FEF, the magnitude of visual responses in the SC did not vary across SAT conditions. Also unlike FEF, the activity of SC movement neurons when saccades were initiated was equivalent in Fast and Accurate trials. Saccade-related neural activity in SC, but not FEF, varied with saccade peak velocity. These results extend our understanding of the cortical and subcortical contributions to SAT. NEW & NOTEWORTHY Neurophysiological mechanisms of speed-accuracy tradeoff (SAT) have only recently been investigated. This article reports the first replication of SAT performance in nonhuman primates, the first report of variation of saccade dynamics with SAT, the first description of superior colliculus contributions to SAT, and the first description of the origin of errors during SAT. These results inform and constrain new models of distributed decision making.