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We present a simple model which can account for the stereoscopic sensitivity of praying mantis predatory strikes. The model consists of a single "disparity sensor": a binocular neuron sensitive to stereoscopic disparity and thus to distance from the animal. The model is based closely on the known behavioural and neurophysiological properties of mantis stereopsis. The monocular inputs to the neuron reflect temporal change and are insensitive to contrast sign, making the sensor insensitive to interocular correlation. The monocular receptive fields have a excitatory centre and inhibitory surround, making them tuned to size. The disparity sensor combines inputs from the two eyes linearly, applies a threshold and then an exponent output nonlinearity. The activity of the sensor represents the model mantis's instantaneous probability of striking. We integrate this over the stimulus duration to obtain the expected number of strikes in response to moving targets with different stereoscopic disparity, size and vertical disparity. We optimised the parameters of the model so as to bring its predictions into agreement with our empirical data on mean strike rate as a function of stimulus size and disparity. The model proves capable of reproducing the relatively broad tuning to size and narrow tuning to stereoscopic disparity seen in mantis striking behaviour. Although the model has only a single centre-surround receptive field in each eye, it displays qualitatively the same interaction between size and disparity as we observed in real mantids: the preferred size increases as perceived prey distance increases beyond the preferred distance. We show that this occurs because of a stereoscopic "false match" between the leading edge of the stimulus in one eye and its trailing edge in the other; further work will be required to find whether such false matches occur in real mantises. Importantly, the model also displays realistic responses to stimuli with vertical disparity and to pairs of identical stimuli offering a "ghost match", despite not being fitted to these data. This is the first image-computable model of insect stereopsis, and reproduces key features of both neurophysiology and striking behaviour. Author summary: The praying mantis is the only insect so far known to compute depth using stereoscopic (3D) vision. Mantis stereopsis appears to be simpler than human stereopsis and most machine sterovision algorithms. A computational model of mantis stereopsis may therefore be beneficial to the field of robotics, particularly where computational power is limited. Using a combination of behavioural observations and neurophysiological data, we propose a very simple model structure to describe the prey capture response in the praying mantis. We used the limited available data on the mantis' size and distance preferences for its prey to train our model parameters. Our simple model is able to qualitatively reproduce previously unexplained characteristics of our training data, and predicts key observations in additional empirical data that was not included in the model training. Whilst we believe our model to be only a partial and heavily simplified account of mantis stereopsis, our results are supportive of our model structure as an approximation of the size and disparity sensors used by the mantis when catching its prey. [ABSTRACT FROM AUTHOR]