Object recognition relies on the hierarchical processing of visual information along the primate ventral stream. Artificial neural networks (ANNs) recently achieved unprecedented accuracy in predicting neuronal responses in different cortical areas and primate behavior. In this talk, I will present an extension of this approach, in which hundreds of different hierarchical models were tested to quantitatively assess how well they explain primate primary visual cortex (V1) across a wide range of experimentally characterized functional properties. We found that, for some ANNs, individual artificial neurons in early and intermediate layers have functional properties that are remarkably similar to their biological counterparts, and that the distributions of these properties over all neurons approximately match the corresponding distributions in primate V1. Still, none of the candidate models was able to account for all the functional properties, suggesting that current network architectures might not be capable of fully explaining primate V1 at the single neuron level. Since some ANNs have “V1 areas” that more precisely approximate primate V1 than others, we investigated whether a more brain-like V1 model also leads to better models of object recognition behavior. Indeed, over a set of 48 ANN models optimized for object recognition, V1 similarity was positively correlated with behavioral predictivity. This result supports the widespread view that the complex visual representations required for object recognition are derived from low-level functional properties, but it also demonstrates – for the first time – that working to build better models of low-level vision has tangible payoffs in explaining complex visual behaviors. Moreover, the set of functional V1 benchmarks presented here can be used as a gradient to search for better models of V1, which will likely result in better models of the primate ventral stream.