Abstract : Research in the brain and cognitive sciences attempts to uncover the neural mechanisms underlying intelligent behavior. Due to the complexities of brain processing, studies necessarily had to start with a narrow scope of experimental investigation and computational modeling. I will argue that it is time for our field to take the next step: build system models that capture neural mechanisms and supported behaviors in entire domains of intelligence. To make progress on system models, we are developing the Brain-Score platform which, to date, hosts over 50 benchmarks of neural and behavioral experiments that models can be tested on. By systematically evaluating a wide variety of model candidates, we not only identify models beginning to match a range of brain data (~50% explained variance), but also discover key relationships: Models’ brain scores are predicted by their object categorization performance in vision and their next-word prediction performance in language. The better models predict internal neural activity, the better they match human behavioral outputs, with architecture substantially contributing to brain-like representations. Using the integrative benchmarks, we develop improved state-of-the-art system models that more closely match shallow recurrent neuroanatomy, predict primate temporal processing, and are more robust to image corruptions. Finally, I will argue that the newest generation of models can be used to predict the behavioral effects of neural interventions, and to drive new experiments.