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Description: The feeling of confidence in what we see can influence our future behaviour and learning. Understanding how the brain computes confidence is important goal of researchers. As such, researchers have identified a host of potential models. Yet, they rarely test a wide range of models against each other to find the best at predicting choice behaviour. The study had human participants compare their confidence for pairs of easy perceptual decisions, reporting if they had higher confidence in the first or second decision. We tested twelve models, covering all three types of models proposed in previous studies, finding strong support for two models. The winning Heuristic model combines all three factors affecting choice uncertainty with an idiosyncratic weighting to compute confidence. The other winning model uses a transformation where the strength of the sensory signal is scaled according to sensory uncertainty. This latter model outperformed the frequently-used unscaled version of this model, suggesting that the scaling transformation should be adopted in future studies. We also assessed the agreement of confidence reports in identical decision scenarios. Humans had higher agreement than almost all model predictions. We propose using confidence agreement intentionally as a second performance benchmark of model fit.