We introduce a Generalized Nested Logit model of demand for bundles that can be estimated sequentially and virtually eliminates any challenge of dimensionality related to large choice sets. We use it to investigate quantity discounts for carbonated soft drinks by simulating a counterfactual with linear pricing. The prices of quantities up to 1L decrease by −31.5% while those of larger quantities increase by +14.8%. Purchased quantities decrease by −20.4%, associated added sugar by −23.8%, and industry profit by −20.5%. Consumer surplus however reduces only moderately, suggesting that linear pricing may be effective in limiting added sugar intake.