In this paper, we develop and analyze a model of framing under ambiguity. Frames are circumstances, unobservable to the analyst, that shape the agent’s perception of the relevant ambiguity. The analyst observes a choice correspondence that represents the set of possible choices under the various decision frames. Our first result provides axioms that are equivalent to a multi-multiple prior model; that is, there is a collection of multiple prior models with a common utility index so that the choice correspondence consists of the optimizers of the models in the collection. Furthermore, we characterize the extent to which the analyst can identify the parameters of the model, that is, the extent to which the frames can be inferred from behavior. To capture the degree to which frames affect choice, we introduce two comparative notions; the first says that one agent is more decisive than another if the former’s choice correspondence is a subset of the latter’s. The second, less demanding notion says that an agent is more consistent than another agent if the former has a unique choice whenever the latter does. We characterize both comparative measures in terms of the model parameters. Agents who recognize that they are subject to different frames may learn by combining their frames into a single model. Our final result characterizes the behavioral implications of this form of learning.