This article investigates channel capacity under mismatched stochastic decoding. Feinstein- and Verdú-Han-type bounds on the error probability of coded communication are derived for sequences of $n$-letter channels and decoding metrics. These are used to obtain an information-spectrum formula for the corresponding channel capacity: the supremum, over all input distribution sequences, of the limit inferior in probability of the sequence of normalized mismatched information densities. This formula yields the mismatched analog of the Verdú-Han channel capacity formula. For discrete-memoryless channels and product metrics, uniform integrability allows to show that the limit product-space improvements of the generalized mutual information and the LM rate give the mismatch capacity under stochastic decoding. This establishes the stochastic decoding counterpart of the Csiszár-Narayan conjecture. Relationships among the capacity formulas of related decoders are investigated. A number of examples are discussed including the case of binary-input binary-output channels and decoding metrics, where capacity is characterized in single-letter form.

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