As others have mentioned, when the assumptions of conditional independence are met, Gaussian NaiveBayes and MaxEnt will asymptotically learn the same classifier. Except NB will learn it in O(log dimensions) instead of O(dimensions) examples. So in those cases and where you have fewer examples you'll want to use NB.

Even when the independence assumptions are not met, NB will often produce a good classifier if you don't care about the accuracy of the probabilities. NB is also online, resistant to the curse of dimensionality and for categorical data will learn polynomial decision boundaries. So the gain in the trade-off is in ease of implementation (you can write one in a dozen lines or less) that you can throw something close to your raw data at and often get good enough results without any ahead of time training, kernels or regularization.

You are right though that MaxEnt (some sparsity capturing algorithms are as powerful as SVMs) will in general outperform naive bayes and that the averaged perceptron probably has, on average, the best performance/ease of implementation ratio. But for many implementors/problems, where clarity is paramount, those distinctions will not be worth the cost.

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