Input metrics evaluate the progress of testing in terms of features of inputs present in a test suite. Previous works, as early as the 1950s, established a number of such metrics, but few endeavored to compare them. This paper does so by utilizing existing methods proposed for other metric classes in partition testing literature. After defining and reviewing common input metrics, we begin with a short case study revealing that typical empirical comparison strategies are fundamentally insufficient for comparing metrics. Then, we demonstrate how one rigorously improves a standard metric by defining and implementing $k$-alt-path, a new metric which reduces redundancy while improving sensitivity over $k$-path. Each of the other common input metrics are then systematically compared before discussing the implications of our findings. With these contributions, we bring forward partition testing analysis methods that justify and form a strategy for future research in refining input metrics.

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