We establish a sharp variable-occurrence threshold for polynomial expected-time convergence for the Moser--Tardos algorithm and the pure random-walk WalkSAT on 3-CNF formulas. For every formula with $m$ clauses and at most two occurrences per variable, both algorithms terminate in $O(m)$ expected updates. This occurrence bound is sharp: we construct an infinite family with at most three occurrences per variable on which both algorithms require $2^{Θ(\sqrt{m})}$ expected updates. Surprisingly, this dynamical transition occurs strictly below the classical NP-completeness threshold at variable-occurrence degree four. We further establish exponential separations between the two algorithms on satisfiable 3-CNF formulas. On one family, WalkSAT terminates in $O(m^4)$ expected flips, whereas MT requires $2^{Ω(m)}$ expected resampling steps. On another, MT terminates in $O(m)$ expected resampling steps, whereas WalkSAT requires $2^{Ω(m)}$ expected flips. These constructions exploit the different ways in which single-variable flips and simultaneous resampling propagate local violations. Our results show that the algorithms' expected running times are incomparable even up to polynomial overhead on the same inputs.

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