We study convergence lower bounds of without-replacement stochastic gradient descent (SGD) for solving smooth (strongly-)convex finite-sum minimization problems. Unlike most existing results focusing on final iterate lower bounds in terms of the number of components π and the number of epochs πΎ, we seek bounds for arbitrary weighted average iterates that are tight in all factors including the condition number π . For SGD with Random Reshuffling, we present lower bounds that have tighter π dependencies than existing bounds. Our results are the first to perfectly close the gap between lower and upper bounds for weighted average iterates in both strongly-convex and convex cases. We also prove weighted average iterate lower bounds for arbitrary permutation-based SGD, which apply to all variants that carefully choose the best permutation. Our bounds improve the existing bounds in factors of π and π and thereby match the upper bounds shown for a recently proposed algorithm called GraB.