We revisit the $k$-mismatch problem in the streaming model on a pattern of length $m$ and a streaming text of length $n$, both over a size-$σ$ alphabet. The current state-of-the-art algorithm for the streaming $k$-mismatch problem, by Clifford et al. [SODA 2019], uses $\tilde O(k)$ space and $\tilde O\big(\sqrt k\big)$ worst-case time per character. The space complexity is known to be (unconditionally) optimal, and the worst-case time per character matches a conditional lower bound. However, there is a gap between the total time cost of the algorithm, which is $\tilde O(n\sqrt k)$, and the fastest known offline algorithm, which costs $\tilde O\big(n + \min\big(\frac{nk}{\sqrt m},σn\big)\big)$ time. Moreover, it is not known whether improvements over the $\tilde O(n\sqrt k)$ total time are possible when using more than $O(k)$ space. We address these gaps by designing a randomized streaming algorithm for the $k$-mismatch problem that, given an integer parameter $k\le s \le m$, uses $\tilde O(s)$ space and costs $\tilde O\big(n+\min\big(\frac {nk^2}m,\frac{nk}{\sqrt s},\frac{σnm}s\big)\big)$ total time. For $s=m$, the total runtime becomes $\tilde O\big(n + \min\big(\frac{nk}{\sqrt m},σn\big)\big)$, which matches the time cost of the fastest offline algorithm. Moreover, the worst-case time cost per character is still $\tilde O\big(\sqrt k\big)$.