Yes. This follows by time change: The process $(X^i_t)$ equals the process $(1+t+W_{\tau_i(t)})$ in distribution, where
$$\tau_i(t):=\int_0^t\sigma_i(s)^2\,ds,$$
so that $\tau_2\ge\tau_1$ and hence for the corresponding inverse functions we have $\tau_2^{-1}\le\tau_1^{-1}$.
It follows that
$$\begin{aligned}
&\inf_{s\in[0,t]}(1+s+W_{\tau_2(s)}) \\
&=\inf_{u\in[0,\tau_2(t)]}(1+\tau_2^{-1}(u)+W_u) \\
&\le\inf_{u\in[0,\tau_1(t)]}(1+\tau_1^{-1}(u)+W_u) \\
&=\inf_{s\in[0,t]}(1+s+W_{\tau_1(s)}).
\end{aligned}$$
So, for all real $x$,
$$
\begin{aligned}
&P(\inf\limits_{s\in[0,t]}X^1_s>x) \\
&=P(\inf_{s\in[0,t]}(1+s+W_{\tau_1(s)})>x) \\
&\ge
P(\inf_{s\in[0,t]}(1+s+W_{\tau_2(s)})>x) \\
&=P(\inf\limits_{s\in[0,t]}X^2_s>x).
\end{aligned}$$
Thus, the desired result follows.