Further I'll use the following notation: $V^\pi(s)$ would be the value of a state under some (in most cases - arbitrary) policy $\pi$. There is a class of optimal policies with the same optimal value function $V^*(s)$. To measure "distances" between different value functions the max norm is used:
$$\lVert V^\pi - V^{\pi'}\rVert_\infty = \max_{s \in S} \left| V^\pi(s) - V^{\pi'}(s)\right|$$
The Bellman operator ${\mathcal B}$ is a central concept for reasoning about RL algorithms. It takes a value function and returns a new one. (I'm using your notation there except keeping more general dependency of expected reward $R(s) = \max_aR(s,a)$)
$${\mathcal B}(V^\pi) = \max_a\left[ R(s,a) + \gamma\sum_{s'}T(s,a,s')V^\pi(s') \right]$$
The Value Iteration algorithm then starts with an arbitrary value function $V_0$ and iteratively applies the Bellman operator $V_t ={\mathcal B}(V_{t-1})$. The optimal value function is the stationary point of ${\mathcal B}$. In other words, it satisfies the Bellman equation:
$$V^* ={\mathcal B}(V^*)$$
Existence of such stationary point, as well as the convergence of the Value Iteration, follows from the contraction property of ${\mathcal B}$:
$$\lVert {\mathcal B}(V^\pi) - {\mathcal B}(V^{\pi'})\rVert_\infty \leq \gamma \lVert V^\pi - V^{\pi'}\rVert_\infty$$
Substituting $V_{t-1}$ and $V_{t-2}$ into the inequality above, we get:
$$\lVert V_t - V_{t-1}\rVert_\infty \leq \gamma \lVert V_{t-1} - V_{t-2}\rVert_\infty$$
Iteratively applying this inequality to itself we get a useful intermediate result:
$$\lVert V_t - V_{t-1} \rVert_\infty \leq \gamma^{t-1} \lVert V_1 - V_0\rVert_\infty \tag{*}\label{*}$$
The expected immediate reward is assumed to be bounded by some value $R_{\max}$
$$ -R_{max} \leq R(s,a) \leq R_{max},\quad \forall s, a$$
Since for any policy, the value of a state is the expectation of the discounted sum of rewards, we can bound it with the sum of geometric series:
$$V^\pi(s_0) = {\mathbb E}\left[R(s_0, a_0) + \gamma R(s_1, a_1) + \gamma^2 R(s_2, a_2) + \dots\right]$$
$$- (1 + \gamma + \gamma^2 + \dots) R_{max} \leq V^\pi(s_0) \leq (1 + \gamma + \gamma^2 + \dots)R_{max}$$
$$\lVert V^\pi\rVert_\infty \leq \frac{R_{max}}{1 - \gamma}$$
We can use it to bound the distance between any pair of value functions:
$$\lVert V^\pi - V^{\pi'}\rVert_\infty \leq \lVert V^\pi\rVert_\infty + \lVert V^{\pi'}\rVert_\infty \leq \frac{2R_{max}}{1 - \gamma} $$
Applying this bound to $\eqref{*}$ we get a convergence bound in terms of a distance from the optimal policy:
$$\lVert V_t - V_{t-1}\rVert_\infty \leq \gamma^{t-1}\frac{2R_{max}}{1 - \gamma} \leq \epsilon $$
Solving for (an integer) $t$ for a given $\epsilon$ I get the final convergence lower bound on the number of iterations:
$$t \geq \left\lceil \frac{\log\frac{(1-\gamma)\gamma\epsilon}{2 R_{max}}}{\log\gamma}\right\rceil$$
For your numbers (in your case $R_{max} = 5$) I get $t \geq 67$ steps. Note that the whole thing doesn't depend on number of states and actions.
