In the near future, ultra deep observations of galaxy clusters with HST or JWST will uncover $300-1000$ lensed multiple images, increasing the current count per cluster by up to an order of magnitude. This will further refine our view of clusters, leading to a more accurate and precise mapping of the total and dark matter distribution in clusters, and enabling a better understanding of background galaxy population and their luminosity functions. However, to effectively use that many images as input to lens inversion will require a re-evaluation of, and possibly upgrades to the existing methods. In this paper we scrutinize the performance of the free-form lens inversion method Grale in the regime of $150-1000$ input images, using synthetic massive galaxy clusters. Our results show that with an increasing number of input images, Grale produces improved reconstructed mass distributions, with the fraction of the lens plane recovered at better than $10%$ accuracy increasing from $40-50%$ for $sim!!150$ images to $65%$ for $sim!1000$ images. The reconstructed time delays imply a more precise measurement of $H_0$, with $lesssim 1%$ bias. While the fidelity of the reconstruction improves with the increasing number of multiple images used as model constraints, $sim 150$ to $sim 1000$, the lens plane rms deteriorates from $sim 0.11$ to $sim 0.28$. Since lens plane rms is not necessarily the best indicator of the quality of the mass reconstructions, looking for an alternative indicator is warranted.
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