Offshore wind-farm collection-system design is a cost-relevant combinatorial optimization problem whose difficulty grows exponentially with turbine count. This paper introduces <strong>OptiWindNet RouteSets</strong>, a database of cable-routing solutions to 13954 distinct problem instances based on built, proposed, and procedurally-generated farm layouts. The database is intended as an open benchmark for routing algorithms, a source of hard instances, a strong baseline for reinforcement-learning solvers, and a training corpus for supervised-learning models for the Wind Farm Cable-Routing Problem.</p> <p>The solutions were produced using the <em>optiwindnet</em> Python package with three solvers: exact mathematical optimization and two meta-heuristics (inexact) – hybrid genetic search (HGS) and Lin–Kernighan–Helsgaun (LKH). Two network topologies are covered: radial (path-based) or branched (tree-based). Each solution contains the feasible network and its metadata (solver used, cable capacity, method configuration, route length, detour overhead, solver runtime, and, for exact runs, a proven optimality gap). The problem instances vary in: number of wind turbines (∈ [50, 200]), maximum cable capacity (∈ [2, 12]), and location geometry. For 63% of the problem instances, a solution with <1% gap is available (and 79% with <2% gap).</p> <p>We analyze: (a) the total length of meta-heuristic solutions compared to their exact counterparts – HGS median increase is 0.0%, while LKH is 0.6%; (b) the instance difficulty as a function of capacity and turbine count – those two variables interact, the difficulty increases monotonically with count, but exhibits a count-dependent peak across capacities; (c) the length reduction of branched topology compared to radial – 0.34% median; (d) the detour-caused increase in length over the solver-optimized objective – 0.16% median.