We propose a novel benchmark inspired by the MathWorks Math Modeling Challenge (https://m3challenge.siam.org), where a green agent defines real-world modeling problem contexts (e.g., housing markets, energy use, or population dynamics) and provides multiple relevant datasets. White agents operate under a fixed budget and must decide which subsets of these datasets to use, then construct mathematical models to forecast future trends. The green agent evaluates submissions by comparing generated forecasts against hidden ground-truth trends, measuring both accuracy and efficiency. Unlike existing benchmarks that focus on single-task accuracy, our benchmark emphasizes decision-making and context-aware reasoning: white agents must choose what data to incorporate and which modeling approach to use. Our contribution is a new environment that combines applied data science with resource-constrained modeling, offering a scalable way to evaluate agents on modeling under limited information.