The ability of large language models (LLMs) to approach non-trivial tasks involving both information retrieval and mathematical reasoning has led to significant research interest in evaluating these properties. Yet, the popularity of reasoning benchmarks, such as the often-used Grade-School Math (GSM) [1] or MATH [2] datasets, is leading to performance saturation (see Figure 1), and can potentially lead to training set contamination. Thus, there is a stringent need to develop new strong benchmarks to evaluate LLM reasoning. We address this by proposing Mathador-LM, a new benchmark for examining the mathematical reasoning properties of LLMs. At a high level, Mathador-LM follows the popular Mathador mathematical game [3], in which a human player is given five base numbers together with a target number, and has to provide a series of calculations, each using one of the four basic arithmetic operations, which result in the target number.