Benchmarks for language models have become essential tools for research. Yet, such benchmarks face a persistent contamination problem, with recent studies finding 25-50% of evaluation datasets appearing in training corpora. This is true even looking at the two-player zero-sum game setting, where most benchmarks are based on popular games, like chess, whose optimal strategies are all over the web. Such contamination hinders the possibility to differentiate memorization and reasoning skills. To rectify these problems, we introduce TCG-Bench, a benchmark based on a new two-player trading card game (TCG), similar in spirit to games like Magic: The Gathering. TCG-Bench offers three key innovations: (1) a contamination-resistant design by separating the publicly released game engine from hidden card implementations, (2) a continuous difficulty spectrum via Monte Carlo simulation that prevents benchmark saturation, and (3) a parallel implementation in English and Arabic, the first multilingual text-based game benchmark to do so. We also formalize a practical threat model and refresh protocol that preserves evaluation integrity even if specific cards leak.Our analysis across 17 models (50,000+ games) reveals that performance declines exponentially with difficulty, while model size correlates only weakly with strategic ability. We also observe cross-linguistic performance gaps between English and Arabic, with a gap of 47.4% at 32B, highlighting the need for multilingual game benchmarks that target reasoning capabilities in the target language. We host a leaderboard showcasing these results and welcome evaluation requests on our private cards.

Large Language Model (LLM) leaderboards based on benchmark rankings are regularly used to guide practitioners in model selection. Often, the published leaderboard rankings are taken at face value — we show this is a (potentially costly) mistake. Under existing leaderboards, the relative performance of LLMs is highly sensitive to (often minute) details. We show that for popular multiple-choice question benchmarks (e.g., MMLU), minor perturbations to the benchmark, such as changing the order of choices or the method of answer selection, result in changes in rankings up to 8 positions. We explain this phenomenon by conducting systematic experiments over three broad categories of benchmark perturbations and identifying the sources of this behavior. Our analysis results in several best-practice recommendations, including the advantage of a *hybrid* scoring method for answer selection. Our study highlights the dangers of relying on simple benchmark evaluations and charts the path for more robust evaluation schemes on the existing benchmarks. The code for this paper is available at [https://github.com/National-Center-for-AI-Saudi-Arabia/lm-evaluation-harness](https://github.com/National-Center-for-AI-Saudi-Arabia/lm-evaluation-harness).