Show HN: I Made 17 AIs Play 7,700 Hands of Poker (chrystianschutz.com)

In a recent benchmark, an AI enthusiast tested 17 AI models, including cheap reasoning language models (LLMs) and decision models, by having them play 7,700 hands of poker. The primary goal was to determine which AI types are more effective in making decisions under uncertainty, particularly in a game where strategy and luck are tightly intertwined. Surprisingly, the inexpensive reasoning LLMs performed comparably to the frontier models, with some even achieving better results than the more costly ones. A simple 30-line Python script outperformed advanced decision models, demonstrating that straightforward simulations can yield superior poker strategies compared to complex AI solutions. This study is significant for the AI/ML community as it challenges the notion that expensive, state-of-the-art AI models are inherently more effective. The findings suggest that budget-friendly options can be just as efficient, if not more so, especially in structured decision-making scenarios like poker. Technical details reveal that the median betting strategies of the decision models often led to unconventional and suboptimal plays, while the LLMs managed to gauge their chances better, showing that training and structure matter significantly in AI performance. The research emphasizes the importance of affordable AI alternatives and offers insights into their potential applications beyond gaming.