The rapid progress in Automated Program Repair (APR) has been fueled by advances in AI, particularly large language models (LLMs) and agent-based systems. SWE-Bench is a benchmark designed to evaluate repair systems using real issues mined from popular open-source Python repositories. Its public leaderboards—SWE-Bench Lite and Verified—have become central platforms for tracking progress and comparing solutions. In this paper, we present the first comprehensive study of these two leaderboards, examining who is submitting solutions, the products behind the submissions, the LLMs employed, and the openness of the approaches. We analyze 99 entries submitted to Lite leaderboard and 133 to Verified. Our results show that most entries on both leaderboards originate from industry, particularly small companies and large publicly traded companies. These submissions often achieve top results, although academic contributions—typically open source—also remain competitive. We also find a clear dominance of proprietary LLMs, especially Claude family, with state-of-the-art results on both leaderboards currently achieved by Claude 4 Sonnet. These findings offer insights into the SWE-Bench ecosystem that can guide greater transparency and diversity in future benchmark-driven research.