A systematic comparison of Large Language Models for automated assignment assessment in programming education: Exploring the importance of architecture and vendor

This study presents the first large-scale, side-by-side comparison of contemporary Large Language Models (LLMs) in the automated grading of programming assignments. Drawing on over 6,000 student submissions collected across four years of an introductory programming course, we systematically analysed the distribution of grades, differences in mean scores and variability reflecting stricter or more lenient grading, and the consistency and clustering of grading patterns across models. Eighteen publicly available models were evaluated: Anthropic (claude-3-5-haiku, claude-opus-4-1, claude-sonnet-4); Deepseek (deepseek-chat, deepseek-reasoner); Google (gemini-2.0-flash-lite, gemini-2.0-flash, gemini-2.5-flash-lite, gemini-2.5-flash, gemini-2.5-pro); and OpenAI (gpt-4.1-mini, gpt-4.1-nano, gpt-4.1, gpt-4o-mini, gpt-4o, gpt-5-mini, gpt-5-nano, gpt-5). Statistical tests, correlation and clustering analyses revealed clear, systematic differences between and within vendor families, with "mini" and "nano" variants consistently underperforming their full-scale counterparts. All models displayed high internal agreement, measured by the intraclass correlation coefficient, with the model consensus but only moderate agreement with human teachers' grades, indicating a persistent gap between automated and human assessment. These findings underscore that the choice of model for educational deployment is not neutral and should be guided by pedagogical goals, transparent reporting of evaluation metrics, and ongoing human oversight to ensure accuracy, fairness and relevance.