A user tested Anthropic's Opus 5.5 model on the SlopCodeBench benchmark, which evaluates an AI's ability to add new features to its own code without introducing errors. The tests revealed that Opus 5.5's code did become messier over time, with increased structural erosion and verbosity in many problems. However, this code messiness did not correlate with a decrease in its ability to pass checkpoints or a higher rate of regression in previously passing tests. Interestingly, while code size impacted cost, code messiness itself did not predict higher costs for subsequent checkpoints. AI
IMPACT Suggests that code messiness may not be a direct predictor of AI model failure in coding tasks, potentially impacting how code quality metrics are used.
RANK_REASON User-conducted research/benchmark of an existing model. [lever_c_demoted from research: ic=1 ai=1.0]
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