The ARC Prize Foundation tested OpenAI's GPT-5.5 and Anthropic's Opus 4.7 against the ARC-AGI-3 benchmark, running 160 games total. Both models scored below 1 percent on tasks that humans solve easily. This poor performance stems from three systematic error patterns that the foundation identified.
The research reveals that even the latest language models fail at basic reasoning challenges. These aren't random mistakes. The errors follow predictable patterns, indicating fundamental limitations in how current AI systems approach problem-solving. Humans tackle these same tasks with minimal difficulty, highlighting the gap between human and artificial reasoning.
The ARC-AGI benchmark tests abstract reasoning and general intelligence by presenting visual puzzles and pattern recognition problems. It has become a key metric for measuring AI progress toward artificial general intelligence. The latest results suggest that current scaling approaches may not solve reasoning deficits on their own.
Understanding these three error patterns offers a roadmap for researchers. Rather than simply making models larger, developers can target the specific reasoning failures that hold current systems back. The foundation's analysis provides concrete data about where AI reasoning breaks down, moving the field beyond vague claims about limitations.