Anthropic's Claude Fable 5 achieved 88% accuracy on FrontierMath's hardest problems, outperforming OpenAI's GPT-5.5 at 75%. The gap represents a dramatic acceleration in AI mathematical reasoning capabilities.
Anthropic's latest model, Claude Fable 5, has set a new benchmark for AI mathematical problem-solving. On FrontierMath's most difficult tier, the model reached 88% accuracy—a significant lead over OpenAI's GPT-5.5, which achieved approximately 75% on the same benchmark.
The performance jump underscores rapid progress in the field. Claude's predecessor, Opus 4.5, scored below 10% on the same FrontierMath tier in early 2026, making Fable 5's result an 80-point improvement in less than a year.
FrontierMath, developed by Epoch AI, tests models on competition-level mathematical problems that typically require advanced reasoning and symbolic manipulation. The benchmark has become a standard measure for evaluating frontier-level AI capabilities in structured problem-solving.
The acceleration in math performance reflects broader advances in AI reasoning. Recent generations of large language models have incorporated improved training techniques, larger parameter counts, and enhanced architectures specifically designed to handle complex reasoning tasks.
Other models have also shown competitive gains. Models from Meta and other labs have demonstrated steady improvements on mathematical benchmarks, though Fable 5's score represents the current leader on this specific benchmark tier.
The implications extend beyond raw performance metrics. Enhanced mathematical reasoning in AI systems could accelerate applications in research, engineering, and scientific discovery. Companies and researchers increasingly view mathematical capability as a proxy for general reasoning ability.
Both Anthropic and OpenAI continue refining their approaches to reasoning. Anthropic has emphasized interpretability and safety alongside capability gains, while OpenAI has focused on scaling and reasoning-specific training methods. The competitive dynamic between major labs is likely to sustain momentum in this area.
As models tackle increasingly difficult mathematical problems, the gap between practical applications and frontier benchmarks continues to narrow, suggesting real-world impact may soon follow these research achievements.
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