Large language models spit out confident nonsense at the worst moments. Ask them for a math proof. Request health advice. Demand a citation. Boom—hallucinations surge. Precision tasks expose the gap between LLMs’ text-generation prowess and their shaky grip on facts. A recent benchmark from Open Resource Application nails it: average accuracy sits at 38.61% for math on Omni-MATH, 52.2% for data analysis via GPQA scores, and just 0.67 on MMLU-Pro for teaching or specific queries. MakeUseOf breaks down how Gemini 3 Pro dominates four of five categories, while GPT-5 mini edges out math. But why? LLMs predict tokens statistically. No real calculation engine. Feed incomplete data on niche topics, and they guess—with conviction. Industry pros know this isn’t new. Yet 2026 data shows progress. Hallucination rates dropped from 38% in 2021 to 8.2% overall, with top models like GPT-4o and Gemini 2.0 hitting 0.7-1.9% on benchmarks. Master of Code . Still unacceptable for finance or medicine.…