Why GPT-5.4, Claude, and Gemini can’t agree on basic, real-world facts
A New Stack investigation reveals that leading large language models—including GPT-5.4, Claude, and Gemini—often produce conflicting answers to straightforward factual questions about real-world events, dates, and common knowledge. The article highlights that even when prompted identically, these frontier models disagree on basic facts, such as historical dates or current events, with no consistent accuracy leader. This disagreement stems from differences in training data, model architecture, and fine-tuning approaches. For developers building applications that rely on factual accuracy, this means no single model can be trusted without verification. The consequence is a need for ensemble methods or external fact-checking tools, increasing complexity and cost. The analysis suggests that until models achieve more reliable grounding, developers must treat LLM outputs as probabilistic rather than authoritative, potentially limiting use cases in domains like journalism, education, and legal research.
Developers cannot trust any single LLM for factual accuracy, requiring cross-verification or fallback systems.