A study has revealed an unexpected twist in artificial intelligence behavior. Researchers found that Polish, not English, emerged as the most effective language for prompting large language models. The finding challenges widespread assumptions about English dominance in AI communication and raises questions about how language structure affects machine comprehension.
The Polish Language Emerges as the Most Precise Language for Advanced AI Tasks
Researchers testing 26 languages across leading AI models discovered that Polish enables more precise and consistent responses than English.
Evaluating 26 Languages Across Different AI Models
The research was a collaboration between the University of Maryland and Microsoft. Additional academic partners contributed to data analysis. The researchers aim to understand how efficiently AI models interpret and execute instructions written in different languages. Their focus was on measuring performance in tasks that required long and complex prompts.
A total of 26 languages representing various families and writing systems were selected. Several major AI models, including those developed by OpenAI, Google, Meta, DeepSeek, and Alibaba, were used. Each model received identical prompts translated into different languages to enable comparisons across systems without bias from cultural or regional contexts.
The experiments emphasized long-context reasoning. This involved models processing extensive text segments reaching up to 128K tokens. The tests simulated realistic scenarios like document summarization or multi-step reasoning over long passages. The team then recorded how accurately each language prompted the AI to produce correct and consistent answers.
Results revealed striking differences in how languages influenced AI performance. Polish ranked first with an accuracy of 88 percent. French followed with 87 percent and Italian with 86 percent. Spanish had 85 percent, and Russian had 84 percent. English placed 6th with 83.9 percent. Chinese ranked near the bottom despite its extensive representation in training datasets.
Polish Challenges the Dominance of the English Language
The team also found that languages using Latin or Cyrillic scripts generally enabled better results compared with languages employing more complex writing systems. This pattern suggested that visual and structural uniformity might make token processing easier for AI. This contrasted with assumptions that larger training corpora always guarantee superior performance.
One possible reason behind Polish success is its grammatical precision. It uses 7 cases and multiple inflectional endings that clarify relationships among words. This structure may reduce ambiguity in AI interpretation and force models to recognize meaning through explicit linguistic cues rather than contextual guesses. The clarity could significantly enhance task consistency.
The implications extend beyond linguistics and into practical applications. The researchers argued that users and developers might achieve better outcomes by prompting AI systems in alternative languages. Governments investing in national language models could benefit from reexamining and utilizing native linguistic structures suitable for local AI systems.
Remember that the study challenges the assumption that English is the most effective medium for communicating with AI. It shows that language selection itself can influence how well AI understands, remembers, and reasons through information. The findings are discussed in a paper that was first published on 3 March 2025 and republished on 30 September. 2025.
FURTHER READING AND REFERENCE
- Kim, Y., Russell, J., Karpinska, M., and Iyyer, M. 2025. “One Ruler to Measure Them All: Benchmarking Multilingual Long-Context Language Models (Version 3).” arXiv. DOI: 48550/ARXIV.2503.01996