The Polish language, often described as one of the most difficult and complex in the world, has unexpectedly proven to be the most effective for artificial intelligence when handling long and sophisticated prompts. A study conducted by researchers from the University of Maryland in collaboration with Microsoft revealed that AI models achieve their best performance when processing Polish text, outperforming many more widely spoken languages, including English.
American scientists tested several advanced AI models, including OpenAI o3-mini-high, Google Gemini 1.5 Flash, and Llama 3.3 (70B), analyzing their ability to correctly interpret long-form prompts of up to 128,000 text units. The results showed that Polish delivered the highest accuracy and response quality at 88%. By comparison, French scored 87%, Italian 86%, while English reached only 83.9%.
The biggest surprise for researchers was that despite having far less training data available in Polish compared to English or Spanish, AI systems demonstrated stronger comprehension and more logical processing of long and complex commands specifically in Polish. This challenges the common assumption that the more widespread a language is, the better AI understands it. Even less common and structurally difficult languages may offer unique advantages for training artificial intelligence algorithms.
The study covered 26 languages and provided a broader perspective on performance rankings. The most effective languages were Polish (88%), French (87%), Italian (86%), Spanish (85%), Russian (84%), English (83.9%), Ukrainian (83.5%), Portuguese (82%), German (81%), and Dutch (80%). These findings surprised many experts, since English is traditionally treated as the default standard for AI development and evaluation.
Researchers suggest that Polish, due to its morphological complexity, rich system of endings, and structural features, forces AI models to analyze context and syntax more carefully. As a result, the system generates more precise and logically consistent answers than in “simpler” languages, where context may be easier to predict.
These conclusions are highly significant for the future development of artificial intelligence worldwide. They demonstrate that less widely spoken languages like Polish can not only keep pace with global standards but even surpass them in terms of processing quality. For Poland, this is also an important signal: local developers and researchers may play a key role in the global AI race by contributing to models that better understand linguistic complexity and nuance.
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Moreover, the study has direct practical implications for companies building AI assistants, chatbots, and voice-based systems. Using Polish as a testing and training language could improve prompt interpretation and enhance the overall quality of generated responses. In other words, Polish is becoming not only a cultural asset but also a technological advantage in the AI era.
Ultimately, this discovery confirms that complex languages with rich grammar and structure, even if less widespread, can provide artificial intelligence with unique benefits. Polish stands as an example that the global AI competition is not determined solely by the number of speakers or the volume of available data, but by how strongly a language encourages models to analyze, compare, and logically process information.
This research opens the door for Poland to take a more active role in shaping the future of AI technologies and highlights an important message: linguistic diversity in AI training is not a weakness – it may become a strategic advantage in the years ahead.
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