AI models cost more when you don’t use English

Large language models often require significantly more tokens to process non-English languages, which increases operational costs and reduces the effective memory capacity for those users. This linguistic disparity creates a performance gap that can limit the accessibility and accuracy of AI tools for non-English speakers. These inefficiencies pose a challenge for global equitable access, particularly as these technologies are increasingly integrated into public services and democratic processes.