Which programming languages are most token-efficient? (martinalderson.com)

Recent analysis into programming languages' token efficiency highlights a growing consideration for software development in the age of AI. The study explores how the context length limitations of large language models (LLMs) influence the practicality of language selection for coding agents. By examining the Rosetta Code dataset, which contains tasks highlighted in nearly 1,000 programming languages, and utilizing OpenAI's GPT-4 tokenizer, the findings reveal a significant difference in token efficiency: Clojure tops the list as the most efficient language, while C ranks as the least, showing a disparity of 2.6 times. This research is particularly significant for the AI/ML community as it suggests that dynamic and functional languages, notably Haskell and F#, could allow for longer, more efficient coding sessions when utilized with LLMs due to their reduced token requirements. The implications are profound; optimizing language choice could enhance coding efficiency, especially as LLMs continue to redefine software engineering practices, reinforcing the need to integrate token efficiency directly into programming language considerations. As computational power expands, these considerations might shift how developers and AI agents engage in software creation.