Cross‑entropy analysis shows Kimi’s responses closely match Claude’s

Researchers performed a cross‑entropy comparison of large language model outputs. The analysis focused on responses generated by Kimi and Claude. Results indicate that Kimi’s answers have a high similarity to Claude’s. The metric quantifies how closely the two models align in language use. Findings suggest comparable performance between the two systems. The study adds insight into model behavior across different LLMs. It may inform developers choosing between Kimi and Claude for projects. Further evaluation could explore additional similarity measures.