A study evaluates over 30 large language models on Japanese grapheme-to-phoneme conversion using 3000 manually annotated sentences. The best LLMs achieve a kana character error rate below 0.52%, outperforming the best conventional tool (1.03%). Parse mode, with rule-based post-processing, performs better than direct mode for most models, and LLM-predicted kana improves TTS pronunciation when fed into kana-input TTS.