[sacred_numbers]

The quality difference is substantial. I don't care if it's wasteful to use something that has many uses for a supposedly narrow task (although I don't see translation as a particularly narrow task anymore than I see writing as a narrow task). I would gladly waste untold trillions of floating point operations for a 1% increase in translation quality. From my experiments, though, it's much higher than 1% increase in translation quality. And regardless of how wasteful the compute is, it's actually cheaper in terms of dollars. Using GPT-3.5 to translate Korean to English would cost about $11 per million words, based on the average characters per token of the small sample of text I gave it. DeepL (the best translation service I could find) costs $25 per million characters, or for my sample text, about $64 per million words. At $11 per million words I can have GPT-3.5 perform multiple translation passes and use it's own judgment to pick the best translation and STILL save money compared to DeepL.