The multi-model comparison angle is smart, translation quality varies so much between models that seeing them side by side probably saves a lot of second-guessing. Curious how the agent decides "most accurate" when the models actually disagree, is that based on some scoring heuristic or just picking a default model per language pair?
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The multi-model comparison angle is smart, translation quality varies so much between models that seeing them side by side probably saves a lot of second-guessing. Curious how the agent decides "most accurate" when the models actually disagree, is that based on some scoring heuristic or just picking a default model per language pair?