This is completely determined by user behavior. They can choose to drag across multiple words to translate it as a phrase. Alternatively, if they click two adjacent words to translate them AND they have the “Merge Phrases” option enabled in the reader page settings then they will merge.
I query Google Translate (basic translations) or OpenAI (context-aware translations) for the translations. These are reasonably fast already, but I also cache the results in MongoDB so that I don’t need to query Google or OpenAI again if we’ve already done that same translation within the past few months.
For accuracy of the LLM powered context-aware translations, I’ve been experimenting with running evals on translations of the same set of words on different LLM models (e.g. gpt-4o, gpt-5, google’s gemma and gemini models, etc) and getting a very good, very expensive reasoning model to judge these results (e.g. claude opus and google gemini pro). I’ve done this for many different languages. This can give me confidence that if I alter the LLM powering the translations in future the translations will be of better quality. As part of these evals I also measure the latency so I can ensure that they aren’t too slow. As you’d expect, the reasoning models are significantly slower than the non-reasoning ones. For now I’m still using gpt-4o and gpt-4o-mini to power all the LLM based features in Readlang, but I’m going to revisit this after the summer, run more evals, and potentially update to newer and better models.