Perplexity’s Best mode automatically chooses a model for each of your queries based on its opinion of which model would give the highest quality response for that particular prompt. In Best mode, then, the model you’re actually talking to can change from prompt to prompt.
Because large language models are stateless, the whole conversation, from the very beginning, is passed back to the model each time you send a prompt. In Best mode, whatever model Perplexity chooses will get the entire conversation history, including previous responses written by a different model.
I thought that perhaps an LLM might balk somehow at a transcript that claims a model gave a response that in reality it would never give. So I asked Perplexity about that, naturally. Turns out that’s not a problem; LLMs take the conversation transcript at face value and do what they always do: use it to predict the next response. In fact, you could synthesize a transcript with complete non-sequiturs for the model’s previous “responses” and it’d work perfectly well, in the sense that you’d get a response that’s reasonable for the context.
One implication of this modus operandi is that if a conversation shows the model responding one way in past turns, it’s more likely to respond the same way in the future. (You could use this to send a few fake conversational turns that reinforce instructions in the system prompt.)
And that brings us to why Perplexity keeps writing in Chinese. In Best mode, Perplexity can use DeepSeek to respond to some requests, even though you can’t select that model from the UI. And DeepSeek, being a Chinese model trained on lots of Chinese text, has a penchant for randomly responding in Chinese even if you’re prompting it in another language. Once that pattern has been established in a conversation, Perplexity keeps doing it. Even other models do it once DeepSeek has done it a few times!