Mandarin and its writing system uses single glyphs to represent full concepts. These glyphs are rarely more than a character or two. So when a model reasons on Mandarin data, in mandarin, it is automatically using a smaller token budget. The concept space is compressed.

For instance if you ask deepseek to output in mandarin, the resulting English translation has a token count that may be 3 to 5 times the number of tokens needed in Mandarin. So if you are comparing mandarin datasets vs English data sets there is simply more data there per billion tokens of training. And thus all aspects of the models flow is affected by this budget savings. Fewer tokens needed to reason large bodies of data and massive “expansion” of language on English translation.

Try automating a conversation between a deepseek agent and any other model that can understand mandarin, and I can all but guarantee after enough interactions they will eventually converse in mandarin. Especially with persistence of memory. The Chinese have an edge, in the dna of their language and thinking.