The vocabulary size is fairly small (128,256) for a multilingual model. I would guess it doesn't require many additional parameters to support these 5 languages as many tokens can be shared.
Typically, multilingual capabilities consume 20-30% of model parameters in small LLMs, primarily in token embeddings and early transformer layers. Monolingual variants of similar models often perform better on English benchmarks with the same parameter count.
