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Large language models are trained with tokenizers that map text to a fixed vocabulary, yet the resulting token distribution is highly imbalanced: a few words dominate the stream while most occur rarely. Recent practice favours ever-larger vocabularies, but it is unclear whether the benefit comes from better word segmentation or from amplifying this frequency skew. To this end, we perform a controlled study that scales the vocabulary of a constant-size Transformer from 24K to 196K symbols while holding data, compute and optimisation unchanged. Above 24K every common word is already a single token, so further growth only increases imbalance. Word-level loss decomposition shows that larger vocabularies reduce cross-entropy almost exclusively by lowering uncertainty on the ~$2,500$ most frequent words, even though loss on the rare tail rises. Same frequent words cover roughly $80\%$ of tokens in downstream benchmarks, this training advantage transfers intact. We further show that enlarging model parameters with a fixed tokenizer yields the same frequent-word benefit, revealing a shared mechanism behind vocabulary and model scaling. Our results recast “bigger vocabularies help” as “sharper frequency imbalance helps,” offering a simple, principled knob for tokenizer–model co-design and clarifying the loss dynamics that govern language-model scaling in pre-training.