Tokenization and generation differ across Transformer versions due to updates in tokenizers, model architectures, and decoding strategies. Changes in vocabulary, padding, or special tokens can affect output length and format. Upgraded generation methods may also modify behavior, influencing fluency, repetition, or coherence across different model versions.