π-PrimeNovo introduces the first non-autoregressive transformer model for de novo peptide sequencing. Unlike most transformer-based methods, π-PrimeNovo predicts all amino acids in the sequence simultaneously, avoiding the error accumulation typical for next-token prediction strategies. The model is trained with a connectionist temporal classification loss to enhance the global coherence of the peptide sequence and avoid the local ambiguities that can arise in non-autoregressive sequence generation. This loss function operates at the sequence level rather than at the token level. Finally, π-PrimeNovo includes a novel precise mass control unit that implements a CUDA-accelerated knapsack-type dynamic programming algorithm to ensure that the predicted sequence matches the precursor mass during decoding.