Diffusion Language Models, From Scratch to Production

Diffusion language models generate text by repeatedly refining an entire masked sequence rather than producing tokens left to right, enabling parallel decoding and bidirectional context but introducing quality risks when many dependent tokens are committed at once. Masked diffusion extends BERT-style masked language modeling across all corruption levels, training a bidirectional denoiser to recover masked tokens and generating by progressively unmasking a blank sequence; a small character-level implementation illustrates that more denoising steps improve coherence at the expense of speed. Production evaluations comparing Inception Mercury 2 with Claude Haiku 4.5 on code tasks found Mercury roughly twice as fast but less accurate overall, matching Haiku on easy tasks, slipping slightly on medium tasks, and falling substantially behind on hard tasks, although its highest reasoning-effort setting improved hard-task performance with added latency. Diffusion models are therefore best suited to speed-sensitive, relatively short, parallelizable work such as code completion, on-device inference, and high-throughput serving, while autoregressive models remain preferable for maximum quality, long dependency-heavy outputs, and token-by-token streaming.

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