Originally published on AI Tech Connect.
What you need to know Different generation process. Autoregressive models write left to right, one token at a time. Diffusion language models start from noise or a fully masked sequence and refine the whole thing in parallel over a series of denoising steps. Planning comes first. A 2026 line of work proposes generating in a continuous latent space — organising the global meaning of a passage before any discrete word is committed. Speed is the headline claim. Parallel refinement can decouple latency from sequence length. Commercial diffusion LLMs report throughput far above typical autoregressive baselines. Maturity is the catch. Tooling, fine-tuning recipes and serving stacks are early, and several efficiency studies warn the speed story is not yet universal. Treat 2026 as an evaluation…
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