Mercury 3: How Inception Labs Scaled Diffusion LLMs to 1,000 Tokens Per Second

When most people think of large language models, they picture the same basic loop: generate one token, append it to the context, generate the next. Autoregressive decoding has been the default for years, and for good reason — it works. But it also has a hard ceiling on throughput, because each token depends on every token before it. Inception Labs has been quietly building a different kind of model, and with Mercury 3 , released on October 19, 2026, they've pushed diffusion-based language models to a scale where that ceiling starts to matter.

What Makes a Diffusion LLM Different

Standard autoregressive models generate text left-to-right, one token at a time. Each step is conditioned on the full sequence so far, which means you can't parallelize across output positions — you have to wait for position n before you can compute position n+1 .…