Why Diffusion Models Could Change Developer Workflows in 2026 | The JetBrains AI Blog

Diffusion models, particularly diffusion large language models (d-LLMs), are emerging as a transformative approach to coding assistants by better mirroring the non-linear and iterative nature of coding, where developers frequently edit, refactor, and revisit code sections. Unlike traditional autoregressive models that generate code in a strict sequence, diffusion models are capable of considering both past and future contexts, akin to how programmers often work by jumping back and forth through code. This allows for more intuitive handling of structured logic, long-range dependencies, and order-sensitive code constraints. Diffusion models excel in tasks such as code infilling and refactoring, where maintaining global coherence across edits is crucial. While they currently face challenges such as reduced quality when attempting to generate multiple tokens simultaneously, and limitations in open-source ecosystem maturity, their potential for faster generation and flexible editing positions them as a promising tool for developers. As they evolve, diffusion models may become integral to coding workflows by offering more dynamic and context-aware support, aligning closely with the actual coding practices of developers.