ByteDance's "iLLaDA" is a diffusion language model that keeps up with Qwen2.5

Diffusion language models take a different approach. They start with a sequence of placeholders, called masked tokens, and refine them across multiple passes in parallel. It's similar to how image models shape a picture from noise. Every position can attend to every other position at the same time, making the process bidirectional.

iLLaDA is part of a broader movement that includes Google. In June 2026, Google DeepMind released DiffusionGemma. That model generates text about four times faster via diffusion but scores worse on benchmarks like MMLU and code than the similarly sized autoregressive Gemma 4. Google recommends it for low-latency use cases, not quality-critical production.

DiffusionGemma takes a different approach. It's built on the Gemma 4 backbone, a 25-billion-parameter mixture-of-experts model that swaps only the generation method to prioritize speed. iLLaDA, short for "improved LLaDA," goes the other way. It's a dense 8B model trained from scratch, focused on quality.

The question behind all of this is whether a diffusion model built from the ground up can actually keep up with autoregressive models. A direct numerical comparison between the two is tough, though. Google uses partly different and harder benchmark variants, and DiffusionGemma plays in a different weight class.

What iLLaDA can do

The team pretrained iLLaDA on 12 trillion tokens, up from 2.3 trillion for its predecessor LLaDA, and fine-tuned it for twelve epochs. According to the paper, iLLaDA-Base improves sharply over LLaDA, jumping 21.6 points on the reasoning test BBH, for example. On average it hits 63.9 points, edging just past the autoregressive Qwen2.5 7B at 63.3.

The comparison with the competing diffusion model Dream 7B also favors iLLaDA. Dream wasn't trained from scratch but fine-tuned from an existing Qwen2.5 checkpoint. iLLaDA still beats Dream on average, 63.9 vs. 61.4, even without the head start of a strong autoregressive base. Dream only holds a slight edge on coding benchmarks.

A gap remains at the instruct level. iLLaDA-Instruct scores 67.1 points while Qwen2.5 7B Instruct hits 77.1, with math and code driving most of the difference. The authors blame this on the extra reinforcement learning alignment in Qwen2.5, which iLLaDA lacks. In the paper's appendix, they also note that the model can get stuck in reasoning loops on harder tasks.