# DiffusionGemma 26B-A4B ![Organization](https://img.shields.io/badge/Org-Google%20DeepMind-4285F4) ![Architecture](https://img.shields.io/badge/Architecture-Diffusion%20LLM-8957e5) ![Total params](https://img.shields.io/badge/Total%20Params-26B-1f6feb) ![Active params](https://img.shields.io/badge/Active%20Params-4B-2da44e) ![License](https://img.shields.io/badge/License-Gemma%20ToU-d29922) ![Weights](https://img.shields.io/badge/Weights-Open-2da44e) > **Quick answer:** DiffusionGemma 26B-A4B is Google DeepMind's open-weight diffusion-based language model — a fundamentally different architecture from standard autoregressive LLMs. Rather than generating tokens left-to-right, it iteratively denoises an entire output sequence in parallel, with 26B total parameters and 4B active per denoising step. ## At a Glance **Where DiffusionGemma leads** - Novel architecture: generates entire sequences in parallel via iterative denoising, not token-by-token - Can refine and edit outputs during generation — unlike autoregressive models, intermediate steps are accessible - 4B active parameters per step gives lower per-step compute than a 26B dense autoregressive model - Research platform for diffusion LLM capabilities and limitations **Where it lags** - Diffusion LLMs currently trail autoregressive models on most standard benchmarks (MMLU, coding, reasoning) - Streaming output is fundamentally different — partial results are lower quality until convergence - Smaller developer ecosystem than standard transformer models **Best for:** research on diffusion-based generation, controlled text editing tasks, and applications where parallel refinement is preferable to sequential decoding. ## What DiffusionGemma Is Diffusion language models apply the iterative denoising approach from image diffusion (Stable Diffusion, DALL-E) to text generation. The model starts with a noisy sequence and progressively refines it over multiple forward passes until the output converges to a coherent, high-quality text sequence. DiffusionGemma 26B-A4B is Google DeepMind's research contribution to this space — using the Gemma 4 architecture (Mixture of Experts, 26B total / 4B active) as the backbone for a diffusion LLM. The open-weight release enables the research community to study, fine-tune, and benchmark diffusion-based language generation. ## Specifications | Field | Value | |---|---| | Organization | Google DeepMind | | Architecture | Diffusion LLM (not autoregressive) | | Total parameters | 26 billion | | Active parameters per step | 4 billion | | License | [Gemma Terms of Use](https://ai.google.dev/gemma/terms) | | Modality | Text | | Generation method | Iterative denoising | | HuggingFace | [google/diffusiongemma-26b-a4b](https://huggingface.co/google/diffusiongemma-26b-a4b) | ## Related Models & Benchmarks - Compare: [Gemma 4 26B-A4B](/models/google/gemma-4-26b-a4b) · [Gemma 4 31B](/models/google/gemma-4-31b) - Benchmarks: [MMLU-Pro](/benchmarks/mmlu-pro) ## Frequently Asked Questions

What makes DiffusionGemma different from standard LLMs?

Standard LLMs generate text one token at a time, left to right. DiffusionGemma generates the entire output simultaneously through iterative refinement — starting from noise and converging to a coherent sequence across multiple forward passes.

Is DiffusionGemma competitive with autoregressive models?

Not yet at frontier quality levels on standard benchmarks, but it is a rapidly developing research area and represents Google DeepMind's public contribution to open diffusion LLM research.

--- *Last updated 2026-06-19.* ## Pricing Open weights under Gemma Terms of Use — self-host at no cost. ## DiffusionGemma 26B A4B vs Alternatives | Model | MMLU-Pro | License | |---|---|---| | **DiffusionGemma 26B A4B** | 77.6% | Gemma ToU | | [Gemma 4 26B A4B](/models/google/gemma-4-26b-a4b) | — | Gemma ToU | | [Gemma 4 12B](/models/google/gemma-4-12b) | 77.2% | Gemma ToU | DiffusionGemma 26B A4B uses diffusion decoding (vs autoregressive). Similar MMLU-Pro to Gemma 4 12B (77.6% vs 77.2%). --- *Scores from Benchgen evaluations. Last updated 2026-07-24.*

DiffusionGemma 26B-A4B — Files