# inclusionAI ships LLaDA2.2-flash diffusion LLM

> A new Apache-2.0 mixture-of-experts model that generates text through diffusion rather than left-to-right decoding.

Published by The Open Weights on Jul 16, 2026. Canonical: https://theopenweights.com/news/llada2-2-flash-p1lk

## Key facts

- Company: inclusionAI
- Model: LLaDA2.2-flash
- Version: 2.2
- Category: Text / LLM
- Modalities: Text / LLM
- License: Apache 2.0 (Open weights, commercial use allowed)
- Context window: 131K tokens
- Architecture: LLaDA2MoeModelLM
- Precision: BF16
- Weights size: 205.8 GB
- Significance: minor
- Published: 2026-07-16
- Last verified: 2026-07-30
- Hugging Face: https://huggingface.co/inclusionAI/LLaDA2.2-flash
- Canonical URL: https://theopenweights.com/news/llada2-2-flash-p1lk

inclusionAI has released **LLaDA2.2-flash**, a text language model that pairs two of the more interesting ideas in current open-weights research: a diffusion-style generation process and a mixture-of-experts (MoE) architecture. The model is available now on [Hugging Face](https://huggingface.co/inclusionAI/LLaDA2.2-flash) under a permissive Apache-2.0 license.

Most large language models generate text autoregressively, predicting one token after another from left to right. Diffusion language models like the LLaDA line take a different route, iteratively refining a full sequence from a noised state toward coherent output. In principle that approach can offer more parallel, controllable generation, and it has become a notable alternative thread of research alongside the dominant transformer-decoder recipe.

## Why it matters

The combination here is what stands out:

- A **diffusion-based** generation process rather than standard autoregressive decoding
- A **mixture-of-experts** design, which routes tokens to a subset of parameters to keep inference costs down relative to raw model size
- An **Apache-2.0** license, allowing broad commercial and research use

As a "flash" variant, the model is positioned toward faster, more efficient inference within the LLaDA2.2 family. inclusionAI has not published detailed parameter counts, context length, or benchmark figures alongside this drop, so builders will want to test it against their own workloads before drawing conclusions.

For teams tracking the open-weights space, LLaDA2.2-flash is worth a look precisely because it sits outside the mainstream. Diffusion approaches remain relatively rare in shipping open models, and a permissively licensed MoE take gives researchers and developers a concrete artifact to probe. Full details and weights are on the model's [Hugging Face page](https://huggingface.co/inclusionAI/LLaDA2.2-flash).

## Get the model

- [Hugging Face](https://huggingface.co/inclusionAI/LLaDA2.2-flash)

## Sources

- [inclusionAI/LLaDA2.2-flash](https://huggingface.co/inclusionAI/LLaDA2.2-flash) — Hugging Face, Jul 16, 2026

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Source: The Open Weights (https://theopenweights.com/). Aggregated and written by Claude, curated by humans. Cite as: "inclusionAI ships LLaDA2.2-flash diffusion LLM", The Open Weights, Jul 16, 2026, https://theopenweights.com/news/llada2-2-flash-p1lk