FLUX 3, a new multimodal foundation model, is now available in early access, marking a strategic shift toward unified visual intelligence that jointly processes images, video, audio, and language within a single architecture. Rather than training on isolated data streams, the model learns how objects persist, move, and interact acoustically, treating each modality as complementary evidence of an underlying physical reality. Built on the Self-Flow methodology, FLUX 3 aligns multimodal generation and understanding efficiently, enabling simultaneous synthesis of video and audio from text prompts or visual references while preserving temporal and causal consistency. In preliminary evaluations, FLUX 3 demonstrates measurable advantages across content creation and physical AI applications. The video module generates up to twenty-second clips with native audio at 720p resolution, excelling in human facial expression rendering, accurate sound-to-event synchronization, and multilingual comprehension. When provided with reference imagery, the model maintains character and scene consistency across extended sequences. The image synthesis component supports diverse stylistic outputs, variable aspect ratios, and high-fidelity text rendering across multiple languages, showing marked improvements in complex prompt adherence over previous iterations. Beyond generative media, FLUX 3 extends into action prediction and robotics. By leveraging its video backbone as a dynamics-aware foundation, specialized action models can be finetuned using minimal task-specific data. Early collaborations include FLUX-mimic, a partnership with Mimic Robotics that applies the foundation to dexterous manipulation, with deployment testing already underway at Audi manufacturing facilities. Early human preference benchmarks indicate strong reception, with FLUX 3 outperforming several competing video models by margins ranging from fifty-two to ninety-three percent in head-to-head comparisons. The development team has outlined a phased rollout strategy centered on iterative early access windows, rigorous safety testing, and continuous feedback integration. Image capabilities will open to early access in the coming weeks, accompanied by forthcoming technical documentation detailing the underlying flow-matching architecture. Future development will focus on expanding interactive editing, simulation environments, computer use interfaces, and broader physical AI integrations. The team is simultaneously advancing next-generation research aimed at unifying perceptual, action, and language prediction within a single model. Engineering and research opportunities are currently available in Germany and the United States as the project progresses toward its official release.