FLUX 3 is Black Forest Labs' attempt at one model across images, video, audio and robot action prediction. Here is what the official page claims and how to try it.
Read the full page: https://flux-3-dev.github.io/
The interesting claim here is consolidation: one model covering images, video with native audio, and action prediction for robotics, rather than four models stitched together behind an interface. If you generate images or short clips, that matters mainly for consistency between them. If you were hoping for a small local model, this is not that conversation, and the official page does not commit to weights for this release. Pricing is not on the landing page, so check the official pricing page before planning a budget. Synexa is the route we use when the priority is calling FLUX-family models from code with one endpoint and paying per run instead of managing another vendor account.
- One model instead of four — The framing on the official page is deliberate and worth taking seriously: a single multimodal model spanning image, video, audio and action prediction, describ
- Video with sound in one pass — The video description is the most specific part of the page. Clips run up to twenty seconds in a single generation, which is longer than the few-second outputs
- Images and the text rendering claim — For still images the page emphasises grounding in the real world, a wide range of styles, complex prompt handling, and highly accurate text rendering. That last
- Action prediction is a different audience — The fourth modality is not for creative teams at all. The site describes it as unifying perception, simulation and execution for robotics, taking visual observa
- Access, pricing and calling it from code — Getting in starts at the vendor dashboard, with a playground for trying prompts, an API section and a pricing page in the navigation, plus an enterprise track a
Try Synexa: synexa.ai
An independent page about FLUX 3, not affiliated with or endorsed by Black Forest Labs; all trademarks and product names belong to their respective owners.
Last reviewed: 2026-09-22