Flux 3
blackforestlabs/flux-3/text-to-videoflux-3-text-to-video- 🆕 Black Forest Labs' first video-capable Flux, generating from text prompts alone
- 🧠 Single multimodal model spanning image, video, audio and action prediction
- 🔧 Built on Self-Flow, unifying understanding and generation in one flow-matching model
- 💬 Native audio, sound effects and multilingual dialogue generated jointly with video
- 📏 Clips can be agentically chained into sequences lasting several minutes
- 🎯 BFL reports improved complex-prompt handling and text generation over earlier Flux
- 🌐 Wide style range, from camcorder footage to animation and cinematics
- 🏢 Sibling endpoints cover image-to-video and keyframe-driven generation
Black Forest Labs is a generative AI company based in Freiburg im Breisgau, Germany, founded by former members of Stability AI. The lab is best known for developing the Flux family of text-to-image models, which generate images from natural language prompts and have quickly…
Explore 6 more models by Flux (text-to-image model) →Flux 3 extends the Flux line from still images to video and audio: a multimodal foundation model that covers image, video, audio and action prediction within one network. This catalog entry is the text-to-video endpoint, turning a written prompt into a video clip with sound, while the related Flux 3 image-to-video endpoint animates a supplied still and Flux 3 First Last Frame interpolates between two defined keyframes for controlled transitions.
Architecturally, the model is described by BFL as being trained with Self-Flow, an approach that places multimodal understanding and generation inside a single flow-matching model, with compute and data scaled across video, images and audio simultaneously rather than training separate systems. A practical consequence is joint generation: video and audio arrive in the same pass, so ambience, effects and dialogue — including multilingual speech — are aligned with on-screen events.
Compared with its same-family predecessors, the difference is a change of modality. Flux 2 Pro and Flux 2 Max are image generators focused on photorealism, multi-reference control and text rendering, with Flux 2 Max Edit handling inpainting-style edits; none produce motion or sound. On the generation quality side, BFL reports that in preliminary mid-training evaluations Flux 3 already showed significant improvement over earlier Flux versions in handling complex prompts and generating legible text.
For longer-form work, individual clips can be chained agentically into multi-shot sequences running several minutes, with visual references helping keep characters consistent across scenes. Stylistic coverage described by BFL ranges from handheld camcorder-style footage to animation and cinematic looks.
This About section is AI-generated from public sources via VeniceStats + Venice inference, with no human editing. It may contain inaccuracies.
Daily usage · Flux 3 · Organic sellers
Top sellers · Flux 3 · Organic
- Open Forge72.2%
- Venice.ai Proxy20.6%
- antseed-tidal-falcon-88cd6.6%
- ▲ Apex Ant0.6%
| Seller | Health | Rep↓ | Sales | Video | Routing | $ / s | $ / video |
|---|---|---|---|---|---|---|---|
| Open Forge 0x1d90…b0aa | healthy | 91.11 | $53.55 | 5.3 min | #1 | $0.17 | — |
| ▲ Apex Ant 0x73b4…e736 | — | 81.71 | $0.42 | 5 s | #3 | $0.0841 | — |
| antseed-tidal-falcon-88cd 0x80d1…88cd | — | 12.78 | $4.90 | 25 s | gated | $0.138 | — |
| Venice.ai Proxy 0x1f22…18c9 | failingupstream error · 3.7 h | 86.84 | $15.26 | 2.7 min | #2 | $0.1029 | — |
"Best price" and the seller table are live Antseed catalog data (advertised $/1M tokens — or $ per generated image for unit-billed image models — not settled amounts). Reputation = buyer trust score (0-100, the Antseed SDK's own formula). "Routing" = the SDK's default buyer routing (what the VPR desktop app ships with): a trust ≥ 60 gate on the effective reputation, then cheapest-first among routable sellers; live failover state (per-peer cooldowns) is buyer-side runtime and not included. Model knowledge (TLDR, provider, About) via the VeniceStats enrichment layer. Advertised catalog, not the model used in any specific purchase. "Usage on Antseed" counts only settlements whose buyers share the per-model split on-chain (metadata v2/v3, opt-in), so every usage figure is a lower bound.