How does FLUX 3 Action perform on benchmarks?

The model achieves a 42.92% success rate on the RoboLab benchmark, outperforming the Cosmos 3 Nano model, which scores 36.8%.

Is FLUX 3 Action available via a public API?

No, there is no public API for FLUX 3 Action. Access to the open weights is currently restricted to selected research and commercial partners under specific licensing terms.

What are the hardware requirements for FLUX 3 Action?

Because there is no hosted API, users must host their own infrastructure. The model requires significant compute resources for joint video-action inference and may require external correction or hybrid policies for fine-grained control tasks.

FLUX 3 Action Model Explained: Specs and Performance

Q: What is FLUX 3 Action?

FLUX 3 Action is a 7-billion-parameter world action model developed by Black Forest Labs that integrates camera data, robot state, and language instructions to generate physical actions and predict video dynamics.

FLUX 3 Action Benchmarks and Access. A macro close-up of a high-end GPU cluster rack with its front panel removed, showcasing a dense array of glowing LED status indicators reflecting off a mirrored liquid-cooling manifold pipe in a dark, — GPUniq

Data as of October 1, 20263 sources

Drafted with AI tools and edited by a human. The figures and conclusions were checked by a GPUniq editor.

TL;DR

FLUX 3 Action is a 7-billion-parameter world action model from Black Forest Labs. It scores a 42.92% success rate on the RoboLab benchmark. That beats Cosmos 3 Nano, which scores 36.8%. The model is released as open weights under BFL licensing terms, but general access is restricted to partners and no public API exists.

What Is FLUX 3 Action?

FLUX 3 Action (F3A) is a 7-billion-parameter world action model built by Black Forest Labs on the FLUX 3 multimodal backbone. It takes camera observations, the robot's current state, and natural language instructions as inputs. The model processes those feeds to output physical action sequences while jointly predicting future video dynamics.

Input Streams	Processing Engine	Generated Outputs
Cameras, Robot State, Language Command	FLUX 3 Action (7B)	Physical Actions, Predicted Video Dynamics

Classical robotics setups split vision, planning, and control into separate codebases. FLUX 3 Action operates as an open-weight vision-language-action architecture that pairs visual prediction directly with motor execution.

Benchmarks

Reporting by VentureBeat puts FLUX 3 Action at a 42.92% overall success rate on the RoboLab benchmark. A guidance-distilled checkpoint scores 42.2% on the same tests.

Model	Parameters	RoboLab Success Rate	Access Type
FLUX 3 Action (Base)	7B	42.92%	Partner-gated
FLUX 3 Action (Guidance-distilled)	-	42.20%	Partner-gated
Cosmos 3 Nano	-	36.80%	-

The model achieves faster runtimes per second of robot motion than some existing architectures. Yet tradeoffs exist. BFL notes that the model falls behind certain VLA options when factoring in the total compute cost and latency of joint video-action prediction. In experimental settings, it can struggle with task ordering or fine-grained control unless paired with external correction routines in a hybrid policy setup.

API Availability

Black Forest Labs released open weights on September 23, 2026. You cannot plug an API key into a remote endpoint today because no public API exists.

Request Step	Handling Body	Outcome
Developer Application	Black Forest Labs	Manual Partner Gating
Partner Clearance	Targeted Research & Enterprise	Direct Weight Access

Access remains gated. BFL rolls out access through selected research and commercial partners under specific licensing terms.

Hardware and Deployment

Running joint video-action inference on a 7B model takes heavy compute. Running joint video-action inference demands significant compute resources.

Joint prediction adds cost and latency compared to some vision-language-action models.
Real-time motor loops need hybrid execution where FLUX 3 Action sets actions and external routines handle low-level motor corrections.
BFL notes that joint video-action inference demands significant compute resources.

Without a hosted API, deployment requires hosting your own infrastructure. Generative action outputs must pass through external verification and fallback layers to ensure physical safety on target hardware.

Methodology

Facts are compiled from the vendor's own announcement and documentation, linked below, at the time of writing; where the vendor hasn't published a number it is marked as such. GPUniq prices, when the model is already available here, come from our live catalog.