The practice of running the identical prompt through several AI models — the video tests SeaArt Dream Pro 5.0, Niji Journey Pro, and Soul Cinema — to see which one produces the best result for a specific asset or shot, then using that model's output and discarding the rest.
This differs from Multi-Model Specialization and Compositing: testing/selection is a bake-off that keeps one winner per prompt, while specialization-and-compositing combines partial outputs from several models into a single final asset (e.g. via a face-swap merge). Testing is how a creator discovers which model to trust for a given kind of shot in the first place.
Before committing to a model for a recurring asset type (e.g. a specific character or environment), run the same prompt across the available models and compare results side by side; adopt the winner as the default for that asset type going forward.
Из тем: Unsorted, Workflow Tooling: Node-Based Pipelines, Claude, and Multi-Model Chains