YouziDuanju
Member
Model pricing makes AI video look easy to compare: one generation costs X. In practice that number has been misleading for us.
A cheap clip is expensive if identity, wardrobe, props or camera direction drift and the shot cannot be edited into the sequence. The metric I find more useful is:
total generation + upscaling + audio + correction cost / accepted seconds in the final cut.
I would also log rejected takes by reason: identity drift, continuity error, motion failure, lip-sync/audio, or story mismatch. That separates a model that is cheap but wasteful from one that costs more per attempt but produces usable footage.
For people running AI video, image or short-form content sites: what do you measure today? Cost per attempt, cost per finished minute, revision hours, or something tied to retention/revenue?
A cheap clip is expensive if identity, wardrobe, props or camera direction drift and the shot cannot be edited into the sequence. The metric I find more useful is:
total generation + upscaling + audio + correction cost / accepted seconds in the final cut.
I would also log rejected takes by reason: identity drift, continuity error, motion failure, lip-sync/audio, or story mismatch. That separates a model that is cheap but wasteful from one that costs more per attempt but produces usable footage.
For people running AI video, image or short-form content sites: what do you measure today? Cost per attempt, cost per finished minute, revision hours, or something tied to retention/revenue?
