GPT Image 2.5 Stilled Animation Test Results: Can It Loop?

An access-blocked draft outlines a planned benchmark for evaluating “stilled animation,” in which GPT Image 2.5 Sunburst and Flare would generate 16-frame image grids that are locally extracted and played as GIFs, but it reports no completed requests, quality results, costs, or model winner. The proposed study uses five tasks—waving, coffee pouring, sneaker rotation, a walk cycle, and stationary-bike pedaling—with three independent requests per model and task, while separately assessing layout accuracy, identity consistency, frame-to-frame motion, physical relationships, contact points, and loop seams. Additional illustrative cases involving a caterpillar-to-butterfly transformation, rotating DNA, and blooming peony explain how stable scene anchors can distinguish intended subject motion from background, camera, lighting, or geometry drift. The workflow specifies generation settings, fixed prompts, 4-by-4 grid extraction, 8 fps playback, preservation of raw files, and clear separation of unaltered outputs from any repaired versions. It also emphasizes that image sheets and locally assembled GIFs are not native animation or video generation, that individual cells are correlated rather than independent observations, and that usable-animation cost can only be calculated from actual billing, qualified outputs, and separately recorded inspection or repair labor.

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