Status, October 2026: OpenAI closed the Sora app on 26 April 2026, and it shut down the Sora 2 models and the Videos API on 24 September 2026, so new Sora work has no official access route. This guide now shows how to test Wan 2.5 as a replacement on your own products. Record the exact version and access route you test, because both change.
Choose a model by examining the path from product reference to an approved ad. When assessing Wan 2.5 or another replacement, track image quality, repeatability and practical work together.
If you would rather brief finished ads than run the models yourself, AI Vidia's AI video ads service starts from your product photos and a signed product fact sheet and delivers finished 9:16 ads.
Describe what needs to be repeated
A single attractive clip may be enough for a limited assignment. Ongoing production requires using the same product in new scenes and a process others can understand. State which need applies.
Choose a representative video with your product, message and frequently needed movement. A spectacular scene unrelated to your ads may be interesting without answering the practical production question.
Check the actual access
Record model version, service and settings. Wan can run in different production setups, and a model can be withdrawn, as Sora 2 was, so check the provider's deprecation notices before you plan a series.
Identify what the producer must configure and maintain. Technical operation and extra file handling belong in the workload. If a service limits a required feature, make that visible in the assessment.
A hypothetical lamp collection
Suppose three lamps need the same visual direction, but differ in shade shape and size. Start with one lamp and a limited scene, then repeat the assignment with the other products.
Check whether the producer can switch products without carrying the first lamp's details into the others. Inspect colour, proportions, cable and lighting depiction. Record recurring defects as problems in the tested process.
This hypothetical example defines useful observations. It does not establish that any model is better for product series.
