Sora Is Winding Down: What to Use for AI Video Instead

Sora is going away. OpenAI ended the consumer Sora experience in April 2026, and the Sora API is scheduled to shut off on 24 September 2026. If you built anything on top of it, or simply got used to it as your default text to video tool, you have a migration on your hands rather than a preference to reconsider.

The good news is that the timing is unusually kind. AI video in 2026 is in far better shape than it was when Sora first made everyone pay attention, and the replacements are not consolation prizes.

What actually changes

Two separate things are ending, and it is worth keeping them apart. The consumer app is already gone, which affects people who generated clips directly in the product. The API shutdown is the one with a deadline attached, and it affects anything programmatic: pipelines, integrations, internal tools, side projects.

If you are in the second group, the practical question is not which model is prettiest. It is which one covers the specific job your pipeline was doing, and how quickly you can validate that the output holds up.

Work out which job you actually need covered

"AI video" is not one capability, and this is where most migrations go sideways. People swap in whichever model was ranked highest that week, then discover it does not do the thing their workflow depended on. Sort your use case first.

• Text to video: a written prompt becomes a clip, with nothing else supplied.
• Image to video: you already have a frame you like and want it to move.
• Audio driven video: a voice track or piece of music dictates the timing.
• Video to video: you have footage and want to transform it, transfer motion, or fix lips against new audio.

Those are genuinely different problems. A model that is excellent at the first can be useless at the fourth.

What to move to, by job

One detail worth knowing about Wan 2.7: its open weights mean self-hosting is a realistic option if you ever need to move off a hosted service entirely. That is a meaningful difference from a closed model, and this whole episode is a decent argument for caring about it.

The bar has moved since Sora launched

Part of what made Sora feel singular in its first demos was that nothing else was close. That gap has largely closed. The current generation of video models routinely produces clips in the eight to twenty second range at native resolution, with audio that stays in sync, physics that mostly behaves, and characters who survive a cut without turning into someone else.

That last point is the one worth dwelling on, because character consistency across shots was the practical blocker for anything longer than a single clip. When a face drifts between cuts you do not have a sequence, you have a collection of unrelated shots. Holding identity steady is what turns generated video from a novelty into something you can actually edit together.

So if your mental model of AI video was formed by the first Sora demos, it is probably out of date in both directions. The ceiling is higher than you remember, and the field is far less dependent on any single lab.

The lesson worth taking from this

Sora is not the only thing being retired this year. Google has said it is retiring Imagen in August 2026 and pointing users to its Nano Banana models instead. Two high profile retirements in one year, from two of the largest labs, is a pattern rather than a coincidence.

The takeaway is not that any particular lab is unreliable. It is that model availability is now a moving target, and building a workflow that depends on exactly one model from exactly one vendor is a fragile position no matter how good that model is today.

A migration that will not waste your week

1. List the clips you actually produce, sorted by how often you make them. Migrate the common ones first.
2. Take three real prompts from past work, not new test prompts. Old prompts expose differences that clean ones hide.
3. Run each through the replacement model and compare against the Sora output you already have.
4. Adjust prompt wording before concluding the model is worse. Models respond to different phrasing, and a like for like prompt is rarely optimal on a new one.
5. Only then decide whether anything in the pipeline genuinely needs rebuilding.

Step four is the one people skip. A prompt tuned over months against one model is not a fair test of another, and plenty of "this replacement is worse" verdicts are really "I have not learned this model yet."

The short version

Sora's consumer app is already gone and the API has a shutdown date, so anything built on it needs a plan. Sort your work by job first, since text to video, image to video, audio driven video, and video to video are different problems with different answers. Test with real prompts from past projects, and give yourself room to re-tune wording before judging the result.

You can try the replacements side by side in the studio, or see how each video tool is set up on the video features page.