How Long Should an AI Video Be? 1,367 Posts Later

Every "how long should your video be" article I've read quotes the same platform documentation back at you. I wanted to know what happens in practice, so I went and counted: 1,367 published posts built from 460 distinct generated clips, across Instagram, Facebook, TikTok and YouTube, over roughly a month of running two stores.

The answer turned out to be less about a number and more about where the number comes from. Here's what I actually landed on.

The short answer: 15 seconds

Nearly everything I ship is 15 seconds, and that's not a stylistic choice — it's what the constraints converge on.

Generation models mostly cap a single continuous shot somewhere around 10 to 15 seconds. Going longer means stitching, and stitching means a cut, and a cut is where AI video gives itself away. A single unbroken 15-second shot reads as footage. Two 8-second shots joined together read as AI.

Fifteen seconds is also long enough to establish a place, show the product in motion, and land a line of voiceover, which is genuinely all a product clip has to do.

Why shorter usually isn't better

The common advice is "shorter is better, attention spans are short." In my numbers that's not what happened.

Clips under about 8 seconds underperformed consistently — not because people wouldn't watch them, but because there wasn't enough there to watch. A 6-second clip shows one thing. There's no arc, no reveal, nothing that makes someone stay past the first beat, and no room for a voiceover that says anything.

The retention argument also cuts the other way. A 6-second clip that gets 60% watched and a 15-second clip that gets 60% watched are not equivalent — the second one held someone almost three times as long. Watch time, not watch percentage, is what actually gets rewarded.

Where longer earns its place

There are three cases where I'll go past 15 seconds, and only three.

Something has to be explained. If the product's value isn't visible — software, a service, anything where you have to say what it does — 30 to 45 seconds is the honest length. Trying to compress an explanation into 15 seconds produces a clip that's fast and unconvincing.

The format is a list. "Five ways to wear this" needs five beats. Each beat is roughly 5 seconds, so the length falls out of the structure rather than being chosen.

It's for YouTube proper, not Shorts. Different platform, different economics. Longer form on the main YouTube feed is a separate discipline — see AI video for YouTube Shorts for where the line sits.

Length by platform, in practice

I publish the same clip to four platforms, which is itself the finding: one length works everywhere, and cutting platform-specific variants was effort that never paid back.

TikTok technically rewards longer, and there's real evidence that clips over a minute get more distribution. But that's a different production — scripted, multi-scene, and not something an image-to-video pipeline produces well. At 15 seconds TikTok performs fine.

Instagram Reels is where 15 seconds is most clearly correct. It's short enough to loop, and a loop that isn't obviously a loop counts as a rewatch.

Facebook behaves almost identically to Instagram for this content, which makes sense given the shared delivery.

YouTube Shorts is the most forgiving. Anything under 60 seconds qualifies and 15 sits comfortably inside it.

The loop is the trick nobody mentions

This is the single highest-leverage thing I found, and it's a length decision disguised as an editing decision.

If your last frame is close to your first frame, the clip loops seamlessly, and a viewer will often sit through it two or three times before realising it restarted. On a 15-second clip that turns 15 seconds of watch time into 40. Every platform's ranking treats that as a strong signal.

You don't need matching frames exactly — you need the same setting, similar framing and similar light. Starting and ending on a wide shot of the same street does it. Ending on an extreme close-up when you opened wide does not.

What about the end card?

I put a two-second brand card at the end of every clip, and I'll defend it, but be honest about the cost: those two seconds are the least-watched part of the video and they break the loop.

The compromise I settled on is to keep it short, keep it visually continuous with the clip rather than a hard cut to a logo, and keep every word of it in the middle of the frame. Both Instagram and TikTok cover the bottom of the screen with their own interface, so text near the bottom edge is text nobody reads.

How to decide for your own content

Don't start from a target length. Start from the structure and let the length fall out:

Count the beats. How many distinct things does the viewer need to see? Multiply by about 4 seconds. That's your length.

Check it fits one shot. If your answer is over 15 seconds, ask whether you can cut a beat rather than adding a cut. Usually you can.

Make it loop. Whatever you land on, end near where you started.

Then stop optimising length and go make more of them. Across 460 clips, the variation in performance driven by length was small compared with the variation driven by the opening two seconds — and vastly smaller than the variation driven by simply publishing consistently. Length is worth getting roughly right once, and then ignoring.

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One setting decides more of this than the prompt does: generate in the ratio you will publish in. Cropping a 16:9 generation down to vertical throws away about 70% of the frame width and leaves roughly 608 real pixels across, upscaled back to 1080 for delivery - which is the most common reason a clip looks soft without anyone being able to say why. Set the aspect ratio in the settings rather than describing it in the prompt, then verify the pixels before the file reaches a scheduler. Per-platform ratios, resolution advice and the safe-area margins in AI video aspect ratios.

Hands are the one thing that did not get solved. Faces, lighting and cloth all improved; a hand closing around a cup handle still grows a sixth finger for four frames, and viewers register that something is wrong without being able to name it. Three causes stack: a hand is high-articulation detail in very few pixels, the training data is heavy on hands doing nothing, and nothing in the architecture knows a finger count has to stay constant between frames. What actually helps is staging rather than prompting — start from a real photograph where the hands are already right, give the hand one committed job instead of a reach-and-grasp, keep it small or out of frame, and cut the clip shorter so drift has less room to accumulate. What does not help is negative-prompting the anatomy. The six fixes, and the four shots not worth attempting, in AI video still breaks hands.

If you want to compare what lengths and structures are working for other people running this at volume, that's the kind of thing we pull apart together. Join the AI Video Generator community on Skool.