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How long will it take to finish a YouTube course playlist at 1.5x speed?

WhiteUI · A plain-English guide to playlist watch time

Watching at 1.5x speed is the most common study shortcut, and the maths behind it is simple: divide the total length by 1.5. The hard part is knowing the total in the first place — and remembering that faster watching still needs breaks.

Two bars comparing a playlist length at normal speed, 1 hour 27 minutes 50 seconds, with 1.5x speed, 58 minutes 33 seconds
In plain words: divide the total by 1.5. A 1:27:50 playlist takes 58:33 at 1.5x, saving 29:17 — about a third of the time, not half.

The 1.5x rule

At 1.5x, every 90 minutes of video takes 60 minutes of your time. You save a third of the original length, which is why a long course suddenly looks finishable.

When 1.5x helps, and when it does not

It works well for lectures you already half-know and for review. For brand-new, dense material, 1.5x can cost more in re-watching than it saves — try 1.25x first for those sections.

Plan around the saved time honestly

Do not spend the saved third on more videos in the same session. Use it for notes, practice or a break, or the speed gain evaporates into fatigue.

Worked example

Take three videos — 10:00, 12:30 and 1:05:20. Added together they are 1 hour, 27 minutes and 50 seconds (1:27:50). At 1.5x, divide 5,270 seconds by 1.5 to get 3,513 seconds — 58 minutes and 33 seconds. That is 29 minutes and 17 seconds saved.

Frequently asked questions

How much time does 1.5x actually save?

A third of the original length. A 3-hour playlist takes 2 hours at 1.5x.

Is 1.5x good for exams?

For revision, usually yes. For first-time learning of hard topics, 1x or 1.25x is often faster overall because you re-watch less.

Can the calculator do 1.5x for me?

Yes — enter the durations, pick 1.5x, and it shows the adjusted time and the days to finish at your daily minutes.

Try it yourself

Paste your video durations and get the total, the time at faster speeds, and the days to finish.

The calculator is being rebuilt — check back soon.