What is a virality score? How AI clipping tools rate clips

When an AI clipping tool cuts a long video into clips, it puts a number on each one. The number is the tool's prediction of how the clip will do once it's posted, made from the clip itself: its words, and in some tools its sound and picture. It isn't a count of anything that has happened. Nobody has seen the clip yet.

The names differ. OpusClip, quso.ai, Klap, StreamLadder, Submagic and Spikes Studio call theirs a virality score, Eklipse and Vizard a viral score, and Clyper a hook score. What they're for is the same: one long video gives you ten, twenty or thirty clips, more than you'll post in a day, and the score puts them in order. OpusClip's help center says it sorts clips from highest to lowest score by default, and Clyper sorts them by hook score as they come in.

Two things follow. A score is made for ranking, so its best use is comparing one video's clips with each other. And the same number means different things in different tools. quso.ai's help center says every clip gets between 75 and 99: it ranks the clips, then gives the top ones scores near 99 and spreads the rest down to 75. A 75 there is the bottom of the range; in Clyper, 75 counts as strong. For how a tool finds the moments in the first place, see how AI clip generators work.

Here's what each tool's own pages say about its score, read on October 27, 2026. Where a page doesn't say how the score is made, the table says so.

OpusClip says the most about how its score is built. Its post on its API lists signals from the transcript (hook strength, narrative arc, information density, emotional valence, quotability), from the audio (pace shifts, pitch range, background reactions, pause patterns) and from the picture (gesture intensity, facial expression). It says the model was trained on hundreds of thousands of clip and performance pairs from real social platforms, calibrated to predict above-median performance on TikTok, Reels and Shorts, and gives one hit rate: scores of 70 and up predict above-median performance about 75% of the time on talking-head content. It doesn't say how that was measured. None of the other pages we read gave an accuracy figure.

Klap's is the most open about single clips: its API returns a written explanation beside each score, and the example in its docs reads “Strong hook in the first seconds and a clear, quotable takeaway.” Vizard's post on its Model v2 says it breaks each score down across several dimensions, among them hook strength, clear takeaway and story flow. For everything else about these tools, from prices to what links they take, see AI clipping tools compared.

Put the tools' own descriptions side by side and the same few things come up:

• The opening. OpusClip, quso.ai and Vizard all name the hook: OpusClip's hook, quso.ai's hook and flow, Vizard's hook strength (“does the opening stop the scroll?”). The apps measure the same moment. YouTube's Shorts analytics show Stayed to watch: the percentage of times viewers stayed past the initial seconds of a Short.
• Whether it stands alone and pays off. OpusClip's flow and value, Vizard's story flow (“can the clip stand entirely on its own?”) and clear takeaway. By those descriptions, a clip that needs the rest of the video, or stops before its point, should score lower.
• Sound and picture, in some tools. OpusClip lists pace, pitch, gestures and facial expressions. Eklipse, built for gaming streams, weighs chat activity, voice intensity and visual motion around a peak, and even the source's resolution and bitrate.
• Trends. OpusClip and quso.ai both list trend; quso.ai describes it as how relevant and timely the content is.

So a score is a read on what's inside the clip. It says the most about the part you control, the opening, and nothing about what happens outside the clip once it's posted. What makes an opening work is in hook examples for short videos.

Clyper's score is called a hook score because that's what it rates. When Clyper reads a video, a language model goes through the whole transcript, picks the moments that stand on their own and scores each from 0 to 100: how likely its opening sentence is to stop someone scrolling, and the clip to keep them to the end. It's asked to be strict: 90 or above is rare and exceptional, 75 to 89 is strong, and 60 to 74 is decent.

The score sets the order and decides what makes the cut. Clips are sorted by hook score, and each shows its rank and score. On Auto, Clyper keeps to moments with a real hook and gives you fewer clips rather than pad the list with weak ones. When you choose a number, it fills it with the next-best moments and scores them honestly, in the 50s if that's what they are. Nothing under 50 is kept.

780:22

680:59

640:54

On the site's own example clips, from 4 videos, the hook scores run from 64 to 80. That's the point of a strict scale: if most clips scored 90, the number wouldn't help you choose.

It's read from the words. The hook score rates what's said in the opening and how the clip plays out from there; it doesn't see the picture or hear the tone of voice, so a moment carried by a look or a laugh can score lower than it plays. That's one more reason to watch the low scorers before you skip them. On Pro and Studio, Clyper can also open each clip with its hook as a headline for the first 4 seconds, which gives a slower opening a second hook in text; more in how to clip videos with AI.

A score is made from the clip before it's posted. Everything that happens after is outside it:

• Your audience. TikTok has said there's no one For You feed: each person's is unique to them. YouTube says its recommendations start from each viewer's own habits, and that “the importance of each signal depends on you.” The same clip can find its people on one account and miss on another, and the score is the same on both.
• Timing. When you post, what else is out that day, whether the topic is in the news. quso.ai's help center: “Actual performance depends on many factors including timing, audience, and platform algorithms.”
• The post around it. The caption, the cover, the account it goes on. OpusClip's own post: “Some 60-scored clips outperform 85-scored ones because of timing, accompanying caption, or platform context.”
• Whether it gets posted. Eklipse puts it bluntly: “plenty of 90+ scores stall because the creator never posted them.”
• The rules. A score doesn't know a campaign's brief, or whether a clip will be approved. See why clipping campaigns reject clips.

The tools say as much. Eklipse: “Viral score is a guide, not a verdict.” quso.ai: “a prediction based on content analysis, not a guarantee.” OpusClip calls scores “ranking signals, not green/red lights.” What the apps themselves reward is in the TikTok algorithm and YouTube Shorts algorithm guides.

• Post in score order, mostly. The top of the list goes out first, so your likeliest clip gets the most time to find its audience. Don't read much into a gap of a few points: between a 74 and a 72, post the one you'd stop scrolling for.
• Don't throw the low scorers away. Eklipse writes that plenty of clips with 60-something scores “go off because the moment is genuinely funny or unexpected.” A low score often means a slow first line, and that you can fix: trim to the next sentence, or add a headline that says what's coming. The free hook generator helps with the words.
• Test, and keep notes. Post a mix of high and low scorers, and write each clip's score beside how it did: its views, and on Shorts, Stayed to watch. After a few weeks you'll see whether the high scores do better on your account, and by how much. That tells you how far to trust your tool's score, which no tool can tell you.
• Learn from the winners, not the numbers. When a clip takes off, look at how it opens, and look for more openings like it in the next video. What a good retention rate is helps you read the numbers.

Not by asking. Eklipse says its viral score is generated automatically and can't be ranked or overridden by hand; it re-scores a clip after you edit it, and its advice is to improve what goes in: stream at higher quality, keep the kill feed in view, trim dead air. Clyper scores each moment when it picks it, so trimming a clip in the editor changes the clip, not the number.

What raises a clip's chances is what the scores look at: an opening line that makes people need the next one, a moment that makes sense on its own, and an ending right after the payoff. Those are yours to get right, whatever the number says. How to make clips that go viral covers each, and can you use AI for clipping? covers where the AI's judgment ends and yours begins.