Opus Clip’s virality score runs 0 to 99. ScaleReach’s runs 0 to 100. Klap scores every clip but publishes no scale anywhere on its own site, and Descript, Submagic and Vizard publish no ranked score on any page I could read. That is six tools and four different answers to what a comparison table would flatten into one tick in a “virality score” column.
This post compares three features that every roundup lists and nobody defines: virality scoring, speaker tracking, and captions. Disclosure: I run ScaleReach, so five of these six are competitors and I have an obvious interest. No affiliate links. The rule I set myself was that a feature only goes in the table if the vendor states it on a page the vendor controls — its own site, docs, or help centre. Where a vendor’s own pages are silent, the table says “not verified” instead of “no”, because absence from a marketing page is not proof a feature is missing. Everything here was read on 2026-09-23 except the Descript rows, read 2026-09-17 for that tool’s own review.
Prices are deliberately absent. We already normalise those in the AI video clipping tools roundup; this post is only about what the software does.
The 60-second verdict
If you sort by score before publishing, only Opus Clip and ScaleReach publish a ranked score with a documented scale. Klap scores clips without publishing the range.
If your source is a multi-speaker podcast, ask whether the tool tracks faces or the active speaker. Opus Clip uses voice and motion cues; ScaleReach adds audio diarization on top of face detection; Klap describes facial recognition plus speech detection.
If captions are the whole job, Submagic publishes the largest caption-language count at 123 and a 99% accuracy claim.
Bottom line: the three features in every comparison table are the three least comparable things in the category. Scores use different scales and different inputs, “speaker tracking” describes two different mechanisms, and a single “languages” number quietly merges transcription, translation and dubbing, which are three separate capabilities with three different counts inside the same product.
What does each tool actually publish?
One row per tool, one column per claim, every cell traceable to that vendor’s own page.
| Tool | Ranked score | Scale | Tracking mechanism it describes | Caption languages (and of what kind) |
|---|---|---|---|---|
| Opus Clip | Yes | 0–99 | Active speaker via voice and motion cues, plus manual per-scene subject tracking | Not verified on the pages read |
| ScaleReach | Yes | 0–100 | Face detection always, audio diarization when multiple speakers are detected | Transcription-driven, 24 style templates |
| Klap | Yes | Not published on its site; 0–100 observed in-product | Facial recognition plus speech detection; AI Reframe 2 picks a layout | 52 languages, edit and transcribe |
| Vizard | Not documented; observed in-product | Not published | Reframes to centre “key subjects”; mechanism not described | 100+ or 130+ translation, depending which Vizard page you read |
| Descript | No | — | Center Active Speaker, an AI tool | 25 transcription, 61 caption translation, 30 audio dubbing |
| Submagic | Not verified | — | Not verified on the pages read | 123 caption languages, 99% accuracy claimed |
Sources, all read 2026-09-23 unless noted: help.opus.pro virality score and subject tracking; klap.app; vizard.ai, Find Good Clips and help.vizard.ai; submagic.co; descript.com/pricing and help.descript.com Create Clips, read 2026-09-17. ScaleReach rows are read from our own source code.
Two cells deserve naming rather than a footnote.
Vizard is the messiest row in the table. Neither its homepage, its Find Good Clips page, nor its help centre article on how Vizard works names a virality score. The only mention of one anywhere on vizard.ai is inside a customer testimonial. Third-party reviews flatly contradict each other — one competitor’s review asserts Vizard has no score at all, another asserts every clip gets a 0-to-100 rating that Vizard “markets hardest”. Our own 30-day Vizard review did see a viral score in the product. So the score appears to exist and to be undocumented, which is a different finding from either third-party claim, and the table says “not documented” rather than “no”.
Submagic: its homepage and FAQ cover captions, Magic Clips, silence removal, B-roll, avatars, scheduling and 4K export, and mention neither a score nor speaker tracking. I have not tested Submagic hands-on, so those two cells stay unverified rather than negative.
Is the virality score a comparable feature?
No, and this is the part worth reading twice if you are choosing a tool on the strength of its scoring.
A score is only as good as what it is computed from, and two of the three tools that publish one also publish the inputs. Opus Clip evaluates four things: Hook (does the opening grab attention and relate to the topic), Flow (does it move logically to a satisfying conclusion), Value (does it resonate and create a personal connection), and Trend (is it aligned with current interests). When you use its ClipAnything model it also checks the clip against your prompt.
That is a defensible rubric, and knowing it changes how you read the number. A clip can score badly on Trend while being exactly right for your audience, and no amount of staring at “62” tells you which factor dragged it down.
Two practical gates people miss:
- Opus Clip’s score is plan-gated. It is available on Pro and Starter. Free-plan users do not see it at all.
- The scale is 0–99, not 0–100. A third-party guide I checked publishes 0–100. The vendor’s own help page says 0 to 99. That is a small thing that tells you something larger: most feature tables in this category are copied from each other rather than read off the source.
ScaleReach scores 0–100, and since I can read our own schema rather than our marketing, here is what is stored alongside the number: a written viralityReason, an improvementSuggestion (a viewer-perspective edit to push the clip toward 96+, left empty once a clip already scores 96 or higher), a hooks array, an emotions array, and a recommendedPlatforms list. Clips are indexed by score so the library sorts on it.
I think the bare number is the least useful part of that payload. “This clip scores 71” is not actionable. “This clip scores 71, the hook is weak, and here is the cut that would fix it” is. If you are comparing scoring features, compare what comes with the score, not whether the score exists.
Klap is the one row where our own pages need reconciling, and it is worth stating plainly rather than quietly picking one. Klap’s site publishes no range. Our 30-day hands-on Klap review reports a 0–100 scale, observed in the product during paid testing. Both are true: the scale exists in the UI and is absent from the marketing. A table cell sourced only to vendor pages has to say “not published”, which is why this one carries both.
And the honest caveat on all of it, ours included: a score predicts nothing on its own. Two things from our own measurements say so. Across the tools I have run video through for the Klap and Vizard reviews, high-scored clips beat low-scored clips on average while individual top-scored clips underperformed regularly. And in our own clip data, 82% of scored clips landed at 80 or above — a distribution that bunches that hard is a usable sort key and a poor yes/no signal. Treat any score in this category, ours included, as triage rather than a forecast.
Speaker tracking or face tracking: what is the difference?
This is the distinction that decides whether a tool survives a four-person podcast, and “auto-reframe” hides it completely.
Face tracking is vision only. Find faces, keep them in frame. It has no idea who is talking, so on a multi-speaker recording it can sit politely on someone who is listening.
Active-speaker detection combines vision with audio. Opus Clip’s documentation is explicit that it “identifies the active speaker using voice and motion cues”, then shifts the frame gradually rather than cutting. Klap describes facial recognition for framing and says its algorithm “relies heavily on speech detection”, which is why its own FAQ recommends it for podcasts, interviews and talks over other content.
Our implementation sits in between, and since I can read it, I will describe it precisely rather than flatter it. smart_crop.py runs MediaPipe BlazeFace face detection on every clip and picks one of four layouts: face-tracked 9:16 for a talking head, a stacked split for two detected speakers, a letterbox for groups of four or more, and a centre crop when it finds no face at all. It predicts through brief detection gaps using velocity rather than freezing and jumping, and it places the head about 20% down the frame instead of dead centre. Audio diarization via pyannote.audio runs conditionally — only when multiple speakers are detected, and it is skipped entirely on single-speaker video because it adds roughly two minutes of processing for no benefit.
So: always face-aware, speaker-aware when there is more than one speaker to disambiguate. That is an accurate description of a real trade-off, not a feature bullet.
Opus Clip is the only tool in this set whose documentation describes manual tracking as a first-class feature, and it is genuinely the most flexible thing here. You select a scene on the timeline, click Tracker, click the subject, and it tracks that subject for that scene — and the subject does not have to be a person. Its docs name products, animals, vehicles and text, with a chef’s hands and a drone as examples. Automatic tracking prioritises speakers, so for a product or an animal you are meant to reach for manual. Nothing else in this comparison publishes an equivalent.
Descript lists Center Active Speaker as an AI tool, available on every paid tier and limited on Free. Vizard says it centres “key subjects” in vertical format without describing how. For Submagic I found nothing to cite.
Are caption language counts comparable?
No, and this is the sloppiest column in every roundup including, historically, the ones on this site.
“Languages” collapses three different capabilities:
- Transcription — turning speech into text in the language spoken
- Caption translation — rendering that text in a different language
- Audio dubbing — generating new speech in a different language
Descript makes the distinction impossible to ignore because it publishes all three separately: 25 transcription languages, 61 caption-translation languages, 30 audio-dubbing languages. Three numbers, one product. Any table that gives Descript a single “languages” figure has picked one and discarded the others.
Against that, read the other counts carefully:
| Tool | Published figure | What it actually covers |
|---|---|---|
| Submagic | 123 | Caption languages, with a 99% accuracy claim |
| Klap | 52 | Editing and transcribing, with the full list published |
| Descript | 25 / 61 / 30 | Transcription / caption translation / dubbing, stated separately |
| Vizard | 100+ or 130+ | Translation. Its homepage says over 100, its Find Good Clips page says 130+ |
Vizard’s two different numbers are on two pages of the same site on the same day. That is not a gotcha, it is the reason the rule is to read the vendor’s page and cite the URL and date — because even then a vendor can disagree with itself, and you want the reader to be able to check which page you used.
Where the comparison gets thinner is caption styling, because almost nobody quantifies it. ScaleReach ships 24 caption style templates, and I can be specific about what a template controls because it is a typed object in our codebase: font family and size, text and background colour with opacity, x/y position, alignment, animation, word-level highlight colour with a scale factor, shadow, outline colour and width, text transform, and words per line. Word-level highlighting, the moving-highlight effect that most short-form captions use, is a per-template flag rather than a global setting.
Submagic’s pitch is caption quality above everything else and it is the one tool here whose whole product is that axis, so a template count from us is not a claim to beat it on polish. If captions are the entire job, its 123 languages and a 99% accuracy claim are the numbers to go and test on your own audio.
What each tool does better
A comparison written by a competitor is worth nothing if it cannot name where it loses.
Opus Clip has the best-documented scoring in the category — a published scale, four named factors, and a stated plan gate — and the only manual, any-subject tracking. If you clip products, animals or screen content rather than faces, it is the only tool here that describes a workflow for it.
Klap publishes its full 52-language list rather than a bare number, and AI Reframe 2 selects a layout per scene including split-screen, screencast and gaming presets, which is a harder problem than centring a head.
Vizard is the most explicit about what its detection is looking for — content clarity, speaker energy, delivery, and key topical transitions — and it publishes the widest scheduled-publishing surface I verified, covering YouTube, TikTok, Shorts, Instagram, X, LinkedIn and Facebook.
Descript is the only one that separates transcription, translation and dubbing instead of merging them, which is the honest way to publish those numbers, and its Create Clips run is steerable with a topic or goal and configurable from 10 seconds to 5 minutes.
Submagic owns the captions axis outright on published numbers, and it pairs that with silence removal, B-roll insertion and 4K 60fps export.
Where we lose: we do not publish a caption-language count in the way Submagic and Klap do, our diarization is conditional rather than always-on, and we ship nothing like Opus Clip’s manual subject tracker. If your footage is a product demo rather than a person talking, that gap is the one that will bite.
Which tool fits which source footage?
Single talking head → any of them
Every tool in this set is built for this and will handle it. Pick on price and on whether you need an editor afterwards, not on these three features.
Two-person interview → Opus Clip, Klap, or ScaleReach
You want the active speaker, not whoever is on camera. These three describe an audio component in their tracking. Ours only engages diarization once it sees more than one speaker, which is exactly this case.
Four-plus panel → verify before you commit
Nobody in this set publishes a reliability figure for heavy speaker overlap. Ours drops to a letterbox at four or more faces rather than guessing, which is a deliberate choice and also an admission that per-speaker framing at that density is unreliable. Run one real episode through any candidate before buying a year.
Product demo, gameplay, or screen recording → Opus Clip
Automatic tracking prioritises speakers everywhere in this comparison. Opus Clip is the only one documenting manual tracking of non-human subjects, and Klap’s AI Reframe 2 at least names screencast and gaming layouts.
Multilingual publishing → Descript or Submagic
Descript if you need dubbing separated from captions and stated honestly. Submagic if the job is captions at maximum language coverage.
FAQ
What is a virality score and is it accurate?
A virality score is a number an AI clip maker assigns to each clip to predict engagement. Opus Clip’s runs 0–99 and is built from four factors it publishes: hook, flow, value and trend. ScaleReach’s runs 0–100 and ships with a written reason and a suggested edit. Accuracy is limited: in hands-on testing across tools, high-scored clips outperform low-scored ones on average while individual top-scored clips regularly underperform. Use a score to triage a batch, not to predict a single clip.
Which AI clip makers have a virality score?
Of the six tools checked on 2026-09-23, Opus Clip and ScaleReach publish a ranked score with a documented scale, and Klap scores clips without publishing the range on its own site. Descript identifies engaging moments but publishes no ranked score. Neither Vizard’s nor Submagic’s own pages name one, though third-party reviews disagree about Vizard. Verify on the vendor’s page before trusting a comparison table.
What is the difference between face tracking and speaker tracking?
Face tracking is vision only: it keeps detected faces in frame with no knowledge of who is talking, so it can centre a listener on a multi-speaker recording. Active-speaker detection adds audio. Opus Clip states it identifies the active speaker from voice and motion cues, Klap describes facial recognition plus speech detection, and ScaleReach runs face detection always plus audio diarization when it detects more than one speaker. The distinction only matters on multi-speaker footage.
How many languages do AI captions support?
It depends what “support” means, which is why the numbers look incomparable. Submagic publishes 123 caption languages, Klap 52 for editing and transcribing, and Vizard either 100+ or 130+ for translation depending which of its own pages you read. Descript splits the capability properly: 25 transcription languages, 61 for caption translation and 30 for audio dubbing. Transcribing, translating and dubbing are three different features.
Does every AI clip maker reframe video to 9:16 automatically?
Every tool in this comparison converts landscape to vertical automatically, but the mechanism and the options differ. Descript offers portrait 9:16 and square 1:1 presets on its Create Clips run. Klap’s AI Reframe 2 selects a layout per scene including split-screen, screencast and gaming. ScaleReach picks among four layouts including a stacked split for two speakers and a letterbox for groups of four or more. Opus Clip adds manual per-scene subject tracking.
Can AI clip makers track something other than a person?
Mostly no. Automatic tracking prioritises speakers in every tool checked here. Opus Clip is the exception: its documentation describes manual subject tracking for non-human subjects including products, animals, vehicles and on-screen text, selected per scene from the timeline. If your footage is a product demo or a cooking shot rather than a person talking, that capability is the one to shortlist on.
The bottom line
The three features every AI clip maker roundup compares are the three that resist comparison hardest. A virality score exists on three of these six tools, on two different published scales and one undisclosed one, computed from inputs only two vendors describe. “Speaker tracking” covers both vision-only face following and genuine audio-plus-vision active-speaker detection, and the difference is invisible in a tick-box table while being the thing that decides whether a panel episode comes out watchable. Caption language counts merge transcription, translation and dubbing, which Descript proves by publishing 25, 61 and 30 for the same product.
The practical takeaway is smaller than the table. Work out which of the three actually constrains you — sorting a big batch, framing multiple speakers, or publishing in languages you do not speak — and verify that one feature on the vendor’s own page on the day you buy. Two of the discrepancies in this post came from a vendor disagreeing with its own other page, and one from a third-party guide publishing a scale the vendor does not use. A feature table is a starting point for questions, not an answer.
Last reviewed: September 2026
Next planned refresh: December 2026
Update hooks: whether Vizard names a virality score on its own pages; whether Submagic publishes speaker tracking; Opus Clip’s 0–99 scale and plan gate; Klap’s 52-language count and whether it publishes a score range; Descript’s three language counts; Vizard reconciling the 100+ and 130+ figures; our own template count and diarization behaviour if smart_crop.py changes.
About the author
Hevin K runs ScaleReach, an AI clip maker with a 0–100 virality score, face and speaker-aware reframing and 24 caption templates. Five of the six tools compared here are competitors and there is no affiliate relationship with any of them. Every competitor claim is cited to a page that vendor controls, and cells the vendor’s own pages did not answer are marked “not verified” rather than guessed.
