Choosing a video model for advertising is a different decision than choosing one for art. Nobody is impressed by a dreamy shot that mangles the logo. What matters commercially is narrower and harsher: does the packaging stay legible, does the product keep its shape as the camera moves, does the hand holding the bottle look like a hand? A recent ad-focused leaderboard put ten of the current models through exactly that kind of scrutiny, and the results are useful even if you never touch the platform that ran it.
Here is what the ranking said, how it was built, and — more importantly — how to turn a leaderboard into a model you can actually ship an ad with.
The top of the board
On the ad-creative snapshot, three models pulled clearly ahead of the rest, with a long, tight pack behind them.
| Rank | Model | Ad score |
|---|---|---|
| 1 | Seedance 2.5 | 1,072 |
| 2 | Wan 3.0 | 1,043 |
| 3 | Seedance 2.0 | 1,031 |
| 4 | Google Omni Flash | 1,010 |
| 5 | MiniMax H3 | 1,000 |
| 6 | Seedance 2.0 Mini | 990 |
| 7 | HappyHorse 1.1 | 981 |
| 8 | Flux 3 Video | 971 |
| 9 | Grok Imagine 1.5 | 944 |
| 10 | Kling 3.0 Omni | 939 |
Read those numbers carefully. They are relative positions inside one specific board, not grades, and they do not transfer to other categories like cinematic or lip-sync work. A 29-point gap between first and second is a ranking signal, not proof that one model is meaningfully better at your product than the other. Treat the order as a shortlist generator, not a verdict.
How the testing actually works
The method matters more than the ranking, because it is what makes the comparison fair. Every model gets the identical prompt and settings. Exactly one output per model enters judging under a fixed selection rule — nobody cherry-picks the best take. Model identities are hidden, left/right placement is randomized, and judges make a forced either/or choice on each criterion rather than scoring on a loose 1-to-10 scale. Those pairwise results convert to relative scores through a Bradley-Terry model, and expert creative votes carry three times the weight of general-taste votes.
Published results come with 95% confidence intervals. When two models' intervals overlap, the ranking difference is not statistically decisive — a crucial caveat most "top model" headlines quietly drop. And for transparency: the board is run by a single platform, a tiny fraction of judges were affiliated with it, and no model provider paid for placement.
The four criteria that map to real ad failures
The composite ad score is 30% general video competence plus four ad-specific axes at 17.5% each. Each one corresponds to a way a generated ad actually dies in review:
Text and logo rendering. Does the on-screen brand mark hold together, or does the packaging copy warp and the fine print turn to mud? Anything legally important still belongs in post-production overlays.
Product and brand consistency. Do color, proportion, container geometry and logo placement stay stable across frames? Generated objects love to drift as a shot progresses.
Product realism. Do hands, surfaces, shadows, weight and contact points behave? Weak physics is instantly readable as fake.
Scene-product logic. Does the product plausibly fit the action and setting? A gorgeous scene that fails to communicate the offer is still a failed ad.
General video competence is the baseline: coherent motion and visual stability. A practical pass gate is simply "no distracting deformation, no unstable subject, no motion that breaks the intended shot."
What the board does not measure is the part advertisers care about most: click-through, conversion, ROAS. Speed and price are reported separately and excluded from quality scores, and it makes no judgment about licensing or platform-ad-policy compliance.
What different teams actually need
The ranking is one lens; your job is another. Agencies need repeatable output that clears client review without bleeding one client's visual identity into another — per-customer product, color and logo fidelity plus enough volume for campaign variants. DTC and ecommerce teams live or die on packaging stability across demos and hooks; a shifted label or warped bottle turns an otherwise good clip into unusable footage.
Performance marketers need multiple scroll-stopping hooks around one controlled concept, holding product, benefit and subject sequence constant so they can isolate whether a new hook actually moved the numbers. Small businesses usually need decent footage with no studio and no dedicated editor, which rewards a toolchain that minimizes manual cleanup while still leaving room for a pre-publish review.
Localization and multi-format delivery sit outside model choice entirely: translated overlays need their own review, and vertical and horizontal placements often need genuine recomposition, not a crop.
The failure modes to watch for
Product drift is the headline problem: packaging changes shape, color, proportion or label detail mid-motion. Logo drift is its cousin — letterforms distort, the mark migrates, or it vanishes between frames. Human-led creative adds its own continuity issues: an actor's face, wardrobe, proportions or apparent age can shift between takes, and unnatural facial motion makes AI-generated UGC feel uncanny.
Weak source material amplifies all of it. Blurry labels, occluded packaging and inconsistent reference angles generate more rejects, and re-rolling burns credits without building a repeatable method. Clean references and simpler shots remove avoidable variance: one primary action per shot, protected brand details, and camera moves that respect the physical scene.
A controlled way to test before you spend
The disciplined workflow is worth stealing regardless of which model wins the board:
Lock one brief before testing. Approved product references, logo, colors, offer, audience, placements, duration, aspect ratio and a single measurable objective. Fix the model version, settings, reference set and retry count.
Run three repeatable tasks: a product beauty shot, a person using the product, and several different openings for the same product — same generation budget per model per task.
Separate baseline from tuning rounds. Baseline uses identical prompts across models; tuning may adjust prompt structure per model, but tasks, duration, count and pass checks stay fixed so optimization does not skew the comparison.
Define pass checks before generating. A qualified output preserves required logo shape, packaging text, product color, proportion and physical contact.
Score raw output before editing. Judge text/logo rendering, consistency, realism and scene logic on what the model produced; score post-production usability separately so overlays and cuts do not inflate the model's own grade.
Do the cost math honestly. Pass rate is passing videos divided by all videos generated; cost per passing video is total spend divided by passing videos. If nothing passes, say so rather than rounding up.
Push small or legally exact text into post-production, build several hooks on one approved concept, and only promote variants that clear every mandatory product and logo check into real media.
The takeaway
A leaderboard shortlists; a controlled test decides. Use the ranking to pick two or three candidates — Seedance 2.5, Wan 3.0 and Seedance 2.0 currently sit at the top for ad work — then run them against your own locked brief with fixed pass checks and identical budgets. Keep product shape, packaging color and logo stable across the whole clip, keep the offer readable at delivery sizes, and let live A/B testing, not a visual benchmark, tell you what actually converts. The highest score on someone else's board is a starting hypothesis, never a guarantee.
