Video advertising · November 2026 Edition
StackAdapt vs Vibe.co
Four of fourteen models named StackAdapt first on the direct prompt; zero named Vibe.co. StackAdapt was named by fourteen of the fourteen models and Vibe.co by nine and StackAdapt carries 26 labels and Vibe.co 18, so the shares are not directly comparable.
Named in seven categories this edition.
Named in two categories this edition.
First-choice share22%2%Of first choices across the direct, paraphrase, budget and scale prompts, 0 to 100.
Negative rate0%11%Negative labels as a share of the product's labels, 0 to 100.
Rank in category#2#7A position in a field of 13; printed, not drawn.
Labels2618A count; the two differ.
The two percentage rows are drawn on one 0 to 100 track, StackAdapt reading right to left. Rank and label count are printed, not drawn.LinkedIn Video Ads was named alongside these two in thirteen of the fourteen direct answers. LinkedIn Video Ads vs StackAdapt · LinkedIn Video Ads vs Vibe.co · StackAdapt vs YouTube Ads
Share is the count of first choices across the direct, paraphrase, budget and scale prompts over all fourteen models, for a mid-market B2B company; rank is within the category; every quote names the model and the prompt it came from. Both figures come from the video advertising page.
By framing
How many of the fourteen models made each the first choice, per way of asking, and how many argued against it.
StackAdaptFirst choices, of fourteen modelsVibe.co
Direct40
Paraphrase701 against Vibe.co
Comparative01
Budget-constrained011 against Vibe.co
Scale-constrained00
Negative00
Bars are first choices, 0 to 14 each sideModels that argued againstA model can name both, so the two sides of a row do not sum to fourteen.
Every model, every framing
The eighty-four answers behind the chart above, one cell each: where StackAdapt and Vibe.co stood in it.
| Model | Direct | Paraphrase | Comparative | Budget-constrained | Scale-constrained | Negative |
|---|---|---|---|---|---|---|
| Claude Haiku 4.5 | | | ||||
| GPT-5.4 mini | | |||||
| Gemini 3.5 Flash | | |||||
| Perplexity Sonar | | | | | ||
| Grok 4.20 | | |||||
| Mistral Small | | | ||||
| DeepSeek V4 Flash | | | ||||
| Llama 4 Maverick | | | ||||
| Qwen 3.7 Flash | | |||||
| Kimi K2 | | | ||||
| GLM 4.7 FlashX | | | ||||
| MiniMax M2.5 | | |||||
| GPT-6 Luna | | |||||
| Muse Glimmer 30B | | | |
StackAdapt Vibe.co first choice named as an alternative argued againstblank: not namedEach cell is one answer, StackAdapt on the left and Vibe.co on the right.The direct prompt
The plain question, one answer per model, grouped by where StackAdapt and Vibe.co stood in it.
StackAdapt first, Vibe.co an alternative
4 of 14 modelsVibe.co was named in the answer but not as the choice, or not at all.Claude Haiku 4.5LinkedIn Video Ads, StackAdapt alternatives: Vibe.co, Vidyard, Wistia
Perplexity SonarStackAdapt alternatives: Demandbase CTV, LinkedIn Video Ads, Vibe.co, YouTube Ads
Llama 4 MaverickStackAdapt
Muse Glimmer 30BLinkedIn Video Ads, StackAdapt alternatives: YouTube Ads
Neither was the first choice, one was named
4 of 14 modelsThe answer put something else first and named one of the two as an alternative.DeepSeek V4 FlashLinkedIn Video Ads alternatives: Demandbase CTV, StackAdapt, YouTube Ads
Qwen 3.7 FlashLinkedIn Video Ads, YouTube Ads alternatives: Demandbase CTV, Google Display, Meta Ads, StackAdapt, The Trade Desk
Kimi K2LinkedIn Video Ads alternatives: StackAdapt, Vibe.co, YouTube Ads
GLM 4.7 FlashXLinkedIn Video Ads alternatives: Demandbase CTV, Spotify Ads, StackAdapt, YouTube Ads
Neither was named
6 of 14 modelsThe answer made no first choice from these two in this category.GPT-5.4 miniLinkedIn Video Ads alternatives: YouTube Ads
Gemini 3.5 FlashLinkedIn Video Ads alternatives: MNTN, Meta Ads, YouTube Ads
Grok 4.20LinkedIn Video Ads alternatives: YouTube Ads
Mistral SmallLinkedIn Video Ads, YouTube Ads alternatives: Demandbase CTV
MiniMax M2.5LinkedIn Video Ads alternatives: Demandbase CTV, YouTube Ads
GPT-6 LunaLinkedIn Video Ads alternatives: YouTube Ads
Bold names in an answer are the products the judge labeled a first choice; a model naming several gives each of them that label. The full answer text for every row is in the record.
By buyer segment
The same question asked on behalf of a different buyer. Each standing is computed within its segment and they are never added together. The figures above are the mid-market standing, which is the one the category orders by.
Small business
Mid-marketThe figures above
The order flips: StackAdapt leads at mid-market.
StackAdapt22%#2 of 13Vibe.co2%#7 of 13Enterprise
The order flips: Vibe.co leads at enterprise.
Vibe.co4%#– of 11StackAdapt0%#6 of 11What the models said about StackAdapt
No label in this category carried a quote.
What the models said about Vibe.co
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Five of five in this category shown.
“campaigns can start at $500, though its B2B depth should be validated” Perplexity Sonar · paraphrase prompt · soft negative
“Vibe $1,500/mo” Muse Glimmer 30B · budget prompt · soft negative
“Vibe.co is the top choice for most performance marketers — 100% direct premium streaming inventory, full self-serve controls” Claude Haiku 4.5 · comparative prompt · first choice
“For the absolute best value and ease of use on a limited budget, Vibe and Meta (Facebook/Instagram) are top choices.” Mistral Small · budget prompt · first choice
“Vibe is designed for performance marketers, DTC brands, B2B teams, and in-house marketing teams that want self-serve video advertising” Claude Haiku 4.5 · direct prompt · alternative
Also compared
LinkedIn Video Ads vs StackAdaptLinkedIn Video Ads vs Vibe.coStackAdapt vs YouTube AdsStackAdapt vs TikTok AdsStackAdapt vs Meta AdsStackAdapt vs Demandbase CTVStackAdapt vs The Trade DeskYouTube Ads vs Vibe.coTikTok Ads vs Vibe.coMeta Ads vs Vibe.coDemandbase CTV vs Vibe.coVibe.co vs The Trade DeskAll twenty-eight head-to-head pages and the full video ads standing
Comparisons are drawn for the top eight products in each category, each against each. The output is the models' output; nothing here is a recommendation by the index.