Video advertising · September 2026 Edition
StackAdapt vs TikTok Ads
Two of twelve models named StackAdapt first on the direct prompt; zero named TikTok Ads. Both were named by all twelve models and StackAdapt carries 31 labels and TikTok Ads 33, so the shares are not directly comparable.
Named in nine categories this edition.
By TikTok, Ireland, founded 2016. Named in five categories this edition.
First-choice share24%7%Of first choices across the direct, paraphrase, budget and scale prompts, 0 to 100.
Negative rate3%30%Negative labels as a share of the product's labels, 0 to 100.
Rank in category#1#6A position in a field of 9; printed, not drawn.
Labels3133A 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.YouTube Ads was named alongside these two in eight of the twelve direct answers. StackAdapt vs YouTube Ads · StackAdapt vs LinkedIn Campaign Manager · StackAdapt vs Meta Ads
Share is the count of first choices across the direct, paraphrase, budget and scale prompts over all twelve 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 twelve models made each the first choice, per way of asking, and how many argued against it.
StackAdaptFirst choices, of twelve modelsTikTok Ads
Direct201 against StackAdapt
Paraphrase80
Comparative01
Budget-constrained023 against TikTok Ads
Scale-constrained11
Negative007 against TikTok Ads
Bars are first choices, 0 to 12 each sideModels that argued againstA model can name both, so the two sides of a row do not sum to twelve.
Every model, every framing
The seventy-two answers behind the chart above, one cell each: where StackAdapt and TikTok Ads 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 4.1 Fast | | | | | | |
| Mistral Small | | | ||||
| DeepSeek V4 Flash | | | | | | |
| Llama 4 Maverick | | | ||||
| Qwen 3.7 Flash | | | | | ||
| Kimi K2 | | | | | | |
| GLM 4.7 FlashX | | | | | ||
| MiniMax M2.5 | | | | |
StackAdapt TikTok Ads first choice named as an alternative argued againstblank: not namedEach cell is one answer, StackAdapt on the left and TikTok Ads on the right.The direct prompt
The plain question, one answer per model, grouped by where StackAdapt and TikTok Ads stood in it.
StackAdapt first, TikTok Ads not the choice
2 of 12 modelsTikTok Ads was named in the answer but not as the choice, or not at all.Claude Haiku 4.5StackAdapt alternatives: AdRoll ABM, MNTN, Vibe.co, YouTube Ads
Perplexity SonarStackAdapt alternatives: Demandbase, LinkedIn Campaign Manager, Vibe.co
Neither was the first choice, one was named
2 of 12 modelsThe answer put something else first and named one of the two as an alternative.DeepSeek V4 FlashLinkedIn Campaign Manager alternatives: StackAdapt, YouTube Ads
Kimi K2LinkedIn Campaign Manager alternatives: StackAdapt, YouTube Ads
Neither was named
8 of 12 modelsThe answer made no first choice from these two in this category.GPT-5.4 miniLinkedIn Campaign Manager alternatives: YouTube Ads
Gemini 3.5 FlashLinkedIn Campaign Manager, YouTube Ads alternatives: MNTN, VISTY, Vibe.co
Grok 4.1 FastLinkedIn Campaign Manager alternatives: Demandbase CTV, YouTube Ads
Mistral SmallLinkedIn Video Ads alternatives: Vidyard, Wistia, YouTube Ads
Llama 4 Maverickno first choice alternatives: Goldcast, Vibe.co
Qwen 3.7 FlashLinkedIn Campaign Manager, YouTube Ads via Google Video/PMax campaigns alternatives: Meta Ads, Reddit Ads
GLM 4.7 FlashXLinkedIn Campaign Manager alternatives: Meta Ads, YouTube Ads
MiniMax M2.5LinkedIn Video Ads alternatives: Vidyard, Wistia
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
TikTok Ads leads by six points.
TikTok Ads8%#5 of 9StackAdapt2%#6 of 9Mid-marketThe figures above
The order flips: StackAdapt leads at mid-market.
StackAdapt24%#1 of 9TikTok Ads7%#6 of 9Enterprise
Level: the same share of first choices.
StackAdapt0%#7 of 10TikTok Ads0%#10 of 10What the models said about StackAdapt
No label in this category carried a quote.
What the models said about TikTok Ads
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Five of six in this category shown.
“High IVT rates ... strict policies leading to frequent account bans/suspensions ... complaints of funds withheld” Grok 4.1 Fast · negative prompt · hard negative
“Fraudulent Advertisers: Similar issues to Meta, with many scammers posing as legitimate brands” GLM 4.7 FlashX · negative prompt · hard negative
“Meta (Facebook & Instagram) and TikTok offer the best combination of low entry costs and high return on investment” Qwen 3.7 Flash · budget prompt · first choice
“For the absolute most limited budget, start with TikTok for its lowest entry costs and highest engagement rates” Kimi K2 · budget prompt · first choice
“For your size, self-serve platforms like Meta/Google scale well initially, with programmatic for advanced needs.” Grok 4.1 Fast · scale prompt · first choice
Also compared
StackAdapt vs YouTube AdsStackAdapt vs LinkedIn Campaign ManagerStackAdapt vs Meta AdsStackAdapt vs Vibe.coStackAdapt vs LinkedIn Video AdsYouTube Ads vs TikTok AdsLinkedIn Campaign Manager vs TikTok AdsMeta Ads vs TikTok AdsVibe.co vs TikTok AdsTikTok Ads vs LinkedIn Video AdsAll twenty-one 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.