Audio and podcast advertising · October 2026 Edition
AudioGo vs StackAdapt
Two of fourteen models named AudioGo first on the direct prompt; eight named StackAdapt. AudioGo was named by thirteen of the fourteen models and StackAdapt by eleven and AudioGo carries 37 labels and StackAdapt 19, so the shares are not directly comparable.
Named in one category this edition.
Named in eight categories this edition.
First-choice share34%20%Of first choices across the direct, paraphrase, budget and scale prompts, 0 to 100.
Negative rate3%0%Negative labels as a share of the product's labels, 0 to 100.
Rank in category#1#2A position in a field of 11; printed, not drawn.
Labels3719A count; the two differ.
The two percentage rows are drawn on one 0 to 100 track, AudioGo reading right to left. Rank and label count are printed, not drawn.AudioGo vs Spotify Ads Manager · AudioGo vs Acast · AudioGo vs Libsyn 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 audio and podcast 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.
AudioGoFirst choices, of fourteen modelsStackAdapt
Direct28
Paraphrase22
Comparative00
Budget-constrained110
Scale-constrained41
Negative001 against AudioGo
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 AudioGo and StackAdapt 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 | | | | |||
| GPT-6 Luna | | | ||||
| Muse Glimmer 30B | | | |
AudioGo StackAdapt first choice named as an alternative argued againstblank: not namedEach cell is one answer, AudioGo on the left and StackAdapt on the right.The direct prompt
The plain question, one answer per model, grouped by where AudioGo and StackAdapt stood in it.
AudioGo first, StackAdapt not the choice
2 of 14 modelsStackAdapt was named in the answer but not as the choice, or not at all.Kimi K2Acast, AudioGo alternatives: B2B PodPros, Hey Sid, Spotify Ad Studio
Muse Glimmer 30BAudioGo alternatives: Hey Sid, Spotify Ad Studio
StackAdapt first, AudioGo an alternative
8 of 14 modelsAudioGo was named in the answer but not as the choice, or not at all.Claude Haiku 4.5StackAdapt alternatives: Acast, Hey Sid, Spotify Advertising
Gemini 3.5 FlashStackAdapt alternatives: AdsWizz, Demandbase, SXM Media, Terminus
Perplexity SonarStackAdapt alternatives: Spotify Ads Manager
Grok 4.1 FastStackAdapt alternatives: AdsWizz AudioMax, Megaphone, Spotify Audience Network, The Trade Desk
Mistral SmallStackAdapt, The Trade Desk alternatives: Spotify Advertising
DeepSeek V4 FlashStackAdapt alternatives: AudioGo, Intentsify
Qwen 3.7 FlashStackAdapt alternatives: Hey Sid
MiniMax M2.5StackAdapt alternatives: Spotify Ads, The Trade Desk
Neither was the first choice, one was named
2 of 14 modelsThe answer put something else first and named one of the two as an alternative.Llama 4 MaverickTriton Digital alternatives: AudioGo, StackAdapt
GLM 4.7 FlashXHey Sid alternatives: Libsyn Ads, Spotify Ads, StackAdapt
Neither was named
2 of 14 modelsThe answer made no first choice from these two in this category.GPT-5.4 miniSpotify Ads Manager alternatives: Amazon Ads, Amazon DSP
GPT-6 LunaSpotify Ads Manager alternatives: Acast
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
AudioGo leads by forty-nine points.
AudioGo49%#1 of 10StackAdapt0%#9 of 10Mid-marketThe figures above
AudioGo leads by fourteen points.
AudioGo34%#1 of 11StackAdapt20%#2 of 11Enterprise
The order flips: StackAdapt leads at enterprise.
StackAdapt17%#1 of 14AudioGo6%#5 of 14What the models said about AudioGo
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Four of five in this category shown.
“a $250 campaign is more of a test and not scalable... only supports pre-produced audio” Mistral Small · negative prompt · soft negative
“AudioGo is the safest recommendation from the search results because it combines a low entry point with broad access and simple self-serve setup” Perplexity Sonar · budget prompt · first choice
“All-in-One Audio Network | AudioGO, Triton, AdsWizz | You want to cast a wide net across hundreds of smaller podcasts” Qwen 3.7 Flash · scale prompt · first choice
“I'd suggest starting with Acast or AudioGo — both offer low entry points ($250), self-serve flexibility, and strong targeting” Kimi K2 · direct prompt · first choice
What the models said about StackAdapt
No label in this category carried a quote.
Also compared
AudioGo vs Spotify Ads ManagerAudioGo vs AcastAudioGo vs Libsyn AdsAudioGo vs Triton DigitalAudioGo vs Spotify Audience NetworkAudioGo vs Spotify Ad StudioStackAdapt vs Spotify Ads ManagerStackAdapt vs AcastStackAdapt vs Libsyn AdsStackAdapt vs Triton DigitalStackAdapt vs Spotify Audience NetworkStackAdapt vs Spotify Ad StudioAll twenty-eight head-to-head pages and the full audio advertising 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.