AudioGo vs Libsyn Ads
Two of fourteen models named AudioGo first on the direct prompt; zero named Libsyn Ads. AudioGo was named by thirteen of the fourteen models and Libsyn Ads by eleven and AudioGo carries 37 labels and Libsyn Ads 22, so the shares are not directly comparable.
Named in one category this edition.
Named in one category this edition.
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
AudioGoFirst choices, of fourteen modelsLibsyn Ads
Across every category in the October 2026 Edition, AudioGo and Libsyn Ads were named in the same answer nineteen times, of the 112 answers naming AudioGo and the 39 naming Libsyn Ads. In those answers Libsyn Ads took the first choice two times and AudioGo nine.
Every model, every framing
| 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 Libsyn Ads first choice named as an alternative argued againstblank: not namedEach cell is one answer, AudioGo on the left and Libsyn Ads on the right.The direct prompt
AudioGo first, Libsyn Ads not the choice
2 of 14 modelsLibsyn Ads was named in the answer but not as the choice, or not at all.Neither was the first choice, one was named
3 of 14 modelsThe answer put something else first and named one of the two as an alternative.Neither was named
9 of 14 modelsThe answer made no first choice from these two in this category.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
AudioGo49%#1 of 10Libsyn Ads4%#5 of 10AudioGo34%#1 of 11Libsyn Ads2%#5 of 11What 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 Libsyn 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.
“Drawbacks: Limited to specific genres and audiences; low scalability for general consumer brands.” Muse Glimmer 30B · negative prompt · soft negative
“Cheaper CPM (~$12-26), but bigger min.” Grok 4.1 Fast · budget prompt · soft negative
“For better alternatives, consider platforms like Podbean or Libsyn Ads, which tend to offer more favorable terms for creators.” Claude Haiku 4.5 · negative prompt · first choice
“Libsyn Ads (70/30 split, $24-26 CPM, transparent) | Published rates, IAB-aligned, established track record” DeepSeek V4 Flash · negative prompt · first choice
“Safer alternatives: Libsyn Ads (transparent 70/30 splits for mid-tier shows)” Grok 4.1 Fast · negative prompt · first choice
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.