Goldcast vs ON24
Three of fourteen models named Goldcast first on the direct prompt; zero named ON24. Goldcast was named by thirteen of the fourteen models and ON24 by fourteen and Goldcast carries 27 labels and ON24 42, so the shares are not directly comparable.
Named in three categories this edition.
San Francisco, United States, founded 1998. 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 webinar and virtual event platforms page.
By framing
GoldcastFirst choices, of fourteen modelsON24
Across every category in the November 2026 Edition, Goldcast and ON24 were named in the same answer sixty-two times, of the 83 answers naming Goldcast and the 125 naming ON24. In those answers ON24 took the first choice seventeen times and Goldcast five.
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 4.20 | | | | | | |
| 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 | | |
Goldcast ON24 first choice named as an alternative argued againstblank: not namedEach cell is one answer, Goldcast on the left and ON24 on the right.The direct prompt
Goldcast first, ON24 an alternative
3 of 14 modelsON24 was named in the answer but not as the choice, or not at all.Neither was the first choice, one was named
8 of 14 modelsThe answer put something else first and named one of the two as an alternative.Neither was named
3 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
Goldcast0%#– of 14ON240%#14 of 14ON2435%#1 of 15Goldcast4%#5 of 15What the models said about Goldcast
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Four of five in this category shown.
“relatively new player in the market, and its pricing is not transparent” Llama 4 Maverick · negative prompt · soft negative
“My default pick would be Goldcast for a mid-sized B2B company” GPT-6 Luna · paraphrase prompt · first choice
“Goldcast — Best Overall for B2B Marketing & Growth” Gemini 3.5 Flash · paraphrase prompt · first choice
“Goldcast – *Best Overall for B2B Marketing Teams*” Gemini 3.5 Flash · direct prompt · first choice
What the models said about ON24
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Six of eight in this category shown.
“Enterprise-grade demand gen platform with quote-only pricing — likely overkill (and overpriced) for most mid-sized teams” DeepSeek V4 Flash · paraphrase prompt · hard negative
“Avoid ON24 unless you're a larger enterprise” Kimi K2 · direct prompt · hard negative
“Higher price point, steeper learning curve, and a legacy interface compared to newer platforms like Goldcast.” Gemini 3.5 Flash · direct prompt · soft negative
“For a mid-sized B2B company, ON24 is the strongest overall recommendation” Perplexity Sonar · paraphrase prompt · first choice
“Shortlist ON24 instead if marketing attribution, personalization, and always-on content conversion justify a higher-priced enterprise platform.” Perplexity Sonar · direct prompt · alternative
“ON24 is the better "grow into it" option—but it's typically more of an enterprise purchase and can be overkill for some mid-sized teams” GPT-5.4 mini · paraphrase prompt · alternative
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.