One of fourteen models named Zoom Webinars first on the direct prompt; one named Airmeet. Zoom Webinars was named by fourteen of the fourteen models and Airmeet by eleven and Zoom Webinars carries 71 labels and Airmeet 31, so the shares are not directly comparable.
By Zoom, San Jose, United States, founded 2011. Named in four categories 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 webinar and virtual event platforms page.
Across every category in the November 2026 Edition, Zoom Webinars and Airmeet were named in the same answer fifty-four times, of the 224 answers naming Zoom Webinars and the 69 naming Airmeet. In those answers Airmeet took the first choice two times and Zoom Webinars twelve.
| 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 |
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.
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Six of seven in this category shown.
“Zoom's Events privacy statement says it may process data for legitimate interests including improving products, ensuring safety, and sending marketing communications/advertising related to events. That doesn't make it "bad," but it does mean you should be cautious” GPT-5.4 mini · negative prompt · soft negative
“Zoom Events/Webinars for marketing-heavy programs. It is strong for large live broadcasts, but registration pages, reminders, branding, and marketing analytics can be limited” Perplexity Sonar · negative prompt · soft negative
“marketing features (registration optimization, CRM sync, engagement analytics) are more limited than purpose-built webinar platforms” DeepSeek V4 Flash · direct prompt · soft negative
“Strengths: Extremely user-friendly, reliable HD video, polls, Q&A, chat, recording, large-scale (up to 10,000+), integrations” Grok 4.20 · comparative prompt · first choice
“For most mid-sized B2B companies, Zoom Webinar or Demio offer the best balance of features, reliability, and value.” GLM 4.7 FlashX · paraphrase prompt · first choice
“Zoom remains the de‑facto standard for reliable, high‑quality webinars and is a safe bet for most businesses.” MiniMax M2.5 · comparative prompt · first choice
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Six of eight in this category shown.
“describe it as a "cautionary tale" due to poor business practices, sudden trust breaches, disappointing performance, and integration limitations” Grok 4.20 · negative prompt · hard negative
“Avoid or approach with extreme caution: WebinarJam, Airmeet (due to business ethics), and Events.com.” Kimi K2 · negative prompt · hard negative
“G2 reviewers report capacity issues with the virtual backstage and problems queueing up speakers” Claude Haiku 4.5 · negative prompt · soft negative
“Airmeet and Livestorm are the top recommendations due to their strong feature sets” Mistral Small · direct prompt · first choice
“Airmeet is best for mid-market and enterprise B2B teams prioritizing branded engagement” Llama 4 Maverick · paraphrase prompt · first choice
“Airmeet, Remo, and SpatialChat differentiate by creating “close‑to‑in‑person” networking experiences.” MiniMax M2.5 · comparative 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.