Three of fourteen models named Calendly first on the direct prompt; zero named Google Calendar Appointment Scheduling. Calendly was named by fourteen of the fourteen models and Google Calendar Appointment Scheduling by ten and Calendly carries 72 labels and Google Calendar Appointment Scheduling 14, so the shares are not directly comparable.
United States, founded 2013. Named in three categories this edition.
By Google, Mountain View, California, 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 meeting scheduling page.
Across every category in the October 2026 Edition, Calendly and Google Calendar Appointment Scheduling were named in the same answer thirty-four times, of the 236 answers naming Calendly and the 35 naming Google Calendar Appointment Scheduling. In those answers Google Calendar Appointment Scheduling took the first choice one time and Calendly twenty.
| 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 |
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 eight in this category shown.
“Calendly gets pricey for teams (sharp jumps to $10+/user/month); limited customization or integrations in lower plans frustrate some users.” Grok 4.1 Fast · negative prompt · soft negative
“Free plan limited to one event type and one calendar connection; Round-robin/routing features require the $16/seat/mo Teams plan” DeepSeek V4 Flash · budget prompt · soft negative
“Calendly says it isn't intended for collecting sensitive personal information and doesn't currently comply with HIPAA” GPT-6 Luna · negative prompt · soft negative
“Calendly is particularly well-suited for mid-market and enterprise teams that prioritize control, scalability, and long-term alignment with CRM-driven sales operations.” Llama 4 Maverick · paraphrase prompt · first choice
“Sales-led mid-sized B2B with HubSpot/Salesforce CRM: Calendly Team/Enterprise for routing, round-robin and automatic CRM record creation.” Muse Glimmer 30B · paraphrase prompt · first choice
“start with HubSpot Meetings (if willing to use HubSpot CRM) or Calendly's free tier - both cover basic scheduling needs well” MiniMax M2.5 · budget 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.
“Google Calendar/HubSpot free tools can work, but they are generally less sales-focused” Perplexity Sonar · budget prompt · soft negative
“(Low cost, but lack the sophisticated sales qualification features mentioned above)” Qwen 3.7 Flash · scale prompt · soft negative
“Google Calendar can be hard to coordinate with multiple people” Perplexity Sonar · negative prompt · soft negative
“Google Calendar Appointment Scheduling — Best Free Option” GLM 4.7 FlashX · budget prompt · first choice
“If you already live in Google or Microsoft 365: try Google Calendar appointment schedules or Microsoft Bookings first.” GPT-6 Luna · comparative prompt · alternative
“Choose Google Calendar Appointment Scheduling if you mainly need basic booking and live in Google Workspace.” GPT-5.4 mini · 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.