Calendly vs Greenhouse
Three of fourteen models named Calendly first on the direct prompt; two named Greenhouse. Both were named by all fourteen models and Calendly carries 55 labels and Greenhouse 42, so the shares are not directly comparable.
Named in seven categories this edition.
Named in thirteen categories 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 interview scheduling page.
By framing
CalendlyFirst choices, of fourteen modelsGreenhouse
Across every category in the October 2026 Edition, Calendly and Greenhouse were named in the same answer sixty-five times, of the 249 answers naming Calendly and the 454 naming Greenhouse. In those answers Greenhouse took the first choice one time and Calendly nineteen.
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 | | | | | |
Calendly Greenhouse first choice named as an alternative argued againstblank: not namedEach cell is one answer, Calendly on the left and Greenhouse on the right.The direct prompt
Calendly first, Greenhouse an alternative
3 of 14 modelsGreenhouse was named in the answer but not as the choice, or not at all.Greenhouse first, Calendly not the choice
2 of 14 modelsCalendly was named in the answer but not as the choice, or not at all.Neither was the first choice, one was named
6 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
Calendly41%#1 of 15Greenhouse3%#7 of 15Calendly20%#1 of 12Greenhouse8%#5 of 12What the models said about Calendly
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Five of six in this category shown.
“Avoid using general-purpose tools (like Doodle or basic free tiers of Calendly) for serious hiring workflows” DeepSeek V4 Flash · negative prompt · hard negative
“Calendly (Specifically for large Enterprise/Tech sectors): ... it is notably banned by Amazon for employee use. This is due to concerns over data leakage” Qwen 3.7 Flash · negative prompt · soft negative
“Calendly - Very popular and widely used. Has a free tier for basic use... For very small teams (1-5 people): Calendly's free plan or BizBot's free tier should suffice” MiniMax M2.5 · budget prompt · first choice
“Most companies with limited budgets start with Calendly's free plan and upgrade only when they need team scheduling features” Kimi K2 · budget prompt · first choice
“1. Calendly - Best for: Sales teams, recruiters, consultants, and anyone needing to schedule 1:1 or team meetings.” Mistral Small · comparative prompt · first choice
What the models said about Greenhouse
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Two of two in this category shown.
“Avoid full ATS suites like Lever/Greenhouse unless free tiers suffice, as they're pricier for scale.” Grok 4.1 Fast · budget prompt · hard negative
“Greenhouse and Lever are the top recommendations due to their scalability, integration capabilities, and fit for structured recruiting processes” Mistral Small · direct 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.