Interview scheduling · October 2026 Edition
Calendly vs Ashby
Three of fourteen models named Calendly first on the direct prompt; one named Ashby. Calendly was named by fourteen of the fourteen models and Ashby by eleven and Calendly carries 55 labels and Ashby 18, so the shares are not directly comparable.
Named in seven categories this edition.
Named in eight categories this edition.
First-choice share20%2%Of first choices across the direct, paraphrase, budget and scale prompts, 0 to 100.
Negative rate33%0%Negative labels as a share of the product's labels, 0 to 100.
Rank in category#1#8A position in a field of 12; printed, not drawn.
Labels5518A count; the two differ.
The two percentage rows are drawn on one 0 to 100 track, Calendly reading right to left. Rank and label count are printed, not drawn.Greenhouse was named alongside these two in eleven of the fourteen direct answers. Calendly vs GoodTime · Calendly vs Lever · Calendly vs ModernLoop
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
How many of the fourteen models made each the first choice, per way of asking, and how many argued against it.
CalendlyFirst choices, of fourteen modelsAshby
Direct312 against Calendly
Paraphrase101 against Calendly
Comparative30
Budget-constrained90
Scale-constrained005 against Calendly
Negative0010 against Calendly
Bars are first choices, 0 to 14 each sideModels that argued againstA model can name both, so the two sides of a row do not sum to fourteen.
Every model, every framing
The eighty-four answers behind the chart above, one cell each: where Calendly and Ashby stood in it.
| 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 Ashby first choice named as an alternative argued againstblank: not namedEach cell is one answer, Calendly on the left and Ashby on the right.The direct prompt
The plain question, one answer per model, grouped by where Calendly and Ashby stood in it.
Calendly first, Ashby not the choice
3 of 14 modelsAshby was named in the answer but not as the choice, or not at all.Grok 4.1 FastCalendly alternatives: Chili Piper, Greenhouse, Lever
DeepSeek V4 FlashCal.com, Calendly alternatives: Greenhouse, Lever
GLM 4.7 FlashXCalendly alternatives: GoodTime, Greenhouse, Lever
Ashby first, Calendly not the choice
1 of 14 modelsCalendly was named in the answer but not as the choice, or not at all.GPT-5.4 miniAshby alternatives: Calendly Sales, GoodTime, Prelude
Neither was the first choice, one was named
4 of 14 modelsThe answer put something else first and named one of the two as an alternative.Claude Haiku 4.5ModernLoop alternatives: Ashby, GoodTime, Greenhouse
Gemini 3.5 FlashModernLoop alternatives: Ashby, Calendly’s Prelude, GoodTime
Qwen 3.7 FlashGoodTime alternatives: Ashby, Lever
Kimi K2ModernLoop alternatives: Ashby, Calendly, GoodTime, Greenhouse, Lever
Neither was named
6 of 14 modelsThe answer made no first choice from these two in this category.Perplexity SonarGoodTime alternatives: Calendly Sales, Greenhouse, ModernLoop
Mistral SmallGreenhouse, Lever alternatives: Cal.com
Llama 4 MaverickLever alternatives: Cal.com, Greenhouse, Joveo, Workforce Now
MiniMax M2.5Lever alternatives: Cal.com, Chili Piper, Greenhouse
GPT-6 LunaModernLoop alternatives: GoodTime, Greenhouse
Muse Glimmer 30BGreenhouse, Jobvite alternatives: GoodTime, candidate.fyi
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
The same question asked on behalf of a different buyer. Each standing is computed within its segment and they are never added together. The figures above are the mid-market standing, which is the one the category orders by.
Small business
Calendly leads by thirty-six points.
Calendly41%#1 of 15Ashby5%#5 of 15Mid-marketThe figures above
Enterprise
What 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 Ashby
No label in this category carried a quote.
Also compared
Calendly vs GoodTimeCalendly vs LeverCalendly vs ModernLoopCalendly vs GreenhouseCalendly vs Cal.comCalendly vs KoalendarGoodTime vs AshbyLever vs AshbyModernLoop vs AshbyGreenhouse vs AshbyCal.com vs AshbyKoalendar vs AshbyAll twenty-eight head-to-head pages and the full interview scheduling standing
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.