GoodTime vs Greenhouse
Two of fourteen models named GoodTime first on the direct prompt; two named Greenhouse. Both were named by all fourteen models and GoodTime carries 49 labels and Greenhouse 42, so the shares are not directly comparable.
Named in one category 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
GoodTimeFirst choices, of fourteen modelsGreenhouse
Across every category in the October 2026 Edition, GoodTime and Greenhouse were named in the same answer seventy-one times, of the 155 answers naming GoodTime and the 454 naming Greenhouse. In those answers Greenhouse took the first choice four times and GoodTime thirty.
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 | | | |
GoodTime Greenhouse first choice named as an alternative argued againstblank: not namedEach cell is one answer, GoodTime on the left and Greenhouse on the right.The direct prompt
GoodTime first, Greenhouse an alternative
2 of 14 modelsGreenhouse was named in the answer but not as the choice, or not at all.Greenhouse first, GoodTime an alternative
2 of 14 modelsGoodTime was named in the answer but not as the choice, or not at all.Neither was the first choice, one was named
10 of 14 modelsThe answer put something else first and named one of the two as an alternative.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
Greenhouse3%#7 of 15GoodTime2%#11 of 15GoodTime16%#2 of 12Greenhouse8%#5 of 12GoodTime55%#1 of 13Greenhouse7%#3 of 13What the models said about GoodTime
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Six of eight in this category shown.
“What to avoid if on a tight budget: Enterprise-level tools like GoodTime or ModernLoop” DeepSeek V4 Flash · budget prompt · hard negative
“Users report technical issues, including frequent crashes, missing time slots, and incorrect calendar availability display.” GLM 4.7 FlashX · negative prompt · soft negative
“Caveat: It has a steeper price tag (often starting around $15,000 to $40,000/year) and a longer implementation curve” Gemini 3.5 Flash · paraphrase prompt · soft negative
“look toward standalone partners like Goodtime or Gem as they are designed specifically for the volume and complexity typical of 500-person companies” Qwen 3.7 Flash · scale prompt · first choice
“Platforms like candidate.fyi, GoodTime, and ModernLoop are specifically designed to handle complex, multi-stage panel loops” Gemini 3.5 Flash · scale prompt · first choice
“For complex panels, frequent rescheduling, or lots of recruiting coordination, compare GoodTime and ModernLoop.” GPT-6 Luna · 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.