Index › HR › Talent acquisition › Interview scheduling › GoodTime vs Lever
Interview scheduling · November 2026 Edition

GoodTime vs Lever

Three of fourteen models named GoodTime first on the direct prompt; zero named Lever. GoodTime was named by fourteen of the fourteen models and Lever by eleven and GoodTime carries 54 labels and Lever 18, so the shares are not directly comparable.

GoodTime

accepted challenger

Named in two categories this edition.

Lever

accepted challenger

Named in twelve categories this edition.

First-choice share12%8%Of first choices across the direct, paraphrase, budget and scale prompts, 0 to 100.
Negative rate22%22%Negative labels as a share of the product's labels, 0 to 100.
Rank in category#3#6A position in a field of 15; printed, not drawn.
Labels5418A count; the two differ.
The two percentage rows are drawn on one 0 to 100 track, GoodTime reading right to left. Rank and label count are printed, not drawn.ModernLoop was named alongside these two in thirteen of the fourteen direct answers. ModernLoop vs GoodTime · ModernLoop vs Lever · Calendly vs GoodTime

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.
GoodTimeFirst choices, of fourteen modelsLever
Direct302 against GoodTime
Paraphrase342 against GoodTime · 1 against Lever
Comparative40
Budget-constrained006 against GoodTime
Scale-constrained00
Negative112 against GoodTime · 3 against Lever
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 GoodTime and Lever stood in it.
ModelDirectParaphraseComparativeBudget-constrainedScale-constrainedNegative
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
GoodTime Lever first choice named as an alternative argued againstblank: not namedEach cell is one answer, GoodTime on the left and Lever on the right.

The direct prompt

The plain question, one answer per model, grouped by where GoodTime and Lever stood in it.

GoodTime first, Lever an alternative

3 of 14 modelsLever was named in the answer but not as the choice, or not at all.
GPT-5.4 miniGoodTime alternatives: Ashby, Calendly, Greenhouse, Lever
Perplexity SonarGoodTime alternatives: Ashby, Cal.com, Lever, ModernLoop
Mistral SmallGoodTime alternatives: Calendly, ModernLoop, Paradox

Neither was the first choice, one was named

8 of 14 modelsThe answer put something else first and named one of the two as an alternative.
Claude Haiku 4.5ModernLoop alternatives: Ashby, GoodTime, Prelude
Gemini 3.5 FlashModernLoop alternatives: Ashby, GoodTime, candidate.fyi
Llama 4 MaverickModernLoop alternatives: Calendly, GoodTime
Qwen 3.7 FlashCalendly, ModernLoop alternatives: GoodTime, Greenhouse, Lever
Kimi K2ModernLoop alternatives: GoodTime
GLM 4.7 FlashXModernLoop alternatives: GoodTime, Grayscale
MiniMax M2.5ModernLoop alternatives: Cal.com, GoodTime, VidCruiter
GPT-6 LunaModernLoop alternatives: Ashby's native scheduling, GoodTime

Neither was named

3 of 14 modelsThe answer made no first choice from these two in this category.
Grok 4.20ModernLoop alternatives: Grayscale, Prelude
DeepSeek V4 FlashModernLoop alternatives: Calendly
Muse Glimmer 30BModernLoop alternatives: Grayscale, Prelude

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
GoodTime leads by two points.
GoodTime2%#7 of 16
Lever0%#– of 16
The full small business standing →
Mid-marketThe figures above
GoodTime leads by four points.
GoodTime12%#3 of 15
Lever8%#6 of 15
The full mid-market standing →
Enterprise
GoodTime leads by fifty-five points.
GoodTime55%#1 of 13
Lever0%#11 of 13
The full enterprise standing →

What 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 - Enterprise-focused tools like GoodTime, VidCruiter, or Paradox” Kimi K2 · budget prompt · hard negative
“Avoid these for budgets: Tools like GoodTime, Ashby, Paradox, or ModernLoop” Grok 4.20 · budget prompt · hard negative
“Avoid GoodTime (unless you're enterprise and test heavily)” DeepSeek V4 Flash · negative prompt · hard negative
“If you already have an ATS (like Greenhouse/Lever): Add GoodTime. It saves hiring managers significantly more time than Calendly” Qwen 3.7 Flash · paraphrase prompt · first choice
“GoodTime for most mid-market B2B teams that take hiring seriously and want a scalable recruiting workflow.” GPT-5.4 mini · direct prompt · first choice
“Highly rated for mid-market and enterprise use, especially for structured, multi-stage hiring processes” Mistral Small · direct prompt · first choice

What the models said about Lever

Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Two of two in this category shown.

“complaints such as with Lever: "It will schedule calls back to back and you can't set up a 10-minute buffer window."” Muse Glimmer 30B · negative prompt · soft negative
“I'd lean toward Greenhouse or Lever for their balance of features, scalability, and B2B-focused functionality.” MiniMax M2.5 · paraphrase prompt · first choice
Also compared

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.