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Applicant tracking systems · September 2026 Edition

Greenhouse vs Lever

Nine of twelve models named Greenhouse first on the direct prompt; one named Lever. Both were named by all twelve models and Greenhouse carries 50 labels and Lever 43, so the shares are not directly comparable.

Greenhouse

endorsed leader

Named in thirteen categories this edition.

Lever

accepted challenger

Named in nine categories this edition.

First-choice share47%7%Of first choices across the direct, paraphrase, budget and scale prompts, 0 to 100.
Negative rate6%12%Negative labels as a share of the product's labels, 0 to 100.
Rank in category#1#4A position in a field of 13; printed, not drawn.
Labels5043A count; the two differ.
The two percentage rows are drawn on one 0 to 100 track, Greenhouse reading right to left. Rank and label count are printed, not drawn.Workable was named alongside these two in ten of the twelve direct answers. Greenhouse vs Breezy HR · Greenhouse vs Zoho Recruit · Greenhouse vs Ashby

Share is the count of first choices across the direct, paraphrase, budget and scale prompts over all twelve 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 applicant tracking systems page.

By framing

How many of the twelve models made each the first choice, per way of asking, and how many argued against it.
GreenhouseFirst choices, of twelve modelsLever
Direct911 against Lever
Paraphrase101
Comparative60
Budget-constrained112 against Greenhouse · 2 against Lever
Scale-constrained101 against Lever
Negative201 against Greenhouse · 1 against Lever
Bars are first choices, 0 to 12 each sideModels that argued againstA model can name both, so the two sides of a row do not sum to twelve.

Across every category in the September 2026 Edition, Greenhouse and Lever were named in the same answer 198 times, of the 370 answers naming Greenhouse and the 255 naming Lever. In those answers Lever took the first choice ten times and Greenhouse forty-five.

Every model, every framing

The seventy-two answers behind the chart above, one cell each: where Greenhouse and Lever stood in it.
ModelDirectParaphraseComparativeBudget-constrainedScale-constrainedNegative
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
Greenhouse Lever first choice named as an alternative argued againstblank: not namedEach cell is one answer, Greenhouse on the left and Lever on the right.

The direct prompt

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

Both were the first choice

1 of 12 modelsThe answer named them together, and the judge labeled each a first choice.
MiniMax M2.5Greenhouse, Lever alternatives: Pinpoint, Workable, Zoho Recruit

Greenhouse first, Lever an alternative

8 of 12 modelsLever was named in the answer but not as the choice, or not at all.
GPT-5.4 miniGreenhouse alternatives: Lever, SmartRecruiters, Workable
Perplexity SonarGreenhouse alternatives: Ashby, Pinpoint, Workable
Grok 4.1 FastGreenhouse alternatives: Lever, Workable
Mistral SmallGreenhouse, Pinpoint alternatives: Lever, Workable
DeepSeek V4 FlashGreenhouse, Workable alternatives: Ashby
Qwen 3.7 FlashGreenhouse alternatives: Lever, Pinpoint, Workable
Kimi K2Greenhouse alternatives: Ashby, Pinpoint, Workable
GLM 4.7 FlashXGreenhouse alternatives: Ashby, BambooHR, Lever, Workable

Neither was the first choice, one was named

2 of 12 modelsThe answer put something else first and named one of the two as an alternative.
Gemini 3.5 FlashAshby alternatives: BambooHR, Greenhouse, Lever, Rippling
Llama 4 MaverickPinpoint alternatives: Greenhouse, JazzHR, Lever, Workable

Neither was named

1 of 12 modelsThe answer made no first choice from these two in this category.
Claude Haiku 4.5no first choice

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
Lever leads by two points.
Lever2%#9 of 11
Greenhouse0%#11 of 11
The full small business standing →
Mid-marketThe figures above
The order flips: Greenhouse leads at mid-market.
Greenhouse47%#1 of 13
Lever7%#4 of 13
The full mid-market standing →
Enterprise
Greenhouse leads by forty-seven points.
Greenhouse47%#1 of 8
Lever0%#7 of 8
The full enterprise standing →

What the models said about Greenhouse

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

“don't publish prices and can cost $3,500\u2013$6,000+/year \u2014 avoid these on a limited budget” DeepSeek V4 Flash · budget prompt · hard negative
“Greenhouse is repeatedly positioned as the best choice for teams that want consistent interview processes and better reporting.” Perplexity Sonar · comparative prompt · first choice
“If you are an established mid-market company focused on structured interviews & reducing bias: Choose Greenhouse.” Gemini 3.5 Flash · comparative prompt · first choice
“I'd start with Greenhouse if you expect to grow headcount and value structured hiring + deep integrations” DeepSeek V4 Flash · 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.

“avoid these on a limited budget” DeepSeek V4 Flash · budget prompt · hard negative
“Cons: Analytics are historically less robust than Ashby; since acquisition by Employ Inc., some users report slower product development cycles.” Gemini 3.5 Flash · scale prompt · soft negative
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.