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Index › Talent acquisition › Recruiting CRM › LinkedIn Recruiter vs Greenhouse
Recruiting CRM and sourcing · October 2026 Edition

LinkedIn Recruiter vs Greenhouse

One of fourteen models named LinkedIn Recruiter first on the direct prompt; zero named Greenhouse. LinkedIn Recruiter was named by fourteen of the fourteen models and Greenhouse by nine and LinkedIn Recruiter carries 34 labels and Greenhouse 13, so the shares are not directly comparable.

LinkedIn Recruiter

criticized challenger

Named in seven categories this edition.

Greenhouse

accepted challenger

Named in thirteen categories this edition.

First-choice share4%4%Of first choices across the direct, paraphrase, budget and scale prompts, 0 to 100.
Negative rate26%0%Negative labels as a share of the product's labels, 0 to 100.
Rank in category#7#8A position in a field of 10; printed, not drawn.
Labels3413A count; the two differ.
The two percentage rows are drawn on one 0 to 100 track, LinkedIn Recruiter reading right to left. Rank and label count are printed, not drawn.Gem was named alongside these two in nine of the fourteen direct answers. Gem vs LinkedIn Recruiter · Gem vs Greenhouse · Lever vs LinkedIn Recruiter

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 Recruiting CRM and sourcing page.

By framing

How many of the fourteen models made each the first choice, per way of asking, and how many argued against it.
LinkedIn RecruiterFirst choices, of fourteen modelsGreenhouse
Direct101 against LinkedIn Recruiter
Paraphrase02
Comparative801 against LinkedIn Recruiter
Budget-constrained103 against LinkedIn Recruiter
Scale-constrained00
Negative004 against LinkedIn Recruiter
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 LinkedIn Recruiter and Greenhouse 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
GPT-6 Luna
Muse Glimmer 30B
LinkedIn Recruiter Greenhouse first choice named as an alternative argued againstblank: not namedEach cell is one answer, LinkedIn Recruiter on the left and Greenhouse on the right.

The direct prompt

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

LinkedIn Recruiter first, Greenhouse not the choice

1 of 14 modelsGreenhouse was named in the answer but not as the choice, or not at all.
GPT-5.4 miniLinkedIn Recruiter alternatives: Hiring Pro

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.
Grok 4.1 FastLever alternatives: Gem, Greenhouse, Pin, Workable, hireEZ
DeepSeek V4 FlashGem alternatives: Greenhouse, Lever, Workable
Kimi K2Gem, hireEZ alternatives: LinkedIn Recruiter, SeekOut
GPT-6 LunaGem alternatives: LinkedIn Recruiter, SeekOut

Neither was named

9 of 14 modelsThe answer made no first choice from these two in this category.
Claude Haiku 4.5Jobvite, Workable alternatives: Gem, hireEZ
Gemini 3.5 FlashGem alternatives: Juicebox, hireEZ
Perplexity SonarGem, hireEZ alternatives: Fetcher, Findem, SeekOut
Mistral SmallLever alternatives: Guideflow, Intervue, Pin
Llama 4 Maverickno first choice
Qwen 3.7 FlashGem alternatives: Apollo.io, SeekOut
GLM 4.7 FlashXno first choice
MiniMax M2.5Lever alternatives: Pin, Recruit CRM
Muse Glimmer 30BLever alternatives: Gem, Workable, hireEZ

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
LinkedIn Recruiter leads by five points.
LinkedIn Recruiter5%#6 of 12
Greenhouse0%#– of 12
The full small business standing →
Mid-marketThe figures above
Level: the same share of first choices.
LinkedIn Recruiter4%#7 of 10
Greenhouse4%#8 of 10
The full mid-market standing →
Enterprise
LinkedIn Recruiter leads by two points.
LinkedIn Recruiter6%#6 of 12
Greenhouse4%#8 of 12
The full enterprise standing →

What the models said about LinkedIn Recruiter

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

“Overpriced Platforms with Opaque Pricing - LinkedIn Recruiter Corporate: $10,800–$15,000/year with 3-seat minimums” Kimi K2 · negative prompt · hard negative
“Tools like LinkedIn Recruiter cost north of $9K/year per seat—this is justified by network size, but many alternatives provide similar value at a fraction of the cost.” GLM 4.7 FlashX · negative prompt · soft negative
“The gold standard network with the highest candidate-intent database, though it lacks advanced multi-channel outbound automation on its own.” Gemini 3.5 Flash · comparative prompt · soft negative
“### 1. LinkedIn Recruiter - Best for: Enterprise-scale sourcing - Key difference: Direct access to the largest professional network” MiniMax M2.5 · comparative prompt · first choice
“The most widely used platform, leveraging the world's largest professional database (290M+ profiles).” Qwen 3.7 Flash · comparative prompt · first choice
“The default industry standard — by far the largest professional network with 1B+ profiles.” DeepSeek V4 Flash · comparative prompt · first choice

What the models said about Greenhouse

No label in this category carried a quote.

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.