| Category | Function | Share | Rank | Negative rate | Labels | Quadrant | Since September 2026 |
|---|---|---|---|---|---|---|---|
| Recruiting CRM and sourcing | Talent acquisition | 15% | 2 of 111 | 0% | 17 | accepted challenger | ▼−10Since September 2026: 20% → 11%, −10 points. Inside the 13-point floor: within noise. Read over the models both editions asked. |
| Interview scheduling | Talent acquisition | 11% | 3 of 75 | 9% | 34 | accepted challenger | ▲+3Since September 2026: 8% → 11%, +3 points. Inside the 13-point floor: within noise. Read over the models both editions asked. |
| Applicant tracking systems | Talent acquisition | 7% | 6 of 65 | 2% | 44 | accepted challenger | ▲+1Since September 2026: 7% → 8%, +1 point. Inside the 13-point floor: within noise. Read over the models both editions asked. |
| Staffing agency platforms | Talent acquisition | 5% | 6 of 136 | 10% | 10 | accepted challenger | |
| Talent intelligence | Performance and talent management | 2% | 15 of 90 | 0% | 4 | under 10 labels · led by Pin at 27% | ▲+2Since September 2026: 0% → 2%, +2 points. Inside the 13-point floor: within noise. Read over the models both editions asked. |
| Job advertising and distribution | Talent acquisition | 2% | 11 of 111 | 0% | 2 | under 10 labels · led by JobTarget at 23% | ▲+2Since September 2026: 0% → 2%, +2 points. Inside the 13-point floor: within noise. Read over the models both editions asked. |
| Recruitment marketing and career sites | Talent acquisition | 2% | 16 of 104 | 7% | 14 | accepted challenger | ▲+2Since September 2026: 0% → 2%, +2 points. Inside the 13-point floor: within noise. Read over the models both editions asked. |
| AI recruiting assistants | Talent acquisition | 0% | 18 of 90 | 20% | 20 | accepted challenger | =heldSince September 2026: 0% → 0%, ±0 points. Inside the 13-point floor: within noise. Read over the models both editions asked. |
| Employee onboarding | Onboarding and employee experience | 0% | 23 of 87 | 0% | 3 | under 10 labels · led by Rippling at 25% | =heldSince September 2026: 0% → 0%, ±0 points. Inside the 13-point floor: within noise. Read over the models both editions asked. |
| Candidate assessment | Talent acquisition | 0% | 72 of 118 | 0% | 1 | under 10 labels · led by TestGorilla at 52% | newNew since September 2026: not ranked then, 0% now. |
| HR AI assistants | Onboarding and employee experience | 0% | 126 of 142 | 100% | 1 | under 10 labels · led by MeBeBot at 21% | newNew since September 2026: not ranked then, 0% now. |
| Pay equity | Compensation and total rewards | 0% | 58 of 73 | 0% | 1 | under 10 labels · led by Syndio at 12% | newNew since September 2026: not ranked then, 0% now. |
| Technical hiring assessments | Talent acquisition | 0% | 37 of 71 | 0% | 1 | under 10 labels · led by HackerRank at 17% | newNew since September 2026: not ranked then, 0% now. |
| Category | September 2026 | Now | Change | Reading | Rank |
|---|---|---|---|---|---|
| Recruiting CRM | 20% | 11% | ▼−10Since September 2026: 20% → 11%, −10 points. Inside the 13-point floor: within noise. Read over the models both editions asked. | Within noise | Rank 1 → 2 of 111 |
| Staffing agency tools | 0% | 6% | newNew since September 2026: not ranked then, 6% now. | New this edition | Rank 6 of 136, unchanged |
| Interview scheduling | 8% | 11% | ▲+3Since September 2026: 8% → 11%, +3 points. Inside the 13-point floor: within noise. Read over the models both editions asked. | Within noise | Rank 6 → 3 of 75 |
| Talent intelligence | 0% | 2% | ▲+2Since September 2026: 0% → 2%, +2 points. Inside the 13-point floor: within noise. Read over the models both editions asked. | Within noise | Rank 30 → 15 of 90 |
| Job advertising | 0% | 2% | ▲+2Since September 2026: 0% → 2%, +2 points. Inside the 13-point floor: within noise. Read over the models both editions asked. | Within noise | Rank 22 → 11 of 111 |
| Recruitment marketing | 0% | 2% | ▲+2Since September 2026: 0% → 2%, +2 points. Inside the 13-point floor: within noise. Read over the models both editions asked. | Within noise | Rank 36 → 16 of 104 |
| Applicant tracking syste | 7% | 8% | ▲+1Since September 2026: 7% → 8%, +1 point. Inside the 13-point floor: within noise. Read over the models both editions asked. | Within noise | Rank 4 → 6 of 65 |
| AI recruiting assistants | 0% | 0% | =heldSince September 2026: 0% → 0%, ±0 points. Inside the 13-point floor: within noise. Read over the models both editions asked. | Within noise | Rank 24 → 18 of 90 |
| Employee onboarding | 0% | 0% | =heldSince September 2026: 0% → 0%, ±0 points. Inside the 13-point floor: within noise. Read over the models both editions asked. | Within noise | Rank 41 → 23 of 87 |
| Candidate assessment | 0% | 0% | newNew since September 2026: not ranked then, 0% now. | New this edition | Rank 72 of 118, unchanged |
| HR AI assistants | 0% | 0% | newNew since September 2026: not ranked then, 0% now. | New this edition | Rank 126 of 142, unchanged |
| Pay equity | 0% | 0% | newNew since September 2026: not ranked then, 0% now. | New this edition | Rank 58 of 73, unchanged |
| Technical hiring assessm | 0% | 0% | newNew since September 2026: not ranked then, 0% now. | New this edition | Rank 37 of 71, unchanged |
Shares here are read over the models both editions asked, so they can differ by a point or two from the standing above, which counts every model in this edition.
The floor is 13 points of share, measured: how far the models move a leader on their own when the same questions are asked twice with nothing changed. A larger change is movement; a smaller one is noise, and both are shown. Movement is read over the twelve models both editions asked; GPT-6 Luna, Muse Glimmer 30B joined this edition and are in the standing but not yet in the comparison. How the floor is measured · The editions
| Model | First choice | Alternative | Mention | Negative | Labels |
|---|---|---|---|---|---|
| Claude Haiku 4.5 | 2 | 1 | 2 | 0 | 5 |
| GPT-5.4 mini | 1 | 3 | 0 | 0 | 4 |
| Gemini 3.5 Flash | 1 | 7 | 2 | 2 | 12 |
| Perplexity Sonar | 1 | 5 | 4 | 1 | 11 |
| Grok 4.1 Fast | 3 | 10 | 1 | 1 | 15 |
| Mistral Small | 3 | 2 | 2 | 0 | 7 |
| DeepSeek V4 Flash | 2 | 9 | 6 | 0 | 17 |
| Llama 4 Maverick | 1 | 1 | 5 | 0 | 7 |
| Qwen 3.7 Flash | 0 | 7 | 9 | 5 | 21 |
| Kimi K2 | 0 | 12 | 0 | 2 | 14 |
| GLM 4.7 FlashX | 1 | 9 | 2 | 0 | 12 |
| MiniMax M2.5 | 7 | 3 | 3 | 0 | 13 |
| GPT-6 Luna | 1 | 4 | 0 | 0 | 5 |
| Muse Glimmer 30B | 3 | 4 | 2 | 0 | 9 |
Verbatim evidence the judge attached to positive labels.
“If you want a single platform that handles both active applicants and passive candidates with strong nurturing and automation: Go with Lever or Ashby.” GLM 4.7 FlashX · Staffing agency tools · paraphrase prompt · first choice
“Lever stands out as a top choice because it integrates sourcing, nurturing, and applicant tracking into a single system” Mistral Small · Recruiting CRM · direct prompt · first choice
“focus on mid-market fits like Lever (CRM-strong), Greenhouse (structured processes), Workable (affordable scaling)” Grok 4.1 Fast · Applicant tracking syste · scale prompt · first choice
“Lever - Best for mid-market to enterprise teams hiring at scale... AI-native recruiting platform with scheduling” Llama 4 Maverick · Interview scheduling · direct prompt · first choice
Verbatim evidence attached to negative labels. A warning on a product with few labels is a warning; on a product with many, it is one voice among them.
“Avoid full ATS suites like Lever/Greenhouse unless free tiers suffice, as they're pricier for scale.” Grok 4.1 Fast · Interview scheduling · budget prompt · hard negative
“Class-action attorneys have launched broad investigations into popular tools like Greenhouse, Lever by Employ, and Ashby.” Gemini 3.5 Flash · AI recruiting assistants · negative prompt · soft negative
“better suited to internal hiring than external or high-volume staffing, so they can be a poor fit for agencies” Perplexity Sonar · Staffing agency tools · negative prompt · soft negative
“their native scheduling tools are decent for simple 1:1s, but they lack advanced interviewer load balancing” Gemini 3.5 Flash · Interview scheduling · direct prompt · soft negative
Citations exist only for the models that return a source list, five of the fourteen in this edition, so these counts come from 275 of the 307 answers that named Lever and are not a share of its labels.
662 of the 844 domain citations in answers naming Lever came from somebody else's page.
Pages are listed as the models cited them.
Search figures are US estimates from DataForSEO, read September 28, 2026; AI search demand is its modeled, directional estimate, not a count of queries to any assistant. The answers are this edition's. Two measurements side by side: neither is read as the cause of the other.
| Kind | Pages | Last 90 days | 2025-10 to 2026-09 | Latest | Categories named |
|---|---|---|---|---|---|
| Blog | 328 | 2 | 2026-09-09 | ||
| Case study | 24 | 1 | 2026-08-06 | ||
| Comparison | 19 | 6 | 2026-08-26 | ||
| Glossary or explainer | 3 | 0 | 2023-01-21 | ||
| Template or tool | 1 | undated |
Every page lever.co exposes, subdomains included. Kind is read from the address and title. The last 90 days, the latest date and the twelve months count pages by when they were published, from the site's feeds, a date in the address, or the page's own publication date, read from up to a hundred of its most recently changed pages; a page that says only when it last changed is counted in its kind but not in when, so the recent counts are a floor, and a kind none of whose pages gives a publication date reads undated. Read September 29, 2026.
An email the morning each edition publishes: where this product moved, where it held, and by how much against the noise floor. One address, confirmed by a click; a stop link in every email.
Already following? Everything you follow, with a stop for each.
What Lever's own pages state, read September 28, 2026: lever.co, lever.co/pricing, lever.co/security, lever.co/solutions/dei, lever.co/case-studies. A claimed page can correct any of them.
Claiming is free and changes nothing in the data. A claimed page shows a verified contact who is told when each edition publishes and when Lever's standing changes by more than the noise floor; the right to propose corrections to the vendor table, meaning names the judge wrote that should or should not read as Lever, applied by version and listed in the change log; and a one-line description supplied by the vendor and marked as such.
A new claim receives the current edition's vendor brief for Lever by email, built from the raw record of the edition. It shows:
Lever is among the products AI models recommend first in Recruiting CRM and sourcing this edition. Each badge says so in the buyer's words, names the edition, and links to the standing. The next edition issues a new badge; this one stays true as a record of October 2026.
Lever is among the products AI models recommend first in Interview scheduling this edition. Each badge says so in the buyer's words, names the edition, and links to the standing. The next edition issues a new badge; this one stays true as a record of October 2026.
Badges read on light and dark pages. Alt text carries the claim, the category, the edition and the index, so it stays a citation where the image does not load.