| Category | Function | Share | Rank | Negative rate | Labels | Quadrant | Since September 2026 |
|---|---|---|---|---|---|---|---|
| Candidate assessment | Talent acquisition | 0% | 52 of 118 | 20% | 5 | under 10 labels · led by TestGorilla at 52% | =heldSince September 2026: 0% → 0%, ±0 points. Inside the 13-point floor: within noise. Read over the models both editions asked. |
| AI recruiting assistants | Talent acquisition | 0% | 69 of 90 | 25% | 4 | under 10 labels · led by Manatal at 31% | =heldSince September 2026: 0% → 0%, ±0 points. Inside the 13-point floor: within noise. Read over the models both editions asked. |
| Talent intelligence | Performance and talent management | 0% | 87 of 90 | 67% | 3 | under 10 labels · led by Pin at 27% | =heldSince September 2026: 0% → 0%, ±0 points. Inside the 13-point floor: within noise. Read over the models both editions asked. |
| HR AI assistants | Onboarding and employee experience | 0% | 138 of 142 | 100% | 2 | under 10 labels · led by MeBeBot at 21% | =heldSince September 2026: 0% → 0%, ±0 points. Inside the 13-point floor: within noise. Read over the models both editions asked. |
| People analytics | Workforce planning and analytics | 0% | 109 of 114 | 100% | 2 | under 10 labels · led by Crunchr at 22% | newNew since September 2026: not ranked then, 0% now. |
| HR compliance and policy management | Core HR and payroll | 0% | 88 of 97 | 100% | 1 | under 10 labels · led by Rippling at 19% | newNew since September 2026: not ranked then, 0% now. |
| Technical hiring assessments | Talent acquisition | 0% | 68 of 71 | 100% | 1 | under 10 labels · led by HackerRank at 17% | goneNot named this edition; 0% in September 2026. |
| Category | September 2026 | Now | Change | Reading | Rank |
|---|---|---|---|---|---|
| Candidate assessment | 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 81 → 52 of 118 |
| 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 80 → 69 of 90 |
| Talent intelligence | 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 73 → 87 of 90 |
| HR AI 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 102 → 138 of 142 |
| People analytics | 0% | 0% | newNew since September 2026: not ranked then, 0% now. | New this edition | Rank 109 of 114, unchanged |
| HR compliance | 0% | 0% | newNew since September 2026: not ranked then, 0% now. | New this edition | Rank 88 of 97, unchanged |
| Technical hiring assessm | 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 43 → 68 of 71 |
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 | 0 | 1 | 2 | 0 | 3 |
| GPT-5.4 mini | 0 | 0 | 0 | 0 | 0 |
| Gemini 3.5 Flash | 0 | 0 | 0 | 1 | 1 |
| Perplexity Sonar | 0 | 0 | 0 | 0 | 0 |
| Grok 4.1 Fast | 0 | 0 | 0 | 0 | 0 |
| Mistral Small | 0 | 0 | 2 | 0 | 2 |
| DeepSeek V4 Flash | 0 | 0 | 0 | 1 | 1 |
| Llama 4 Maverick | 0 | 0 | 0 | 0 | 0 |
| Qwen 3.7 Flash | 0 | 1 | 0 | 1 | 2 |
| Kimi K2 | 0 | 0 | 0 | 1 | 1 |
| GLM 4.7 FlashX | 0 | 0 | 0 | 1 | 1 |
| MiniMax M2.5 | 0 | 1 | 1 | 2 | 4 |
| GPT-6 Luna | 0 | 0 | 0 | 0 | 0 |
| Muse Glimmer 30B | 0 | 0 | 0 | 3 | 3 |
Verbatim evidence the judge attached to positive labels.
“Uses games and AI to assess cognitive abilities and work style, good for various roles” Claude Haiku 4.5 · Candidate assessment · paraphrase prompt · alternative
“Uses neuroscience-based games and AI to assess candidates beyond just resumes.” MiniMax M2.5 · AI recruiting assistants · paraphrase prompt · alternative
“Best For: Reducing unconscious bias in the early screening stages.” Qwen 3.7 Flash · Talent intelligence · comparative prompt · alternative
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.
“Game-based or neuroscience assessments marketed as predicting job performance without published I/O validation and transparent adverse-impact statistics.” Muse Glimmer 30B · Technical hiring assessm · negative prompt · hard negative
“Privacy / data reuse concerns... "This study suggests that candidate results data is saved and ‘used' for 330 days."” Muse Glimmer 30B · Candidate assessment · negative prompt · hard negative
“Research found the platform's design "replicated bias instead of erasing it" despite claims of neutrality.” Kimi K2 · HR AI assistants · negative prompt · hard negative
“Research has documented significant issues... the system was found to replicate rather than eliminate bias” MiniMax M2.5 · Talent intelligence · negative prompt · hard negative
Citations exist only for the models that return a source list, five of the fourteen in this edition, so these counts come from 27 of the 40 answers that named Pymetrics and are not a share of its labels.
Sixty-six of the sixty-six domain citations in answers naming Pymetrics 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.
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 Pymetrics's own pages state, read October 5, 2026: pymetrics.com. 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 Pymetrics'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 Pymetrics, 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 Pymetrics by email, built from the raw record of the edition. It shows: