| Category | Function | Share | Rank | Negative rate | Labels | Quadrant | Since September 2026 |
|---|---|---|---|---|---|---|---|
| Pay equity | Compensation and total rewards | 7% | 7 of 73 | 23% | 13 | accepted challenger | ▲+6Since September 2026: 2% → 9%, +6 points. Inside the 13-point floor: within noise. Read over the models both editions asked. |
| Compensation management | Compensation and total rewards | 4% | 11 of 140 | 0% | 5 | under 10 labels · led by Pave at 18% | newNew since September 2026: not ranked then, 2% now. |
| Compensation benchmarking data | Compensation and total rewards | 0% | 79 of 148 | 0% | 1 | under 10 labels · led by Pave at 19% | newNew since September 2026: not ranked then, 0% now. |
| Category | September 2026 | Now | Change | Reading | Rank |
|---|---|---|---|---|---|
| Pay equity | 2% | 9% | ▲+6Since September 2026: 2% → 9%, +6 points. Inside the 13-point floor: within noise. Read over the models both editions asked. | Within noise | Rank 11 → 7 of 73 |
| Compensation management | 0% | 2% | newNew since September 2026: not ranked then, 2% now. | New this edition | Rank 11 of 140, unchanged |
| Compensation benchmarkin | 0% | 0% | newNew since September 2026: not ranked then, 0% now. | New this edition | Rank 79 of 148, 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 | 0 | 0 | 0 | 2 |
| GPT-5.4 mini | 0 | 0 | 0 | 0 | 0 |
| Gemini 3.5 Flash | 1 | 3 | 0 | 0 | 4 |
| Perplexity Sonar | 0 | 1 | 0 | 0 | 1 |
| Grok 4.1 Fast | 1 | 0 | 0 | 1 | 2 |
| Mistral Small | 0 | 1 | 0 | 1 | 2 |
| DeepSeek V4 Flash | 0 | 1 | 0 | 0 | 1 |
| Llama 4 Maverick | 1 | 0 | 0 | 0 | 1 |
| Qwen 3.7 Flash | 0 | 1 | 0 | 0 | 1 |
| Kimi K2 | 0 | 1 | 0 | 0 | 1 |
| GLM 4.7 FlashX | 0 | 0 | 1 | 0 | 1 |
| MiniMax M2.5 | 0 | 1 | 0 | 0 | 1 |
| GPT-6 Luna | 1 | 0 | 0 | 0 | 1 |
| Muse Glimmer 30B | 0 | 0 | 0 | 1 | 1 |
Verbatim evidence the judge attached to positive labels.
“Barley is ideal for mid-market and enterprise HR teams needing simple compensation analytics and pay band management.” Claude Haiku 4.5 · Pay equity · direct prompt · first choice
“Barley - ideal for mid-market and enterprise HR teams needing simple compensation analytics and pay band management.” Llama 4 Maverick · Pay equity · direct prompt · first choice
“If you want a dedicated, easy-to-use tool for merit cycles and raises: Go with Workleap Compensation.” Gemini 3.5 Flash · Compensation management · budget prompt · first choice
“choose Workleap if tying performance and pay decisions together is the bigger goal” GPT-6 Luna · Compensation management · paraphrase 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.
“Lesser-known or smaller tools (e.g., Barley, Deel) may lack the depth required for enterprise-level analysis.” Mistral Small · Pay equity · negative prompt · hard negative
“Tools Lacking Enterprise Depth (e.g., smaller/low-cost options like Barley... flag them as "avoid for scale"” Grok 4.1 Fast · Pay equity · negative prompt · hard negative
“"Avoid for your scale: Smaller tools like Barley ($5/user/month) or Deel lack enterprise depth"” Muse Glimmer 30B · Pay equity · 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 38 of the 38 answers that named Workleap Compensation and are not a share of its labels.
153 of the 166 domain citations in answers naming Workleap Compensation 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 Workleap Compensation's own pages state, read September 28, 2026: workleap.com/compensation, workleap.com/demo-compensation, workleap.com/fr/remuneration, workleap.com/glossary/total-rewards, workleap.com/glossary/pay-transparency. 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 Workleap Compensation'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 Workleap Compensation, 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 Workleap Compensation by email, built from the raw record of the edition. It shows: