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Index › Compensation and total rewards › Pay equity › Figures vs Pave
Pay equity · October 2026 Edition

Figures vs Pave

Zero of fourteen models named Figures first on the direct prompt; one named Pave. Figures was named by nine of the fourteen models and Pave by ten and Figures carries 15 labels and Pave 16, so the shares are not directly comparable.

Figures

accepted challenger

Named in three categories this edition.

Pave

accepted challenger

Named in six categories this edition.

First-choice share7%5%Of first choices across the direct, paraphrase, budget and scale prompts, 0 to 100.
Negative rate7%19%Negative labels as a share of the product's labels, 0 to 100.
Rank in category#4#6A position in a field of 14; printed, not drawn.
Labels1516A count; the two differ.
The two percentage rows are drawn on one 0 to 100 track, Figures reading right to left. Rank and label count are printed, not drawn.Payscale was named alongside these two in nine of the fourteen direct answers. Syndio vs Figures · Syndio vs Pave · PayAnalytics vs Figures

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 pay equity page.

By framing

How many of the fourteen models made each the first choice, per way of asking, and how many argued against it.
FiguresFirst choices, of fourteen modelsPave
Direct01
Paraphrase41
Comparative00
Budget-constrained01
Scale-constrained00
Negative001 against Figures · 3 against Pave
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 Figures and Pave 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
Figures Pave first choice named as an alternative argued againstblank: not namedEach cell is one answer, Figures on the left and Pave on the right.

The direct prompt

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

Pave first, Figures not the choice

1 of 14 modelsFigures was named in the answer but not as the choice, or not at all.
Qwen 3.7 FlashPave alternatives: Payscale, Phodata, Workleap Compensation

Neither was the first choice, one was named

2 of 14 modelsThe answer put something else first and named one of the two as an alternative.
GLM 4.7 FlashXSyndio alternatives: Aeqium, Affirmity, Compport, Figures, HiBob, Pave, PayAnalytics, Payscale, Workday Pay Equity, beqom Pay Equity
MiniMax M2.5HiBob alternatives: Aeqium, Pave, Payscale, Workleap Compensation

Neither was named

11 of 14 modelsThe answer made no first choice from these two in this category.
Claude Haiku 4.5Workleap Compensation alternatives: Compport, OpenComp, PayAnalytics, Payscale, Syndio, Trusaic PayParity
GPT-5.4 miniSyndio alternatives: PayAnalytics, Trusaic PayParity
Gemini 3.5 FlashPayAnalytics alternatives: CandorIQ, Pequity, Syndio, Trusaic PayParity
Perplexity SonarAeqium alternatives: HiBob, Payscale, Salary.com Pay Equity, Workleap Compensation
Grok 4.1 FastWorkleap Compensation alternatives: Aeqium, HiBob, Payscale
Mistral SmallAeqium alternatives: Sysarb, Workleap Compensation
DeepSeek V4 FlashPayAnalytics alternatives: Compport, Payscale, Syndio
Llama 4 MaverickWorkleap Compensation alternatives: Affirmity, Effy
Kimi K2PayAnalytics, Syndio alternatives: Payscale, Trusaic PayParity
GPT-6 LunaPayscale alternatives: Syndio, Trusaic PayParity
Muse Glimmer 30BPayAnalytics, beqom Pay Equity alternatives: Syndio, Trusaic PayParity

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
Pave leads by four points.
Pave6%#2 of 13
Figures2%#– of 13
The full small business standing →
Mid-marketThe figures above
The order flips: Figures leads at mid-market.
Figures7%#4 of 14
Pave5%#6 of 14
The full mid-market standing →
Enterprise
Level: the same share of first choices.
Figures0%#6 of 8
Pave0%#– of 8
The full enterprise standing →

What the models said about Figures

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

“Tools requiring heavy analyst intervention (e.g., certain configurations of Aeqium, Figures)” DeepSeek V4 Flash · negative prompt · soft negative
“If EU-based or global: Figures offers the best balance of compliance features and usability for mid-market” Kimi K2 · paraphrase prompt · first choice
“I'd suggest starting with Figures or Syndio as they balance functionality with mid-market accessibility” MiniMax M2.5 · paraphrase prompt · first choice
“Top Recommendation: Figures HR (if EU/UK focus) or Syndio (if global/US focus)” DeepSeek V4 Flash · paraphrase prompt · first choice

What the models said about Pave

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

“smaller/low-cost options like Barley, Deel basic tiers, Pave, or Equity.com basic... "avoid for scale"” Grok 4.1 Fast · negative prompt · hard negative
“"Examples to Avoid: Pave, Barley, Equity.com (basic tiers)."” Muse Glimmer 30B · negative prompt · hard negative
“well-known platforms (e.g., Pave, Equity.com) may have basic tiers that are not suitable for comprehensive pay equity analysis” Mistral Small · negative 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.