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Compensation management · November 2026 Edition

Pave vs CompUp

Seven of fourteen models named Pave first on the direct prompt; one named CompUp. Pave was named by fourteen of the fourteen models and CompUp by seven and Pave carries 40 labels and CompUp 13, so the shares are not directly comparable.

Pave

accepted challenger

Named in five categories this edition.

CompUp

accepted challenger

Named in three categories this edition.

First-choice share25%9%Of first choices across the direct, paraphrase, budget and scale prompts, 0 to 100.
Negative rate2%0%Negative labels as a share of the product's labels, 0 to 100.
Rank in category#1#3A position in a field of 18; printed, not drawn.
Labels4013A count; the two differ.
The two percentage rows are drawn on one 0 to 100 track, Pave reading right to left. Rank and label count are printed, not drawn.Pave vs Aeqium · Pave vs Lattice Compensation · Pave vs HRSoft Compensation Management

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 compensation management page.

By framing

How many of the fourteen models made each the first choice, per way of asking, and how many argued against it.
PaveFirst choices, of fourteen modelsCompUp
Direct71
Paraphrase60
Comparative20
Budget-constrained04
Scale-constrained10
Negative001 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 Pave and CompUp stood in it.
ModelDirectParaphraseComparativeBudget-constrainedScale-constrainedNegative
Claude Haiku 4.5
GPT-5.4 mini
Gemini 3.5 Flash
Perplexity Sonar
Grok 4.20
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
Pave CompUp first choice named as an alternative argued againstblank: not namedEach cell is one answer, Pave on the left and CompUp on the right.

The direct prompt

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

Pave first, CompUp not the choice

7 of 14 modelsCompUp was named in the answer but not as the choice, or not at all.
Gemini 3.5 FlashCaptivateIQ, Pave alternatives: CompLogix, Comprehensive.io, Everstage, HiBob, Rippling, Salesforce Spiff
Grok 4.20Pave alternatives: Aeqium, HRSoft Compensation Management, Lattice Compensation, beqom
Mistral SmallAeqium, Pave, Ravio alternatives: Compport, Decusoft Compose, HRSoft Compensation Management, beqom
DeepSeek V4 FlashPave alternatives: Aeqium, OpenComp
Qwen 3.7 FlashPave alternatives: Decusoft Compose, Everstage, Payscale, QuotaPath, beqom
Kimi K2Aeqium, Pave alternatives: HRSoft Compensation Management, Lattice Compensation, beqom
GLM 4.7 FlashXHRSoft Compensation Management, Pave alternatives: Aeqium, beqom

CompUp first, Pave an alternative

1 of 14 modelsPave was named in the answer but not as the choice, or not at all.
MiniMax M2.5CompUp alternatives: Deel, Pave

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.
Perplexity SonarAeqium alternatives: CaptivateIQ, Comprehensive.io, Deel, Pave
Muse Glimmer 30BHRSoft Compensation Management, SalaryCube Comp Planning alternatives: CaptivateIQ, CompUp, Everstage, Pave, QuotaPath, Ravio, Salary.com CompAnalyst, beqom

Neither was named

4 of 14 modelsThe answer made no first choice from these two in this category.
Claude Haiku 4.5no first choice
GPT-5.4 miniComprehensive.io alternatives: Compport, Lattice Compensation, Paycom, Payscale Ascent, Salary.com CompAnalyst
Llama 4 Maverickno first choice
GPT-6 LunaCaptivateIQ alternatives: QuotaPath, Salesforce Spiff

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 twenty-three points.
Pave27%#1 of 19
CompUp4%#– of 19
The full small business standing →
Mid-marketThe figures above
Pave leads by sixteen points.
Pave25%#1 of 18
CompUp9%#3 of 18
The full mid-market standing →
Enterprise
The order flips: CompUp leads at enterprise.
CompUp5%#– of 17
Pave4%#6 of 17
The full enterprise standing →

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.

“Pave for benchmarking if you're outside its core tech/startup niche” DeepSeek V4 Flash · negative prompt · soft negative
“Choose a Modern Tech Platform (Pave, Comprehensive, Barley) if: You are a high-growth or tech company where equity is a significant portion of total rewards.” Gemini 3.5 Flash · comparative prompt · first choice
“Pave is frequently cited as the top choice for mid-market companies that need to unify both sales commissions and corporate compensation” Qwen 3.7 Flash · direct prompt · first choice

What the models said about CompUp

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

“CompUp offers the best value at $3/employee/year with strong reviews and dedicated mid-market features.” MiniMax M2.5 · direct prompt · first choice
“Start with Aeqium (free) or CompUp (~$3/user/month) for general compensation planning.” Kimi K2 · budget prompt · first choice
“CompUp appears to be the best dedicated compensation-management option” Perplexity Sonar · budget prompt · first choice
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.