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Compensation management · November 2026 Edition

Aeqium vs QuotaPath

Three of fourteen models named Aeqium first on the direct prompt; zero named QuotaPath. Aeqium was named by thirteen of the fourteen models and QuotaPath by eleven and Aeqium carries 32 labels and QuotaPath 15, so the shares are not directly comparable.

Aeqium

accepted challenger

Named in three categories this edition.

QuotaPath

accepted challenger

Named in one category this edition.

First-choice share23%2%Of first choices across the direct, paraphrase, budget and scale prompts, 0 to 100.
Negative rate0%0%Negative labels as a share of the product's labels, 0 to 100.
Rank in category#2#7A position in a field of 18; printed, not drawn.
Labels3215A count; the two differ.
The two percentage rows are drawn on one 0 to 100 track, Aeqium reading right to left. Rank and label count are printed, not drawn.Pave was named alongside these two in ten of the fourteen direct answers. Pave vs Aeqium · Pave vs QuotaPath · Aeqium vs CompUp

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.
AeqiumFirst choices, of fourteen modelsQuotaPath
Direct30
Paraphrase90
Comparative01
Budget-constrained11
Scale-constrained00
Negative00
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 Aeqium and QuotaPath 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
Aeqium QuotaPath first choice named as an alternative argued againstblank: not namedEach cell is one answer, Aeqium on the left and QuotaPath on the right.

The direct prompt

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

Aeqium first, QuotaPath not the choice

3 of 14 modelsQuotaPath was named in the answer but not as the choice, or not at all.
Perplexity SonarAeqium alternatives: CaptivateIQ, Comprehensive.io, Deel, Pave
Mistral SmallAeqium, Pave, Ravio alternatives: Compport, Decusoft Compose, HRSoft Compensation Management, beqom
Kimi K2Aeqium, Pave alternatives: HRSoft Compensation Management, Lattice Compensation, beqom

Neither was the first choice, one was named

6 of 14 modelsThe answer put something else first and named one of the two as an alternative.
Grok 4.20Pave alternatives: Aeqium, HRSoft Compensation Management, Lattice Compensation, beqom
DeepSeek V4 FlashPave alternatives: Aeqium, OpenComp
Qwen 3.7 FlashPave alternatives: Decusoft Compose, Everstage, Payscale, QuotaPath, beqom
GLM 4.7 FlashXHRSoft Compensation Management, Pave alternatives: Aeqium, beqom
GPT-6 LunaCaptivateIQ alternatives: QuotaPath, Salesforce Spiff
Muse Glimmer 30BHRSoft Compensation Management, SalaryCube Comp Planning alternatives: CaptivateIQ, CompUp, Everstage, Pave, QuotaPath, Ravio, Salary.com CompAnalyst, beqom

Neither was named

5 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
Gemini 3.5 FlashCaptivateIQ, Pave alternatives: CompLogix, Comprehensive.io, Everstage, HiBob, Rippling, Salesforce Spiff
Llama 4 Maverickno first choice
MiniMax M2.5CompUp alternatives: Deel, Pave

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
Level: the same share of first choices.
Aeqium2%#– of 19
QuotaPath2%#7 of 19
The full small business standing →
Mid-marketThe figures above
Aeqium leads by twenty-one points.
Aeqium23%#2 of 18
QuotaPath2%#7 of 18
The full mid-market standing →
Enterprise
Aeqium leads by two points.
Aeqium2%#12 of 17
QuotaPath0%#– of 17
The full enterprise standing →

What the models said about Aeqium

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

“Best for mid-sized organizations (200 to 6,000 employees) replacing spreadsheet merit planning and prioritizing fast implementation” Claude Haiku 4.5 · paraphrase prompt · first choice
“Aeqium, Pave, and Lattice are the top recommendations due to their balance of features, scalability, and user-friendliness” Mistral Small · paraphrase prompt · first choice
“Start with SalaryCube or Aeqium for dedicated compensation management with strong mid-market capabilities.” GLM 4.7 FlashX · paraphrase prompt · first choice

What the models said about QuotaPath

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

“includes options like QuotaPath, Compease by HR Performance Solutions, and Leapsome” Llama 4 Maverick · budget prompt · first choice
“Sales commissions & AI planning | QuotaPath” GLM 4.7 FlashX · comparative prompt · first choice
“Offers AI-powered, dynamic commission plans and is noted for being affordable, making it a strong choice for smaller teams” Mistral Small · budget prompt · alternative
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.