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Compensation management · September 2026 Edition

Comprehensive vs Aeqium

Two of twelve models named Comprehensive first on the direct prompt; one named Aeqium. Comprehensive was named by twelve of the twelve models and Aeqium by nine and Comprehensive carries 49 labels and Aeqium 23, so the shares are not directly comparable.

Comprehensive

accepted challenger

Named in two categories this edition.

Aeqium

accepted challenger

Named in three categories this edition.

First-choice share15%6%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#3A position in a field of 29; printed, not drawn.
Labels4923A count; the two differ.
The two percentage rows are drawn on one 0 to 100 track, Comprehensive reading right to left. Rank and label count are printed, not drawn.Payscale Ascent was named alongside these two in eight of the twelve direct answers. Pave vs Comprehensive · Pave vs Aeqium

Share is the count of first choices across the direct, paraphrase, budget and scale prompts over all twelve 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 twelve models made each the first choice, per way of asking, and how many argued against it.
ComprehensiveFirst choices, of twelve modelsAeqium
Direct21
Paraphrase62
Comparative01
Budget-constrained53
Scale-constrained30
Negative00
Bars are first choices, 0 to 12 each sideModels that argued againstA model can name both, so the two sides of a row do not sum to twelve.

The direct prompt

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

Comprehensive first, Aeqium an alternative

2 of 12 modelsAeqium was named in the answer but not as the choice, or not at all.
Perplexity SonarComprehensive, HiBob alternatives: CaptivateIQ, CompLogix, Payscale Ascent, Performio, QuotaPath, SalaryCube
Kimi K2CompLogix, Comprehensive, Pave, Pequity alternatives: Aeqium, CompUp, Compport, Everstage, HiBob, Lattice Compensation

Aeqium first, Comprehensive not the choice

1 of 12 modelsComprehensive was named in the answer but not as the choice, or not at all.
GLM 4.7 FlashXAeqium, CompLogix, SalaryCube alternatives: HiBob, Pave, Payscale Ascent, SalaryCube Comp Planning

Neither was the first choice, one was named

2 of 12 modelsThe answer put something else first and named one of the two as an alternative.
Gemini 3.5 FlashCaptivateIQ, Everstage, Pave alternatives: Aeqium, CompLogix, Compport, Comprehensive, Pequity, Performio, Qobra, QuotaPath
Grok 4.1 FastCompLogix alternatives: Comprehensive, HiBob, Paylocity, Payscale Ascent, Salary.com CompAnalyst, SimplyMerit

Neither was named

7 of 12 modelsThe answer made no first choice from these two in this category.
Claude Haiku 4.5HiBob HRIS, Payscale Ascent, QuotaPath alternatives: Everstage, Lattice, Paycom, Paylocity, Payscale, Xactly
GPT-5.4 miniCaptivateIQ, Lattice Compensation alternatives: HRSoft, Payscale, SAP SuccessFactors Compensation, Varicent, Xactly, Xactly Incent, beqom
Mistral SmallEverstage, HiBob, Payscale Ascent alternatives: CompLogix, Compport, Paylocity, QuotaPath
DeepSeek V4 FlashLattice, Pave, QuotaPath alternatives: CaptivateIQ, CompLogix, Compport, Everstage, HiBob, Lattice Compensation, Payscale
Llama 4 MaverickEverstage alternatives: HiBob HRIS, Payscale Ascent
Qwen 3.7 FlashDeel, HiBob alternatives: Figures, Lattice, Payfactors, Payscale Ascent, Qommet, Salary.com
MiniMax M2.5CompLogix, HiBob HRIS, Paylocity, Payscale Ascent alternatives: HiBob, 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
Comprehensive leads by five points.
Comprehensive7%#5 of 22
Aeqium2%#11 of 22
The full small business standing →
Mid-marketThe figures above
Comprehensive leads by nine points.
Comprehensive15%#2 of 29
Aeqium6%#3 of 29
The full mid-market standing →
Enterprise
The order flips: Aeqium leads at enterprise.
Aeqium1%#– of 22
Comprehensive0%#– of 22
The full enterprise standing →

What the models said about Comprehensive

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

“Comprehensive: Best all-in-one platform for replacing spreadsheets with custom, scalable compensation workflows. Suitable for companies with 100 to 2,000 employees.” Llama 4 Maverick · paraphrase prompt · first choice
“Choose Comprehensive if you want the best balance of flexibility, speed, and mid-market fit without going enterprise-heavy.” Perplexity Sonar · direct prompt · first choice
“Highly recommended for mid-sized teams, offering dedicated compensation planning with budget controls and structured approvals.” Mistral Small · paraphrase prompt · first choice

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 three in this category shown.

“Aeqium (Best for Overall Compensation Planning) ... offers a completely free plan specifically designed for smaller teams transitioning away from spreadsheets.” Gemini 3.5 Flash · budget prompt · first choice
“Built from the ground up as an *execution* platform... AI-assisted cycles, no-code config, audit trails... Fast rollout.” DeepSeek V4 Flash · comparative prompt · first choice
“Best low‑cost solution: Aeqium (free basic plan) or Empxtrack (free HR offerings).” GLM 4.7 FlashX · budget prompt · first choice
Also compared
Pave vs ComprehensivePave vs AeqiumThe full compensation management standing

Comparisons are drawn for the top three products in each category. The output is the models' output; nothing here is a recommendation by the index.