GTM AI Index
Index Revenue operations Commissions › Commissionly vs Performio
Sales commissions and incentive compensation · September 2026 Edition

Commissionly vs Performio

Zero of twelve models named Commissionly first on the direct prompt; zero named Performio. Commissionly was named by eleven of the twelve models and Performio by twelve and Commissionly carries 12 labels and Performio 36, so the shares are not directly comparable.

Commissionly

accepted challenger

Named in one category this edition.

Performio

accepted challenger

Named in two categories this edition.

First-choice share14%4%Of first choices across the direct, paraphrase, budget and scale prompts, 0 to 100.
Negative rate8%6%Negative labels as a share of the product's labels, 0 to 100.
Rank in category#4#7A position in a field of 13; printed, not drawn.
Labels1236A count; the two differ.
The two percentage rows are drawn on one 0 to 100 track, Commissionly reading right to left. Rank and label count are printed, not drawn.CaptivateIQ was named alongside these two in eleven of the twelve direct answers. Everstage vs Commissionly · Everstage vs Performio · CaptivateIQ vs Commissionly

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 sales commissions and incentive compensation page.

By framing

How many of the twelve models made each the first choice, per way of asking, and how many argued against it.
CommissionlyFirst choices, of twelve modelsPerformio
Direct00
Paraphrase01
Comparative00
Budget-constrained701 against Performio
Scale-constrained01
Negative001 against Commissionly · 1 against Performio
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.

Every model, every framing

The seventy-two answers behind the chart above, one cell each: where Commissionly and Performio 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
Commissionly Performio first choice named as an alternative argued againstblank: not namedEach cell is one answer, Commissionly on the left and Performio on the right.

The direct prompt

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

Neither was the first choice, one was named

5 of 12 modelsThe answer put something else first and named one of the two as an alternative.
Claude Haiku 4.5Qobra alternatives: CaptivateIQ, Everstage, Performio, Visdum
Gemini 3.5 FlashEverstage alternatives: CaptivateIQ, Performio, QuotaPath, Salesforce Spiff
Grok 4.1 FastCaptivateIQ alternatives: Everstage, Performio, Qobra, QuotaPath, Salesforce Spiff, Visdum
GLM 4.7 FlashXCaptivateIQ alternatives: Everstage, Performio, QuotaPath
MiniMax M2.5Everstage, QuotaPath alternatives: CaptivateIQ, Performio

Neither was named

7 of 12 modelsThe answer made no first choice from these two in this category.
GPT-5.4 miniCaptivateIQ alternatives: Salesforce Spiff, Varicent, Xactly
Perplexity SonarQobra alternatives: CaptivateIQ
Mistral SmallEverstage alternatives: CaptivateIQ, Qobra, QuotaPath, Visdum
DeepSeek V4 FlashEverstage alternatives: CaptivateIQ, QuotaPath, Salesforce Spiff
Llama 4 MaverickQobra alternatives: CaptivateIQ, Everstage, Salesforce Spiff
Qwen 3.7 FlashQobra alternatives: Everstage, QuotaPath
Kimi K2Everstage alternatives: CaptivateIQ, Qobra, QuotaPath

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
Commissionly leads by fifteen points.
Commissionly15%#2 of 10
Performio0%#– of 10
The full small business standing →
Mid-marketThe figures above
Commissionly leads by ten points.
Commissionly14%#4 of 13
Performio4%#7 of 13
The full mid-market standing →
Enterprise
The order flips: Performio leads at enterprise.
Performio11%#5 of 10
Commissionly0%#– of 10
The full enterprise standing →

What the models said about Commissionly

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

“These platforms trade flexibility for ease of use... you may quickly hit the limits” Gemini 3.5 Flash · negative prompt · soft negative
“Commissionly offers the best combination of affordability, speed of implementation, and essential features” Mistral Small · budget prompt · first choice
“For a limited budget, Commissionly and Sales Cookie stand out as the most cost-effective options” Claude Haiku 4.5 · budget prompt · first choice
“Commissionly is the most budget-oriented choice for very small teams with simple commission plans” Perplexity Sonar · budget prompt · first choice

What the models said about Performio

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

“What to Avoid on a Budget - Visdum, CaptivateIQ, Performio” Qwen 3.7 Flash · budget prompt · hard negative
“Low-Moderate Caution... "No real what-if forecasting"” Kimi K2 · negative prompt · soft negative
“I'd recommend CaptivateIQ or Performio as the top choices” MiniMax M2.5 · paraphrase prompt · first choice
“Shortlist CaptivateIQ, Performio, and Everstage” DeepSeek V4 Flash · scale prompt · first choice
“If you have more complex commission structures, Performio or CaptivateIQ might be worth the higher investment.” MiniMax M2.5 · direct 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.