Gane Index
All indexesEvery index and its categoriesGTMMarketing, sales, customer and revenue operations softwareITSecurity, IT operations, cloud, developer and data softwareHRHR, payroll, recruiting, performance and learning softwareFinanceAccounting, planning, spend, billing and treasury software
Claim your listing
Index › HR › Core HR and payroll › PEOs › Rippling vs Insperity
PEO providers · November 2026 Edition

Rippling vs Insperity

Two of fourteen models named Rippling first on the direct prompt; zero named Insperity. Both were named by all fourteen models and Rippling carries 39 labels and Insperity 44, so the shares are not directly comparable.

Rippling

accepted challenger

Named in thirty-three categories this edition.

Insperity

accepted challenger

Named in five categories this edition.

First-choice share11%7%Of first choices across the direct, paraphrase, budget and scale prompts, 0 to 100.
Negative rate5%16%Negative labels as a share of the product's labels, 0 to 100.
Rank in category#4#5A position in a field of 7; printed, not drawn.
Labels3944A count; the two differ.
The two percentage rows are drawn on one 0 to 100 track, Rippling reading right to left. Rank and label count are printed, not drawn.TriNet was named alongside these two in fourteen of the fourteen direct answers. ADP TotalSource vs Rippling · ADP TotalSource vs Insperity · Justworks vs Rippling

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 PEO providers page.

By framing

How many of the fourteen models made each the first choice, per way of asking, and how many argued against it.
RipplingFirst choices, of fourteen modelsInsperity
Direct20
Paraphrase23
Comparative10
Budget-constrained101 against Rippling · 4 against Insperity
Scale-constrained00
Negative101 against Rippling · 3 against Insperity
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 Rippling and Insperity 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
Rippling Insperity first choice named as an alternative argued againstblank: not namedEach cell is one answer, Rippling on the left and Insperity on the right.

The direct prompt

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

Rippling first, Insperity an alternative

2 of 14 modelsInsperity was named in the answer but not as the choice, or not at all.
Gemini 3.5 FlashRippling alternatives: ADP TotalSource, Insperity, TriNet
Llama 4 MaverickRippling alternatives: ADP TotalSource, Paychex PEO, TriNet

Neither was the first choice, one was named

11 of 14 modelsThe answer put something else first and named one of the two as an alternative.
Claude Haiku 4.5ADP TotalSource alternatives: Engage PEO, Insperity, TriNet
Perplexity SonarADP TotalSource alternatives: Deel PEO, Rippling, TriNet
Grok 4.20Justworks alternatives: ADP TotalSource, Rippling, TriNet
Mistral SmallADP TotalSource, TriNet alternatives: Insperity, Rippling
DeepSeek V4 FlashADP TotalSource alternatives: Insperity, Rippling, TriNet
Qwen 3.7 FlashADP TotalSource alternatives: Insperity, Rippling, TriNet
Kimi K2ADP TotalSource alternatives: Insperity, Paychex PEO, Rippling, TriNet
GLM 4.7 FlashXADP TotalSource alternatives: Paychex PEO, Rippling, TriNet
MiniMax M2.5ADP TotalSource alternatives: Insperity, Paychex PEO, Rippling, TriNet
GPT-6 LunaADP TotalSource alternatives: Insperity, TriNet
Muse Glimmer 30BADP TotalSource, Paychex PEO alternatives: Insperity, TriNet

Neither was named

1 of 14 modelsThe answer made no first choice from these two in this category.
GPT-5.4 miniADP TotalSource alternatives: Justworks, TriNet

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
Rippling leads by seven points.
Rippling9%#2 of 7
Insperity2%#5 of 7
The full small business standing →
Mid-marketThe figures above
Rippling leads by four points.
Rippling11%#4 of 7
Insperity7%#5 of 7
The full mid-market standing →
Enterprise
Rippling leads by two points.
Rippling2%#5 of 8
Insperity0%#6 of 8
The full enterprise standing →

What the models said about Rippling

No label in this category carried a quote.

What the models said about Insperity

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

“Large enterprise-focused PEOs (like ADP TotalSource or Insperity) often require custom quotes and long contracts” Qwen 3.7 Flash · budget prompt · soft negative
“Insperity is the most frequently cited fit for 25–500 employees with hands-on support teams” Muse Glimmer 30B · paraphrase prompt · first choice
“my default recommendation would be TriNet or Insperity” GPT-5.4 mini · paraphrase prompt · first choice
“the default recommendation would be Insperity” Perplexity Sonar · paraphrase prompt · first choice
Also compared
ADP TotalSource vs RipplingADP TotalSource vs InsperityJustworks vs RipplingJustworks vs InsperityTriNet vs RipplingTriNet vs InsperityRippling vs Paychex PEOInsperity vs Paychex PEOAll fifteen head-to-head pages and the full peos standing

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.