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People analytics · November 2026 Edition

Visier vs BambooHR

One of fourteen models named Visier first on the direct prompt; zero named BambooHR. Visier was named by fourteen of the fourteen models and BambooHR by twelve and Visier carries 47 labels and BambooHR 31, so the shares are not directly comparable.

Visier

criticized challenger

Named in seven categories this edition.

BambooHR

accepted challenger

Named in twenty-five categories this edition.

First-choice share9%5%Of first choices across the direct, paraphrase, budget and scale prompts, 0 to 100.
Negative rate36%6%Negative labels as a share of the product's labels, 0 to 100.
Rank in category#4#5A position in a field of 13; printed, not drawn.
Labels4731A count; the two differ.
The two percentage rows are drawn on one 0 to 100 track, Visier reading right to left. Rank and label count are printed, not drawn.ChartHop was named alongside these two in twelve of the fourteen direct answers. ChartHop vs Visier · ChartHop vs BambooHR · Crunchr vs Visier

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 people analytics page.

By framing

How many of the fourteen models made each the first choice, per way of asking, and how many argued against it.
VisierFirst choices, of fourteen modelsBambooHR
Direct105 against Visier
Paraphrase313 against Visier
Comparative130
Budget-constrained024 against Visier
Scale-constrained101 against Visier
Negative104 against Visier · 2 against BambooHR
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 Visier and BambooHR 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
Visier BambooHR first choice named as an alternative argued againstblank: not namedEach cell is one answer, Visier on the left and BambooHR on the right.

The direct prompt

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

Visier first, BambooHR not the choice

1 of 14 modelsBambooHR was named in the answer but not as the choice, or not at all.
Gemini 3.5 FlashChartHop, Visier alternatives: HiBob, Lattice, Leapsome, Rippling

Neither was the first choice, one was named

5 of 14 modelsThe answer put something else first and named one of the two as an alternative.
Llama 4 MaverickHiBob alternatives: 15Five, BambooHR, ChartHop, Crunchr, Culture Amp, Rippling
GLM 4.7 FlashXCrunchr alternatives: BambooHR, ChartHop
MiniMax M2.5Crunchr alternatives: BambooHR, ChartHop, HiBob
GPT-6 LunaChartHop alternatives: One Model, Visier
Muse Glimmer 30BCrunchr alternatives: BambooHR, ChartHop, HiBob, Knoetic, PeoplePilot, ThriveSparrow

Neither was named

8 of 14 modelsThe answer made no first choice from these two in this category.
Claude Haiku 4.5PeoplePilot alternatives: Crunchr, HiBob People Analytics, Lattice, Orgnostic
GPT-5.4 miniHiBob alternatives: Crunchr, Orgvue, Perceptyx
Perplexity SonarCrunchr alternatives: ChartHop, Culture Amp, Lattice
Grok 4.20ChartHop alternatives: HiBob, Lattice
Mistral SmallChartHop, Crunchr alternatives: HiBob
DeepSeek V4 FlashChartHop alternatives: Crunchr, Culture Amp, HiBob, One Model
Qwen 3.7 FlashHiBob alternatives: ChartHop, Crunchr
Kimi K2ChartHop, Crunchr

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
BambooHR leads by forty-six points.
BambooHR46%#1 of 17
Visier0%#17 of 17
The full small business standing →
Mid-marketThe figures above
The order flips: Visier leads at mid-market.
Visier9%#4 of 13
BambooHR5%#5 of 13
The full mid-market standing →
Enterprise
Visier leads by sixty-five points.
Visier65%#1 of 12
BambooHR0%#– of 12
The full enterprise standing →

What the models said about Visier

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

“Options to Avoid on a Tight Budget - Visier and Crunchr (enterprise tier)” DeepSeek V4 Flash · budget prompt · hard negative
“Avoid Visier, Workday, or other enterprise platforms” Perplexity Sonar · budget prompt · hard negative
“Enterprise platforms like Visier, One Model, or Workday Peakon often cost tens of thousands of dollars annually and require dedicated data teams.” Gemini 3.5 Flash · budget prompt · soft negative
“Visier offers dedicated people analytics with custom quotes and provides predictive models and an AI assistant for enterprise people analytics functions” Claude Haiku 4.5 · comparative prompt · first choice
“Widely recognized as the market leader in pure-play people analytics, designed specifically for large-scale enterprise environments.” Muse Glimmer 30B · comparative prompt · first choice
“Choose Visier or ChartHop if you have a mature HR function, multiple HR systems, and want to minimize data cleaning effort” Qwen 3.7 Flash · comparative prompt · first choice

What the models said about BambooHR

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

“BambooHR or Zoho People gives you analytics inside your HRIS for ~$10/emp/mo or ~$1.25/emp/mo with no extra integration” Muse Glimmer 30B · 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.