HR AI Index
Index Vendors › GoodHabitz · September 2026 Edition
2 categories · Ranked

GoodHabitz

23Judge labels
2First choices
0Negative labels
8 of 12Models named it
2Categories
September 2026 Edition. Every number here is derived from the raw labels under vendor table vv2026-09.1, every buyer segment counted.
Best standing
2% in Online course libraries for enterprise buyers
Rank 7 of 73 in the mid-market standing
0 of 12 models made it the first choice on the direct prompt; 0% of its 8 labels there were negative.
By buyer segmentRead the same way at every buyer size.
In online course libraries · each standing computed within its segment · bars are 0 to 100 · the accent bar is the product's own best reading

Standing by category

Every category where a model named GoodHabitz for a mid-market B2B company. Share is first choices across the direct, paraphrase, budget and scale prompts; rank is within every product named in that category.
CategoryFunctionShareRankNegative rateLabelsQuadrant
Online course librariesLearning and development2%7 of 730%8under 10 labels · led by Udemy Business at 32%
Skills intelligencePerformance and talent management0%89 of 1620%1under 10 labels · led by TalentGuard at 19%

Movement

This is the first edition on this tier, so no move can be computed for GoodHabitz yet. From the next edition this section shows, per buyer segment, whether its share moved by more than the measured noise floor.

By model

How each model treated GoodHabitz across every prompt where it was named for a mid-market B2B company. Twelve models, six prompts per category.
ModelFirst choiceAlternativeMentionNegativeLabels
Claude Haiku 4.500000
GPT-5.4 mini00000
Gemini 3.5 Flash00000
Perplexity Sonar02002
Grok 4.1 Fast00000
Mistral Small00101
DeepSeek V4 Flash00101
Llama 4 Maverick10203
Qwen 3.7 Flash00000
Kimi K200000
GLM 4.7 FlashX00101
MiniMax M2.501001

By framing

Which of the six questions produced the naming. By model says how often; this says asked what. The first-choice count on the right carries the marks of the models that produced it.
FramingLabels by classFirst choices
Direct4 labelsNone
Paraphrase4 labelsNone
Comparative11 labelsNone
Budget-constrained3 labels2
Scale-constrained1 labelNone
Negative0 labelsNone
First choiceAlternativeMentionNegative23 labels in all, every segment counted; 2 of the 2 first choices count toward share, since the comparative and negative framings do not. The bar is one segment per label class, to scale within the framing.

What the models said for it

Verbatim evidence the judge attached to positive labels.

“includes platforms such as Mitti, Proprofs Training, and GoodHabits” Llama 4 Maverick · Online course libraries · budget prompt · first choice
“Best if your main goal is soft skills and engagement-led development” Perplexity Sonar · Online course libraries · direct prompt · alternative
“Best if you need behavioral/soft-skills content: GoodHabitz” Perplexity Sonar · Online course libraries · paraphrase prompt · alternative
“Best for: Engaging, practical content” MiniMax M2.5 · Online course libraries · paraphrase prompt · alternative

And against it

Verbatim evidence attached to negative labels. A warning on a product with few labels is a warning; on a product with many, it is one voice among them.

No model argued against it.

Named alongside

The products named in the same answers as GoodHabitz, over the 23 answers that named it. Took the first choice instead counts the answers where the other product was the first choice and GoodHabitz was named but was not.
ProductSame answerTook the first choice insteadHead to head
LinkedIn Learning15 of 234Not in the top three
Coursera for Business15 of 233Not in the top three
Udemy Business14 of 230Not in the top three
Skillsoft Percipio11 of 231Not in the top three
Pluralsight10 of 230Not in the top three
Go19 of 230Not in the top three
OpenSesame5 of 230Not in the top three
TalentLMS4 of 231Not in the top three
Absorb LMS4 of 230Not in the top three
BizLibrary4 of 230Not in the top three
A head-to-head page exists where both products are in a category's top three. The other rows are the same fact without a page behind them, so they link to the product instead.

What carried it into the answer

The sites and pages cited by the answers that named GoodHabitz. A fact about retrieval, not a lever on the model.

Citations exist only for the models that return a source list, four of the twelve in this edition, so these counts come from 23 of the 23 answers that named GoodHabitz and are not a share of its labels.

Names read as GoodHabitz

What the judge wrote, as written, with how often. The vendor table decides that these count as GoodHabitz; a claim can dispute any of them.
GoodHabits 1Goodhabitz 1
Is this your product?

Claim this page

Claiming is free and changes nothing in the data. A claimed page shows a verified contact who is told when each edition publishes and when GoodHabitz's standing changes by more than the noise floor; the right to propose corrections to the vendor table, meaning names the judge wrote that should or should not read as GoodHabitz, applied by version and listed in the change log; and a one-line description supplied by the vendor and marked as such.

It does not get any change to labels, shares or verdicts, any preview, or any say over which quotes appear. A verification link goes to your work email; an address at goodhabitz.com is approved on the spot, any other address is reviewed by hand.

Your name and company appear on the claimed page, or the company alone if you ask below. A title and a LinkedIn address appear there too if you give them, and are left off if you do not. Your email address is never published.

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