GTM AI Index
Index Vendors › Hyperbound · September 2026 Edition
1 category · Ranked

Hyperbound

34Judge labels
1First choices
1Negative labels
12 of 12Models named it
1Category
September 2026 Edition. Every number here is derived from the raw labels under vendor table v2026-09-16.3, every buyer segment counted.
Best standing
2% in Sales coaching for small business buyers
Rank 26 of 124 in the mid-market standingaccepted challenger
0 of 12 models made it the first choice on the direct prompt; 0% of its 10 labels there were negative.
By buyer segmentRead the same way at every buyer size.
In sales coaching · 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 Hyperbound 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
Sales coaching and trainingSales0%26 of 1240%10accepted challenger

Movement

This is the first edition on this tier, so no move can be computed for Hyperbound yet. The next is due October 1, 2026. 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 Hyperbound across every prompt where it was named for a mid-market B2B company. Twelve models, six prompts per category.
ModelFirst choiceAlternativeMentionNegativeLabels
Claude Haiku 4.500101
GPT-5.4 mini00000
Gemini 3.5 Flash00101
Perplexity Sonar01001
Grok 4.1 Fast00000
Mistral Small00000
DeepSeek V4 Flash01203
Llama 4 Maverick01001
Qwen 3.7 Flash00101
Kimi K210001
GLM 4.7 FlashX00101
MiniMax M2.500000

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
Direct7 labelsNone
Paraphrase0 labelsNone
Comparative13 labels1not counted in share
Budget-constrained3 labelsNone
Scale-constrained8 labels1
Negative3 labels1not counted in share
First choiceAlternativeMentionNegative34 labels in all, every segment counted; 1 of the 3 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.

“For practice/roleplay, look at Hyperbound or PitchMonster” Kimi K2 · Sales coaching · negative prompt · first choice
“Tough Tongue AI, Hyperbound, and other sales training platforms that offer a free or low-cost pilot or trial” Llama 4 Maverick · Sales coaching · budget prompt · alternative
“Choose Hyperbound or Second Nature if you want reps to practice before they go live” Perplexity Sonar · Sales coaching · comparative prompt · alternative
“Highly rated for AI roleplay and practice” DeepSeek V4 Flash · Sales coaching · negative 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 Hyperbound, over the 34 answers that named it. Took the first choice instead counts the answers where the other product was the first choice and Hyperbound was named but was not.
ProductSame answerTook the first choice insteadHead to head
Gong31 of 349Not in the top three
MindTickle27 of 348Not in the top three
Second Nature20 of 341Not in the top three
Chorus by ZoomInfo20 of 340Not in the top three
SalesHood17 of 341Not in the top three
Allego15 of 341Not in the top three
PitchMonster15 of 341Not in the top three
Seismic14 of 341Not in the top three
Highspot13 of 341Not in the top three
Trainual12 of 342Not 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 Hyperbound. 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 7 of the 34 answers that named Hyperbound and are not a share of its labels.

Domains cited

alpharun.com6
docebo.com5
pipeline.zoominfo.com5
ringcentral.com5
cirrusinsight.com4
firstsales.io4
hyperbound.aiYour site4
learn.g2.com4
mindtickle.com4
pitchmonster.io4

Forty-one of the forty-five domain citations in answers naming Hyperbound came from somebody else's page.

Pages cited

Pages are listed as the models cited them.

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 Hyperbound'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 Hyperbound, 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 hyperbound.ai 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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