GTM AI Index

Index › Vendors › Sobot · September 2026 Edition

1 category · Named, not ranked

Sobot

3Judge labels

0First choices

2Negative labels

3 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.

Standing

1 label, too few to rank

A product needs 10 labels in a category before a share or quadrant is stated. Sobot was named 1 time in Support chatbots, where Intercom led with 35%. The labels and the evidence are below, counted exactly.

By buyer segmentRead the same way at every buyer size.

Small business72 of 84

under the label threshold

Mid-market93 of 106

under the label threshold

Enterprise70 of 122

under the label threshold

In support chatbots · 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 Sobot 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.

Category	Function	Share	Rank	Negative rate	Labels	Quadrant
Chatbots and conversational AI for support	Customer	0%	93 of 106	100%	1	under 10 labels · led by Intercom at 35%

Movement

This is the first edition on this tier, so no move can be computed for Sobot 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 Sobot across every prompt where it was named for a mid-market B2B company. Twelve models, six prompts per category.

Model	Negative	Labels
Claude Haiku 4.5	0	0
GPT-5.4 mini	0	0
Gemini 3.5 Flash	0	0
Perplexity Sonar	1	1
Grok 4.1 Fast	0	0
Mistral Small	0	0
DeepSeek V4 Flash	0	0
Llama 4 Maverick	0	0
Qwen 3.7 Flash	0	0
Kimi K2	0	0
GLM 4.7 FlashX	0	0
MiniMax M2.5	0	0

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

Direct1 labelNone

Paraphrase0 labelsNone

Comparative0 labelsNone

Budget-constrained0 labelsNone

Scale-constrained0 labelsNone

Negative2 labelsNone

First choiceAlternativeMentionNegative3 labels in all, every segment counted; 0 of the 0 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.

No positive label carried a quote.

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.

“Capterra flags Sobot as quote-based, which makes it less suitable for teams that want transparent published pricing upfront.” Perplexity Sonar · Support chatbots · negative prompt · soft negative

Named alongside

The products named in the same answers as Sobot, over the 3 answers that named it. Took the first choice instead counts the answers where the other product was the first choice and Sobot was named but was not.

ProductSame answerTook the first choice insteadHead to head

Intercom2 of 31Not in the top three

Zendesk2 of 31Not in the top three

Freshchat2 of 30Not in the top three

Tidio1 of 31Not in the top three

Chatbase1 of 30Not in the top three

Crisp1 of 30Not in the top three

Drift1 of 30Not in the top three

Gorgias1 of 30Not in the top three

LiveAgent1 of 30Not in the top three

Salesforce Service Cloud with Agentforce1 of 30Not 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 Sobot. 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 1 of the 3 answers that named Sobot and are not a share of its labels.

Domains cited

assembled.com1

botpress.com1

capterra.com1

chatbase.co1

chatbot.com1

chatspark.io1

cnbc.com1

featurebase.app1

nurix.ai1

rajeshkumar.xyz1

Ten of the ten domain citations in answers naming Sobot came from somebody else's page.

Pages cited

assembled.com/blog/ai-chat-agents-customer-support1

botpress.com/blog/customer-service-chatbot-platforms1

capterra.com/resources/best-chatbot-software1

capterra.com/resources/customer-service-chatbot-platforms1

chatbase.co/blog/best-chatbot-software1

chatbot.com/blog/common-chatbot-mistakes1

chatspark.io/blog/ai-chatbots-customer-support-common-mistakes-businesses-avoid1

cnbc.com/2026/04/01/ai-chatbot-customer-service-complaints-refunds.html1

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 Sobot'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 Sobot, 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 sobot.io 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.