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

Agent Frank

28Judge labels
5First choices
1Negative labels
11 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
9% in AI SDR for small business buyers
Rank 12 of 71 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 ai sdr · 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 Agent Frank 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
AI SDR agentsSales2%12 of 710%10accepted challenger

Movement

This is the first edition on this tier, so no move can be computed for Agent Frank 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 Agent Frank across every prompt where it was named for a mid-market B2B company. Twelve models, six prompts per category.
ModelFirst choiceAlternativeMentionNegativeLabels
Claude Haiku 4.500202
GPT-5.4 mini00000
Gemini 3.5 Flash01001
Perplexity Sonar00000
Grok 4.1 Fast00000
Mistral Small00101
DeepSeek V4 Flash01001
Llama 4 Maverick00000
Qwen 3.7 Flash01102
Kimi K201001
GLM 4.7 FlashX10001
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
Direct6 labels3
Paraphrase2 labelsNone
Comparative7 labelsNone
Budget-constrained12 labels2
Scale-constrained0 labelsNone
Negative1 labelNone
First choiceAlternativeMentionNegative28 labels in all, every segment counted; 5 of the 5 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.

“Top Pick: Agent Frank (Salesforge) - $499/month” GLM 4.7 FlashX · AI SDR · budget prompt · first choice
“If you want a highly scalable, fully autonomous email worker: Go with Agent Frank at $499/month.” Gemini 3.5 Flash · AI SDR · budget prompt · alternative
“Agent Frank at $416/mo (on annual billing) is the cheapest turnkey solution with proven ROI” Qwen 3.7 Flash · AI SDR · budget prompt · alternative
“Best Value Turnkey Option... If you can stretch to ~$500/month” DeepSeek V4 Flash · AI SDR · budget 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 Agent Frank, over the 28 answers that named it. Took the first choice instead counts the answers where the other product was the first choice and Agent Frank was named but was not.
ProductSame answerTook the first choice insteadHead to head
AiSDR20 of 283Not in the top three
Apollo.io15 of 286Not in the top three
Artisan14 of 281Not in the top three
11x13 of 280Not in the top three
Instantly10 of 281Not in the top three
Clay8 of 280Not in the top three
Reply.io8 of 280Not in the top three
SalesTools.io7 of 284Not in the top three
Amplemarket7 of 283Not in the top three
Jeeva7 of 282Not 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 Agent Frank. 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 4 of the 28 answers that named Agent Frank and are not a share of its labels.

Domains cited

getvoip.com4
amplemarket.com3
devcommx.com3
salesforge.ai3
artisan.co2
clay.com2
getbreakout.ai2
miniloop.ai2
saleshandy.com2
snov.io2

Twenty-five of the twenty-five domain citations in answers naming Agent Frank came from somebody else's page.

Pages cited

Pages are listed as the models cited them.

Names read as Agent Frank

What the judge wrote, as written, with how often. The vendor table decides that these count as Agent Frank; a claim can dispute any of them.
Agent Frank (by Salesforge) 2Agent Frank (Salesforge) 1Agent Frank by Salesforge 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 Agent Frank'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 Agent Frank, 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 agent.nexus 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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