Gong

Named in 32 judge labels across 1 category by 6 of 6 models in the September 2026 Edition. 13 first choices, 5 negative labels. Every number here is derived from the raw labels under vendor table v2026-09-08.5.

Best standing

59% in Conversation intel

Rank 1 of 50 products named in conversational intelligence and call recording, endorsed leader. 5 of 6 models made it the first choice on the direct prompt; 16% of its 32 labels there were negative.

Standing by category

Every category where a model named Gong. Share is first choices across the direct, paraphrase, budget and scale prompts; rank is within every product named in that category.

Category	Vertical	First choices	Rank	Negative rate	Labels	Quadrant
Conversational intelligence and call recording	Sales	59%	1 of 50	16%	32	endorsed leader

By model

How each model treated Gong across every prompt where it was named. Six models, six prompts per category.

Model	First choice	Alternative	Mention	Negative	Labels
Claude Opus 5	3	0	2	1	6
Claude Opus 4.8	3	0	2	1	6
GPT-6 Astra	4	0	0	1	5
GPT-5.6 Sol	3	0	1	1	5
Gemini 3.1 Pro	4	0	1	1	6
Perplexity Sonar Pro	2	1	1	0	4

What the models said for it

Verbatim evidence the judge attached to positive labels.

“I'd recommend Gong if your priority is conversation intelligence for sales coaching, deal risk, and pipeline visibility” Perplexity Sonar Pro · Conversation intel · paraphrase prompt · first choice

“Gong is widely regarded as the market-defining platform... remains the most feature-rich option for large organizations” Claude Opus 4.8 · Conversation intel · comparative prompt · first choice

“Default recommendation: Gong if you have budget ... and coaching/deal inspection is a strategic priority.” Claude Opus 5 · Conversation intel · direct prompt · first choice

“Gong is typically positioned as the deepest option for enterprise revenue teams and pipeline/deal insight” Perplexity Sonar Pro · Conversation intel · comparative prompt · first choice

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.

“What to avoid on a limited budget... it's built for funded sales orgs: annual contracts only, a platform fee on top of seats” Claude Opus 5 · Conversation intel · budget prompt · hard negative

“First, why to avoid the big names (if budget is your constraint)... Gong and Chorus, are powerful but expensive.” Claude Opus 4.8 · Conversation intel · budget prompt · hard negative

“the first rule is to avoid enterprise tools like Gong or Chorus” Gemini 3.1 Pro · Conversation intel · budget prompt · hard negative

“enterprise suites such as Gong should be limited to calls where their analytics justify the expanded data footprint” GPT-5.6 Sol · Conversation intel · negative prompt · soft negative

Names read as Gong

What the judge wrote, as written, with how often. The vendor table decides that these count as Gong; a claim can dispute any of them.

Every label used the canonical name.

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

It does not get any change to labels, shares or quadrants, any preview, or any say over which quotes appear. Sponsorship is separate: a sponsor funds categories or buyer dimensions and is named on what it funded, and the rules are the same for every sponsor.