Gong vs Jiminny
Three of fourteen models named Gong first on the direct prompt; one named Jiminny. Gong was named by fourteen of the fourteen models and Jiminny by ten and Gong carries 63 labels and Jiminny 23, so the shares are not directly comparable.
By Gong.io, Ramat Gan, Israel, founded 2015. Named in fourteen categories this edition.
Named in three categories this edition.
Share is the count of first choices across the direct, paraphrase, budget and scale prompts over all fourteen models, for a mid-market B2B company; rank is within the category; every quote names the model and the prompt it came from. Both figures come from the conversational intelligence and call recording page.
By framing
GongFirst choices, of fourteen modelsJiminny
Across every category in the October 2026 Edition, Gong and Jiminny were named in the same answer seventy times, of the 578 answers naming Gong and the 79 naming Jiminny. In those answers Jiminny took the first choice eight times and Gong twenty.
Every model, every framing
| Model | Direct | Paraphrase | Comparative | Budget-constrained | Scale-constrained | Negative |
|---|---|---|---|---|---|---|
| Claude Haiku 4.5 | | | | | ||
| GPT-5.4 mini | | | | | ||
| Gemini 3.5 Flash | | | | | | |
| Perplexity Sonar | | | | | ||
| Grok 4.20 | | | | | | |
| Mistral Small | | | | | | |
| DeepSeek V4 Flash | | | | | | |
| Llama 4 Maverick | ||||||
| Qwen 3.7 Flash | | | | | | |
| Kimi K2 | | | | | | |
| GLM 4.7 FlashX | | | | |||
| MiniMax M2.5 | | | | | ||
| GPT-6 Luna | | | | | ||
| Muse Glimmer 30B | | | | | |
Gong Jiminny first choice named as an alternative argued againstblank: not namedEach cell is one answer, Gong on the left and Jiminny on the right.The direct prompt
Gong first, Jiminny not the choice
3 of 14 modelsJiminny was named in the answer but not as the choice, or not at all.Jiminny first, Gong an alternative
1 of 14 modelsGong was named in the answer but not as the choice, or not at all.Neither was the first choice, one was named
9 of 14 modelsThe answer put something else first and named one of the two as an alternative.Neither was named
1 of 14 modelsThe answer made no first choice from these two in this category.Bold names in an answer are the products the judge labeled a first choice; a model naming several gives each of them that label. The full answer text for every row is in the record.
By buyer segment
Gong0%#11 of 11Jiminny0%#7 of 11What the models said about Gong
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Four of four in this category shown.
“you should immediately bypass enterprise-level conversation intelligence (CI) giants like Gong and Chorus by ZoomInfo” Gemini 3.5 Flash · budget prompt · hard negative
“Gong.io — The Most Expensive and Complicated Option ... Pricing transparency issues (major red flag)” DeepSeek V4 Flash · negative prompt · hard negative
“Gong is widely considered the market leader in Revenue Intelligence... If you have the budget ($1k-$1.5k/user/month) and want rigorous sales optimization, this is the one.” Qwen 3.7 Flash · paraphrase prompt · first choice
“The market-leading conversation intelligence platform focused on revenue analytics... For large sales teams needing deep revenue intelligence: Gong or Clari Copilot.” GLM 4.7 FlashX · comparative prompt · first choice
What the models said about Jiminny
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Four of five in this category shown.
“Jiminny is noted for being priced out of reach for mid-market sales teams” Mistral Small · negative prompt · soft negative
“Jiminny: The Best Overall for Mid-Market Coaching & Adoption ... Jiminny is the top choice.” Gemini 3.5 Flash · direct prompt · first choice
“Shortlist: Avoma, Jiminny, Chorus (if using ZoomInfo), and Clari Copilot” Kimi K2 · scale prompt · first choice
“Jiminny is the strongest mid-market option.” Gemini 3.5 Flash · paraphrase prompt · first choice
Comparisons are drawn for the top eight products in each category, each against each. The output is the models' output; nothing here is a recommendation by the index.