Three of fourteen models named Gong Revenue Intelligence Platform first on the direct prompt; one named Aviso. Gong Revenue Intelligence Platform was named by fourteen of the fourteen models and Aviso by ten and Gong Revenue Intelligence Platform carries 65 labels and Aviso 24, so the shares are not directly comparable.
By Gong.io, Ramat Gan, Israel, founded 2015. Named in one category this edition.
By Aviso AI, Redwood City, United States, founded 2012. Named in one category 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 revenue intelligence and forecasting page.
Across every category in the November 2026 Edition, Gong Revenue Intelligence Platform and Aviso were named in the same answer sixty-three times, of the 188 answers naming Gong Revenue Intelligence Platform and the 78 naming Aviso. In those answers Aviso took the first choice five times and Gong Revenue Intelligence Platform fourteen.
| Model | DirectGR | ParaphraseGR | ComparativeGR | Budget-constrainedGR | Scale-constrainedGR | NegativeGR |
|---|---|---|---|---|---|---|
| Claude Haiku 4.5 | GR | GR | GR | |||
| GPT-5.4 mini | GR | GR | GR | |||
| Gemini 3.5 Flash | GR | GR | GR | GR | GR | GR |
| Perplexity Sonar | GR | GR | GR | GR | ||
| Grok 4.20 | GR | GR | GR | GR | GR | GR |
| Mistral Small | GR | |||||
| DeepSeek V4 Flash | GR | GR | GR | GR | ||
| Llama 4 Maverick | GR | |||||
| Qwen 3.7 Flash | GR | GR | GR | GR | GR | |
| Kimi K2 | GR | GR | GR | GR | GR | |
| GLM 4.7 FlashX | GR | GR | GR | |||
| MiniMax M2.5 | GR | GR | ||||
| GPT-6 Luna | GR | GR | GR | GR | GR | |
| Muse Glimmer 30B | GR | GR | GR | GR |
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.
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Six of eight in this category shown.
“What to Avoid (Budget Traps)* Gong / Clari (Enterprise Tiers): ... making them difficult for companies with tight cash flow.” Qwen 3.7 Flash · budget prompt · hard negative
“Avoid for Tight Budgets: Enterprise tools like Gong, Clari, People.ai, or Salesforce Revenue Intelligence” Grok 4.20 · budget prompt · hard negative
“What to Avoid on a Tight Budget - Gong: $1,600+/user/year + $5,000–$50,000 annual platform fee” Kimi K2 · budget prompt · hard negative
“Gong and Clari are the most clearly positioned as dedicated revenue intelligence / revenue AI platforms.” GPT-5.4 mini · comparative prompt · first choice
“Gong is generally the gold standard for revenue intelligence because it captures actual conversations” Qwen 3.7 Flash · direct prompt · first choice
“Choose Gong if you want to improve rep performance through coaching and call analysis.” Qwen 3.7 Flash · comparative prompt · first choice
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Six of eight in this category shown.
“What to Avoid: - Aviso – Powerful AI ... but enterprise-focused and likely overkill for mid-market needs.” Kimi K2 · paraphrase prompt · hard negative
“not consistently recommended as a top choice. Buyers should carefully assess its AI call intelligence accuracy” Mistral Small · negative prompt · soft negative
“More enterprise-oriented; powerful AI but likely overkill for smaller mid-market” DeepSeek V4 Flash · paraphrase prompt · soft negative
“Best for mid-market to enterprise teams needing deep pipeline analytics and AI-native forecasting... Aviso is a strong alternative.” Mistral Small · paraphrase prompt · first choice
“Clari or Aviso | Best-in-class data enforcement and "Truth" engine.” Qwen 3.7 Flash · paraphrase prompt · first choice
“Revenue.io and Aviso are standout choices” Mistral Small · direct 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.