Qualified vs HubSpot Conversations
Seven of fourteen models named Qualified first on the direct prompt; four named HubSpot Conversations. Qualified was named by fourteen of the fourteen models and HubSpot Conversations by twelve and Qualified carries 47 labels and HubSpot Conversations 21, so the shares are not directly comparable.
Named in four categories this edition.
By HubSpot, Cambridge, United States, founded 2006. 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 conversational marketing platforms page.
By framing
QualifiedFirst choices, of fourteen modelsHubSpot Conversations
Across every category in the November 2026 Edition, Qualified and HubSpot Conversations were named in the same answer thirty-two times, of the 136 answers naming Qualified and the 66 naming HubSpot Conversations. In those answers HubSpot Conversations took the first choice seven times and Qualified fourteen.
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 | | | |
Qualified HubSpot Conversations first choice named as an alternative argued againstblank: not namedEach cell is one answer, Qualified on the left and HubSpot Conversations on the right.The direct prompt
Qualified first, HubSpot Conversations an alternative
7 of 14 modelsHubSpot Conversations was named in the answer but not as the choice, or not at all.HubSpot Conversations first, Qualified an alternative
4 of 14 modelsQualified was named in the answer but not as the choice, or not at all.Neither was the first choice, one was named
3 of 14 modelsThe answer put something else first and named one of the two as an alternative.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
HubSpot Conversations21%#2 of 14Qualified4%#7 of 14Qualified25%#1 of 8HubSpot Conversations7%#5 of 8Qualified47%#1 of 9HubSpot Conversations2%#7 of 9What the models said about Qualified
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Four of five in this category shown.
“Extremely powerful for ABM and Salesforce-native teams, but likely overkill for mid-sized companies” Kimi K2 · paraphrase prompt · soft negative
“If you are heavily invested in Salesforce and have a dedicated sales motion, Qualified is the stronger pipeline driver.” Qwen 3.7 Flash · direct prompt · first choice
“My default pick: Qualified—if you're a mid-sized B2B company with Salesforce and meaningful inbound or ABM traffic.” GPT-6 Luna · paraphrase prompt · first choice
“Qualified is the current market reference for pipeline chat and is the closest enterprise feature match to Drift.” Muse Glimmer 30B · direct prompt · first choice
What the models said about HubSpot Conversations
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Three of four in this category shown.
“HubSpot Conversations or Intercom (Fin) are the most widely recommended for mid-market B2B” Claude Haiku 4.5 · direct prompt · first choice
“For most mid-market B2B companies, HubSpot Conversations is the best default choice” Perplexity Sonar · direct prompt · first choice
“For a typical mid-market B2B company, HubSpot is the safest default” GPT-6 Luna · 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.