Intercom vs Qualified
Five of fourteen models named Intercom first on the direct prompt; one named Qualified. Intercom was named by fourteen of the fourteen models and Qualified by thirteen and Intercom carries 54 labels and Qualified 33, so the shares are not directly comparable.
United States, founded 2011. Named in twelve categories this edition.
Named in five 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 marketing platforms page.
By framing
IntercomFirst choices, of fourteen modelsQualified
Across every category in the October 2026 Edition, Intercom and Qualified were named in the same answer eighty-three times, of the 470 answers naming Intercom and the 117 naming Qualified. In those answers Qualified took the first choice twenty-six times and Intercom six.
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 4.1 Fast | | | | | | |
| 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 | | | | |
Intercom Qualified first choice named as an alternative argued againstblank: not namedEach cell is one answer, Intercom on the left and Qualified on the right.The direct prompt
Both were the first choice
1 of 14 modelsThe answer named them together, and the judge labeled each a first choice.Intercom 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
8 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
Intercom5%#4 of 11Qualified0%#11 of 11Qualified44%#1 of 6Intercom8%#3 of 6What the models said about Intercom
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Two of two in this category shown.
“Top picks: Intercom (sales-focused), HubSpot (all-in-one CRM), Drift (conversational AI), Tidio/Zendesk (affordable multi-channel).” Grok 4.1 Fast · scale prompt · first choice
“Choose Intercom if you need a single tool for both buyer-facing sales qualification and post-sale/support, with Fin AI deflection” Muse Glimmer 30B · direct prompt · first choice
What the models said about Qualified
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Five of six in this category shown.
“Qualified, another major B2B player, was acquired by Salesforce. Its conversational AI capabilities (formerly "Piper") are now integrated directly into Salesforce's Agentforce.” Gemini 3.5 Flash · scale prompt · soft negative
“More expensive (quote-based, starting around $40K-68K/year)” GLM 4.7 FlashX · direct prompt · soft negative
“with Qualified as the default choice if your primary goal is *more meetings and sales-qualified conversations*” Perplexity Sonar · paraphrase prompt · first choice
“For mid-sized companies specifically, Drift or Qualified are typically the strongest choices” Claude Haiku 4.5 · paraphrase prompt · first choice
“if you need pipeline chat and visitor de-anonymization, a Drift/Qualified-type engine fits” Muse Glimmer 30B · comparative 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.