DealHub vs HubSpot Commerce Hub
Fourteen of fourteen models named DealHub first on the direct prompt; zero named HubSpot Commerce Hub. DealHub was named by fourteen of the fourteen models and HubSpot Commerce Hub by eleven and DealHub carries 57 labels and HubSpot Commerce Hub 25, so the shares are not directly comparable.
Named in five categories this edition.
By HubSpot, Cambridge, United States, founded 2006. Named in two 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 CPQ page.
By framing
DealHubFirst choices, of fourteen modelsHubSpot Commerce Hub
Across every category in the October 2026 Edition, DealHub and HubSpot Commerce Hub were named in the same answer forty-two times, of the 184 answers naming DealHub and the 75 naming HubSpot Commerce Hub. In those answers HubSpot Commerce Hub took the first choice four times and DealHub fifteen.
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 | | | | |
DealHub HubSpot Commerce Hub first choice named as an alternative argued againstblank: not namedEach cell is one answer, DealHub on the left and HubSpot Commerce Hub on the right.The direct prompt
DealHub first, HubSpot Commerce Hub an alternative
14 of 14 modelsHubSpot Commerce Hub was named in the answer but not as the choice, or not at all.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 Commerce Hub14%#3 of 10DealHub12%#4 of 10DealHub57%#1 of 12HubSpot Commerce Hub2%#5 of 12DealHub2%#6 of 9HubSpot Commerce Hub0%#– of 9What the models said about DealHub
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Five of five in this category shown.
“avoid enterprise-grade CPQs like Salesforce CPQ, Oracle CPQ Cloud, or DealHub initially” Qwen 3.7 Flash · budget prompt · hard negative
“Avoid Enterprise Tools: ... DealHub (~$60)” Grok 4.20 · budget prompt · hard negative
“The Agile Mid-Market Leader: DealHub.io ... highly rated for mid-market teams because of its no-code RevOps administration and rapid deployment time” Gemini 3.5 Flash · scale prompt · first choice
“DealHub is frequently cited as the top choice for mid-market organizations due to its balance of power and ease of use.” Qwen 3.7 Flash · direct prompt · first choice
“DealHub is widely regarded as the best CPQ (Configure, Price, Quote) software for mid-market B2B companies” Mistral Small · direct prompt · first choice
What the models said about HubSpot Commerce Hub
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Four of five in this category shown.
“These tools sit on the lighter end of the CPQ spectrum... They will break down if you try to build complex multi-layered product hierarchies” Gemini 3.5 Flash · comparative prompt · soft negative
“PandaDoc, Proposify, or HubSpot Quotes are the best starting points” Mistral Small · budget prompt · first choice
“Look instead at modern, agile, and mid-market-focused CPQ tools (like Dealsign, PandaDoc, or HubSpot CPQ)” Gemini 3.5 Flash · negative prompt · alternative
“if you need basic quoting functionality only, HubSpot's free tier might be sufficient to start” MiniMax M2.5 · budget prompt · alternative
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.