Two of fourteen models named DemandTools first on the direct prompt; zero named ZoomInfo Operations. DemandTools was named by fourteen of the fourteen models and ZoomInfo Operations by twelve and DemandTools carries 51 labels and ZoomInfo Operations 26, so the shares are not directly comparable.
Named in one category this edition.
By ZoomInfo, Vancouver, Washington, United States, founded 2000. 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 CRM data quality and deduplication page.
Across every category in the October 2026 Edition, DemandTools and ZoomInfo Operations were named in the same answer fifty-two times, of the 155 answers naming DemandTools and the 72 naming ZoomInfo Operations. In those answers ZoomInfo Operations took the first choice zero times and DemandTools twelve.
| 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 |
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.
“The interface is widely noted as outdated compared to more modern competitors, and it's not well-suited for single-record edits” Claude Haiku 4.5 · negative prompt · hard negative
“Avoid heavy, platform-level orchestration engines (like Openprise) or rigid legacy suites (like DemandTools)” Gemini 3.5 Flash · negative prompt · hard negative
“Enterprise data quality suites like DemandTools, RingLead, or Cloudingo are powerful, but they often require annual commitments ranging from $2,500 to over $10,000.” Gemini 3.5 Flash · budget prompt · soft negative
“DemandTools is the most established native Salesforce data quality platform... best for mid-market to enterprise Salesforce teams that need a comprehensive data quality suite” Claude Haiku 4.5 · direct prompt · first choice
“Dedupely for HubSpot/Pipedrive users or DemandTools for Salesforce users, because both are explicitly positioned as low-cost deduplication tools” Perplexity Sonar · budget prompt · first choice
“It's the most versatile and mature tool (20+ years of evolution), trusted by administrators who want fine-grained control” DeepSeek V4 Flash · comparative prompt · first choice
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Six of seven in this category shown.
“RingLead is notorious for being incredibly complex to set up and manage... ZoomInfo is widely criticized in industry circles for aggressive, multi-year auto-renewal clauses” Gemini 3.5 Flash · negative prompt · hard negative
“powerful, but they often require annual commitments ranging from $2,500 to over $10,000” Gemini 3.5 Flash · budget prompt · soft negative
“May be over-featured and expensive for smaller mid-market companies” Kimi K2 · direct prompt · soft negative
“I'd recommend starting with Sweep or RingLead if you're on Salesforce” MiniMax M2.5 · paraphrase prompt · first choice
“Best if you want a broader revenue-ops / routing / account data stack, not just dedupe: ZoomInfo Operations” GPT-5.4 mini · direct prompt · alternative
“designed for B2B revenue teams needing ongoing enrichment, deduplication, and compliance in one platform” Perplexity Sonar · direct 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.