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Index › GTM › Revenue operations › CRM data quality › ZoomInfo vs OpenRefine
CRM data quality and deduplication · November 2026 Edition

ZoomInfo vs OpenRefine

Zero of fourteen models named ZoomInfo first on the direct prompt; zero named OpenRefine. ZoomInfo was named by nine of the fourteen models and OpenRefine by ten and ZoomInfo carries 15 labels and OpenRefine 16, so the shares are not directly comparable.

ZoomInfo

accepted challenger

Vancouver, Washington, United States, founded 2000. Named in fifteen categories this edition.

OpenRefine

criticized challenger

Named in three categories this edition.

First-choice share2%2%Of first choices across the direct, paraphrase, budget and scale prompts, 0 to 100.
Negative rate20%44%Negative labels as a share of the product's labels, 0 to 100.
Rank in category#6#7A position in a field of 13; printed, not drawn.
Labels1516A count; the two differ.
The two percentage rows are drawn on one 0 to 100 track, ZoomInfo reading right to left. Rank and label count are printed, not drawn.Insycle was named alongside these two in fourteen of the fourteen direct answers. Insycle vs ZoomInfo · Insycle vs OpenRefine · Dedupely vs ZoomInfo

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.

By framing

How many of the fourteen models made each the first choice, per way of asking, and how many argued against it.
ZoomInfoFirst choices, of fourteen modelsOpenRefine
Direct00
Paraphrase101 against OpenRefine
Comparative101 against OpenRefine
Budget-constrained012 against ZoomInfo · 1 against OpenRefine
Scale-constrained00
Negative001 against ZoomInfo · 4 against OpenRefine
Bars are first choices, 0 to 14 each sideModels that argued againstA model can name both, so the two sides of a row do not sum to fourteen.

Every model, every framing

The eighty-four answers behind the chart above, one cell each: where ZoomInfo and OpenRefine stood in it.
ModelDirectParaphraseComparativeBudget-constrainedScale-constrainedNegative
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
ZoomInfo OpenRefine first choice named as an alternative argued againstblank: not namedEach cell is one answer, ZoomInfo on the left and OpenRefine on the right.

The direct prompt

The plain question, one answer per model, grouped by where ZoomInfo and OpenRefine stood in it.

Neither was named

14 of 14 modelsThe answer made no first choice from these two in this category.
Claude Haiku 4.5DemandTools alternatives: Cloudingo, Dedupely, Insycle, LeadAngel, ZoomInfo Operations
GPT-5.4 miniInsycle alternatives: Cloudingo, DemandTools, HubSpot Operations Hub
Gemini 3.5 FlashInsycle alternatives: Clay, DataGroomr, Dedupely, Validity
Perplexity SonarInsycle alternatives: Cloudingo, DemandTools, Openprise, Tofu, ZoomInfo OperationsOS
Grok 4.20Insycle alternatives: Dedupely, DemandTools
Mistral SmallInsycle
DeepSeek V4 FlashInsycle alternatives: Cloudingo, Dedupely, DemandTools
Llama 4 MaverickCleanlist alternatives: Dedupely, DemandTools, Findymail CRM Datacare, Insycle
Qwen 3.7 FlashCleanlist alternatives: Clay, Dedupely, DemandTools, Insycle
Kimi K2Insycle alternatives: Cloudingo, Dedupely, DemandTools
GLM 4.7 FlashXInsycle alternatives: Cleanlist, Cloudingo, Dedupely, DemandTools
MiniMax M2.5Insycle alternatives: Cleanlist, Cloudingo, Dedupely
GPT-6 LunaInsycle alternatives: Cloudingo, DemandTools
Muse Glimmer 30BInsycle alternatives: Dedupely, DemandTools

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

The same question asked on behalf of a different buyer. Each standing is computed within its segment and they are never added together. The figures above are the mid-market standing, which is the one the category orders by.
Small business
Level: the same share of first choices.
ZoomInfo0%#– of 11
OpenRefine0%#10 of 11
The full small business standing →
Mid-marketThe figures above
Level: the same share of first choices.
ZoomInfo2%#6 of 13
OpenRefine2%#7 of 13
The full mid-market standing →
Enterprise
ZoomInfo leads by two points.
ZoomInfo2%#9 of 11
OpenRefine0%#– of 11
The full enterprise standing →

What the models said about ZoomInfo

No label in this category carried a quote.

What the models said about OpenRefine

Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Six of seven in this category shown.

“Free Manual Tools (OpenRefine, Excel-based solutions) ... These tools create a false sense of security.” Kimi K2 · negative prompt · hard negative
“Avoid relying solely on manual export/import cycles using tools like Excel or OpenRefine” MiniMax M2.5 · negative prompt · hard negative
“OpenRefine is described as "Bulk deduplication and text normalization on exported CSV files" with the core limitations being "No CRM connection, no automation, no enrichment, learning curve required"” Muse Glimmer 30B · negative prompt · soft negative
“use free OpenRefine for occasional bulk cleanup” GPT-6 Luna · budget prompt · first choice
“OpenRefine (Completely Free): Best if you want zero cost and can handle manual processes.” Grok 4.20 · budget prompt · alternative
“For technical teams on zero budget: OpenRefine for one-time cleanup of exported data” Claude Haiku 4.5 · budget prompt · alternative
Also compared
Insycle vs ZoomInfoInsycle vs OpenRefineDedupely vs ZoomInfoDedupely vs OpenRefineDemandTools vs ZoomInfoDemandTools vs OpenRefineCleanlist vs ZoomInfoCleanlist vs OpenRefineZoomInfo Operations vs ZoomInfoZoomInfo Operations vs OpenRefineAll twenty-one head-to-head pages and the full CRM data quality standing

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.