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Index › Revenue operations › CRM data quality › Cleanlist vs DataGroomr
CRM data quality and deduplication · October 2026 Edition

Cleanlist vs DataGroomr

Three of fourteen models named Cleanlist first on the direct prompt; zero named DataGroomr. Cleanlist was named by ten of the fourteen models and DataGroomr by nine and Cleanlist carries 28 labels and DataGroomr 15, so the shares are not directly comparable.

Cleanlist

accepted challenger

Named in three categories this edition.

DataGroomr

accepted challenger

Named in two categories this edition.

First-choice share10%2%Of first choices across the direct, paraphrase, budget and scale prompts, 0 to 100.
Negative rate0%0%Negative labels as a share of the product's labels, 0 to 100.
Rank in category#4#6A position in a field of 11; printed, not drawn.
Labels2815A count; the two differ.
The two percentage rows are drawn on one 0 to 100 track, Cleanlist reading right to left. Rank and label count are printed, not drawn.DemandTools was named alongside these two in twelve of the fourteen direct answers. Insycle vs Cleanlist · Insycle vs DataGroomr · Dedupely vs Cleanlist

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.
CleanlistFirst choices, of fourteen modelsDataGroomr
Direct30
Paraphrase00
Comparative13
Budget-constrained21
Scale-constrained00
Negative11
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 Cleanlist and DataGroomr stood in it.
ModelDirectParaphraseComparativeBudget-constrainedScale-constrainedNegative
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
Cleanlist DataGroomr first choice named as an alternative argued againstblank: not namedEach cell is one answer, Cleanlist on the left and DataGroomr on the right.

The direct prompt

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

Cleanlist first, DataGroomr not the choice

3 of 14 modelsDataGroomr was named in the answer but not as the choice, or not at all.
Perplexity SonarCleanlist alternatives: DemandTools, HubSpot Operations Hub, ZoomInfo Operations
Mistral SmallCleanlist alternatives: Cloudingo, DemandTools, WinPure Clean & Match
Llama 4 MaverickCleanlist alternatives: DemandTools, HubSpot Operations Hub, Salesforce Duplicate Management

Neither was the first choice, one was named

4 of 14 modelsThe answer put something else first and named one of the two as an alternative.
Grok 4.1 FastInsycle alternatives: Cleanlist, Cloudingo, DemandTools
DeepSeek V4 FlashInsycle alternatives: Cleanlist, DemandTools
MiniMax M2.5Tofu alternatives: Cleanlist, Cloudingo, Insycle
Muse Glimmer 30BCloudingo, Insycle alternatives: Cleanlist, DemandTools

Neither was named

7 of 14 modelsThe answer made no first choice from these two in this category.
Claude Haiku 4.5DemandTools alternatives: Cloudingo, Dedupely, HubSpot's native duplicate management tool, Integrate.io, Openprise, ZoomInfo
GPT-5.4 miniInsycle alternatives: Cloudingo, DemandTools, ZoomInfo Operations
Gemini 3.5 FlashInsycle alternatives: Cloudingo, DemandTools, Koalify
Qwen 3.7 FlashDatagma alternatives: Apption, FullContact, HubSpot Breeze Intelligence
Kimi K2DemandTools, Insycle
GLM 4.7 FlashXInsycle alternatives: Cloudingo, DemandTools, MatchLogic, ZoomInfo Operations
GPT-6 LunaInsycle alternatives: Cloudingo, DemandTools, HubSpot"s native data-quality tools

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
Cleanlist leads by seven points.
Cleanlist9%#3 of 13
DataGroomr2%#7 of 13
The full small business standing →
Mid-marketThe figures above
Cleanlist leads by eight points.
Cleanlist10%#4 of 11
DataGroomr2%#6 of 11
The full mid-market standing →
Enterprise
Level: the same share of first choices.
Cleanlist0%#9 of 11
DataGroomr0%#– of 11
The full enterprise standing →

What the models said about Cleanlist

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

“Cleanlist stands out as the best overall solution due to its affordability, ease of use, and comprehensive feature set” Mistral Small · direct prompt · first choice
“the best data quality tools for RevOps and CRM teams in 2026 are Cleanlist for verified enrichment and cleansing” Muse Glimmer 30B · comparative prompt · first choice
“The best CRM data quality and deduplication tool for a mid-market B2B company is Cleanlist” Llama 4 Maverick · direct prompt · first choice

What the models said about DataGroomr

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

“Choose DataGroomr if you want a set-and-forget AI solution that minimizes administrative overhead.” Qwen 3.7 Flash · comparative prompt · first choice
“DataGroomr is best for Salesforce users seeking AI-driven, automated data quality and enrichment.” Mistral Small · comparative prompt · first choice
“look at cloud-native, user-friendly tools like Cloudingo, Insycle, or DataGroomr” Gemini 3.5 Flash · negative prompt · first choice
Also compared

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.