Five of fourteen models named Oracle Data Masking and Subsetting first on the direct prompt; one named Delphix. Oracle Data Masking and Subsetting was named by ten of the fourteen models and Delphix by fourteen and Oracle Data Masking and Subsetting carries 22 labels and Delphix 37, so the shares are not directly comparable.
Named in one category this edition.
Named in one category 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 data masking and test data management page.
Across every category in the October 2026 Edition, Oracle Data Masking and Subsetting and Delphix were named in the same answer forty-six times, of the 69 answers naming Oracle Data Masking and Subsetting and the 103 naming Delphix. In those answers Delphix took the first choice twelve times and Oracle Data Masking and Subsetting five.
| 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 seven in this category shown.
“Oracle Data Masking and Subsetting is native to Oracle Enterprise Manager and only sensible if your estate is Oracle. Using it outside Oracle adds integration risk and limited coverage.” Muse Glimmer 30B · negative prompt · soft negative
“Best only if you're already an Oracle shop; not flexible for non-Oracle databases” DeepSeek V4 Flash · negative prompt · soft negative
“Great if locked-in, but can't handle hybrid/multi-cloud without extras.” Grok 4.1 Fast · negative prompt · soft negative
“I'd suggest starting with Oracle Data Masking and Subsetting if you're in an Oracle environment, or Delphix if you have a multi-database environment” MiniMax M2.5 · direct prompt · first choice
“Oracle Data Masking and Subsetting is the best pick from these results for a typical mid-market B2B company” Perplexity Sonar · direct prompt · first choice
“Start with Oracle Data Masking if you're in an Oracle ecosystem—explicitly called best for mid-size enterprises” Grok 4.1 Fast · direct prompt · first choice
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Six of eight in this category shown.
“What to Avoid (Unless You Have Enterprise Budget) ... Excellent but expensive” Kimi K2 · paraphrase prompt · hard negative
“Delphix (Perforce) - while it is a comprehensive tool, it focuses on test data management and data virtualization as much as masking itself.” Llama 4 Maverick · negative prompt · soft negative
“without the steep learning curve and infrastructure costs associated with enterprise giants like Delphix or IBM InfoSphere” Qwen 3.7 Flash · paraphrase prompt · soft negative
“or Delphix if you have a multi-database environment. Both offer the right balance of features, scalability, and pricing for mid-market needs.” MiniMax M2.5 · direct prompt · first choice
“Go with Delphix if your bottleneck is database provisioning speed and your infrastructure budget allows for heavy virtualization tooling.” Gemini 3.5 Flash · comparative prompt · first choice
“Choose Delphix or Informatica for broad database support + DevOps/test data pipelines with virtualization and policy automation.” 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.