Four of fourteen models named Microsoft Power Automate Process Mining first on the direct prompt; two named UiPath Process Mining. Microsoft Power Automate Process Mining was named by eleven of the fourteen models and UiPath Process Mining by fourteen and Microsoft Power Automate Process Mining carries 21 labels and UiPath Process Mining 50, 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 process mining page.
Across every category in the October 2026 Edition, Microsoft Power Automate Process Mining and UiPath Process Mining were named in the same answer forty-one times, of the 62 answers naming Microsoft Power Automate Process Mining and the 152 naming UiPath Process Mining. In those answers UiPath Process Mining took the first choice zero times and Microsoft Power Automate Process Mining seventeen.
| Model | DirectMP | ParaphraseMP | ComparativeMP | Budget-constrainedMP | Scale-constrainedMP | NegativeMP |
|---|---|---|---|---|---|---|
| Claude Haiku 4.5 | MP | |||||
| GPT-5.4 mini | ||||||
| Gemini 3.5 Flash | MP | MP | MP | MP | MP | |
| Perplexity Sonar | MP | MP | MP | |||
| Grok 4.1 Fast | MP | |||||
| Mistral Small | MP | |||||
| DeepSeek V4 Flash | MP | MP | ||||
| Llama 4 Maverick | ||||||
| Qwen 3.7 Flash | ||||||
| Kimi K2 | MP | MP | ||||
| GLM 4.7 FlashX | MP | |||||
| MiniMax M2.5 | MP | |||||
| GPT-6 Luna | MP | MP | MP | |||
| Muse Glimmer 30B | MP |
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.
“Avoid or be cautious with Microsoft Power Automate Process Mining if you expect high volumes” Mistral Small · negative prompt · hard negative
“I wouldn’t start with Microsoft Power Automate Process Mining if the budget is especially tight” GPT-6 Luna · budget prompt · soft negative
“it can carry heavy add-on licensing costs (such as a $5,000/month tenant capacity add-on)” Gemini 3.5 Flash · negative prompt · soft negative
“Best for Microsoft-Centric Companies: Microsoft Power Automate Process Mining ... this is the most seamless and cost-effective commercial option.” Gemini 3.5 Flash · budget prompt · first choice
“Best Overall & Best for Microsoft Ecosystems: Microsoft Power Automate Process Mining (formerly Minit)” Gemini 3.5 Flash · direct prompt · first choice
“UiPath Process Mining or Microsoft Power Automate Process Mining are the safest bets” DeepSeek V4 Flash · 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.
“standalone mining feels limited. Enterprise pricing ($24K–$200K+ annually reported) and integration complexity for non-RPA users.” Grok 4.1 Fast · negative prompt · soft negative
“Verdict: Be cautious, particularly if you need robust data connectivity or accurate process analytics out of the box.” DeepSeek V4 Flash · negative prompt · soft negative
“Caution: be cautious if you do not already use UiPath RPA, have messy/unstructured event logs and limited ETL capacity” Muse Glimmer 30B · negative prompt · soft negative
“I'd usually recommend UiPath Process Intelligence if you want the best balance of discovery, automation follow-through, and scalability” GPT-5.4 mini · paraphrase prompt · first choice
“UiPath Process Mining | Automation teams, RPA integration | $25/user/mo | 4.4/5” Grok 4.1 Fast · scale prompt · first choice
“Best overall for many mid-market B2B firms: UiPath Process Mining” GPT-5.4 mini · direct 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.