Five of fourteen models named UiPath first on the direct prompt; zero named Automation Anywhere. Both were named by all fourteen models and UiPath carries 71 labels and Automation Anywhere 63, so the shares are not directly comparable.
Named in five categories this edition.
Named in four 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 robotic process automation page.
Across every category in the November 2026 Edition, UiPath and Automation Anywhere were named in the same answer 183 times, of the 231 answers naming UiPath and the 240 naming Automation Anywhere. In those answers Automation Anywhere took the first choice zero times and UiPath seventy-nine.
| Model | Direct | Paraphrase | Comparative | Budget-constrained | Scale-constrained | Negative |
|---|---|---|---|---|---|---|
| Claude Haiku 4.5 | ||||||
| GPT-5.4 mini | ||||||
| Gemini 3.5 Flash | ||||||
| Perplexity Sonar | ||||||
| Grok 4.20 | ||||||
| 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 |
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.
“Some AI models explicitly recommend avoiding UiPath unless you have a clear, large-scale need and budget” Mistral Small · negative prompt · hard negative
“Avoid if: You are a small team with limited budget → Stay away from UiPath” Qwen 3.7 Flash · negative prompt · hard negative
“Enterprise-grade leaders such as UiPath continue to dominate overall RPA market share and enterprise rankings, but they are most often positioned for enterprise with dedicated automation teams.” Muse Glimmer 30B · direct prompt · soft negative
“UiPath offers a user-friendly drag-and-drop interface with comprehensive toolset and strong community support, though it has high costs and a complex licensing model” Claude Haiku 4.5 · comparative prompt · first choice
“UiPath and Microsoft Power Automate are the top recommendations due to their scalability, strong feature sets, and integration capabilities” Mistral Small · paraphrase prompt · first choice
“You want the market leader with proven scalability and AI | UiPath | Best overall feature set, strong community, and clear path to scale.” GLM 4.7 FlashX · paraphrase prompt · first choice
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Six of six in this category shown.
“often criticized for being targeted at large enterprises and may not be suitable for smaller businesses” Mistral Small · negative prompt · hard negative
“Has faced criticism for pricing complexity and occasional performance issues in large enterprises, though it remains a major player.” MiniMax M2.5 · negative prompt · soft negative
“Review summaries repeatedly mention pricing complexity, steep learning curve, and debugging / integration issues.” GPT-5.4 mini · negative prompt · soft negative
“focus your evaluation on established enterprise leaders with broad ecosystems: UiPath, Microsoft Power Automate, and Automation Anywhere” Gemini 3.5 Flash · negative prompt · first choice
“a balanced shortlist is often UiPath / Automation Anywhere for scale and AI capabilities” Muse Glimmer 30B · scale prompt · first choice
“provides powerful cognitive automation with extensive bot store, strong AI/ML integration, and high scalability, but has a steeper learning curve and high costs” Claude Haiku 4.5 · comparative 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.