Eleven of fourteen models named Microsoft Power BI first on the direct prompt; zero named Domo. Microsoft Power BI was named by fourteen of the fourteen models and Domo by eleven and Microsoft Power BI carries 71 labels and Domo 24, so the shares are not directly comparable.
By Microsoft, Vancouver, Canada, founded 1975. Named in five categories this edition.
American Fork, United States, founded 2010. Named in five 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 dashboards and data visualization page.
Across every category in the October 2026 Edition, Microsoft Power BI and Domo were named in the same answer sixty-one times, of the 266 answers naming Microsoft Power BI and the 78 naming Domo. In those answers Domo took the first choice zero times and Microsoft Power BI thirty-three.
| 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 eight in this category shown.
“Dataset limits and performance: Pro tier caps at 1GB, slows on large data/DAX queries... When to avoid: Non-Microsoft stacks, massive datasets” Grok 4.1 Fast · negative prompt · soft negative
“If you are outside the Microsoft ecosystem, need platform-agnostic deployment, or don't want to invest in DAX and premium-capacity administration.” Perplexity Sonar · negative prompt · soft negative
“Why to be cautious: Power BI is incredibly powerful and cost-effective, but mostly if you are already in a Microsoft/Azure environment.” Gemini 3.5 Flash · negative prompt · soft negative
“Microsoft Shop? ... $\rightarrow$ Power BI. It integrates natively, has deep Excel integration ... usually the cheapest option” Qwen 3.7 Flash · scale prompt · first choice
“Microsoft Power BI — *Best Overall Value* ... Start with Power BI if you're cost-conscious and in the Microsoft ecosystem” Kimi K2 · direct prompt · first choice
“Power BI is almost always the default recommendation. It is widely considered the industry standard for mid-market companies” Gemini 3.5 Flash · paraphrase 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.
“long-standing complaints about consumption-based credit pricing that makes annual cost hard to predict” Muse Glimmer 30B · negative prompt · hard negative
“If you need strict data sovereignty or already have a strong warehouse and don't want to duplicate data into another cloud.” Perplexity Sonar · negative prompt · soft negative
“advises small-to-medium businesses to avoid these unless they have dedicated data teams and large budgets” MiniMax M2.5 · negative prompt · soft negative
“Domo - a strong fit for mid-to-large organizations that need an all-in-one, self-service cloud analytics platform with broad integrations.” Llama 4 Maverick · paraphrase prompt · first choice
“Cloud-native BI plus data app platform. Strong mobile experience. But requires a bigger budget commitment.” DeepSeek V4 Flash · direct prompt · alternative
“Domo is particularly well aligned with mid-market organizations focused on self-service analytics” Muse Glimmer 30B · direct 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.