Thirteen of fourteen models named Microsoft Power BI first on the direct prompt; zero named Qlik Sense. Both were named by all fourteen models and Microsoft Power BI carries 76 labels and Qlik Sense 38, so the shares are not directly comparable.
By Microsoft, Vancouver, Canada, founded 1975. Named in four categories this edition.
By Qlik, King of Prussia, United States, founded 1993. 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 dashboards and data visualization page.
Across every category in the November 2026 Edition, Microsoft Power BI and Qlik Sense were named in the same answer ninety-six times, of the 281 answers naming Microsoft Power BI and the 102 naming Qlik Sense. In those answers Qlik Sense took the first choice zero times and Microsoft Power BI fifty-one.
| 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.
“Extremely popular and cost-effective, but watch for specialist needs (like building complex models), reconciliation errors, and AI that doesn't always meet hype.” Grok 4.20 · negative prompt · soft negative
“Power BI deeply locks you into Microsoft and can struggle with performance and governance when querying massive, complex datasets” Claude Haiku 4.5 · negative prompt · soft negative
“Power BI, for example, has meaningful differences between Free, Pro, and PPU, and sharing/collaboration can require paid licenses” GPT-5.4 mini · negative prompt · soft negative
“The strongest general-purpose choices are Microsoft Power BI, Tableau, Looker, Qlik, ThoughtSpot, Sigma, Sisense, Domo, Metabase, and Zoho Analytics.” Perplexity Sonar · comparative prompt · first choice
“Microsoft Power BI is the most-deployed mid-market BI platform globally... Power BI is the lowest-friction, cost-predictable default.” Muse Glimmer 30B · direct prompt · first choice
“Default / cost-effective for most mid-sized B2B: Microsoft Power BI. Strong Microsoft integration, Excel familiarity, low entry price” Muse Glimmer 30B · 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.
“Proprietary tools with limited data export options (e.g., some Qlik or Tableau deployments) can make migration difficult later.” MiniMax M2.5 · negative prompt · soft negative
“Products such as Sisense, Domo, and Qlik may be capable, but quote-led pricing makes total cost and renewal exposure harder to assess.” Perplexity Sonar · negative prompt · soft negative
“Sigma, Looker, Amazon Quick Sight and Qlik's agentic features are commonly flagged for extra due diligence.” Muse Glimmer 30B · negative prompt · soft negative
“Qlik Sense (if you need strong data exploration)” GLM 4.7 FlashX · scale prompt · first choice
“Choose Qlik if associative exploration, complex data discovery, and minimizing dependence on a single cloud ecosystem are priorities.” Perplexity Sonar · paraphrase prompt · alternative
“Choose Qlik Sense if you want to discover hidden relationships in your data through associative analysis” Kimi K2 · 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.