Zero of fourteen models named Microsoft Excel first on the direct prompt; one named Cube. Microsoft Excel was named by thirteen of the fourteen models and Cube by eleven and Microsoft Excel carries 19 labels and Cube 26, so the shares are not directly comparable.
Named in thirteen categories this edition.
Named in nine 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 financial modeling and scenario planning page.
| 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. Three of three in this category shown.
“For the absolute best value, Microsoft Excel is the top choice due to its low cost (or free if already owned) and unmatched flexibility.” Mistral Small · budget prompt · first choice
“Microsoft Excel is widely regarded as the best financial modeling software for companies with a limited budget.” Grok 4.20 · budget prompt · first choice
“The most cost-effective starting point. Many successful companies use Excel through their early stages.” Kimi K2 · budget prompt · first choice
No label in this category carried a quote.
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.