Three of fourteen models named Workday Adaptive Planning first on the direct prompt; one named Cube. Workday Adaptive Planning was named by ten of the fourteen models and Cube by eleven and Workday Adaptive Planning carries 20 labels and Cube 26, so the shares are not directly comparable.
Named in seven 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.
Across every category in the November 2026 Edition, Workday Adaptive Planning and Cube were named in the same answer 175 times, of the 498 answers naming Workday Adaptive Planning and the 352 naming Cube. In those answers Cube took the first choice thirty times and Workday Adaptive Planning fourteen.
| 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. One of one in this category shown.
“Skip enterprise platforms like Anaplan, OneStream, Oracle EPM, or Workday Adaptive Planning” DeepSeek V4 Flash · budget prompt · hard negative
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.