Two of fourteen models named Pigment first on the direct prompt; three named Workday Adaptive Planning. Pigment was named by thirteen of the fourteen models and Workday Adaptive Planning by ten and Pigment carries 23 labels and Workday Adaptive Planning 20, so the shares are not directly comparable.
Named in eight categories this edition.
Named in seven 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, Pigment and Workday Adaptive Planning were named in the same answer 176 times, of the 230 answers naming Pigment and the 498 naming Workday Adaptive Planning. In those answers Workday Adaptive Planning took the first choice eleven times and Pigment twenty-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.
No label in this category carried a quote.
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
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.