Two of twelve models named Pigment first on the direct prompt; zero named Anaplan. Pigment was named by ten of the twelve models and Anaplan by twelve and Pigment carries 21 labels and Anaplan 41, so the shares are not directly comparable.
Named in six categories this edition.
Named in six categories this edition.
Share is the count of first choices across the direct, paraphrase, budget and scale prompts over all twelve 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 corporate performance management page.
Across every category in the September 2026 Edition, Pigment and Anaplan were named in the same answer 156 times, of the 186 answers naming Pigment and the 438 naming Anaplan. In those answers Anaplan took the first choice twenty-two times and Pigment twenty-two.
| 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 |
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. Two of two in this category shown.
“you generally want to avoid heavy enterprise platforms like Anaplan or Oracle (which cost tens of thousands annually)” Qwen 3.7 Flash · budget prompt · hard negative
“Anaplan or Workday Adaptive Planning are the top recommendations due to their scalability, strong FP&A features” Mistral Small · paraphrase prompt · first choice
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.