Pigment vs Planful
Eight of fourteen models named Pigment first on the direct prompt; three named Planful. Both were named by all fourteen models and Pigment carries 44 labels and Planful 54, so the shares are not directly comparable.
Named in eight categories this edition.
Named in ten 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 FP&A platforms page.
By framing
PigmentFirst choices, of fourteen modelsPlanful
Across every category in the November 2026 Edition, Pigment and Planful were named in the same answer 134 times, of the 230 answers naming Pigment and the 402 naming Planful. In those answers Planful took the first choice thirteen times and Pigment twenty-five.
Every model, every framing
| 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 | | | | |
Pigment Planful first choice named as an alternative argued againstblank: not namedEach cell is one answer, Pigment on the left and Planful on the right.The direct prompt
Pigment first, Planful an alternative
8 of 14 modelsPlanful was named in the answer but not as the choice, or not at all.Planful first, Pigment an alternative
3 of 14 modelsPigment was named in the answer but not as the choice, or not at all.Neither was the first choice, one was named
3 of 14 modelsThe answer put something else first and named one of the two as an alternative.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.
By buyer segment
Pigment19%#1 of 14Planful7%#5 of 14What the models said about Pigment
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Six of seven in this category shown.
“65% of FP&A professionals raising concerns about this platform... substantial negative sentiment among practitioners.” Claude Haiku 4.5 · negative prompt · hard negative
“Avoid the mid-market "contact us" vendors (Anaplan, Workday Adaptive Planning, Pigment, Planful)” Kimi K2 · budget prompt · hard negative
“What to Avoid on a Limited Budget ... Pigment | ~$74,000/year” DeepSeek V4 Flash · budget prompt · hard negative
“If you want the safest all-around choice for mid-market B2B, Pigment offers the best balance of power, modern UI, and scalability.” DeepSeek V4 Flash · direct prompt · first choice
“For most mid-market B2B companies, Pigment offers the best balance of modern functionality, ease of use, and mid-market pricing.” GLM 4.7 FlashX · direct prompt · first choice
“or Pigment if you want the most comprehensive AI-native platform and have the budget for it” MiniMax M2.5 · direct prompt · first choice
What the models said about Planful
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Three of three in this category shown.
“60% of FP&A professionals expressed criticism about Planful. Users report that "the planning feature was almost impossible to use".” Claude Haiku 4.5 · negative prompt · hard negative
“Avoid the mid-market "contact us" vendors (Anaplan, Workday Adaptive Planning, Pigment, Planful)” Kimi K2 · budget prompt · hard negative
“Planful is best for horizontal mid-market and enterprise finance teams that value packaged AI features and integrated FP&A plus close, and is a G2 Leader” Claude Haiku 4.5 · direct 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.