Zero of fourteen models named PriceLabs first on the direct prompt; one named PROS Smart Price Optimization & Management. PriceLabs was named by ten of the fourteen models and PROS Smart Price Optimization & Management by thirteen and PriceLabs carries 23 labels and PROS Smart Price Optimization & Management 42, so the shares are not directly comparable.
Named in one category this edition.
Named in one category 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 pricing optimization page.
Across every category in the November 2026 Edition, PriceLabs and PROS Smart Price Optimization & Management were named in the same answer nineteen times, of the 54 answers naming PriceLabs and the 130 naming PROS Smart Price Optimization & Management. In those answers PROS Smart Price Optimization & Management took the first choice three times and PriceLabs one.
| Model | DirectPRPS | ParaphrasePRPS | ComparativePRPS | Budget-constrainedPRPS | Scale-constrainedPRPS | NegativePRPS |
|---|---|---|---|---|---|---|
| Claude Haiku 4.5 | ||||||
| GPT-5.4 mini | PS | PS | ||||
| Gemini 3.5 Flash | PS | PS | PS | |||
| Perplexity Sonar | PS | PS | PRPS | |||
| Grok 4.20 | PRPS | PRPS | PS | PR | PRPS | |
| Mistral Small | PS | PS | PRPS | PR | PS | |
| DeepSeek V4 Flash | PS | PS | PRPS | PR | PR | |
| Llama 4 Maverick | PR | PS | ||||
| Qwen 3.7 Flash | PS | PR | PR | PS | ||
| Kimi K2 | PS | PS | PRPS | PS | PR | |
| GLM 4.7 FlashX | PS | PS | PS | PR | PS | |
| MiniMax M2.5 | PS | PS | PS | PR | PS | |
| GPT-6 Luna | PS | |||||
| Muse Glimmer 30B | PS | PS | PRPS | PR | PR |
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. Six of eight in this category shown.
“Avoid retail/hospitality-focused tools like PriceLabs or Omnia unless your B2B overlaps there.” Grok 4.20 · direct prompt · hard negative
“Avoid pure e-commerce tools: Options like PriceLabs, PriceShape ... are less ideal for B2B” Grok 4.20 · paraphrase prompt · hard negative
“Mixed but notable complaints on Trustpilot include syncing errors... Test extensively if using for dynamic pricing.” Grok 4.20 · negative prompt · soft negative
“PriceLabs (4.9/5, 250 reviews) — Best for vacation rentals/hospitality, transparent starting price of $9.99/month” DeepSeek V4 Flash · negative prompt · first choice
“PriceLabs is highly recommended for small businesses, with a starting price as low as $9.99/month” Mistral Small · budget prompt · first choice
“PriceLabs stands out as the best pricing optimization platform for companies with a limited budget” Grok 4.20 · budget prompt · first choice
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Six of eight in this category shown.
“Other platforms that have been criticized include PROS Smart Price Optimization & Management, Zilliant, and Competera.” Llama 4 Maverick · negative prompt · hard negative
“It's recommended to avoid unless absolutely necessary due to high costs and complexity.” Mistral Small · negative prompt · hard negative
“you should generally avoid established enterprise giants like Vendavo or PROS” GLM 4.7 FlashX · negative prompt · hard negative
“Large enterprise with complex B2B pricing? → PROS, Vendavo, or Vistaar depending on whether you prioritize optimization science (PROS)” DeepSeek V4 Flash · comparative prompt · first choice
“Best for: Large enterprises with complex dynamic pricing... Deep optimization science with decades of AI pedigree” Kimi K2 · comparative prompt · first choice
“Pricefx and PROS are often the top two to evaluate” MiniMax M2.5 · 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.