Zero of twelve models named Tipalti first on the direct prompt; one named Stampli. Tipalti was named by seven of the twelve models and Stampli by eight and Tipalti carries 12 labels and Stampli 10, so the shares are not directly comparable.
Named in twelve categories this edition.
Named in eight 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 procure-to-pay page.
Across every category in the September 2026 Edition, Tipalti and Stampli were named in the same answer 118 times, of the 465 answers naming Tipalti and the 152 naming Stampli. In those answers Stampli took the first choice twelve times and Tipalti nineteen.
| 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.
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. One of one in this category shown.
“Tipalti: The gold standard for global AP automation, multi-currency payouts, and vendor tax compliance.” Gemini 3.5 Flash · scale prompt · first choice
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. One of one in this category shown.
“Best as a focused AP add\u2011on, not a full P2P replacement.” GLM 4.7 FlashX · direct prompt · soft 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.