Five of fourteen models named BILL first on the direct prompt; zero named Wave. BILL was named by thirteen of the fourteen models and Wave by fourteen and BILL carries 25 labels and Wave 37, so the shares are not directly comparable.
Named in fifteen categories this edition.
Named in seventeen 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 invoicing software page.
| 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 | ||||||
| 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.
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. One of one in this category shown.
“BILL and Invoiced are the top recommendations due to their advanced automation, AI capabilities, and scalability.” Mistral Small · direct prompt · first choice
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Two of two in this category shown.
“Use with Extreme Caution: Wave (only for small, low-risk transactions; avoid for high-volume/high-value needs)” Qwen 3.7 Flash · negative prompt · hard negative
“Payment holds \u2013 funds can be frozen with extensive documentation demands” Kimi K2 · negative 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.