Zero of twelve models named BILL Spend & Expense first on the direct prompt; two named Airbase. Both were named by all twelve models and BILL Spend & Expense carries 38 labels and Airbase 19, so the shares are not directly comparable.
Named in five categories this edition.
Named in seven 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 cards page.
Across every category in the September 2026 Edition, BILL Spend & Expense and Airbase were named in the same answer sixty-six times, of the 334 answers naming BILL Spend & Expense and the 167 naming Airbase. In those answers Airbase took the first choice nine times and BILL Spend & Expense four.
| 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. Two of two in this category shown.
“This one comes up repeatedly in negative reviews... Reports of funds being pulled/frozen without clear warning” DeepSeek V4 Flash · negative prompt · hard negative
“Ramp or BILL Spend & Expense are the top choices because they offer robust corporate card programs and expense management tools completely free” Kimi K2 · budget prompt · first choice
No label in this category carried a quote.
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.