Three of fourteen models named Dealfront first on the direct prompt; three named Factors.ai. Dealfront was named by fourteen of the fourteen models and Factors.ai by ten and Dealfront carries 37 labels and Factors.ai 13, so the shares are not directly comparable.
Named in ten categories this edition.
Named in eleven 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 B2B intent data providers 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. Two of two in this category shown.
“For a mid-sized B2B company, Dealfront offers the best balance of intent visibility, mid-market focus, and affordability.” GLM 4.7 FlashX · paraphrase prompt · first choice
“combine Apollo.io (to find the right accounts) with Leadfeeder (to verify interest)” Qwen 3.7 Flash · budget 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.
“If I had to pick one, I'd shortlist Factors.ai Lite: it's listed at $199/month after a trial” GPT-6 Luna · budget 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.