Five of fourteen models named Apollo.io first on the direct prompt; two named Klue. Apollo.io was named by thirteen of the fourteen models and Klue by fourteen and Apollo.io carries 30 labels and Klue 39, so the shares are not directly comparable.
San Francisco, United States, founded 2015. Named in twenty-two categories this edition.
Named in two 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 market intelligence platforms 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 4.20 | ||||||
| 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.
No label in this category carried a quote.
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Three of three in this category shown.
“flag them as "avoid if on a limited budget" because they typically start at $25K–$30K+ per year” Qwen 3.7 Flash · negative prompt · hard negative
“Avoid platforms such as ZoomInfo, AlphaSense, Crayon, Klue, and 6sense” Perplexity Sonar · budget prompt · hard negative
“Klue – best for sales-facing CI + battlecards... If you need sales reps to have up-to-date battlecards, Klue or Crayon is the reference point.” Muse Glimmer 30B · comparative 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.