One of fourteen models named Clay first on the direct prompt; zero named Lusha. Both were named by all fourteen models and Clay carries 37 labels and Lusha 45, so the shares are not directly comparable.
Named in eleven categories this edition.
By Lusha Systems, Boston, United States, founded 2016. Named in five 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 data enrichment page.
Across every category in the October 2026 Edition, Clay and Lusha were named in the same answer ninety times, of the 234 answers naming Clay and the 340 naming Lusha. In those answers Lusha took the first choice two times and Clay six.
| 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. Six of seven in this category shown.
“multi-provider setups built with tools such as Apollo’s waterfall feature or Clay... may make it harder to trace where a particular field came from” GPT-6 Luna · negative prompt · soft negative
“Don't adopt Clay unless you have technical staff to manage workflows.” DeepSeek V4 Flash · negative prompt · soft negative
“requires 3 to 4 additional tools and has unpredictable credit costs” Claude Haiku 4.5 · comparative prompt · soft negative
“Choose Clay if you have a modern, tech-savvy sales/marketing team ... wants the best possible email-finding coverage” Gemini 3.5 Flash · paraphrase prompt · first choice
“Clay (Best for Workflow Orchestration & Waterfall Enrichment): Clay is a market leader for modern GTM teams.” Gemini 3.5 Flash · scale prompt · first choice
“If your strategy is personalized outbound and you want maximum data coverage: Go with Clay.” Gemini 3.5 Flash · comparative prompt · first choice
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Five of five in this category shown.
“Low email accuracy: Independent checks suggest only about 68% email return at roughly 82% accuracy, which is below industry standards.” Mistral Small · negative prompt · hard negative
“Tools that have faced regulatory actions (e.g., Lusha's Italy fine) should be avoided in sensitive jurisdictions.” GLM 4.7 FlashX · negative prompt · hard negative
“Some users report issues with outdated contact information and relatively high pricing for infrequent use.” Claude Haiku 4.5 · negative prompt · soft negative
“Safer bets from reviews: Tools like Lusha (98% email accuracy, free tier)” Grok 4.1 Fast · negative prompt · first choice
“Apollo.io or Lusha are the right entry points. Both offer free tiers” Claude Haiku 4.5 · 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.