Six of fourteen models named Hightouch first on the direct prompt; zero named RudderStack. Hightouch was named by fourteen of the fourteen models and RudderStack by thirteen and Hightouch carries 42 labels and RudderStack 31, so the shares are not directly comparable.
Named in six 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 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 customer data 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.
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. One of one in this category shown.
“Hightouch if your warehouse data model is immature or your team lacks SQL resources, because setup can stall before activation work begins.” Perplexity Sonar · negative prompt · soft negative
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Two of two in this category shown.
“RudderStack is my default pick for a tight budget if you have technical support” GPT-6 Luna · budget prompt · first choice
“Just starting out? → Twilio Segment's free tier or RudderStack's free tier” Kimi K2 · 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.