Four of twelve models named Segment first on the direct prompt; one named Hightouch. Segment was named by nine of the twelve models and Hightouch by eight and Segment carries 22 labels and Hightouch 14, so the shares are not directly comparable.
By Twilio, San Francisco, United States, founded 2008. Named in nine categories this edition.
Named in six 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 customer intelligence and data science page.
Across every category in the September 2026 Edition, Segment and Hightouch were named in the same answer 248 times, of the 532 answers naming Segment and the 457 naming Hightouch. In those answers Hightouch took the first choice sixty-five times and Segment fifty-two.
| 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.
“Cost escalates quickly with MTU-based pricing; Identity resolution inconsistencies reported” Kimi K2 · negative prompt · hard negative
“Do not buy a CDP unless you already have analytics tools” Gemini 3.5 Flash · negative prompt · hard negative
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.