One of twelve models named HiBob first on the direct prompt; zero named Leapsome. HiBob was named by eleven of the twelve models and Leapsome by eleven and HiBob carries 23 labels and Leapsome 31, so the shares are not directly comparable.
Named in sixteen categories this edition.
Named in thirteen 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 performance management 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 |
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.
“Best for mid-sized teams running structured reviews... a smart, modern choice if you want to level up reviews without adding another separate tool” Claude Haiku 4.5 · paraphrase prompt · first choice
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Two of two in this category shown.
“Leapsome: has a real admin learning curve and thinner native integrations than peers.” Llama 4 Maverick · negative prompt · soft negative
“Leaders in this category typically include Lattice, Culture Amp, 15Five, Leapsome, and PerformYard.” Gemini 3.5 Flash · scale 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.