Zero of fourteen models named Testlify first on the direct prompt; zero named CodeSignal. Testlify was named by nine of the fourteen models and CodeSignal by eight and Testlify carries 12 labels and CodeSignal 13, so the shares are not directly comparable.
Named in two categories this edition.
Named in four 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 candidate assessment 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 | ||||||
| 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. Two of two in this category shown.
“starts at $198/month, but a bit pricier” GPT-5.4 mini · budget prompt · soft negative
“Testlify is the clearest pick from these results because it combines a very low entry price with a large test library” Perplexity Sonar · budget prompt · first choice
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. One of one in this category shown.
“The best skills testing tool for hiring for a mid-sized B2B company would be CodeSignal, TestGorilla, or Criteria Corp.” Llama 4 Maverick · paraphrase 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.