Three of twelve models named Absorb LMS first on the direct prompt; zero named Emtrain. Absorb LMS was named by eleven of the twelve models and Emtrain by ten and Absorb LMS carries 17 labels and Emtrain 16, so the shares are not directly comparable.
Named in six categories this edition.
Named in one category 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 compliance training 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.
No label in this category carried a quote.
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Four of five in this category shown.
“Rated 2.3/5 stars on Trustpilot, with complaints about deteriorating customer service and cumbersome user interface” GLM 4.7 FlashX · negative prompt · hard negative
“Emtrain (Best Overall for Culture & Analytics)” GLM 4.7 FlashX · paraphrase prompt · first choice
“Reputable alternatives (high-rated in comparisons): Traliant, Emtrain, Ethena, BizLibrary, Vector Solutions” Grok 4.1 Fast · negative prompt · alternative
“Highly interactive, scenario-based training: SHRM or a provider like Emtrain may be better suited.” GPT-5.4 mini · paraphrase prompt · alternative
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.