WorkRamp vs LearnWorlds
Two of twelve models named WorkRamp first on the direct prompt; zero named LearnWorlds. WorkRamp was named by ten of the twelve models and LearnWorlds by ten and WorkRamp carries 26 labels and LearnWorlds 30, so the shares are not directly comparable.
Named in two 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 Customer education and LMS page.
By framing
WorkRampFirst choices, of twelve modelsLearnWorlds
Across every category in the September 2026 Edition, WorkRamp and LearnWorlds were named in the same answer thirty-five times, of the 67 answers naming WorkRamp and the 90 naming LearnWorlds. In those answers LearnWorlds took the first choice nine times and WorkRamp two.
Every model, every framing
| 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 | | |
WorkRamp LearnWorlds first choice named as an alternative argued againstblank: not namedEach cell is one answer, WorkRamp on the left and LearnWorlds on the right.The direct prompt
WorkRamp first, LearnWorlds an alternative
2 of 12 modelsLearnWorlds was named in the answer but not as the choice, or not at all.Neither was the first choice, one was named
7 of 12 modelsThe answer put something else first and named one of the two as an alternative.Neither was named
3 of 12 modelsThe answer made no first choice from these two in this category.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.
By buyer segment
LearnWorlds35%#2 of 12WorkRamp0%#9 of 12WorkRamp7%#4 of 13LearnWorlds2%#6 of 13WorkRamp0%#9 of 10LearnWorlds0%#10 of 10What the models said about WorkRamp
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Six of eight in this category shown.
“WorkRamp, listed in several rival roundups, currently redirects to a different company's product page (Confirm's "Learn:Up"), which fails identity-verification standards.” Claude Haiku 4.5 · negative prompt · hard negative
“Weak and inflexible reporting - Most common complaint; "not very robust," hard to use” Kimi K2 · negative prompt · hard negative
“Proceed with Caution: WorkRamp ... its acquisition by Learning Pool has introduced transition uncertainty” Gemini 3.5 Flash · negative prompt · soft negative
“Tier 2: The Mid-Market All-Rounders (Highly recommended for 500-person companies)... WorkRamp: An exceptional "hybrid" LMS.” Gemini 3.5 Flash · scale prompt · first choice
“For most mid-market B2B companies, Skilljar, WorkRamp, or Northpass ... would be the strongest choices” MiniMax M2.5 · direct prompt · first choice
“For most mid-market B2B SaaS companies, Northpass or WorkRamp hit the sweet spot” Kimi K2 · direct prompt · first choice
What the models said about LearnWorlds
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Three of four in this category shown.
“For the best overall value: LearnWorlds at $24-29/month offers the most features for the price” Mistral Small · budget prompt · first choice
“SMB-friendly starting at $24-29/month, modern UX, good e-commerce features” DeepSeek V4 Flash · negative prompt · first choice
“LearnWorlds ($24-299/month) - Good balance of features and price” Mistral Small · negative 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.