LearnWorlds vs Thinkific
Zero of fourteen models named LearnWorlds first on the direct prompt; zero named Thinkific. LearnWorlds was named by eleven of the fourteen models and Thinkific by ten and LearnWorlds carries 20 labels and Thinkific 22, so the shares are not directly comparable.
Named in two categories this edition.
Named in two 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 Customer education and LMS page.
By framing
LearnWorldsFirst choices, of fourteen modelsThinkific
Across every category in the October 2026 Edition, LearnWorlds and Thinkific were named in the same answer twenty-eight times, of the 83 answers naming LearnWorlds and the 70 naming Thinkific. In those answers Thinkific took the first choice four times and LearnWorlds four.
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 | | | | |||
| GPT-6 Luna | | |||||
| Muse Glimmer 30B | | |
LearnWorlds Thinkific first choice named as an alternative argued againstblank: not namedEach cell is one answer, LearnWorlds on the left and Thinkific on the right.The direct prompt
Neither was named
14 of 14 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
LearnWorlds18%#2 of 14Thinkific9%#3 of 14LearnWorlds6%#4 of 11Thinkific6%#5 of 11LearnWorlds2%#– of 10Thinkific2%#9 of 10What the models said about LearnWorlds
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Five of five in this category shown.
“LearnWorlds if you need a strong native CRM, enterprise controls, or live training at the center of the experience.” Perplexity Sonar · negative prompt · soft negative
“LearnWorlds gets mixed feedback for strong engagement tools locked behind pricier tiers, plus steeper learning curves.” Grok 4.1 Fast · negative prompt · soft negative
“or LearnWorlds if you're ready to invest ~$25/month for a more polished, branded academy experience” Kimi K2 · budget prompt · first choice
“choose LearnWorlds Pro Trainer for a branded customer academy with free courses” GPT-6 Luna · budget prompt · first choice
“### 1. LearnWorlds Best for: B2B companies with sophisticated needs” GLM 4.7 FlashX · comparative prompt · first choice
What the models said about Thinkific
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Six of eight in this category shown.
“They are generally not a clean fit for branded academies with formal certification tracking, partner onboarding, SSO provisioning, and compliance reporting.” Muse Glimmer 30B · negative prompt · soft negative
“though it may have limitations in terms of separate environments for different user groups” Mistral Small · paraphrase prompt · soft negative
“lower-tier SMB options (like TalentLMS or Thinkific) may trigger security red flags” Gemini 3.5 Flash · direct prompt · soft negative
“I'd suggest starting with Thinkific Plus (if you want simplicity and good value) or Skilljar” MiniMax M2.5 · paraphrase prompt · first choice
“Start with Thinkific Free or Teachable Free” MiniMax M2.5 · budget prompt · first choice
“Thinkific (Best Overall for Flat-Rate Scaling)” Gemini 3.5 Flash · budget 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.