GTM AI Index
Index Customer Customer education › TalentLMS vs LearnWorlds
Customer education and LMS · September 2026 Edition

TalentLMS vs LearnWorlds

Zero of twelve models named TalentLMS first on the direct prompt; zero named LearnWorlds. TalentLMS was named by twelve of the twelve models and LearnWorlds by ten and TalentLMS carries 47 labels and LearnWorlds 30, so the shares are not directly comparable.

TalentLMS

endorsed leader

Named in three categories this edition.

LearnWorlds

accepted challenger

Named in one category this edition.

First-choice share33%2%Of first choices across the direct, paraphrase, budget and scale prompts, 0 to 100.
Negative rate0%0%Negative labels as a share of the product's labels, 0 to 100.
Rank in category#1#6A position in a field of 13; printed, not drawn.
Labels4730A count; the two differ.
The two percentage rows are drawn on one 0 to 100 track, TalentLMS reading right to left. Rank and label count are printed, not drawn.LearnUpon was named alongside these two in nine of the twelve direct answers. TalentLMS vs LearnUpon · TalentLMS vs Skilljar · TalentLMS vs WorkRamp

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

How many of the twelve models made each the first choice, per way of asking, and how many argued against it.
TalentLMSFirst choices, of twelve modelsLearnWorlds
Direct00
Paraphrase40
Comparative00
Budget-constrained101
Scale-constrained00
Negative12
Bars are first choices, 0 to 12 each sideModels that argued againstA model can name both, so the two sides of a row do not sum to twelve.

Across every category in the September 2026 Edition, TalentLMS and LearnWorlds were named in the same answer seventy-five times, of the 128 answers naming TalentLMS and the 90 naming LearnWorlds. In those answers LearnWorlds took the first choice eight times and TalentLMS twenty-four.

Every model, every framing

The seventy-two answers behind the chart above, one cell each: where TalentLMS and LearnWorlds stood in it.
ModelDirectParaphraseComparativeBudget-constrainedScale-constrainedNegative
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
TalentLMS LearnWorlds first choice named as an alternative argued againstblank: not namedEach cell is one answer, TalentLMS on the left and LearnWorlds on the right.

The direct prompt

The plain question, one answer per model, grouped by where TalentLMS and LearnWorlds stood in it.

Neither was the first choice, one was named

8 of 12 modelsThe answer put something else first and named one of the two as an alternative.
GPT-5.4 miniDocebo alternatives: Absorb, LearnUpon, Skilljar, TalentLMS
Gemini 3.5 FlashSkilljar alternatives: Gainsight Customer Education, LearnUpon, TalentLMS, Thought Industries, WorkRamp
Grok 4.1 FastSkilljar alternatives: Absorb LMS, LearnUpon, LearnWorlds, TalentLMS, WorkRamp
Mistral SmallLearnUpon alternatives: Absorb LMS, LearnWorlds, TalentLMS
DeepSeek V4 FlashGainsight Customer Education alternatives: LearnUpon, LearnWorlds, WorkRamp
Qwen 3.7 FlashSkilljar alternatives: Absorb LMS, LearnUpon, TalentLMS, Thinkific, WorkRamp
GLM 4.7 FlashXLearnUpon alternatives: Absorb LMS, Beetsol, LearnWorlds, TalentLMS
MiniMax M2.5Skilljar, WorkRamp alternatives: Gainsight Customer Education, LearnWorlds, TalentLMS

Neither was named

4 of 12 modelsThe answer made no first choice from these two in this category.
Claude Haiku 4.5Skilljar alternatives: Gainsight Customer Education, Thought Industries
Perplexity SonarLearnUpon alternatives: Docebo, Gainsight Customer Education, Skilljar, WorkRamp
Llama 4 Maverickno first choice
Kimi K2Gainsight Customer Education, WorkRamp alternatives: LearnUpon, Skilljar

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

The same question asked on behalf of a different buyer. Each standing is computed within its segment and they are never added together. The figures above are the mid-market standing, which is the one the category orders by.
Small business
TalentLMS leads by fourteen points.
TalentLMS49%#1 of 12
LearnWorlds35%#2 of 12
The full small business standing →
Mid-marketThe figures above
TalentLMS leads by thirty-one points.
TalentLMS33%#1 of 13
LearnWorlds2%#6 of 13
The full mid-market standing →
Enterprise
Level: the same share of first choices.
TalentLMS0%#– of 10
LearnWorlds0%#10 of 10
The full enterprise standing →

What the models said about TalentLMS

Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Three of four in this category shown.

“Safer Alternatives to Consider | TalentLMS | Transparent pricing (starts at $119/month), free plan available, quick deployment” Kimi K2 · negative prompt · first choice
“If you want a formal "Academy" feel ... without enterprise-level prices, TalentLMS is the industry standard.” Gemini 3.5 Flash · budget prompt · first choice
“The best customer education platform for a company with a limited budget is TalentLMS, which offers a free plan” Llama 4 Maverick · budget 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
Also compared

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.