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Learning experience platforms · October 2026 Edition

Learn Amp vs Docebo

One of fourteen models named Learn Amp first on the direct prompt; zero named Docebo. Learn Amp was named by ten of the fourteen models and Docebo by fourteen and Learn Amp carries 14 labels and Docebo 44, so the shares are not directly comparable.

Learn Amp

accepted challenger

Named in one category this edition.

Docebo

criticized challenger

Named in seven categories this edition.

First-choice share12%2%Of first choices across the direct, paraphrase, budget and scale prompts, 0 to 100.
Negative rate0%30%Negative labels as a share of the product's labels, 0 to 100.
Rank in category#5#6A position in a field of 10; printed, not drawn.
Labels1444A count; the two differ.
The two percentage rows are drawn on one 0 to 100 track, Learn Amp reading right to left. Rank and label count are printed, not drawn.Absorb LMS was named alongside these two in eleven of the fourteen direct answers. 360Learning vs Learn Amp · 360Learning vs Docebo · GoSkills vs Learn Amp

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 learning experience platforms page.

By framing

How many of the fourteen models made each the first choice, per way of asking, and how many argued against it.
Learn AmpFirst choices, of fourteen modelsDocebo
Direct101 against Docebo
Paraphrase413 against Docebo
Comparative011 against Docebo
Budget-constrained002 against Docebo
Scale-constrained10
Negative006 against Docebo
Bars are first choices, 0 to 14 each sideModels that argued againstA model can name both, so the two sides of a row do not sum to fourteen.

Every model, every framing

The eighty-four answers behind the chart above, one cell each: where Learn Amp and Docebo 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
GPT-6 Luna
Muse Glimmer 30B
Learn Amp Docebo first choice named as an alternative argued againstblank: not namedEach cell is one answer, Learn Amp on the left and Docebo on the right.

The direct prompt

The plain question, one answer per model, grouped by where Learn Amp and Docebo stood in it.

Learn Amp first, Docebo an alternative

1 of 14 modelsDocebo was named in the answer but not as the choice, or not at all.
Claude Haiku 4.5Learn Amp alternatives: 360Learning, Absorb LMS, Docebo

Neither was the first choice, one was named

10 of 14 modelsThe answer put something else first and named one of the two as an alternative.
GPT-5.4 mini360Learning alternatives: Absorb LMS, CYPHER Learning, Degreed, Docebo, TalentLMS
Gemini 3.5 FlashLearnUpon alternatives: 360Learning, Absorb LMS, Continu, Docebo
Perplexity Sonar360Learning alternatives: Absorb LMS, Docebo, TalentLMS
Grok 4.1 FastAbsorb LMS alternatives: 360Learning, Docebo
Qwen 3.7 FlashAbsorb LMS alternatives: Docebo, iSpring Learn
Kimi K2360Learning, LearnUpon alternatives: Absorb LMS, Docebo, Litmos
GLM 4.7 FlashXAbsorb LMS alternatives: 360Learning, Adobe Learning Manager, Docebo, Go1, TalentLMS
MiniMax M2.5Absorb LMS alternatives: Continu, GoSkills, Learn Amp, TalentLMS
GPT-6 Luna360Learning alternatives: Docebo, LearnUpon
Muse Glimmer 30BAbsorb LMS alternatives: Continu, Learn Amp

Neither was named

3 of 14 modelsThe answer made no first choice from these two in this category.
Mistral SmallSAP SuccessFactors HCM, TalentLMS alternatives: LearnWorlds, Thinkific
DeepSeek V4 Flash360Learning alternatives: Absorb LMS, LearnUpon, TalentLMS
Llama 4 MaverickSAP SuccessFactors HCM alternatives: Kajabi, LearnDash, LearnWorlds, TalentLMS, Teachable, Thinkific

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
Level: the same share of first choices.
Learn Amp0%#– of 11
Docebo0%#11 of 11
The full small business standing →
Mid-marketThe figures above
Learn Amp leads by ten points.
Learn Amp12%#5 of 10
Docebo2%#6 of 10
The full mid-market standing →
Enterprise
Learn Amp is not named for this buyer.
Docebo40%#1 of 7
Learn Amp—not named
The full enterprise standing →

What the models said about Learn Amp

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

“or Learn Amp if you want an all-in-one solution with robust engagement features” Claude Haiku 4.5 · paraphrase prompt · first choice
“My top recommendation would be Learn Amp for most mid-sized B2B companies” MiniMax M2.5 · paraphrase prompt · first choice
“highlighted as the best all-in-one mid-market learning platform” Claude Haiku 4.5 · direct prompt · first choice

What the models said about Docebo

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

“Frequently cautioned against” DeepSeek V4 Flash · negative prompt · hard negative
“These platforms are the gold standards for massive global enterprises, but they present major roadblocks for smaller or leaner teams.” Gemini 3.5 Flash · negative prompt · soft negative
“Docebo is feature-rich and highly configurable, though that depth comes with a corresponding administrative learning curve” Claude Haiku 4.5 · comparative prompt · soft negative
“Docebo is widely considered the market leader in the LXP space.” GLM 4.7 FlashX · comparative 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.