Small business=heldSince September 2026: 0% → 0%, ±0 points. Inside the 13-point floor: within noise. Read over the models both editions asked.
0%from 0% in September 2026
Inside the floor by 13 pointsRank 85 → 103 of 104
Lectora
35Judge labels
1First choices
7Negative labels
12 / 14Models named it
1Category
October 2026 Edition. Every number here is derived from the raw labels under vendor table v2026-10.7, every buyer segment counted.
Best standing
2% in Course authoring tools for enterprise buyers
Rank 19 of 82 in the mid-market standingaccepted challenger
0 of 14 models made it the first choice on the direct prompt; 7% of its 14 labels there were negative.
What the models named instead of Lectora →By buyer segmentRead the same way at every buyer size.
Small business111 of 112
0%5 labels
under the label threshold
Mid-market19 of 82
0%14 labels
accepted challenger
Enterprise8 of 96
2%16 labels
accepted challenger
In course authoring tools · each standing computed within its segment · bars are 0 to 100 · the accent bar is the product's own best reading
Standing by category
Every category where a model named Lectora for a mid-market B2B company. Share is first choices across the direct, paraphrase, budget and scale prompts; rank is within every product named in that category.
| Category | Function | Share | Rank | Negative rate | Labels | Quadrant | Since September 2026 |
|---|---|---|---|---|---|---|---|
| Course authoring tools | Learning and development | 0% | 19 of 82 | 7% | 14 | accepted challenger | =heldSince September 2026: 0% → 0%, ±0 points. Inside the 13-point floor: within noise. Read over the models both editions asked. |
Movement
Since September 2026, Lectora held within the floor in its one category: no change cleared 13 points.
Best category · Course authoring tools · by segment
Mid-market=heldSince September 2026: 0% → 0%, ±0 points. Inside the 13-point floor: within noise. Read over the models both editions asked.
0%from 0% in September 2026
Inside the floor by 13 pointsRank 25 → 19 of 76
Enterprise▲+2Since September 2026: 0% → 2%, +2 points. Inside the 13-point floor: within noise. Read over the models both editions asked.
2%from 0% in September 2026
Inside the floor by 11 pointsRank 13 → 8 of 92
All categories · Mid-market
| Category | September 2026 | Now | Change | Reading | Rank |
|---|---|---|---|---|---|
| Course authoring tools | 0% | 0% | =heldSince September 2026: 0% → 0%, ±0 points. Inside the 13-point floor: within noise. Read over the models both editions asked. | Within noise | Rank 25 → 19 of 82 |
Shares here are read over the models both editions asked, so they can differ by a point or two from the standing above, which counts every model in this edition.
The floor is 13 points of share, measured: how far the models move a leader on their own when the same questions are asked twice with nothing changed. A larger change is movement; a smaller one is noise, and both are shown. Movement is read over the twelve models both editions asked; GPT-6 Luna, Muse Glimmer 30B joined this edition and are in the standing but not yet in the comparison. How the floor is measured · The editions
By model
How each model treated Lectora across every prompt where it was named for a mid-market B2B company. Fourteen models, six prompts per category.ShowHide
By model
How each model treated Lectora across every prompt where it was named for a mid-market B2B company. Fourteen models, six prompts per category.
ShowHide| Model | First choice | Alternative | Mention | Negative | Labels |
|---|---|---|---|---|---|
| Claude Haiku 4.5 | 0 | 1 | 0 | 0 | 1 |
| GPT-5.4 mini | 0 | 2 | 0 | 0 | 2 |
| Gemini 3.5 Flash | 0 | 1 | 0 | 0 | 1 |
| Perplexity Sonar | 0 | 1 | 0 | 0 | 1 |
| Grok 4.1 Fast | 0 | 0 | 1 | 0 | 1 |
| Mistral Small | 0 | 2 | 1 | 0 | 3 |
| DeepSeek V4 Flash | 0 | 0 | 1 | 0 | 1 |
| Llama 4 Maverick | 0 | 0 | 0 | 0 | 0 |
| Qwen 3.7 Flash | 0 | 0 | 1 | 1 | 2 |
| Kimi K2 | 0 | 1 | 0 | 0 | 1 |
| GLM 4.7 FlashX | 0 | 0 | 0 | 0 | 0 |
| MiniMax M2.5 | 0 | 0 | 1 | 0 | 1 |
| GPT-6 Luna | 0 | 0 | 0 | 0 | 0 |
| Muse Glimmer 30B | 0 | 0 | 0 | 0 | 0 |
By framing
Which of the six questions produced the naming. By model says how often; this says asked what. The first-choice count on the right carries the marks of the models that produced it.
FramingLabels by classFirst choices
Direct4 labelsNone
Paraphrase1 labelNone
Comparative14 labelsNone
Budget-constrained2 labelsNone
Scale-constrained8 labels1
Negative6 labelsNone
First choiceAlternativeMentionNegative35 labels in all, every segment counted; 1 of the 1 first choices count toward share, since the comparative and negative framings do not. The bar is one segment per label class, to scale within the framing.
What the models said for it
Verbatim evidence the judge attached to positive labels.
“Lectora is recognized as the choice for serious instructional designers who need deep customization and strong accessibility” Claude Haiku 4.5 · Course authoring tools · comparative prompt · alternative
“Lectora is a close second for enterprises needing advanced compliance and AI features” Mistral Small · Course authoring tools · direct prompt · alternative
“Best for: Complex course structures and users who want a robust, standalone tool.” Mistral Small · Course authoring tools · comparative prompt · alternative
“Lectora if accessibility and more advanced control are top priorities.” GPT-5.4 mini · Course authoring tools · paraphrase prompt · alternative
And against it
Verbatim evidence attached to negative labels. A warning on a product with few labels is a warning; on a product with many, it is one voice among them.
“certain legacy versions of Lectora)... Avoid unless you have a dedicated developer on your staff” Qwen 3.7 Flash · Course authoring tools · negative prompt · soft negative
Named alongside
The products named in the same answers as Lectora, over the 35 answers that named it. Took the first choice instead counts the answers where the other product was the first choice and Lectora was named but was not.ShowHide
Named alongside
The products named in the same answers as Lectora, over the 35 answers that named it. Took the first choice instead counts the answers where the other product was the first choice and Lectora was named but was not.
ShowHideProductSame answerTook the first choice insteadHead to head
A head-to-head page exists where both products are among a category's top eight. The other rows are the same fact without a page behind them, so they link to the product instead.
What carried it into the answer
The sites and pages cited by the answers that named Lectora. A fact about retrieval, not a lever on the model.
Citations exist only for the models that return a source list, five of the fourteen in this edition, so these counts come from 28 of the 35 answers that named Lectora and are not a share of its labels.
Domains cited
elearningindustry.com18
colossyan.com17
intellum.com15
easygenerator.com13
guideflow.com12
ispringsolutions.com12
elearning.adobe.com7
knowlify.com7
learnworlds.com7
smartcat.com7
No domain is on file for Lectora, so its own site is not marked.
Pages cited
Pages are listed as the models cited them.
Search and answers
Where Lectora stands in Google search beside where it stands in the models' answers.ShowHide
Search and answers
Where Lectora stands in Google search beside where it stands in the models' answers.
ShowHideIn search
Google, US estimates- Searches for its name, Google
- 880 a month (“lectora”)
- AI search demand for its name, est.
- 99 a month
- Its own site
- No site of its own on file, so no site figures
In answers
This editionShare of first choices
0%
rank 19 of 82 in course authoring tools
Segment leader
30%
Articulate 360
- First choices
- 1 across its categories
- Named in
- 35 answers
- Its own site cited
- No site on file to match
Search figures are US estimates from DataForSEO, read September 28, 2026; AI search demand is its modeled, directional estimate, not a count of queries to any assistant. The answers are this edition's. Two measurements side by side: neither is read as the cause of the other.
Names read as Lectora
What the judge wrote, as written, with how often. The vendor table decides that these count as Lectora; a claim can dispute any of them.
Lectora (by ELB Learning) 2
Follow Lectora
An email the morning each edition publishes: where this product moved, where it held, and by how much against the noise floor. One address, confirmed by a click; a stop link in every email.
Already following? Everything you follow, with a stop for each.
Is this your product?
Claim this page
Claiming is free and changes nothing in the data. A claimed page shows a verified contact who is told when each edition publishes and when Lectora's standing changes by more than the noise floor; the right to propose corrections to the vendor table, meaning names the judge wrote that should or should not read as Lectora, applied by version and listed in the change log; and a one-line description supplied by the vendor and marked as such.
What a new claim receivesHide what a new claim receives
A new claim receives the current edition's vendor brief for Lectora by email, built from the raw record of the edition. It shows:
- where Lectora is named, by buyer and by framing, and which cells hold its first choices;
- the claims the models make when they name it, ranked, with the strongest and the weakest quoted;
- its vocabulary against the segment leader's, and the pages the models cited;
- who was chosen in the answers that did not name Lectora, and every reason the record gives;
- a battlecard for each top rival: the head-to-head split, why they win, and the reservation quoted against them;
- one page of published figures cleared to show a buyer.