Language Learning Software: AI Visibility Market Discovery Index

Benchmark Summary

Questions This Section Answers

• Who leads the language learning software benchmark in October 2026, and how wide is the gap to the next brand?
• Which brands moved most sharply since July 2026, and where did those changes fall outside normal variation?

Duolingo leads the Language Learning Software benchmark in October 2026 with valid recommendation coverage of 85.5%, up from 81.7% in July 2026. The gap to the next brand, Babbel at 81.5% in October 2026 (up from 74.6% in July 2026), narrowed to 4.0 percentage points. The leader's margin is now thin enough that the category is tightly contested.

The largest coverage increase came from Pimsleur, rising to 72.0% in October 2026 from 63.1% in July 2026, a move beyond normal variation. The largest coverage decline came from Rosetta Stone, falling to 34.5% in October 2026 from 48.5% in July 2026, also beyond normal variation. Pimsleur's rise and Rosetta Stone's fall are the two defining moves of the period.

This month showed significant movement across the category: Babbel and Pimsleur were significant risers, while Memrise and Rosetta Stone were significant decliners. Across the full July-to-October series, Pimsleur's coverage rose in each of the three months since July 2026, reaching 72.0% in October 2026 from 63.1% in July 2026. Memrise declined in each of the two months since August 2026, falling to 25.6% in October 2026 from 32.5% in July 2026. Both multi-month runs are sustained, not one-off swings.

The benchmark began with 800 prompt-surface observations in each month and produced 539 qualified observations in October 2026 after qualification.

Current Benchmark at a Glance

Qualified surface breadth refers to the number of canonical AI surface families with at least one qualified observation. The six families tested are ChatGPT, Copilot, Gemini, Perplexity, AI Overviews, and AI Mode.

For the strategic interpretation of this benchmark, read CiteWorks Studio's analysis of How AI Search Is Recommending Language Learning Software

Research Scope and Qualification

Questions This Section Answers

• How many of the collected prompt-surface observations qualify for the public benchmark?
• Why do brand-level percentages use the qualified observations rather than the full collection?

The public benchmark is narrower than the raw collection universe by design.

Brand-level percentages use the qualified observations as the public denominator, not the raw collection. The qualified set, not the raw collection, is what every published brand figure rests on. See AI Visibility Market Discovery Methodology.

Current Brand Standings

Questions This Section Answers

• Which language learning apps and platforms rank highest by valid recommendation coverage?
• How do presence rate, top-three rate, and rank-one rate differ across the tracked brands?

This table is the primary current-month benchmark view, sorted by valid recommendation coverage.

How to Read the Standings

• Presence rate: Share of qualified observations where the brand is mentioned, regardless of recommendation status.
• Valid recommendation coverage: Share of qualified observations where the brand appears with a valid recommendation.
• Top-three rate: Share of qualified observations where the brand appears in the top three recommended options.
• Rank-one rate: Share of qualified observations where the brand is the first recommendation.
• Net sentiment: Positive minus negative mentions, divided by total mentions with sentiment.

For formulas and denominator rules, see AI Visibility Market Discovery Metric Definitions.

Recommendation Coverage Movement

Questions This Section Answers

• Which brand recorded the largest coverage increase since July 2026, and was the movement sustained?
• Where did Rosetta Stone's coverage decline occur across the July-to-October series?

Largest Increase: Pimsleur

Pimsleur recorded the largest coverage increase, rising to 72.0% in October 2026 from 63.1% in July 2026, a gain of 8.9 percentage points. The gain was beyond normal variation and built across all three months. Pimsleur's coverage rose in each of the three months since July 2026, from 63.1% to 64.4% in August 2026 to 66.8% in September 2026 to 72.0% in October 2026. The brand's raw mention presence rate rose to 75.3% in October 2026 from 70.2% in July 2026, while its top-three rate rose to 50.8% from 27.1%. Net sentiment stood at 1.0 in October 2026.

Largest Decline: Rosetta Stone

Rosetta Stone recorded the largest coverage decline, falling to 34.5% in October 2026 from 48.5% in July 2026, a drop of 14.0 percentage points. The decline was beyond normal variation and the largest of any brand in the category. The brand's raw mention presence rate fell to 42.7% in October 2026 from 58.3% in July 2026. The decline occurred mainly between July and August, with Rosetta Stone's coverage broadly stabilizing between August 2026 at 36.2% and September 2026 at 36.6%, then easing further to 34.5% in October 2026. Most of the damage happened in a single month, not gradually.

Category Leader: Duolingo

Duolingo remains the category leader with valid recommendation coverage of 85.5% in October 2026, up 3.8 percentage points from 81.7% in July 2026. This gain was within normal month-to-month variation. The gap to Babbel, the next closest brand, narrowed to 4.0 percentage points in October 2026 from 7.1 percentage points in July 2026. A stable score still meant a halved lead, because rivals grew faster.

Recommendation Placement Snapshot

Questions This Section Answers

• How does top-three placement differ from first-position placement among the leading language learning brands?
• What does Pimsleur's rank-one rate show that its overall coverage does not?

Coverage alone does not show how prominently a brand is recommended. Placement rates reveal how often brands appear in top-three or first position when recommended.

Pimsleur's top-three rate rose to 50.8% in October 2026 from 27.1% in July 2026, while its rank-one rate rose to 7.0% from 2.3% in July 2026, still well below Babbel's 19.1% and Duolingo's 43.2%. Similar coverage levels can hide very different first-position rates.

Buyer-Intent Distribution

All qualified observations in both months fell into the Brand Recommendation class.

The current public series measures brand recommendation discovery only and does not yet contain qualified observations in the Pricing & Value or Multi-Brand Comparison classes. Pricing, value, and head-to-head comparison have no public signal in this dataset.

Historical Measurement Record

Questions This Section Answers

• Does the July-to-October record show a stable leader or a change at the top of the language learning benchmark?
• Which brand recorded the largest coverage movement in each measurement month?

This is an evergreen benchmark URL. New measurements are added to the same report.

The leader has not changed across four measurements, but Rosetta Stone has set the largest movement in every month since the baseline.

Evidence and Source Layer

The benchmark is built from prompt-level observations that retain the query, AI/search surface, answer, brand outcome, recommendation placement, sentiment, and, where exposed, citations or attributable evidence sources. Source presence is evidence about the information environment, not proof that a source caused the recommendation.

Scope Boundaries

This public benchmark does not measure:

• Market share or revenue attribution
• Attributable sales or conversions
• Every possible AI response, including non-public or platform-specific variations
• Organic-search ranking positions
• Social media mention volume
• Private or sponsored channels
• Causality from a metric movement alone

A movement in this benchmark cannot be read as proof of cause, revenue impact, or market share.

Top 10 Cited Domains

Questions This Section Answers

• Which domains are cited most often in AI responses about language learning software?
• Where do video, community, review, and brand-owned domains appear among the most-cited sources?

Across all AI platform responses in October 2026, the benchmark observed 6,534 total citations from 990 unique domains.

Citations are broadly distributed across the 990 unique domains observed, with the top 10 domains accounting for approximately 39% of all citations. YouTube and Reddit alone account for more than 22% of citations, so video and community sources outweigh any single brand's own domain. Babbel's own domain (babbel.com) appears in the top 10 at rank 6, cited 159 times, and italki's domain (italki.com) appears at rank 10, cited 102 times. Review and comparison sites, including testprepinsight.com, copycatcafe.com, and mezzoguild.com, also feature prominently alongside official brand domains.

About This Benchmark

The LLM Authority Index AI Visibility Market Discovery Index tracks how often brands are discovered and recommended across six major AI and search surfaces. Each monthly measurement captures brand presence, recommendation coverage, placement, and sentiment from a qualified set of public observations.

• AI Visibility Market Discovery Methodology
• AI Visibility Market Discovery Metric Definitions
• AI Visibility Market Discovery Research Standards
• Modeled AI Visibility Authority Value

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The public industry benchmark shows category-level standings. A company-level Authority Index can go deeper.

The public percentage cannot identify the prompts, competitors, or sources causing the result.

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