How AI Search Is Recommending Solar Panels: Monthly Trends

Executive Summary

Qcells leads the solar panel category as of September 2026, reaching 65.5% valid recommendation coverage, up from 59.7% in July — a baseline-to-current move the benchmark classifies as significant. Qcells held a narrow edge over Maxeon (SunPower) in July (59.7% vs. 59.2%), lost that edge to Maxeon in August (60.2% vs. 60.6%), and reclaimed the lead by a wider margin in September as both Qcells and REC Group posted significant gains.

REC Group climbed into second place, rising to 63.4% valid recommendation coverage, up from 57.6% in July — also a significant baseline-to-current move — and up 6.0 points from August alone, the sharpest single-month change recorded across all ten tracked manufacturers this period. Maxeon (SunPower) rose to 62.4%, up from 59.2% in July, but that was not enough to hold the narrow lead it briefly occupied in August, and the brand now sits third.

Eight of the ten tracked brands — Canadian Solar, JinkoSolar, LONGi Solar, Maxeon (SunPower), Mission Solar, Panasonic, Silfab Solar, and Trina Solar — are classified as stable across the series, meaning their movement stayed within normal month-to-month variation. The category's largest coverage gap now separates Qcells at the top from Mission Solar at the bottom, which fell to 2.6%, down from 3.7% in July.

Each monthly run begins with 800 prompt-surface observations (441 unique questions in July, 490 in August, 464 in September) across the benchmark's defined AI/search surface universe. All 800 observations in each month mentioned a tracked brand or competitor. Of those, 726 were relevant and 74 irrelevant in July, 714 were relevant and 86 irrelevant in August, and 702 were relevant and 98 irrelevant in September. Brand-level metrics are calculated within the qualified sets of 595 observations for July and 566 for both August and September.

Key Findings

Benchmark Context

The report separates the raw collection universe from the qualified analysis set. Brand-level recommendation percentages are calculated within the qualified benchmark set.

These stage counts define the denominator used for every brand-level percentage in this report. August sat between these two months with 566 qualified observations and 6 qualified surfaces.

Benchmark-Level Metrics

AI Recommendation Trend

Qcells and REC Group Post the Category's Significant Gains

Qcells reclaimed the category lead in September, after Maxeon (SunPower) held a narrow edge in August. REC Group moved past Maxeon into second place, pushing Maxeon (SunPower) to third. The competitive cluster at the top tightened around Qcells, REC Group, and Maxeon (SunPower), all now above 62% coverage.

Two brands exceeded normal month-to-month variation in September, accounting for most of the category-level change: Qcells and REC Group. Their combined movement, rather than any single smaller shift, drove the reshaping of the leading tier, narrowing the gap between the first and third positions to 3.1 points.

What Changed This Month

Qcells Extends Its Lead

Qcells extended its position at the top of the category in September, reaching 65.5% valid recommendation coverage, up from 59.7% in July — a 5.8-point gain the benchmark flags as significant. The brand's coverage rose for two consecutive months, from 59.7% in July to 60.2% in August to 65.5% in September, even though Maxeon (SunPower) briefly edged ahead on the same metric in August.

Qcells held the highest raw mention presence in the category at 82.5%, essentially flat against its 82.9% baseline. Its top-three recommendation rate rose to 31.6%, up from 30.6%, and its rank-one rate reached 3.5%, up from 3.0%. With 371 valid recommendations in September, the brand combined broad visibility with more frequent recommendation across all measured positions.

Qcells extended its lead despite a slight decline in raw mention presence. The brand is being mentioned slightly less often in AI answers but recommended in a larger share of the qualified set.

Highest-priority diagnostic: Which prompt clusters drove the 5.8-point coverage gain, and which competitor lost the recommendation when Qcells gained it?

REC Group's Significant Rise

REC Group posted the sharpest single-month movement in the category, rising 6.0 points from August to September and reaching 63.4% valid recommendation coverage, up from 57.6% in July. The July-to-September gain of 5.8 points is also flagged as significant.

The brand's rank-one recommendation rate climbed to 27.6% in September, up from 14.8% in July, moving from 88 rank-one recommendations to 156. Its top-three rate rose to 43.3%, up from 37.6%, and its average recommended rank improved to 1.6. Raw mention presence held roughly flat across the series at about 70%, meaning the coverage gain came without a broader visibility increase.

The pattern here is one of elevated recommendation quality rather than elevated visibility. REC Group's overall presence in AI answers did not change much, but the brand won a much larger share of top-three and rank-one placements.

Highest-priority diagnostic: Which evidence sources and prompt types are associated with REC Group's rank-one placements, and are they concentrated on particular surfaces?

Maxeon (SunPower) Slips to Third

Maxeon (SunPower) rose on the primary metric but lost relative position. The brand reached 62.4% valid recommendation coverage in September, up from 59.2% in July, but that gain was not enough to hold the narrow lead it briefly held in August, when its coverage reached 60.6% against Qcells' 60.2%.

Raw mention presence eased from 76.6% to 71.9%, a decline of 4.7 points. The brand's rank-one rate fell to 14.7%, down from 24.7% in July, a decline of 10.0 points. Its top-three rate also slipped to 38.3%, down from 40.2%.

The distinction is between absolute and relative performance. Maxeon (SunPower) remains a strong third with 353 valid recommendations in September, but the brand is winning a smaller share of first-position recommendations than it did two months earlier.

Highest-priority diagnostic: Which prompts moved Maxeon (SunPower) from first to second or third position, and which competitor captured those rank-one placements?

JinkoSolar's Widening Gap to the Leaders

JinkoSolar posted the largest coverage decline among the ten tracked brands, falling from 27.6% in July to 23.5% in September, a drop of 4.1 points. The brand slid to 133 valid recommendations in September, down from 164 in July.

Raw mention presence eased to 45.1% from 47.7%, and the top-three rate fell to 5.3% from 8.7%, a decline of 3.4 points. Rank-one rate held roughly steady at 4.1%, up slightly from 3.5%. The gap between JinkoSolar and the category leaders widened in both August and September, with JinkoSolar now 42.0 points behind Qcells, up from a 32.1-point gap in July.

This is a widening competitive gap rather than a sudden loss. JinkoSolar's coverage declined in both of the last two months while the leaders posted gains, producing the widest gaps recorded in the category.

Highest-priority diagnostic: Which surfaces or prompt types show fewer JinkoSolar recommendations, and is the decline concentrated in specific query clusters?

Buyer-Intent Interpretation

In September 2026, all 566 qualified observations fell into the Brand Recommendation cluster. The pricing and comparison clusters registered no qualified observations, meaning the public benchmark cannot yet answer questions about price sensitivity, value positioning, or head-to-head comparison dynamics in this vertical. The current data shows which brands AI systems recommend for solar panels, but not how those recommendations shift when buyers introduce cost or comparison into their queries.

Brand Opportunity Summary

The benchmark identifies where attention is warranted across all ten tracked brands; a company-level analysis is needed to explain why those patterns are emerging.

Evidence Behind the Benchmark

The aggregate metrics are built from prompt-level observations covering the query, the AI surface, the recommendation outcome, rank, sentiment, and citations where exposed. Company-level analysis can go deeper into prompt, competitor, surface, and evidence patterns to understand what drives coverage differences. Source presence in an AI answer is not automatically treated as proof of causation.

About This Benchmark

This report is part of the LLM Authority Index AI Market Discovery research program.

• AI Industry Market Discovery
• AI Industry Market Discovery Methodology
• AI Industry Market Discovery Metrics
• AI Industry Market Discovery Standards

Report-Specific Interpretation Notes

• Small-count movements: Mission Solar's 15 valid recommendations and Trina Solar's single rank-one placement are based on low absolute counts; month-to-month changes for these brands carry less signal than for brands with hundreds of recommendations.
• Qualified denominator: Brand-level percentages are calculated within the 566 qualified observations, not the 800 raw prompt-surface observations collected. The 98 irrelevant prompts and 136 further-reserved prompts are excluded from public metrics.
• Directional analysis: Movement between July and September identifies where attention is warranted, but does not by itself establish what caused the change. Qcells' and REC Group's significant rises are signals for investigation, not conclusions about cause.

Next Step

The Public Benchmark Shows Where a Brand Is Winning or Losing. A Company-Level Audit Shows Why.

Beneath the aggregate percentages lie the questions that matter for competitive positioning: which high-intent prompts are won and lost, which competitor takes the recommendation when a brand loses it, what attributes AI associates with each solar panel manufacturer, and which external sources shape those answers. Qcells' return to the category lead, REC Group's rank-one surge, and Maxeon (SunPower)'s slide to third are exactly the kind of movement a company-level audit can trace to specific prompts, surfaces, and evidence sources.

A company-specific AI visibility audit maps these prompt, surface, competitor, ranking, sentiment, and evidence-source patterns into a prioritized visibility strategy. The public benchmark cannot identify the prompts, competitors, or sources causing the result. It turns the benchmark's directional signals into actionable intelligence for closing the gap to the leaders or defending a leadership position.

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