When buyers ask AI which solar brand to choose, a handful of names capture most of the answer — SMA, Sungrow, Trina Solar, JinkoSolar, LONGi, and Huawei FusionSolar lead AI's "share of voice," while dozens of real, established solar companies barely register. We know because we audited roughly 200 AI answers across ChatGPT, Gemini, Google AI Overviews, and Perplexity, asking 50 brand-free buyer questions across five European and US markets. This report shows which solar brands AI recommends, why the four engines disagree, and where you have to show up to get cited.
This is a solar-specific companion to our broader study on how AI decides which brands to recommend — same method, but here we keep the solar detail: the full leaderboard, the per-platform splits, and the exact source profiles each engine reads in this industry.
Key findings at a glance
- A few brands own AI solar recommendations. SMA (83 mentions) and Sungrow (82) lead, followed by Trina Solar (70), JinkoSolar (68), LONGi (61), and Huawei FusionSolar (56). Below the top ~6, mentions fall away fast.
- The four engines disagree. The most-mentioned solar brand was Sungrow on ChatGPT, JinkoSolar on Gemini, and SMA on both Google AI Overviews and Perplexity.
- Each engine reads a different slice of the solar web. Google's AI Overviews lean on LinkedIn, YouTube, and SolarPowerEurope; ChatGPT on niche solar-data blogs and pv-magazine; Perplexity on trade press, LinkedIn, and wiki-solar.
- Buyers never named a brand — AI did. Every question was brand-free ("most reliable inverter for a European commercial rooftop"), yet AI returned specific brands almost every time.
- Mentioned isn't recommended isn't liked. The most-mentioned brand wasn't always the explicit "best pick," and the most positively framed brand wasn't a share-of-voice leader.
How we ran the audit
We asked 50 brand-free buyer questions — the kind real commercial and utility solar buyers type, with no company named — to ChatGPT, Gemini, Google AI Overviews, and Perplexity, across Germany, the UK, Italy, Spain, and the US. That's roughly 200 AI answers, each parsed for which brands were mentioned, which were explicitly recommended, which domains were cited, and the sentiment around each brand. AI answers are non-deterministic, so treat the figures as a snapshot of patterns, not a fixed ranking.
The AI share-of-voice leaderboard
Counting every brand mention across all 200 answers, the solar leaderboard is top-heavy:
| Rank | Brand | Total mentions | Platforms covering |
|---|---|---|---|
| 1 | SMA | 83 | 4 / 4 |
| 2 | Sungrow | 82 | 4 / 4 |
| 3 | Trina Solar | 70 | 4 / 4 |
| 4 | JinkoSolar | 68 | 4 / 4 |
| 5 | LONGi | 61 | 4 / 4 |
| 6 | Huawei FusionSolar | 56 | 4 / 4 |
| 7 | JA Solar | 39 | 4 / 4 |
| 8 | NEXTracker | 31 | 4 / 4 |
| 9 | Maxeon | 31 | 4 / 4 |
| 10 | Fronius | 29 | 4 / 4 |
| 11 | SolarEdge | 29 | 4 / 4 |
| 12 | PV Hardware | 22 | 4 / 4 |
The drop from the top six to the rest is steep, and the long tail is brutal: plenty of well-known solar companies landed in the low single digits or didn't appear at all. In AI recommendation, they're effectively invisible — even when buyers never asked for a brand by name.
The engines disagree on who's best
Ask the same questions and the four platforms surface different leaders. By mentions, the top solar brand on each engine was:
| Platform | Most-mentioned solar brand |
|---|---|
| ChatGPT | Sungrow (37) |
| Gemini | JinkoSolar (22) |
| Google AI Overviews | SMA (13) |
| Perplexity | SMA (27) |
There is no single "AI answer" for solar to optimize for. A brand that dominates ChatGPT can be mid-pack on Gemini. For a solar marketing team, that kills the idea of a one-and-done "rank in AI" project — and it creates openings: if a competitor owns ChatGPT but no one owns Perplexity in your niche, that's a gap you can take.
Where AI looks for solar (the part you can act on)
The engines disagree because they read different parts of the solar web. We logged the domains each one cited, and the "source diets" barely overlap:
- ChatGPT leaned on niche solar-data and trade blogs — its most-cited solar sources included surgepv.com, solardataatlas.com, and pv-magazine.com, with a few news/research sites such as Reuters.
- Google AI Overviews were the most UGC-heavy: LinkedIn (19 citations) and YouTube (17) were its top two domains, alongside the SolarPowerEurope association site (16) and even Reddit and Facebook.
- Perplexity favored trade press and reference sources — surgepv.com (21), LinkedIn (19), SolarPowerWorld Online (19), and wiki-solar.org (15).
- Gemini skewed toward UK/EU specialist solar sites most marketers have never heard of — solarpanelsforbusinesses.co.uk (12), renewables.digital (10), lighthief.energy (10).
The specific domains will shift over time, but the shape is the lesson: to be quoted by a given engine, you have to be present on the sources that engine trusts. Getting into Google's AI Overviews for solar likely means a real LinkedIn and YouTube presence plus association coverage; getting cited by ChatGPT means earning mentions on the solar-data blogs and outlets like pv-magazine. The same effort spread evenly is wasted. (For Google specifically, see our guide on how to get cited in Google AI Overviews.)
The leaderboard changes by buyer segment
Split the audit by buyer type and the leaders reshuffle:
| Buyer segment | Top 3 by mentions |
|---|---|
| Commercial rooftop (C&I) | SMA (46), Sungrow (42), Trina Solar (38) |
| Utility-scale developers | Sungrow (40), SMA (36), JinkoSolar (33) |
Same five countries, same four engines — but a different leaderboard, because a commercial-rooftop buyer and a utility developer ask different questions and surface different brands. For a solar company, "our AI visibility" is really several visibilities, one per segment you sell into.
Mentioned, recommended, and liked are three different battles
It's tempting to treat "AI talks about us" as the goal, but the data splits it into three:
- Mentions — how often you appear at all (the leaderboard above).
- Recommendations — who the engine actually endorses as the "best" pick. The most-mentioned brand was not always the most-recommended; on some engines a brand with fewer mentions won the explicit endorsement.
- Sentiment — how positively you're described. The brand framed most positively across answers was not a share-of-voice leader at all.
These move independently. A solar brand can be named constantly but rarely recommended, or described warmly but rarely surfaced. A complete picture tracks all three, per platform and per segment.
What solar companies should do about it
- Measure where you stand. Run brand-free buyer questions ("best solar inverter for a UK warehouse," "most bankable module for a utility project in Spain") through ChatGPT, Perplexity, and Google's AI Overviews and see whether you're named. Our free monthly AI-visibility check shows you how.
- Target the right sources per engine. Figure out which solar sites the engine you care about cites — trade blogs for ChatGPT, LinkedIn/YouTube/associations for Google AI Overviews — and earn a presence there.
- Publish clear, citable content. Structured, well-sourced pages with real authorship and the trust signals Google describes as E-E-A-T are what AI lifts. There's no separate trick beyond the fundamentals.
- Work per segment. Optimize separately for commercial-rooftop and utility buyers — they surface different brands.
- Be consistent. AI share of voice compounds: the brands that own it got there by being referenced again and again.
This is exactly the kind of intelligence QuickCreator produces as a GEO Industry Report — and it's built to do the follow-on work too, from finding the questions buyers ask to drafting the citable content that earns those mentions. If GEO is new to you, start with our explainer on what generative engine optimization is.
Frequently asked questions
Which solar brands does AI recommend most?
Across our audit of ~200 AI answers, SMA (83 mentions) and Sungrow (82) led overall, followed by Trina Solar, JinkoSolar, LONGi, and Huawei FusionSolar. Together a small group of brands captured most of AI's solar "share of voice," while many established companies barely appeared.
Do ChatGPT, Gemini, and Google recommend different solar brands?
Yes. By mentions, the top solar brand was Sungrow on ChatGPT, JinkoSolar on Gemini, and SMA on both Google AI Overviews and Perplexity. There is no single "AI answer" for solar — each engine has its own leaders, so you have to think per platform.
Why do AI engines disagree about solar brands?
Because they cite different sources. Google's AI Overviews lean heavily on LinkedIn, YouTube, and association sites like SolarPowerEurope; ChatGPT on niche solar-data blogs and pv-magazine; Perplexity on trade press and wiki-solar. Each engine recommends the brands that show up in the sources it trusts.
How can a solar company show up in AI answers?
Measure your current AI visibility with brand-free buyer questions, then earn a presence on the specific sources each engine cites in solar, publish clear and credible content with real E-E-A-T, and do it consistently per buyer segment. AI share of voice builds over time, not overnight.
Is this report only about solar?
The data is solar (PV) across five markets, but the patterns — top-heavy share of voice, per-platform divergence, distinct citation sources, and the gap between being mentioned and recommended — hold across industries. Our broader study covers the universal version.
A note on the data and its limits
This was a discovery-mode audit: 50 brand-free questions, each sent once to ChatGPT, Gemini, Google AI Overviews, and Perplexity across five markets, with answers parsed for mentions, recommendations, cited domains, and sentiment. AI answers are non-deterministic, so exact counts shift if you re-run them — treat these as a snapshot of patterns, not fixed rankings. Sentiment used a simple positive/negative lexicon, and some engines surfaced few explicit recommendations, so per-platform "winners" rest on smaller samples than the mention counts. It's one industry in five markets; your numbers will differ. What generalizes is the shape, not the specific figures.
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