We Asked AI the Same Question in English and Hinglish — Here's What Actually Changed

Most people assume that if an AI engine "knows" about a topic, it answers the same way no matter how you ask. We tested that assumption directly, in a small way, using real queries — not a simulation.

Method and sample size

12 paired questions, each written once in plain English and once in Hinglish (the mixed Hindi-English phrasing many Indian customers actually type) — covering realistic small-business categories: digital marketing agencies, GST/CA filing, gyms, pediatric clinics, coaching institutes, salons, web development, real estate, accounting software, physiotherapy, interior design, and one question directly about AI visibility tools themselves. Each pair was sent to 4 AI engines (OpenAI, Anthropic, Google Gemini, Perplexity) in both languages — 96 total real API calls, all on the same day (2026-10-24), to rule out answers simply changing over time between the two runs.

This is a small, disclosed sample — not a national benchmark. 12 question-pairs across 4 engines is enough to show that the gap is real, not enough to claim how big it is across the whole market. Treat every number below as "this is what we saw in this sample," not "this is the rate."

What we found

1. The same question can get a different top recommendation depending on language — even from the same AI engine.

The clearest example: we asked "What's the best AI visibility monitoring tool for Indian agencies?" — once in English, once in Hinglish.

OpenAI, asked in English, led with Otterly.AI. Asked in Hinglish, it led with Peec AI instead — a different top pick, same model, same day.

Perplexity, asked in English, led with Peec AI. Asked in Hinglish, it led with SE Ranking instead.

Two different engines, two different language-dependent swaps, on the exact same question. This isn't a hypothetical risk — it's what actually happened when we asked.

2. Answer length varies a lot by language — not just a little.

Across all 47 analyzable pairs (one pair returned an empty/truncated answer, see limitations below), the average absolute difference in word count between the English and Hinglish answer was 29%, with a median of 20%. In 11 of 47 pairs (about 1 in 4), the two answers differed in length by more than 50% — sometimes the Hinglish answer was dramatically shorter, sometimes the opposite. There's no consistent direction; it depends on the question and the engine.

3. In this sample, no AI engine mentioned Vimarsh — in either language.

Across all 96 responses, zero mentioned Vimarsh by name. Unsurprising for a newer product with no AI-visibility history yet, but it's the honest, unvarnished baseline — exactly the kind of gap this measurement work exists to surface, for any business, not just ours.

Limitations

12 question-pairs, 4 engines, one day. This shows the phenomenon is real, not how often it happens or how large the effect is at scale. A bigger, repeated sample would be needed for anything resembling a benchmark.

One of the 96 responses (about 1%) came back empty — not an error, but the model (OpenAI, one specific query) hit its output token limit while still "thinking," so no visible answer text was produced. We're counting this honestly as a data gap, not excluding it silently.

These are single snapshots. AI engines update their answers as models change and as the web changes under them — a re-run next month could show different specific recommendations, even if the broader pattern (language affects the answer) likely holds.

We are not claiming Hinglish performs "better" or "worse" than English — only that it produces different answers, sometimes substantially so. Drawing a value judgment from this sample would be overclaiming.

Frequently Asked Questions

Does this mean I should only market in Hinglish, or only in English?

No — this data doesn't support that conclusion either way. What it shows is that AI engines don't treat the two as interchangeable, so if you only ever check one language, you're only seeing half the picture of what AI tells your customers.

Is this official research?

No. It's a small, disclosed, methodologically honest sample we ran ourselves and are publishing as-is — not a peer-reviewed study, not a vendor benchmark.