What AI engines actually cite when you ask about LLM gateways

We wrote 27 questions the way a developer asks them, sent them to four engines on one day, and counted where the citations pointed. Here is the distribution — including the findings that argue against our own product strategy.

What we did

The 27 questions are the ones a developer actually types, not what a keyword tool suggests:

• What are the best OpenRouter alternatives?
• Is it safe to route API calls through a third-party LLM gateway?
• Are free LLM APIs reliable enough for real work?
• How do I point Claude Code at a different API base URL?

Each question went to four engines on 2026-09-20. Only one of them returns the sources its answer was built from — Perplexity, reached through OpenRouter. An answer with no citation trail cannot be measured, so the other three engines are not in this dataset at all.

What is left: 613 citation entries across the 27 answers, collapsing to 569 unique URLs and 346 unique domains.

1. No single source dominates

The ten most-cited domains account for 18.6% of all citations. The remaining 81% spreads across 336 domains, and most of those appear exactly once.

This is worth stating plainly, because the standard advice in this space — get into the big awesome list and you are done — does not survive contact with the data. There is no list to win. There is a long tail, and each entry in it is a small bet.

2. The head still has a shape

The GitHub entries are not documentation. They are lists — directories of free APIs and gateways — and issue threads, where someone asks how to change the base URL in Claude Code or Cline and other people answer. Both are places where a project can be named without writing a blog post.

The dev.to entries are all first-person and all contain numbers. Not one of them is a product announcement.

And competitor marketing pages get cited directly — including, for the question about whether a gateway can read your prompts, a competitor's privacy policy and sub-processor list. Those pages are usually written for lawyers. They are being read as answers.

3. Reddit barely appears

Three citations out of 613 — 0.5% — went to reddit.com.

That one deserves a pause, because "Reddit shows up in 40% of AI answers" is repeated everywhere. The figure is real. It is also averaged across every topic imaginable. For developer tooling questions, the engines reached for repositories and tutorials instead. If you were about to spend this quarter on Reddit, the data says spend it elsewhere — for this category, at least.

4. Where YouTube appears, it is someone else's tutorial

All nine YouTube citations are setup tutorials, and eight of them are about one competitor, by name, walking through a configuration the viewer could copy.

What we took away from it

Publish where the engines already read. For this category that is repositories, issue threads, and long-form tutorial posts — not launch announcements.

Your boring pages are answer pages. Pricing, terms, privacy, and sub-processor lists answer real questions that people ask engines. One competitor collected four citations from legal pages in a single question.

Expect the long tail. 81% of citations spread across 336 domains. There is no single placement that fixes visibility, which also means no single competitor can lock it up.

How to use this data

1. Pick one question you want to be cited for, phrased the way a developer would type it. Do not start from a keyword.
2. List the pages that currently answer it. If most of them are repositories or long-form tutorials, that is your target format, not a landing page.
3. Write one page per question with a definition, concrete numbers, and a stated limitation. Pages with numbers and first-person experience dominate the cited set here.
4. Wait, then re-measure. Citation distributions move slowly; give a page a month and check whether it appears in the citation trail for that question.

Per-question spread

The 27 questions did not produce equal citation counts. Comparison questions ("best X alternatives") pulled the widest spread of domains, while safety and pricing questions concentrated on a handful of legal, pricing, and privacy pages. If your content plan is one big comparison post, you are competing in the most crowded bucket; a narrow safety or pricing question is easier to own with one authoritative page.

Sources

• Perplexity help: how citations and sources work: https://www.perplexity.ai/hub/faq
• OpenRouter documentation (the access path used for this study): https://openrouter.ai/docs
• OpenAI API pricing (referenced in the cost questions): https://openai.com/api/pricing/
• Anthropic pricing (referenced in the cost questions): https://www.anthropic.com/pricing

What would change the conclusion

Three things: a different engine with a visible citation trail, a question set weighted toward implementation details instead of comparisons, and a date far enough from a major model release that the cited set stabilizes. Until one of those changes, the practical advice stands: publish where the engines already read, and make the boring pages answerable.

What we would measure next

Two measurements would make this study more useful: the same question set run monthly to see whether the cited domains are stable, and a second engine with a visible citation trail. Without the first, a snapshot cannot separate a trend from a one-day artifact; without the second, every conclusion is conditional on one provider's retrieval stack.

For reference on how citations are exposed, see the Perplexity help center and the OpenRouter documentation.

Limits

One engine. One day. 27 questions, all of them ours, chosen to cover what we care about. A different question set or a different date would produce a different distribution. Three of the four engines returned no citations at all because they do not expose them — so this study says nothing about how those three answer, only about which one is measurable. Treat it as a snapshot, not a trend.

Disclosure

We operate FreeModel, and our product is one of the things these 27 questions ask about. In this round it received zero citations. Every competitor named above was cited more often than we were.

We are publishing the method and the distribution rather than a conclusion, because the number that matters is the one you can re-run. The full write-up, with the same numbers, is on our site: https://freemodel.online/compare/ai-citation-study/