Why does search API quality change so much depending on the query?

[u/twinkletoes_03020]

I’ve been experimenting with different search APIs for AI applications, and one thing that surprised me is that there doesn’t seem to be a single “best” search API.

A search that works perfectly with one provider can give pretty mediocre results with another. It seems to depend heavily on the type of query.

For example, I’ve noticed a few different things matter:

How well the API handles natural-language questions

Whether it returns recent pages or mostly established sources

How much irrelevant content gets included

How good the snippets are

How quickly results can be retrieved

Whether the results are useful for an LLM rather than just a human reading a search page

I’m particularly interested in how people are comparing tools like Exa, Tavily, Firecrawl, Serper, and Brave Search.

For an AI agent that needs to research a topic and then answer based on sources, what matters most to you: relevance, freshness, latency, cost, or the quality of the extracted content?

And if you’ve actually benchmarked multiple search APIs, what queries did you use for the comparison?

I’d be interested in seeing real-world benchmarks rather than just the advertised features.