Brave spent years building its own web crawler and index from scratch — 30 billion pages, more than 100 million daily updates, no dependency on Google or Bing. Linkup, which does not lead with index size at all, is the one claiming the top accuracy spot for 2026 with a 92% F-score on Verified SimpleQA. Two very different bets on what a search API is actually for, and both are being sold to the same person: you, the developer wiring search into an agent.
That tension is the most useful thing happening in this category right now, so let’s sit with it instead of ranking things.
Two definitions of “web search API” are competing
The older shape is the SERP wrapper. You send a query, you get back links and snippets, and everything after that is your problem — fetching pages, stripping boilerplate, chunking, deciding what’s worth keeping. Plenty of products still ship this way, and for some use cases it’s the right call. If you’re building a monitoring tool or a ranking experiment, raw results are the product.
The newer shape is search-plus-extraction: one call, and what comes back is clean content the model can actually read. Reporting on the category describes this as the preferred setup for production agent workflows, and that matches what I keep seeing when I pull these tools apart. The reason is boring and practical. Every extraction pipeline you write yourself is a pipeline you now maintain, and HTML is a hostile environment. Cookie banners, infinite scroll, paywall interstitials, nav menus that outweigh the article. If a vendor absorbs that work and does it competently, you’ve removed a whole class of 2 a.m. debugging.
Why the index question still matters
Brave’s pitch is independence, and I think it’s underrated by people shopping purely on benchmark scores. An API built on someone else’s index inherits that provider’s pricing moves, rate limits, policy changes, and outages. Brave’s own framing is that their search becomes infrastructure rather than a feature endpoint — and whatever you think of the marketing, the underlying argument holds. Owning the crawl means the roadmap is theirs to set.
The flip side: an independent index is a different index. Coverage differs from Google’s, which can be a feature or a problem depending on what your agent is looking for. That’s not a knock, just something you’d want to test against your own query distribution before committing.
About those accuracy claims
Linkup reports a 92% F-score on Verified SimpleQA and positions itself as the most accurate option among sub-second APIs, at €5 per 1,000 standard searches. It also offers native parallel search, which is aimed squarely at agentic workflows where a single user question fans out into six or eight sub-queries that all need to resolve before the model can answer.
Parallel search is one of those features that sounds like a minor optimization and isn’t. If your agent runs searches sequentially, latency stacks. Make it a native capability on the provider side and the whole shape of your orchestration code changes.
Now the honest-reviewer part. I have not independently reproduced that F-score, and neither has anyone else I can point to. Vendor-reported benchmark numbers deserve exactly the trust you’d give any vendor-reported benchmark number: useful as a signal, not as a verdict. SimpleQA-style evaluations measure short factual question answering, which is a real slice of agent behavior but not all of it. If your workload is long-tail technical documentation, pricing pages, or non-English content, a headline accuracy figure tells you less than you’d hope.
The default-tool trap
Tavily and Exa come up as the common LLM-native defaults, and the reporting suggests Linkup edges them on answer quality. I’d read that less as “switch immediately” and more as a reminder that defaults in this space got established fast and stuck. A tool becomes the default because it showed up early with a good SDK and a generous free tier, not because it won a bake-off. Two years later it’s in every tutorial, every starter template, every copy-pasted agent scaffold.
That’s fine until you’re paying production rates for it.
What I’d actually do
- Pull 50 to 100 real queries from your logs. Not synthetic ones. Real ones, including the ugly ambiguous stuff.
- Run them through two or three providers and read the output yourself. Not the scores — the actual returned text.
- Check what the extraction gives you. Is it clean prose, or clean prose plus a footer’s worth of garbage?
- Time the full agent loop, not the single call. Parallel search only pays off if your orchestration can use it.
- Keep the interface swappable. Wrap the provider behind a thin internal function so switching later costs an afternoon, not a sprint.
The category reorganized itself around agents, which means the vendors are now competing on things that are genuinely hard to fake: index ownership, extraction quality, latency under fan-out. That’s a better fight than the snippet-wrapper era ever produced. Just don’t let anyone’s benchmark do your evaluation for you.
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