Artificial Analysis has introduced the Search Index, a benchmark designed to compare how search APIs perform when they are used by AI agents. The focus is not just whether a provider can return links quickly. The index looks at quality, cost, and speed together, because an agent’s real performance depends on all three.
The first group of providers in the benchmark includes Parallel, Exa, Firecrawl, You.com, Tavily, Keenable, and Brave. Each is tested inside the same setup, with GPT-5.6 Luna as the model. Only the search provider changes, which makes the comparison easier to read.
What the Search Index measures
The Search Index is built around a standardized agent environment. The agent runs on Stirrup, an open-source framework from Artificial Analysis, and receives 25 runs per task to search and retrieve web pages.
That design matters because search APIs for AI agents are not being judged as standalone search boxes. They are being tested as tools inside an agent workflow, where the model may need to search, inspect pages, and decide whether the information it found is enough to answer the task.
The index combines three equally weighted benchmarks:
- DeepSearchQA, which contains 900 research questions and requires multiple search queries.
- A BrowseComp subset, which tests 200 hard-to-find facts that require multi-step browsing.
- AA-Omniscience, which covers 600 questions across six knowledge domains.
A tool-free baseline is also included. In that comparison, the model answers without using a search tool. That baseline helps show what the search API adds beyond the model’s own ability to respond.
Why quality can change the cost equation
The benchmark highlights a practical point about agentic search: better search quality can reduce total cost. If an agent receives useful results early, it may need fewer follow-up steps and fewer tokens to complete the task.
Artificial Analysis reports that Parallel Search (advanced) reduces token use by over 40 percent compared with the Basic version. That does not mean every individual search call is cheaper. In this case, per-task search costs rise, but the total cost still comes in lower.
The comparison given is $0.084 versus $0.11. The reason is straightforward: a stronger search result can make the overall agent loop shorter, even if the search component itself costs more per task.
For teams evaluating search APIs, that makes the headline price only part of the picture. The benchmark suggests that search quality can affect how much reasoning and browsing the model has to do after the first result set appears.
Speed alone does not settle the ranking
The Search Index also separates raw query speed from total task speed. A search API can respond quickly and still fail to make the full agent task faster if the results are not strong enough.
Parallel Search (turbo) has the shortest response time per query in the source comparison: 0.51 seconds versus 1.03 seconds for Basic. But the same comparison shows a lower quality score, 67 versus 73.
That lower quality forces the agent to run more passes. As a result, total time per task ends up about the same. The important lesson is that an agent workflow is a chain of decisions, searches, and page retrievals. Faster individual links in that chain do not guarantee a faster final answer.
This is why the benchmark’s three-part framing matters. Quality, cost, and speed are connected. A provider that is fast but weaker on relevance may push more work onto the model. A provider that costs more per search may still reduce the final bill if it helps the model finish with fewer tokens.
Which providers stand out
According to Artificial Analysis, Parallel, Firecrawl, and Parallel (turbo) deliver the best mix of cost and performance in the initial Search Index results. The article does not describe that as a permanent ranking. It presents the current lineup and notes that other providers can apply to join the benchmark.
That open-ended structure is important because the search API market for AI agents is still being evaluated through new measurement methods. The benchmark is not just asking which service is fastest, or which service is cheapest, or which service produces the highest quality score in isolation. It is asking which provider helps an agent complete work efficiently.
The full methodology is public, according to the source. That gives providers and users a way to inspect how the benchmark is built, including the standardized agent setup and the benchmark mix behind the final index.
What this means for AI agent search
The Search Index points to a broader shift in how AI infrastructure is being judged. Search APIs are becoming part of the reasoning loop for AI agents, not just an external lookup feature. In that role, their value depends on how well they help the model move from question to answer.
A strong search API can reduce repeated browsing. A weaker one can make the agent spend more time and tokens checking, searching again, or pulling more pages. The same model, GPT-5.6 Luna, is used across the tests so the provider comparison focuses on the search layer.
For readers following AI agents, the benchmark offers a cleaner way to think about search performance. The best option is not necessarily the one with the fastest query response or the lowest visible search cost. It is the one that helps the agent complete the task with the best overall balance of quality, cost, and speed.