Artificial Analysis Search Index: Benchmark and Leaderboard for Agent Search APIs

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Artificial Analysis has launched a benchmark leaderboard evaluating search APIs used by AI agents to retrieve web information. The index assesses 19 products across 9 providers on quality, latency, and cost using GPT-5.6 Luna (medium) as the uniform answer model.

Key points

  1. Benchmarks 19 search API products from 9 providers, including Perplexity, Parallel, Brave, You.com, Exa, Firecrawl, and TinyFish.
  2. Quality scores represent an equal-weighted average across DeepSearchQA (F1), BrowseComp (exact match), and AA-Omniscience (accuracy).
  3. Uses GPT-5.6 Luna (medium) without search tools as the model-only baseline (score: 33/100).
  4. Perplexity Search (medium) leads overall quality with a score of 80 (+47 points over baseline).
  5. Parallel Search (fast) achieved the lowest latency per task at 18.9s; Keenable Search (realtime) achieved the fastest individual query speed at 0.34s.
  6. Total task cost factors in both the search API pricing and token usage (input, output, reasoning) generated by the answer model.

Why it matters

AI agent performance heavily depends on real-time web retrieval. Suboptimal search results lead to repeated queries and increased reasoning token consumption, driving up both latency and inference costs. This benchmark provides standardized evaluation data for selecting retrieval tools.

Analysis

The benchmark demonstrates that search API selection directly impacts LLM operational costs. While web search significantly improves factuality (lifting GPT-5.6 Luna's score from 33 up to 80 with Perplexity), poor retrieval results force models into deeper reasoning loops and extra search calls, inflating downstream token costs. The Pareto curves presented allow engineering teams to balance retrieval accuracy against total latency and API expenditure based on specific agent workloads.

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Summary generated by AI (Gemini 3.8 Flash, 27 September 2026). It may contain errors – the original source is authoritative.

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