How do web search APIs compare on factual accuracy benchmarks?
Web search APIs are compared on factual accuracy by holding the AI agent, prompt and judge constant, swapping only the search tool, and scoring the agent's answers on a public question set. A search API accuracy benchmark is a public question set scored this way, so it measures the API and the agent together. The common benchmarks test different skills, from single-fact lookup on SimpleQA to multi-hop reasoning and facts that change over time.
| Benchmark | Publisher and date | What it tests | Size |
|---|---|---|---|
| SimpleQA | OpenAI, Oct 2024 | One short fact per question | 4,326 questions |
| SimpleQA Verified | Google DeepMind, Sep 2025 | SimpleQA with corrected labels | 1,000 prompts |
| FRAMES | Google, Sep 2024 | Multi-hop questions that need several sources | 824 questions |
| BrowseComp | OpenAI, Apr 2025 | Hard-to-find facts across many pages | 1,266 problems |
| FreshQA | FreshLLMs authors, since 2023 | Questions whose answers change over time | Updated weekly |
Published results show retrieval raising scores on these benchmarks, by different amounts. The FRAMES paper reports 0.40 accuracy for a model with no retrieval and 0.66 with multi-step retrieval. Use SimpleQA as a sanity check, FRAMES and BrowseComp for research agents, and FreshQA when your agent answers time-sensitive questions. Published results vary in the agent model, the tool-call limit, whether a page-fetch tool was allowed, the judge, the sample size and whether the best run was kept, so compare cost per correct answer and latency from the same run instead of headline accuracy alone. To run your own comparison, see how to measure search quality for an AI agent.
In Firecrawl's July 2026 eval, a GPT-5.4 agent using Firecrawl Search plus Firecrawl Scrape for page fetches scored 94.7% on the full SimpleQA set (best of two sessions), against 43.8% for the same agent with no search tools, and the methodology is published with the result.