Which search API gives an LLM the most up-to-date answers about recent events?

A benchmark run by cloro, a vendor that sells one of the tested search APIs, found that the two Google SERP APIs delivered the most correct answers to an LLM on 358 time-sensitive FreshQA questions, scoring 88.5% (SerpApi) and 88.3% (cloro), ahead of Brave at 84.9%, Exa at 83.0%, Parallel at 82.7% and Tavily at 81.8%. In a paired test, cloro beat Tavily, Exa and Parallel at p of 0.0012 or lower, while its gap to Brave was not significant (p = 0.065). A preliminary run showed that stripping Google's AI Overview from one API's saved results dropped accuracy from 90.5% to 80.2%, though the test could not separate that effect from index freshness.

On 358 questions whose answers change over time, the two Google SERP APIs in the test gave an LLM the most correct answers: 88.5% and 88.3%. Brave followed at 84.9%, then Exa 83.0% , Parallel 82.7% and Tavily 81.8% . Each API got one search per question, and one model answered from the results alone. The test used FreshQA https://github.com/freshllms/freshqa , a public set of questions whose answers change over time. A third of the 358 questions carry a false premise, and the model has to point it out. Six APIs got the same questions on November 5, 2026. | Search API | Correct on FreshQA 358 | 95% interval | $ per 1,000 searches | Median latency | |---|---|---|---|---| | SerpApi | 88.5% | 84.8-91.4 | $7.25 | 8.20 s | | cloro | 88.3% | 84.5-91.2 | $1.92 | 7.96 s | | Brave Search API | 84.9% | 80.8-88.3 | $5.00 | 1.21 s | | Exa | 83.0% | 78.7-86.5 | $7.00 | 0.74 s | | Parallel | 82.7% | 78.4-86.2 | $5.00 | 2.99 s | | Tavily | 81.8% | 77.5-85.5 | $5.00 | 2.00 s | The price is the list price per 1,000 searches on the plan that covers 100,000 searches a month. SerpApi bills a second search when part of the page loads separately, and that happened on 75% of FreshQA questions. The latency is the median time of the search request alone, without the model. A paired test compares two APIs on the same questions and counts the questions that only one of them got right. The two Google SERP APIs had no difference. cloro was more accurate than Tavily, Exa and Parallel, each at p of 0.0012 or lower. The gap to Brave was not significant p = 0.065 . Of the pairs reported among Brave, Exa, Parallel and Tavily, none showed a difference on these questions. The benchmark measures the answers. It did not measure how old each API's index is, so it cannot say how much of the gap comes from index freshness. Two facts from the setup show where the difference is. What each API returns. The two Google SERP APIs return what Google shows for the query: answer blocks such as the AI Overview, then the organic results with titles, links and snippets. SerpApi ran with no cache=true . For the other four, the reader got a title, a URL and an extract of text for each result. Tavily calls it content , Exa highlights , Parallel excerpts , and Brave a description with extra snippets. The AI Overview. Google writes the AI Overview from several top pages, so the answer is often already in one sentence. The organic results only point at the pages that hold it. On a preliminary run of 243 FreshQA questions, the AI Overview was removed from one Google SERP API's saved results, and nothing else changed. Accuracy fell from 90.5% to 80.2%. So in this test, the answer block at the top of Google's page carried a large part of the accuracy on these questions. For the other four APIs, the reader got no such summary. The test cannot separate that effect from the age of each index. The order changes with the question and with what the agent can wait for. Every API got the same setup: include answer , Exa summary were off. gpt-5.6-luna read at most 30,000 characters of results and answered from them alone, or replied "NOT FOUND". For FreshQA, the prompt gives today's date and asks the reader to point out a false premise. gpt-5.6-sol graded each answer correct, incorrect or not attempted with the unchanged OpenAI simple-evals grader prompt. The harness is one Python file with no packages to install. Set OPENAI API KEY and the keys of the APIs you want to compare, then run: python3 benchmark.py --dataset freshqa --providers cloro,serpapi,tavily,exa,parallel,brave --n 100 --workers 4 Run --n 3 first to find a broken key before you spend the budget. --as-of YYYY-MM-DD sets the date in the reader prompt and the FreshQA filter, so you can read the saved responses of an earlier run again with the same questions. This test is run by cloro, which sells one of the two Google SERP APIs in it. For that reason, the harness, the request settings for each API and every graded answer are public in the benchmark repo https://github.com/cloro-dev/web-search-api-benchmark . If a setting is unfair to an API, change it and run it again. Prices were read on November 5, 2026, from each vendor's pricing page. The figures above come from the web search API benchmark https://cloro.dev/blog/web-search-api-benchmark/ , which also has the results on multi-hop and short factual questions, the cost per correct answer, and the tests of which parts of a search result carry the accuracy.