Best web search API by accuracy in this benchmark: Exa fast at 99.3%. Fastest search: Parallel turbo, 348ms (71.3% accuracy). Lowest recorded search price: TinyFish, $0.00 per 1,000 queries within its free-tier limits.
Choose a web search provider for an LLM application that needs current, source-backed answers. Compare 11 providers across 19 configurations, including Exa, Tavily, Brave, Parallel and Firecrawl, on the same questions.
Updated 20 September 2026. Last measured 12 September 2026.
Search accuracy on 300 current company-fact questions, using a fixed extraction process. Latency is mean search-request time. Prices are the benchmark-recorded rate basis, with the same configurations used for the quality results.
| Rank | Provider & configuration | Accuracy | Mean search time | Recorded $ / 1,000 queries | Mean snippet tokens | Official docs |
|---|---|---|---|---|---|---|
| 1 | Exa fasttype=fast/search | 99.3% | 652ms | $7.00type=fast · up to 10 results | 1,987 | Official docs ↗ |
| 2 | Exa instanttype=instant/search | 97.7% | 398ms | $7.00type=instant · up to 10 results | 2,128 | Official docs ↗ |
| 3 | Perplexity lowsearch_context_size=low/search | 97.3% | 1.38s | $5.00Search API · POST /search | 476 | Official docs ↗ |
| 4 | Linkup fastdepth=fast · outputType=searchResults/v1/search | 96.7% | 1.57s | $5.00depth=fast · searchResults | 3,022 | Official docs ↗ |
| 5 | SERPlimit=10google-search74.p.rapidapi.com | 96.0% | 751ms | $3.00Pro overage $0.003/request | 497 | Official docs ↗ |
| 6 | Firecrawlfirecrawl-search/v2/search | 95.3% | 510ms | $5.002 credits / 10 results | 678 | Official docs ↗ |
| 7 | Brave Search llm-contextcount=10/res/v1/llm/context | 94.0% | 601ms | $5.00Search plan · LLM Context | 2,064 | Official docs ↗ |
| 8 | Brave Search webcount=10 · result_filter=web/res/v1/web/search | 93.3% | 630ms | $5.00Recorded search-request rate; see official terms. | 817 | Official docs ↗ |
| 9 | Parallel basicmode=basic/v1/search | 93.3% | 1.68s | $5.00mode=basic · 10 results | 2,330 | Official docs ↗ |
| 10 | Nimble standardsearch_depth=standard · full_content=false · focus=general/v2/search | 93.0% | 861ms | $5.00search_depth=standard · full_content=false | 2,730 | Official docs ↗ |
| 11 | Tavily advancedsearch_depth=advanced/search | 93.0% | 4.29s | $16.002 credits · $0.008 PAYG | 2,210 | Official docs ↗ |
| 12 | Linkup standarddepth=standard · outputType=searchResults/v1/search | 92.0% | 2.55s | $5.00depth=standard · searchResults | 2,983 | Official docs ↗ |
| 13 | TinyFishfree · 30 req/min capapi.search.tinyfish.ai | 92.0% | 2.62s | $0.0030 req/min cap | 441 | Official docs ↗ |
| 14 | You highlights coreextraction_mode=highlights · knowledge=core/v1/search | 92.0% | 889ms | $5.00highlights · knowledge=core · same Web Search price assumption | 2,862 | Official docs ↗ |
| 15 | You highlightsextraction_mode=highlights/v1/search | 90.7% | 628ms | $5.00highlights included in Web Search | 2,837 | Official docs ↗ |
| 16 | Tavily basicsearch_depth=basic/search | 87.7% | 1.88s | $8.001 credit · $0.008 PAYG | 1,639 | Official docs ↗ |
| 17 | Parallel fastmode=fast/v1/search | 86.0% | 942ms | $1.00mode=fast · 10 results | 1,839 | Official docs ↗ |
| 18 | Nimble litesearch_depth=lite · full_content=false · focus=general/v2/search | 75.3% | 3.15s | $1.10search_depth=lite · full_content=false | 413 | Official docs ↗ |
| 19 | Parallel turbomode=turbo/v1/search | 71.3% | 348ms | $1.00mode=turbo · 10 results | 1,853 | Official docs ↗ |
Provider names alone hide useful differences. A fast configuration and a deeper configuration from the same vendor can return different evidence at different prices. Compare the exact endpoint and mode used in the table. Count providers separately from configurations, and use the linked official documentation to reproduce the request.
Start with a minimum accuracy level for the questions your application answers. Among configurations that meet it, compare search latency and price per 1,000 queries. Latency here measures the search request. Your application's response time also includes retrieval processing and model generation. For an agent making repeated searches, use the separate developer or deep-search benchmark.
A successful retrieval gives the model an answer-bearing result, a source URL and enough context to use it. This board measures 300 current company-fact questions using search results and a fixed extraction process. Treat it as evidence for the retrieval step in an LLM application; test your own domain, prompts and final answers before selecting a provider.
Multiply the per-1,000-query price by monthly queries divided by 1,000. For example, a $5 rate implies $500 for 100,000 queries before credits, tax and other services. Include model input tokens, extra result charges and optional scraping in the full budget. The prices shown are the benchmark's recorded rate basis; the source links give the vendor's current terms.
Search accuracy on 300 current company-fact questions, using a fixed extraction process. Latency is mean search-request time. Each row is one measured configuration. Missing measurements appear as a dash.
Open benchmark code ↗ · Public dataset sample ↗ · All benchmark methods →
Best web search API by accuracy in this benchmark: Exa fast at 99.3%. Fastest search: Parallel turbo, 348ms (71.3% accuracy). Lowest recorded search price: TinyFish, $0.00 per 1,000 queries within its free-tier limits.
Search accuracy on 300 current company-fact questions, using a fixed extraction process. Latency is mean search-request time.
The recorded search endpoint's request price. Model-token spend, optional scraping and plan conditions are separate. For rows with task measurements, median task cost is shown in its own column. Check the linked provider terms for current pricing.