How to evaluate a web search API for AI agents
What the benchmark found. Current output of the method: Parallel basic (parallel-basic) leads F1 at 46.5% across 20 configurations on 45 questions.
What this page compares. Vendor benchmarks test on their own samples with their own agent. This page states the method this benchmark uses so it can be reproduced, and shows the board it produces.
How to read it. Each step below is a decision the numbers depend on. The board at the bottom is the current output of the method.
Method in one paragraph. The same agent, prompt, turn budget and tool schema run against every web search API on 45 questions that each define a company set by three or four constraints. Every returned company is resolved to a canonical identity and compared with a human-reviewed gold set; precision, recall, F1 and exact-set are averaged over three runs per question. The model-only baseline is 0.
Most accurate · Fastest · Cheapest · Find companies by criteria · Search + fetch · Full benchmark
The method, step by step
Why hold the agent constant?
Because the question is which search API is better, not which agent. The same model, prompt, turn budget and tool schema run against every provider; only the search tool changes. Anything else confounds the comparison.
Why does the model-only baseline matter?
If the model can answer without search, the benchmark measures recall from training data, not the search API. Every question here combines three or four constraints; with no search tool at all, GPT-5.6-sol scores F1 20.1% on the private set.
Why report precision and recall, not just accuracy?
A company set can fail by including companies that do not meet every constraint (precision) or by omitting members (recall). An agent that pads and one that under-recalls need different fixes; F1 combines them and exact-set accuracy shows how often the set was perfect.
Why repeated runs?
Agent runs are stochastic. Three runs per provider per question, reported as mean and standard deviation, separate a provider's real level from a lucky run.
Ranked by F1, search-only
| Provider | Endpoint & configuration | F1 | Precision | Recall | Exact set | Median time | Mean turns | Median task cost | API list price |
|---|---|---|---|---|---|---|---|---|---|
| Parallel basic | POST /v1/search mode=basicparallel-basic | 46.5 ± 1.9 | 88.7 ± 0.9 | 34.4 ± 2.0 | 3.7 ± 2.6 | 67.6 s | 7.4 | $1.120$0.070 search$1.050 token cost | $0.005 / search |
| Exa deep | POST /search type=deepexa-deep | 45.4 ± 2.0 | 83.2 ± 3.0 | 33.7 ± 1.4 | 1.5 ± 1.3 | 89.5 s | 7.4 | $0.717$0.156 search$0.556 token cost | $0.012 / search |
| Parallel advanced | POST /v1/search mode=advancedparallel-advanced | 44.2 ± 1.4 | 87.6 ± 3.4 | 32.0 ± 1.6 | 2.2 ± 0.0 | 83.4 s | 7.3 | $0.625$0.070 search$0.565 token cost | $0.005 / search |
| Exa instant | POST /search type=instantexa-instant | 43.3 ± 1.0 | 82.6 ± 3.8 | 32.2 ± 0.5 | 3.0 ± 1.3 | 49.8 s | 7.3 | $0.652$0.091 search$0.559 token cost | $0.007 / search |
| Linkup fast | POST /v1/search depth=fastlinkup-fast | 41.1 ± 1.7 | 82.8 ± 1.0 | 30.3 ± 1.5 | 0.7 ± 1.3 | 64.2 s | 7.4 | $0.923$0.070 search$0.853 token cost | $0.005 / search |
| Tavily advanced | POST /search search_depth=advancedtavily-advanced | 41.1 ± 2.3 | 83.7 ± 4.2 | 29.8 ± 1.7 | 2.2 ± 0.0 | 92.2 s | 7.4 | $1.029$0.224 search$0.808 token cost | $0.016 / search |
| Linkup standard | POST /v1/search depth=standardlinkup-standard | 40.6 ± 0.9 | 84.0 ± 7.9 | 29.7 ± 1.4 | 1.5 ± 2.6 | 72.3 s | 7.3 | $0.937$0.070 search$0.867 token cost | $0.005 / search |
| You | POST /v1/searchextraction_mode=highlights | 38.6 ± 0.8 | 79.3 ± 4.9 | 28.3 ± 0.9 | 4.4 ± 0.0 | 48.2 s | 7.5 | $0.971$0.070 search$0.901 token cost | $0.005 / search |
| You | POST /v1/searchextraction_mode=highlights · knowledge=core | 38.1 ± 0.6 | 80.9 ± 1.6 | 27.4 ± 0.9 | 3.0 ± 1.3 | 47.6 s | 7.5 | $0.895$0.070 search$0.827 token cost | $0.005 / search |
| Parallel fast | POST /v1/search mode=fastparallel-fast | 38.0 ± 2.0 | 79.9 ± 4.6 | 27.5 ± 1.4 | 1.5 ± 1.3 | 53.9 s | 7.6 | $0.460$0.014 search$0.447 token cost | $0.001 / search |
| Perplexity | POST /searchsearch_context_size=low | 37.8 ± 2.1 | 79.3 ± 5.7 | 26.8 ± 1.4 | 2.2 ± 2.2 | 48.9 s | 7.7 | $0.334$0.070 search$0.264 token cost | $0.005 / search |
| Tavily basic | POST /search search_depth=basictavily-basic | 36.5 ± 3.2 | 82.5 ± 1.9 | 25.7 ± 2.7 | 1.5 ± 1.3 | 63.0 s | 7.7 | $0.615$0.112 search$0.504 token cost | $0.008 / search |
| Parallel turbo | POST /v1/search mode=turboparallel-turbo | 34.7 ± 2.4 | 80.0 ± 5.2 | 24.8 ± 2.4 | 1.5 ± 1.3 | 46.4 s | 7.6 | $0.419$0.014 search$0.405 token cost | $0.001 / search |
| Nimble | POST /v2/searchsearch_depth=standard · full_content=false · focus=general | 30.7 ± 1.4 | 70.3 ± 3.3 | 21.3 ± 1.1 | 0.7 ± 1.3 | 53.0 s | 7.7 | $0.467$0.070 search$0.398 token cost | $0.005 / search |
| Firecrawl | POST /v2/searchfirecrawl | 30.4 ± 1.1 | 77.3 ± 4.4 | 20.7 ± 0.7 | 2.2 ± 0.0 | 75.0 s | 7.9 | $0.282$0.070 search$0.212 token cost | $0.005 / search |
| Brave Search | GET /res/v1/web/searchbrave | 28.0 ± 1.7 | 66.9 ± 8.5 | 19.3 ± 0.5 | 0.7 ± 1.3 | 43.5 s | 8.0 | $0.268$0.070 search$0.200 token cost | $0.005 / search |
| TinyFish | GET api.search.tinyfish.aitinyfish | 26.6 ± 1.3 | 64.7 ± 1.5 | 17.9 ± 0.8 | 1.5 ± 1.3 | 64.2 s | 7.9 | $0.212$0.000 search$0.212 token cost | $0 / search |
| Nimble | POST /v2/searchsearch_depth=lite · full_content=false · focus=general | 25.3 ± 1.1 | 62.8 ± 1.9 | 17.6 ± 1.2 | 1.5 ± 1.3 | 67.9 s | 7.9 | $0.234$0.015 search$0.220 token cost | $0.0011 / search |
| Seltz | POST /v1/search scope=companiesseltz-companies | 14.5 ± 0.9 | 40.0 ± 3.1 | 9.4 ± 0.7 | 0.0 ± 0.0 | 55.2 s | 7.3 | $1.751$0.070 search$1.681 token cost | $0.005 / search |
| SERP | GET google-search74.p.rapidapi.comserp | 0.4 ± 0.6 | 0.7 ± 1.3 | 0.3 ± 0.4 | 0.0 ± 0.0 | 31.4 s | 7.8 | $0.103$0.036 search$0.065 token cost | $0.003 / search |
F1, precision, recall, and exact-set accuracy are percentages reported as mean ± sample SD across three independent runs; each run aggregates all 45 questions. SD is measured in percentage points. Median time is the median end-to-end time across all runs for each vendor. Median task cost is the median of LLM $ plus search API $ per agent run. API list price is the PAYG unit rate of the search endpoint the harness calls.
Same sort, with a page-reading tool enabled
| Provider | Endpoint & configuration | F1 | Precision | Recall | Exact set | Median time | Mean turns | Median task cost | API list price |
|---|---|---|---|---|---|---|---|---|---|
| Exa deep | POST /search type=deepexa-deep | 48.2 ± 2.1 | 89.4 ± 1.2 | 36.0 ± 2.2 | 2.2 ± 2.2 | 95.8 s | 7.5 | $0.683$0.156 search+fetch$0.526 token cost | $0.012 / search$0.001 / fetch |
| Perplexity | POST /searchsearch_context_size=high | 46.6 ± 2.0 | 87.7 ± 5.9 | 34.7 ± 1.1 | 2.2 ± 2.2 | 53.9 s | 7.5 | $0.504$0.070 search+fetch$0.441 token cost | $0.005 / search |
| Exa instant | POST /search type=instantexa-instant | 44.9 ± 0.9 | 85.9 ± 2.5 | 33.5 ± 0.9 | 5.2 ± 1.3 | 52.5 s | 7.5 | $0.653$0.091 search+fetch$0.557 token cost | $0.007 / search$0.001 / fetch |
| Parallel basic | POST /v1/search mode=basicparallel-basic | 42.3 ± 1.1 | 81.3 ± 2.8 | 31.3 ± 1.1 | 3.0 ± 1.3 | 68.6 s | 7.5 | $1.089$0.061 search+fetch$1.033 token cost | $0.005 / search$0.001 / fetch |
| Parallel advanced | POST /v1/search mode=advancedparallel-advanced | 42.2 ± 1.1 | 87.6 ± 0.3 | 30.1 ± 1.1 | 2.2 ± 0.0 | 80.9 s | 7.4 | $0.599$0.061 search+fetch$0.538 token cost | $0.005 / search$0.001 / fetch |
| Linkup standard | POST /v1/search depth=standardlinkup-standard | 42.0 ± 1.8 | 90.7 ± 2.0 | 30.5 ± 2.0 | 3.0 ± 3.4 | 81.0 s | 7.5 | $0.911$0.070 search+fetch$0.848 token cost | $0.005 / search$0.001 / fetch |
| Tavily advanced | POST /search search_depth=advancedtavily-advanced | 41.0 ± 1.3 | 89.4 ± 6.0 | 29.1 ± 0.7 | 2.2 ± 0.0 | 92.6 s | 7.3 | $0.898$0.195 search+fetch$0.685 token cost | $0.016 / search$0.0032 / fetch |
| Linkup fast | POST /v1/search depth=fastlinkup-fast | 39.9 ± 1.3 | 85.3 ± 3.6 | 28.6 ± 1.3 | 0.7 ± 1.3 | 68.0 s | 7.3 | $0.903$0.061 search+fetch$0.837 token cost | $0.005 / search$0.001 / fetch |
| Parallel fast | POST /v1/search mode=fastparallel-fast | 39.3 ± 3.3 | 82.3 ± 6.6 | 28.2 ± 2.0 | 2.2 ± 0.0 | 55.1 s | 7.6 | $0.441$0.014 search+fetch$0.427 token cost | $0.001 / search$0.001 / fetch |
| You | POST /v1/searchextraction_mode=highlights | 38.5 ± 1.6 | 85.2 ± 2.7 | 26.9 ± 1.7 | 0.7 ± 1.3 | 45.6 s | 7.4 | $0.864$0.065 search+fetch$0.795 token cost | $0.005 / search$0.001 / fetch |
| You | POST /v1/searchextraction_mode=highlights · knowledge=core | 38.5 ± 2.4 | 78.2 ± 2.5 | 28.3 ± 3.0 | 3.7 ± 1.3 | 48.5 s | 7.4 | $0.894$0.065 search+fetch$0.830 token cost | $0.005 / search$0.001 / fetch |
| Tavily basic | POST /search search_depth=basictavily-basic | 38.1 ± 1.4 | 81.2 ± 5.9 | 27.8 ± 0.7 | 2.2 ± 0.0 | 62.0 s | 7.8 | $0.610$0.112 search+fetch$0.507 token cost | $0.008 / search$0.0032 / fetch |
| Parallel turbo | POST /v1/search mode=turboparallel-turbo | 36.0 ± 3.5 | 83.6 ± 4.0 | 25.0 ± 2.6 | 0.0 ± 0.0 | 48.1 s | 7.7 | $0.414$0.013 search+fetch$0.400 token cost | $0.001 / search$0.001 / fetch |
| Firecrawl | POST /v2/searchfirecrawl | 33.2 ± 2.1 | 83.3 ± 0.8 | 22.7 ± 1.8 | 1.5 ± 1.3 | 82.1 s | 7.9 | $0.295$0.063 search+fetch$0.230 token cost | $0.005 / search$0.0025 / fetch |
| Nimble | POST /v2/searchsearch_depth=standard · full_content=false · focus=general · extract formats=[markdown] | 31.3 ± 1.8 | 73.2 ± 1.9 | 21.7 ± 1.6 | 1.5 ± 1.3 | 59.2 s | 7.9 | $0.424$0.424 token cost | $0.005 / search |
| TinyFish | GET api.search.tinyfish.aitinyfish | 30.2 ± 3.5 | 70.9 ± 11.3 | 20.9 ± 2.5 | 0.0 ± 0.0 | 63.4 s | 7.9 | $0.219$0.000 search+fetch$0.219 token cost | $0 / search$0 / fetch |
| Brave Search | GET /res/v1/web/searchbrave | 29.4 ± 1.6 | 73.5 ± 5.8 | 20.4 ± 0.9 | 1.5 ± 1.3 | 45.1 s | 8.0 | $0.285$0.060 search+fetch$0.222 token cost | $0.005 / search |
| Nimble | POST /v2/searchsearch_depth=lite · full_content=false · focus=general · extract formats=[markdown] | 27.1 ± 1.5 | 68.5 ± 4.4 | 18.7 ± 0.9 | 2.2 ± 0.0 | 72.0 s | 8.0 | $0.237$0.237 token cost | $0.0011 / search |
| Seltz | POST /v1/search scope=companiesseltz-companies | 16.3 ± 1.5 | 49.5 ± 2.4 | 10.2 ± 1.2 | 0.0 ± 0.0 | 60.1 s | 7.5 | $1.741$0.070 search+fetch$1.671 token cost | $0.005 / search |
| SERP | GET google-search74.p.rapidapi.comserp | 0.0 ± 0.0 | 0.0 ± 0.0 | 0.0 ± 0.0 | 0.0 ± 0.0 | 33.4 s | 7.8 | $0.102$0.039 search+fetch$0.066 token cost | $0.003 / search |
F1, precision, recall, and exact-set accuracy are percentages reported as mean ± sample SD across three independent runs; each run aggregates all 45 questions. SD is measured in percentage points. Median time is the median end-to-end time across all runs for each vendor. Median task cost is the median of LLM $ plus search/fetch API $ per agent run. API list price is the PAYG unit rate of the search (and fetch) endpoint the harness calls.
Precision vs recall vs F1 for web search APIs → · BrowseComp vs LiveBrowseComp vs multi-turn company search →