Best search API to find companies by investor, location and funding stage
Ranked on the multi-hop search task of the web search benchmark. Each question combines three or four constraints.
Parallel basic (parallel-basic) leads F1 at 46.5% on this run.
Every question here is a company set defined by three or four constraints (investor backing, headquarters geography, accelerator, funding stage, sector). F1 rewards the API that lets the agent recover the complete set without padding. This is search-driven discovery, not a company database lookup.
Most accurate · Fastest · Cheapest · Find companies by criteria · Search + fetch · Full benchmark
Ranking, sorted by f1
| Provider | Endpoint & configuration | F1 | Precision | Recall | Exact set | Median time | Mean turns | Median task cost | API list price |
|---|---|---|---|---|---|---|---|---|---|
| Parallel | 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 | 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 | 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 | 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 | 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 | 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 | 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 |
| Parallel | 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 |
| Parallel | 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 |
| You | POST /v1/searchyou | 33.1 ± 2.4 | 75.7 ± 3.1 | 23.1 ± 1.9 | 1.5 ± 1.3 | 46.2 s | 7.7 | $0.477$0.070 search$0.408 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 |
| 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 (RapidAPI) | 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/fetch API $ per agent run. API list price is the PAYG unit rate of the search (and fetch) endpoint the harness calls.
Search plus fetch, same sort
| Provider | Endpoint & configuration | F1 | Precision | Recall | Exact set | Median time | Mean turns | Median task cost | API list price |
|---|---|---|---|---|---|---|---|---|---|
| Exa | 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 | 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 | 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 | 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 | 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 | 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 | 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 | 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 |
| Parallel | 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 |
| You | POST /v1/searchyou | 34.0 ± 0.9 | 78.8 ± 0.6 | 23.8 ± 1.2 | 3.0 ± 1.3 | 47.4 s | 7.8 | $0.489$0.065 search+fetch$0.427 token cost | $0.005 / 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 |
| 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 |
| 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 (RapidAPI) | 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.
Common questions
Which API can find a list of companies that match several criteria?
Parallel basic (parallel-basic) leads F1 at 46.5% on 45 questions that each define a company set by three or four constraints, such as investor, headquarters location, accelerator and funding stage. The agent is held constant; only the web search API changes.
Is this a company database API or a web search API?
Web search. A company database (enrichment or lookup API) answers from a stored record and cannot assemble a set defined by criteria it does not index. This benchmark measures whether a research agent can build that set from live search results. The model-only baseline is 0: none of these sets is recoverable from training data.
Can I use this to build account lists for GTM?
That is the workflow it approximates: automated account-list building, investor or accelerator portfolio discovery, and geography-scoped market maps. For similar-companies (lookalike) list building, which is a different job, see the lookalikes benchmark.