---
title: "Real-time funding data API (2026): freshest startup funding rounds, measured | Openbenchmarks"
description: "Which funding data API has the freshest, most up-to-date data on new funding rounds? An independent 2026 benchmark measures programmatic funding data providers against dated snapshots, each one a cohort of companies that announced a round inside a one-month window and was put to every provider days after it closed. Exposes real update lag, plus median latency for real-time agent flows."
url: https://openbenchmarks.com/company-funding/real-time-funding-data
---

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[benchmarks](https://openbenchmarks.com/) / [company funding](https://openbenchmarks.com/company-funding) / real-time funding data 

funding data · freshness, measured

# Real-time funding data

Funding rounds are announced continuously, and providers ingest them at different speeds. “Real-time” funding data is therefore a claim you can test, and this benchmark tests it with **dated snapshots**. Each one is a fresh cohort of companies that announced a round inside a one-month window, verified against the primary announcement, then put to every provider within days of the window closing. Four snapshots have run so far, covering rounds announced between 16 Jun 2026 and 13 Sept 2026. The cohort is built after the window closes, so no provider can have been given it in advance. One that has not ingested a round yet returns a stale stage, and that shows up in its score.

Freshness surfaces as **correct latest-stage yield inside the snapshot window**; for agent flows, median latency is measured beside it. 21 programmatic endpoints are scored here, across 227 companies in total. Crunchbase and Harmonic were mapped in from reviewed exports, so their stages are judged the same way, but an export has no query moment relative to an announcement and carries no latency or cost.

The 17 measured providers are Apollo, CompanyEnrich, Crunchbase, Crustdata, Exa, Explorium, Fiber, Firecrawl, Fundable, Harmonic, Nimble, Ocean.io, Parallel, People Data Labs, PredictLeads, Seltz, and ZoomInfo. Several are measured on more than one endpoint, for 23 arms in total.

[open data + code Inputs, normalized provider outputs, and evaluation code are public in openbenchmarks-labs/company-funding. github → ](https://github.com/openbenchmarks-labs/company-funding "Open the public data + code mirror on GitHub") 

\[01\] measured results

## How each provider scored on rounds announced inside the window.

The same request went to every endpoint, so a provider that missed a round missed it on ingestion rather than on phrasing. This is the rolling freshness board, pooled across every dated snapshot, so each row carries the number of companies that provider was measured on. The [company funding benchmark](https://openbenchmarks.com/company-funding) has other vendors that were benchmarked on the same input data. The [methodology](https://openbenchmarks.com/company-funding#methodology) covers cohort design, Ground Truth and the judging rules.

benchmarks/funding/freshness rolling 

__Funding providers compared on rounds announced in the last 30 days (window ending 2026-09-13)__
| Provider | Endpoint & configuration | Measured on | Latest stage correct | Correct when returned | Latest stage returned | Funding fields returned | Fields returned | Median latency | Est. cost | Official docs | Company identified |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Long Running Agent APIs | | | | | | | | | | | |
| [ Exa ](https://exa.ai) | POST /agent/runs effort=medium · JSON schema | **100** company measurements · 2 snapshots | 97.0% | 97.9% | 96.0% | 73.6% | 3.7 / 5 | 27,293 ms | **$10.00** $0.10 / request (medium effort) | [Exa official docs ↗ ](https://exa.ai/docs/reference/agent-api-guide) | 100.0% 100/100 domains |
| [ Firecrawl ](https://firecrawl.dev) | POST /v2/agent model=spark-2 · JSON schema · maxCredits capped per run | **50** company measurements · 1 snapshot | 100.0% | 100.0% | 98.0% | 87.2% | 4.4 / 5 | 102,203 ms | **$1.98** $0.59 / 1,000 credits | [Firecrawl official docs ↗ ](https://docs.firecrawl.dev/features/agent) | 100.0% 50/50 domains |
| [ Firecrawl ](https://firecrawl.dev) | POST /v2/agent model=spark-1-mini · JSON schema · maxCredits capped per run | **100** company measurements · 2 snapshots | 96.0% | 96.9% | 97.0% | 94.4% | 4.7 / 5 | 92,998 ms | **$9.95** $0.59 / 1,000 credits | [Firecrawl official docs ↗ ](https://docs.firecrawl.dev/features/agent) | 100.0% 100/100 domains |
| [ Parallel ](https://parallel.ai) | POST /v1/tasks/runs processor=core · structured JSON output | **181** company measurements · 3 snapshots | 95.0% | 96.0% | 95.6% | 86.7% | 4.3 / 5 | 62,142 ms | **$4.53** $25 / 1,000 Task runs | [Parallel official docs ↗ ](https://docs.parallel.ai/api-reference/tasks/create-task-run) | 100.0% 181/181 domains |
| Web search APIs | | | | | | | | | | | |
| [ Exa ](https://exa.ai) | POST /search type=instant · JSON schema | **100** company measurements · 2 snapshots | 98.0% | 99.0% | 95.0% | 90.2% | 4.5 / 5 | 1,618 ms | **$0.70** $0.007 / request | [Exa official docs ↗ ](https://exa.ai/docs/reference/search) | 100.0% 100/100 domains |
| [ Exa ](https://exa.ai) | POST /search type=deep-reasoning · JSON schema | **181** company measurements · 3 snapshots | 95.6% | 96.0% | 96.7% | 84.8% | 4.2 / 5 | 9,916 ms | **$2.71** $0.015 / request | [Exa official docs ↗ ](https://exa.ai/docs/reference/search) | 100.0% 181/181 domains |
| [ Nimble ](https://www.nimbleway.com) | POST /v2/search search\_depth=standard · focus=general · full\_content=false · 10 results · source-only funding extraction | **46** company measurements · 1 snapshot | 84.8% | 94.6% | 80.4% | 67.8% | 3.4 / 5 | 3,907 ms | **$1.18** $5 / 1,000 searches + extraction tokens | [Nimble official docs ↗ ](https://docs.nimbleway.com/api-reference/search/search) | 95.7% 44/46 domains |
| [ Nimble ](https://www.nimbleway.com) | POST /v2/search search\_depth=lite · focus=general · full\_content=false · 10 results · source-only funding extraction | **46** company measurements · 1 snapshot | 63.0% | 92.9% | 60.9% | 38.7% | 1.9 / 5 | 3,829 ms | **$0.23** $1.10 / 1,000 searches + extraction tokens | [Nimble official docs ↗ ](https://docs.nimbleway.com/api-reference/search/search) | 76.1% 35/46 domains |
| [ Parallel ](https://parallel.ai) | POST /v1/responses model=parallel · reasoning.effort=medium · text.format JSON schema | **100** company measurements · 2 snapshots | 90.0% | 90.6% | 96.0% | 82.6% | 4.1 / 5 | 17,020 ms | **$5.00** $50 / 1,000 Responses requests (medium reasoning) | [Parallel official docs ↗ ](https://docs.parallel.ai/api-reference/responses-api/create-response) | 100.0% 100/100 domains |
| [ Seltz ](https://seltz.ai) | POST /v1/answer scope=news · response\_format JSON | **100** company measurements · 2 snapshots | 71.0% | 85.9% | 78.0% | 57.8% | 2.9 / 5 | 2,109 ms | **\--** Pricing not disclosed | [Seltz official docs ↗ ](https://docs.seltz.ai/api-reference/answer) | 100.0% 100/100 domains |
| [ Seltz ](https://seltz.ai) | POST /v1/answer scope=companies · response\_format JSON | **100** company measurements · 2 snapshots | 22.0% | 32.8% | 67.0% | 62.6% | 3.1 / 5 | 2,118 ms | **\--** Pricing not disclosed | [Seltz official docs ↗ ](https://docs.seltz.ai/api-reference/answer) | 100.0% 100/100 domains |
| GTM data providers | | | | | | | | | | | |
| [ Apollo ](https://www.apollo.io) | GET /api/v1/organizations/enrich | **181** company measurements · 3 snapshots | 59.7% | 69.5% | 85.1% | 70.1% | 3.5 / 5 | 320 ms | **$3.44** $0.0196 / modeled credit | [Apollo official docs ↗ ](https://docs.apollo.io/reference/organization-enrichment) | 97.2% 176/181 domains |
| [ CompanyEnrich ](https://companyenrich.com) | GET /companies/enrich | **181** company measurements · 3 snapshots | 12.7% | 19.1% | 63.5% | 56.8% | 2.8 / 5 | 334 ms | **$1.77** $0.0098 / credit | [CompanyEnrich official docs ↗ ](https://docs.companyenrich.com/reference/get%5Fcompanies-enrich) | 96.7% 175/181 domains |
| [ Crunchbase (exported dataset) ](https://www.crunchbase.com) | Crunchbase self-serve plan export identity-mapped export · not an API run | **81** company measurements · 1 snapshot | 95.1% | 98.7% | 96.3% | 92.6% | 4.6 / 5 | \-- | **\--** Not comparable | [Crunchbase (exported dataset) official docs ↗ ](https://data.crunchbase.com/docs/using-the-api) | 100.0% 81/81 domains |
| [ Crustdata ](https://crustdata.com) | POST /company/enrich exact\_match=true · fields=\[funding\] · 25 domains/batch | **181** company measurements · 3 snapshots | 81.2% | 90.1% | 89.5% | 71.3% | 3.6 / 5 | 1,537 ms | **$54.30** $0.30 / response credit | [Crustdata official docs ↗ ](https://docs.crustdata.com/company-docs/enrichment/introduction) | 96.1% 174/181 domains |
| [ Explorium ](https://www.explorium.ai) | POST /v1/businesses/match + bulk\_enrich funding\_and\_acquisition enrichment | **181** company measurements · 3 snapshots | 5.5% | 13.6% | 36.5% | 40.4% | 2.0 / 5 | 715 ms | **$11.00** $0.04 / request proxy | [Explorium official docs ↗ ](https://explorium-api.readme.io/reference/match%5Fbusinesses) | 44.8% 81/181 domains |
| [ Fiber ](https://www.fiber.ai) | POST /v1/company-search | **181** company measurements · 3 snapshots | 74.6% | 80.2% | 92.3% | 88.3% | 4.4 / 5 | 615 ms | **$4.11** $0.020 / credit | [Fiber official docs ↗ ](https://api.fiber.ai/docs/) | 97.8% 177/181 domains |
| [ Fundable ](https://www.tryfundable.ai) | GET /api/v1/company domain query parameter | **100** company measurements · 2 snapshots | 84.0% | 92.3% | 91.0% | 91.0% | 4.5 / 5 | 1,300 ms | **$3.53** $0.05 / credit | [Fundable official docs ↗ ](https://docs.tryfundable.ai/) | 91.0% 91/100 domains |
| [ Harmonic (exported dataset) ](https://www.harmonic.ai) | Supplied Harmonic company export identity-audited mapping · not an API run | **81** company measurements · 1 snapshot | 81.5% | 81.5% | 100.0% | 91.8% | 4.6 / 5 | \-- | **\--** Pricing not disclosed | [Harmonic (exported dataset) official docs ↗ ](https://console.harmonic.ai/docs/api-reference/introduction) | 100.0% 81/81 domains |
| [ Ocean.io ](https://www.ocean.io) | POST /v2/enrich/company | **181** company measurements · 3 snapshots | 11.1% | 19.4% | 54.1% | 29.4% | 1.5 / 5 | 575 ms | **$1.11** $0.064 / credit | [Ocean.io official docs ↗ ](https://docs.ocean.io) | 96.7% 175/181 domains |
| [ People Data Labs ](https://www.peopledatalabs.com) | GET /v5/company/enrich | **181** company measurements · 3 snapshots | 13.3% | 19.5% | 65.2% | 51.4% | 2.6 / 5 | 325 ms | **$17.38** $0.10 / successful match | [People Data Labs official docs ↗ ](https://docs.peopledatalabs.com/docs/reference-company-enrichment-api) | 92.3% 167/181 domains |
| [ PredictLeads ](https://predictleads.com) | GET /api/v3/companies/{domain}/financing\_events | **181** company measurements · 3 snapshots | 68.0% | 82.4% | 81.8% | 64.3% | 3.2 / 5 | 561 ms | **$4.83** $0.04 / request · 100 free | [PredictLeads official docs ↗ ](https://docs.predictleads.com/guide/extended%5Fcompanies%5Fdataset) | 84.0% 152/181 domains |
| [ ZoomInfo ](https://gtm.ai) | gtm companies enrich --file 10 domains/batch · 4 funding fields | **181** company measurements · 3 snapshots | 72.4% | 85.0% | 84.5% | 85.8% | 4.3 / 5 | 958 ms | **$18.10** $0.10 / credit launch promo · $0.35 standard | [ZoomInfo official docs ↗ ](https://gtm.ai/docs/mcp/tools/enrich-companies) | 90.6% 164/181 domains |

\[02\] the dated snapshots

## What each snapshot covers

The board above pools these together, and each row shows the number of companies that provider was measured on. That number differs by provider because a snapshot is a closed cohort and providers joined the benchmark at different points. A provider present for one snapshot is scored on that cohort alone, so its figures sit on a smaller sample than one present for all of them.

| Snapshot | Rounds announced | Companies | Providers measured |
| --- | --- | --- | --- |
| **September 2026** | 14 Aug 2026 to 13 Sept 2026 | 46 | 2 |
| **August 2026** | 27 Jul 2026 to 26 Aug 2026 | 50 | 19 |
| **August 2026** | 16 Jul 2026 to 15 Aug 2026 | 50 | 18 |
| **July 2026** | 16 Jun 2026 to 15 Jul 2026 | 81 | 13 |

Snapshots are never rebuilt or backfilled once they close. A provider added later is measured on the snapshots that came after it, and the rounds in an earlier window stay judged against what was verifiable at the time.

\[03\] faq

## Common questions about funding data freshness

### What does real-time actually mean for funding data?

Two separate things that are easy to conflate. One is how quickly a provider learns that a round was announced. The other is how fast the call returns. A provider can answer in milliseconds and still not know about a round from last week, so both are measured and reported apart.

### How is update lag measured rather than claimed?

Each cycle builds a new cohort of companies that announced funding inside the trailing window, verifies the events against primary sources, then runs every provider against it while the rounds are still recent. Because the cohort is new each time, a provider cannot have backfilled it in advance.

### Why are recent rounds scored on their own board?

Because finding a round announced this week and holding a correct record of one from last year are different capabilities. Averaging them produces a number that describes neither, and hides the vendors that are strong at exactly one of the two.

### What breaks downstream when funding data lags?

Anything triggered by the round. A sequence that fires on a raise goes out after the moment has passed, and territory or scoring rules that key on stage route the account wrongly until the record catches up. That is why lag is worth measuring separately from whether the stage is eventually right.

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