We built a free tool to benchmark geocoding provider accuracy

François Andrieux — Fri, 24 Apr 2026 10:00:00 GMT

Why we built it

Most teams pick a geocoding provider once and never look back. Usually Google. It is reliable, well-documented, and familiar. But it is also one of the most expensive options, and on some datasets, it is not even the most accurate.

Accuracy varies a lot depending on the country, the address format, and the quality of the input data. A provider that works great for residential addresses might struggle with POI inputs. And it works the other way too: some free, official APIs (like government address registries) outperform paid commercial ones on their home turf. Beyond accuracy, cost and usage rights also differ significantly between providers.

That is why knowing which provider performs best on your data matters. We built a free tool so you can find out in a few minutes, without writing any code and without providing your own API keys.

How the benchmark tool works

You upload a CSV (or Excel file, up to 500 rows), map the columns that form your addresses, and select the providers you want to compare. We run the geocoding on our side; no account, no API keys required from you. The whole thing takes a few minutes. You get a side-by-side accuracy and cost breakdown for each provider, plus a full report by email.

Upload a dataset, map address columns, select providers, and start. No API keys required on your side.

One important detail: we do not count every response as a success. A result is considered valid only if it returns a house number, a street name, and a confidence above 80%. For Google, we also discard GEOMETRIC_CENTER and APPROXIMATE precision types; these place a point somewhere vaguely in an area, which is not useful for industries that need a precise location (logistics, real estate, route planning, insurance).

Accepting every returned coordinate would artificially inflate accuracy scores and hide the silent errors that eventually cause real problems downstream. That's actually a more common problem than you might think: geocoding providers often return false results, and detecting them is not easy. Using confidence scores and other quality rules is the only way to avoid this.

Example 1: 500 US addresses

The first dataset is 500 well-formed US street addresses, the kind of clean, structured input that geocoding providers generally handle best.

Results for 500 US addresses: per-provider accuracy, estimated cost, and Coordable Cascading with +1.4 pts and -97% cost vs a Google-only approach.

Provider	Accuracy	Cost per request
HERE	97.4%	$0.0008
Mapbox	96.8%	$0.0008
US Census	92.2%	Free
Google Geocoding	91.2%	$0.0050
OpenCage	80.6%	$0.0002
Coordable Cascading	98.8%	~$0.0000

Two things stand out. First, HERE outperformed Google by 6 percentage points, while costing 6x less. Second, the US Census (a free, public API that requires no account or key) scored 92.2%, beating Google on this dataset. That is not a fluke: the Census Geocoder is built on TIGER/Line, the official US address dataset, and it handles clean residential addresses very well. We have written more about this in a dedicated comparison.

Running all five providers through Coordable Cascading brought accuracy to 98.8% (494/500 matched) at almost $0 effective cost per request, because US Census resolved the bulk of the file at no charge and the cascade only paid commercial rates for the small fraction it could not handle. That is +1.4 points over the best single provider and -97% cost compared to routing everything through Google.

See geocoding prices in 2026 for a full price comparison.

Example 2: 300 French addresses (mixed POIs and house addresses)

The second dataset is more representative of real-world messiness: 300 French rows mixing residential house addresses with POIs (streets, neighbourhoods, landmarks, business names). No clean housenumber in many rows.

Results for 300 French rows: Google leads single providers on mixed inputs; Coordable Cascading adds +5.6 pts and cuts cost by 93%.

Provider	Accuracy	Cost per request
Google Geocoding	70.7%	$0.0050
HERE	67.3%	$0.0008
Mapbox	62.0%	$0.0008
French BAN	54.3%	Free
OpenCage	31.3%	$0.0002
Coordable Cascading	76.3%	~$0.0004

Google leads here, which makes sense: it handles POIs, business names, and loose address formats better than most providers. The French BAN (the official national address registry, free to use) scores 54.3%, not because it is inaccurate on house addresses, but because it rejects anything that does not look like a structured housenumber + street address. Streets, neighbourhoods, and POI-style rows all come back empty under our strict quality rules. On a dataset of pure French residential addresses, BAN alone typically reaches well above 90%.

The headline numbers stay in the 70% range for this dataset, and that is honest: our quality bar is strict, and mixed input data is genuinely harder. Coordable Cascading reached 76.3% (229/300), +5.6 points over Google, at around $0.0004 per request (a 93% cost reduction). BAN handled the house addresses it could resolve for free; commercial providers covered the rest.

More context on French provider performance: best geocoding providers for France and how we cut French geocoding cost by 67%.

Conclusion

I hope these two examples show how much results can vary from one dataset to another. There is rarely a clear winner among commercial providers, even though some do a genuinely great job. The better strategy is to combine several providers: take advantage of the strengths of each, and prioritize the cheapest ones first. This approach, cascading geocoders, helps reduce the number of incorrect or unmatched geocodings while drastically cutting costs.

What works best truly depends on your country, your address quality, and your specific use case. For example, a clean dataset of US residential addresses is a very different challenge from a mixed French file containing POIs, street-level inputs, neighborhood names, or business names. The right cascade or provider combination for one scenario might not suit the other.

That’s exactly why we built this tool: your results will depend on your actual data and your context. Run it on your own files and see what happens—you may discover that a free public API covers most of your addresses, or that a cheaper commercial provider outperforms the one you're currently paying for. Ultimately, the best strategy is tailored to your country and input quality: experiment, compare, and don’t assume a single provider (or cascade) will be optimal in every situation.

Open the free benchmark tool

Coordable (Posts about cascading)