This work investigates the relationship between the average export prices and the distance between the origin and the destination market in international trade. Distance between trading partners obviously stands at the core of I international trade literature and is strictly related with the issue of how countries and firms compete on export markets when transport costs become increasingly stiff. Heterogeneous-Firm Trade (HFT) models predict that only most competitive firms are able to export on distant markets, where it is more difficult to recover from freight costs. However, this simple concept does not lead to unambiguous predictions on the spatial pattern of average export f.o.b. prices. \\\\\\\\\\\\\\\\r\\\\\\\\\\\\\\\\nThis work uses bilateral product-level data for five top-exporters (US, Germany, China, France, Italy) and all their world partners in order to test the main hypotheses derivable on this issue from theoretical literature. An huge dataset of bilateral international trade flows, the BACI dataset of CEPII, provides data both on values and quantities, allowing to construct products’ unit values (at the 6 digit level of the Harmonized System), approximating average exports’ prices. These data are used as the dependent variable of the econometric test and regressed on a set of explanatory variables: the distance of the destination market from the exporter and other importer’s characteristics. Thus, this test assesses which of the considered theoretical models fit the data best, shedding some light both on the sign and on the determinants of the spatial pattern of average prices. The main finding of this work is that theoretical models not embedding a quality dimension for products do not seem to fit the data very well: it is clear from the econometric test that a positive correlation between average exports’ prices and distance often emerges, denoting a “quality competition” pattern in addition to a possible alternative pattern of “price competition”. Moreover, theoretical models with a quasi-linear demand function (in contrast with a CES one) are able to better explain same evidences emerging from the data. \\\\\\\\\\\\\\\\r\\\\\\\\\\\\\\\\n