This work presents the first high-resolution map of soil organic carbon (SOC) in mainland France, including soils below 30 cm. The research was performed within the framework of GlobalSoilMap (GSM). SOC predictions for different depth layers (0–5 cm, 5–15 cm, 15–30 cm, 30–60 cm, 60–100 cm and > 100 cm) were made at 90 and 500 m resolution for mainland France, along with their upper and lower confidence intervals. The maps were developed using data mining and an elaborate cross-validation scheme. The 90 m maps were compared to 500 m resolution GlobalSoilMap maps and the SoilGrids1km (SG1km) product. The latter is a global model for predicting soil properties for the same depth layers, at 1 km resolution. At 90 and 500 m resolution, the predicted SOC content was unbiased and showed good agreement with the measured SOC, despite the poor model diagnostics and decrease of performance with depth. It was found that the subsoil (> 30 cm) carbon pool for France contributes 49% to the total soil carbon stock. The use of coarser resolution prediction grids resulted in smoother spatial patterns and wider confidence intervals; however, it did not bias the estimated carbon stocks. Applying GlobalSoilMap specifications to France, using a large soil dataset and all the exhaustive spatially available data outperformed SG1km predictions. The overall spatial patterns of the SG1km SOC content were found to be very similar to the GlobalSoilMap maps. However, the SG1km overestimated the SOC content and carbon stocks (> 75% for the total carbon stock, and 100% for the stocks below 30 cm) and showed a similar spatial distribution over the different soil depth layers. The main reason for the overestimation was that the local data used in SG1km was rather small (56 samples) and not representative in terms of SOC content or represented soil types; the profiles had far higher SOC content and this may have propagated in the modelled vertical profile and the kriging part of the residuals. Improvements for SG1km may entail the use of a representative national subsample from large national soil databases. Furthermore, a bottom-up approach such as GlobalSoilMap may be more favourable when considering prediction accuracies, data privacy policies and local acceptance of generated products.