The partitioning of net ecosystem exchange of CO2 (NEE) into photosynthesis and respiration can be challenging and is often associated with assumptions that yield unknown amounts of uncertainty, thereby hindering model development. This occurs because we are inferring two pieces of information from one equation and measurement, NEE. While there are multiple methods for partitioning NEE, each has unique limitations that are difficult to evaluate as these techniques are often not implemented simultaneously. Here we present an analysis of multiple partitioning methods (a non-linear regression model known as the Reichstein method, artificial neural networks (ANN), stable carbon isotopes, and soil respiration (Rsoil) measurements) under ideal field conditions. We measured ecosystem-scale fluxes of stable C isotopes via a new quantum cascade laser (QCL) spectrometer and paired those measurements with abiophysical model CANVEG in order to close a system of equations and solve for gross primary productivity (GPP) and ecosystem respiration (Reco). Isotope-partitioned GPP and Reco were on average 10–13% lower than Reichstein- and ANN-partitioned GPP and Reco. These results suggest that the Reichstein and ANN approaches, which use nighttime NEE to infer daytime Reco, may be overestimating Reco and GPP during the day. This may be attributed to higher plant respiration rates at night compared to the day, otherwise known as the Kok effect. As isotope measurements and theory become more accessible to diverse ecosystems it will grow to be more than a benchmarking technique and into a valuable means of improving understanding of carbon cycling. Future studies are encouraged to evaluate these techniques in increasingly complex ecosystems to determine when significant differences appear between partitioning methods and how those differences influence modeling of terrestrial carbon budgets.