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Characterizing exoplanets’ spectra is a crucial step in understanding the chemical and physical processes shaping their atmospheres and constraining their formation and evolutionary history. The X-SHYNE library is a homogeneous sample of 43 medium-resolution (R_λ~ 8000) infrared (0.3–2.5 μm) spectra of young (<500 Myr), low-mass (<20 M_Jup), and cold (T_eff~600–2000 K) isolated brown dwarfs and wide-separation companions observed with the VLT/X-Shooter instrument. To characterize our targets, we performed a global comparative analysis. We first applied a semiempirical approach. By refining their age and bolometric luminosity, we derived key atmospheric and physical properties, such as T_eff, mass, surface gravity (g), and radius, using the evolutionary modelCOND03. These results were then compared with the results from a synthetic analysis based on three self-consistent atmospheric models: the cloudy modelsExo-REMandSonora Diamondback, and the cloudless modelATMO. To compare our spectra with these grids we used the Bayesian inference codeForMoSA. We found similar L_bolestimates between both approaches, but an underestimated T_efffrom the cloudy models, likely due to a lack of absorbers that could dominate the J and H bands of early L. We also observed a discrepancy in the log(g) estimates, which are dispersed between 3.5 and 5.5 dex for mid-L objects. We interpret this as a bias caused by a range of rotational velocities leading to cloud migration toward equatorial latitudes, combined with a variety of viewing angles that result in different observed atmospheric properties (cloud column densities, atmospheric pressures, etc.). This interpretation is supported by the correlation of the color anomaly Δ(J–K) of each object with log(g) and the parameter f_sedthat drives the sedimentation of the clouds. Finally, while providing robust estimates of [M/H] and C/O for individual objects remains challenging, the X-SHYNE library globally suggests solar values that are consistent with a formation via stellar formation mechanisms. This study highlights the strength of homogeneous datasets in performing comparative analyses, reducing the impact of systematics, and ensuring robust conclusions while avoiding overinterpretation.