Two handheld near infrared (NIR) spectrometers were used to quantify crude protein (CP) content of mixed forage and feedstuff composed of Sweet Bran, distiller's grains, corn silage, and corn stalk. First was a transportable spectrometer, which measured in the visible and NIR ranges (320–2500 nm) with a spectral interval of 1 nm (H1). Second was a smartphone spectrometer, which measured from 900–1700 nm with a spectral interval of 4 nm (H2). Spectral data of 147 forage and feed samples were collected by both handheld instruments and split into calibration (n = 120) and validation (n = 27) sets. For H1, only absorbances in the NIR region (780–2500 nm) were used in the multivariate analyses, while for H2, absorbances in the second and third overtone regions (940–1660 nm) were used. Principal component analysis (PCA) and partial least squares (PLS) regression models were developed using mean-centered data that had been preprocessed using standard normal variate (SNV) or Savitzky-Golay first derivative (SG1) or second derivative (SG2) algorithm. PCA models showed two major groups—one with Sweet Bran and distillers grains, and the other with corn silage and corn stalk. Using H1 spectra, the PLS regression model that best predicted CP followed SG1 preprocessing. This model had low root mean square error of prediction (RMSEP = 2.22%) and high ratio of prediction to deviation (RPD = 5.24). With H2 spectra, the model best predicting CP was based on SG2 preprocessing, returning RMSEP = 2.05% and RPD = 5.74. These values were not practically different than those of H1, indicating similar performance of the two devices despite having absorbance measurements only in the second and third overtone regions with H2. The result of this study showed that both handheld NIR instruments can accurately measure forage and feed CP during screening, quality, and process control applications.