This paper distinguishes two guessing algorithms for decoding binary linear codes: Guessing Noise Decoding (GND) and Guessing Codeword Decoding (GCD). The author proves that GCD is a maximum likelihood (ML) decoding algorithm and is more efficient than GND in most practical applications. The paper also introduces several variants of ordered statistic decoding (OSD) to balance the complexity of Gaussian elimination (GE) and guessing, which can be useful for decoding short block codes in high signal-to-noise ratio (SNR) regions. The main result shows that GCD requires fewer or the same number of guesses as GND, and simulation results validate this analysis, demonstrating that GCD is more efficient, especially for low-rate codes. The paper concludes by emphasizing that no single decoding algorithm is universally optimal and that the choice depends on the specific SNR conditions.This paper distinguishes two guessing algorithms for decoding binary linear codes: Guessing Noise Decoding (GND) and Guessing Codeword Decoding (GCD). The author proves that GCD is a maximum likelihood (ML) decoding algorithm and is more efficient than GND in most practical applications. The paper also introduces several variants of ordered statistic decoding (OSD) to balance the complexity of Gaussian elimination (GE) and guessing, which can be useful for decoding short block codes in high signal-to-noise ratio (SNR) regions. The main result shows that GCD requires fewer or the same number of guesses as GND, and simulation results validate this analysis, demonstrating that GCD is more efficient, especially for low-rate codes. The paper concludes by emphasizing that no single decoding algorithm is universally optimal and that the choice depends on the specific SNR conditions.