A language model is being trained to complete the sentence 'The sky is...'. The training data indicates the correct next word is 'blue'. The model's performance is measured by a value that quantifies the difference between its predicted probabilities and the correct outcome. A lower value indicates a better prediction.

Two different versions of the model produce the following probability distributions for the next word:

• Model Alpha: Assigns a probability of 0.8 to 'blue', 0.1 to 'green', and 0.1 to 'cloudy'.
• Model Beta: Assigns a probability of 0.3 to 'blue', 0.5 to 'cloudy', and 0.2 to 'green'.

Which model, Alpha or Beta, would be assigned a lower performance penalty (i.e., a lower loss value) for this specific prediction? Justify your reasoning.