Perplexity Calculator

Convert loss and perplexity

Perplexity is a standard measure of how well a language model predicts text, and it is the exponential of the cross-entropy loss. This tool converts between the two: given a cross-entropy loss in nats, it computes the perplexity as e raised to that loss, and given a perplexity, it recovers the loss as the natural logarithm. A cross-entropy loss of two nats corresponds to a perplexity of about 7.4.

Intuitively, a perplexity of N means the model is, on average, as uncertain about the next token as if it were choosing uniformly among N equally likely options.

Why perplexity matters

Perplexity is widely reported when training and comparing language models, with a lower value meaning the model predicts the text more confidently and accurately. Because loss is what the model optimises during training while perplexity is the more interpretable number, converting between them is a routine task. The tool also shows the loss in bits, which is the base-two version sometimes used instead of nats.

Perplexity is only comparable across models evaluated on the same data and tokenisation, so treat absolute values with care. All calculation happens locally in your browser.

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