Perplexity and bits per token

One-line definition

Perplexity is the exponentiated average per-token cross-entropy of a language model on a held-out corpus: $\text{PPL}=\exp (\bar{L})$ where $\bar{L}=-\frac{1}{N}{\sum }_{i=1}^N\log p(x_i\mid x_{<i})$. Bits per token (BPT) is the same quantity in ${\log }_2$ units: $\text{BPT}=\bar{L}/\ln 2$.

Why it matters

Perplexity is the dominant intrinsic metric used during pretraining: cheap to compute, smooth, monotonically related to next-token log-likelihood. Loss curves are usually reported as cross-entropy or perplexity. Scaling laws (Kaplan et al., 2020; Chinchilla, 2022) are derived in perplexity / loss space.

But: perplexity is a poor evaluation of model usefulness. Lower perplexity does not reliably mean better summarization, instruction following, code, or chat. For these you need task evals (HumanEval, MMLU, MT-Bench, etc.).

The formula

For a tokenized sequence $x_1,\dots ,x_N$ and a model $p$:

$$\bar{L}=-\frac{1}{N}\sum _{i=1}^N\log p(x_i\mid x_{<i})$$

$\bar{L}$ is the average cross-entropy in nats (natural log) per token. Then:

• Perplexity: $\text{PPL}=\exp (\bar{L})$
• Bits per token: $\text{BPT}=\bar{L}/\ln 2={\log }_2(\text{PPL})$
• Bits per byte: BPT divided by mean bytes per token (depends on tokenizer)

Interpretation of perplexity: the model is “as confused as if it had to choose uniformly among $\text{PPL}$ words.” A perplexity of 20 means the model’s predictions are as good as a uniform distribution over 20 next-token candidates.

Tokenizer dependence

Perplexity is not comparable across tokenizers. Different tokenizers split the same text into different numbers of tokens, so the per-token cross-entropy differs even when the model perfectly captures the underlying language.

To compare across tokenizers, convert to bits per byte (BPB) or bits per character:

$$\text{BPB}=\frac{\text{total bits to encode}}{\text{total bytes in corpus}}$$

This is invariant to tokenization and is the proper cross-tokenizer comparison metric.

Typical numbers (for context)

On a clean English text corpus:

Model	Perplexity	BPB
Trigram (Brown corpus 1992)	~150	~1.5
LSTM (Mikolov 2010, PTB)	~80	.
GPT-2 small (Radford 2019, WebText)	~30	~1.0
GPT-3 (Brown 2020, books)	~10–20	~0.7
Frontier 2024–2026 LLM	~5–10	~0.5

The lower bound (compressed bits per byte of natural English) is estimated at ~0.6 bits/byte (Shannon).

Why perplexity ≠ usefulness

A model that has memorized its training data has perplexity 1 on it but doesn’t generalize. A model with low perplexity on web text may still:

• Generate fluent nonsense in long-form generation.
• Fail at instruction following (perplexity isn’t measured on that distribution).
• Fail at tool use, reasoning, or code (a small perplexity gap can hide a huge capability gap).

Production model evaluation always requires task evals.

Common pitfalls

• Comparing perplexities across tokenizers. Use BPB.
• Reporting perplexity on the training set. Always use a held-out test corpus.
• Reporting perplexity on a domain very different from your eval target. A model with low perplexity on Wikipedia may be terrible at code; pick a test corpus that matches the deployment distribution.
• Treating a perplexity drop as a quality win. Sub-half-bit improvements are common during fine-tuning but don’t translate to user-facing quality.

Related

• Tokenization. Affects how perplexity is computed.
• LLM evals: the hardest part. Why intrinsic metrics fail for production LLMs.