Sequence Padding Calculator
Calculate the padding length needed to align sequences to a fixed model input length.
About Sequence Padding Calculator
Padding brings variable-length sequences up to a fixed maximum length so they can be batched into a single tensor, but every padding token still consumes compute during training (unless masked efficiently), which is pure overhead. This calculator computes the number of padding tokens added for a given sequence and max length, the resulting padding ratio, and the total wasted tokens across a full batch — high ratios signal that bucketing or dynamic padding could meaningfully speed up training.
Formula
padding_tokens = max_seq_len - actual_length
- max_seq_len
- Fixed maximum sequence length
- actual_length
- Actual sequence length in tokens
Worked example
With the default values below, Sequence Padding Calculator returns Padding Tokens Added of 384. Change any input above and every figure here is recalculated in your browser.
Inputs used
- Actual Sequence Lengthaccepts 0–1,000,000 tokens
- 128 tokens
- Max Sequence Lengthaccepts 1–1,000,000 tokens
- 512 tokens
- Batch Size (optional)accepts 1–1,000,000 sequences
- 32 sequences
Results
- Padding Tokens Added
- 384 tokens
- Padded Length
- 512 tokens
- Padding Ratio
- 75.0 %
- Total Padding in Batch
- 12,288 tokens
Step by step
- 1Padded length= 512 tokens
- 2Padding tokens added= 384 tokens
- 3Padding ratio= 75.0%
Assumptions and limits
- The result is derived only from the 3 inputs listed above. Anything not among them is not part of the calculation.
- In particular, this calculator does not take inflation, tax, fees or charges or compounding frequency into account.
- Figures are rounded for display to 1 decimal place, so totals built from several rounded parts can differ slightly from the unrounded arithmetic.
How these calculators are built and tested is described on the Methodology page. If a figure looks wrong, please report it.
Frequently Asked Questions
Does padding affect model accuracy?
Properly masked padding tokens shouldn't affect accuracy directly since attention masks exclude them from loss and attention computation, but they still consume memory and compute.
How can I reduce wasted compute from padding?
Use dynamic padding (pad only to the longest sequence in each batch) or length-based bucketing (group similar-length sequences together) instead of a single global max length.
What if actual length exceeds max length?
In that case the sequence must be truncated rather than padded — see the Sequence Truncation Calculator for that scenario.