Byte Pair Encoding (BPE)
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Practical example
BPE (Byte Pair Encoding) is the algorithm that builds the model's vocabulary by merging frequent byte pairs. "understanding" might split into ["under", "stand", "ing"] — three tokens. Token count ≠ word count. A sentence of 20 words could be 15 tokens (lots of common words) or 60 tokens (rare words, code). This directly affects your per-request cost and latency.
Workflow example
Benchmark token efficiency: for your target domain, count tokens per word (TPW). English text typically has TPW ~1.3; code (Python, JSON) has TPW ~0.6 (efficient); languages like Korean or Arabic can have TPW 5–15× higher than English. If your target language has high TPW, your effective context window is much smaller than advertised. Test with your actual data before committing to a model.