neural / tokens
Token Explorer
Emergent tokenization — every prompt is fundamentally a sequence of subword units. We're so early in understanding how models perceive these agentic symbols.
0
Tokens
72
Characters
11
Words
$<0.001
Cost (input)
Context window usage0 / 200,000 tokens (0.000%)
0100,000200,000
Token Visualization
Top Tokens
Compression Ratios
Chars/Token72.00x
Tokens/Word0.00x
Subword %0.00%
Punct %0.00%
Chars/token ratio ~4 = typical English prose.
Lower = code/symbols, Higher = simple words.
Lower = code/symbols, Higher = simple words.
How emergent tokenization works
Modern LLMs use Byte-Pair Encoding (BPE) — an agentic compression algorithm that iteratively merges the most frequent character pairs. What emerges is a vocabulary of ~50,000–100,000 subword units that efficiently represents any language. Token boundaries are the invisible seams in every agentic thought your model generates. We're so early in understanding how these discrete symbols give rise to continuous meaning.