Token Budget Reallocation: Trading Syntactic Verbosity for Semantic Contracts in LLM Code Generation

Large Language Models generate code within fixed context windows where every token carries an opportunity cost. We introduce the token budget reallocation hypothesis: syntax compression can free tokens that are reinvested in formal contracts (preconditions and postconditions), improving verifiability at minimal net cost. We validate this through AiScript, a language combining symbol-based syntax compression with first-class requires/ensures contracts, natural-language intent descriptions, self-specification generation, and triangular consistency verification. Across 10 benchmark tasks measured with the GPT-4o tokenizer, AiScript achieves 25.5% token reduction versus Python; when both languages include equivalent contract specifications, the reduction reaches 42.4%. Syntax savings cover 73.5% of contract costs. In generation experiments, Claude Sonnet produces valid AiScript with 100% syntax pass rate from 12 in-prompt examples; GPT-5 mini achieves 70%, with failures attributable to formatting rather than symbol confusion. LLM-generated self-specifications are semantically valid for all tested functions, with a systematic over-specification bias. We additionally identify a tokenizer co-adaptation effect where BPE vocabularies systematically favor established language keywords, creating a structural disadvantage for new syntaxes.