Why AI Context Packs Must Adapt Dynamically to Target Model Economics

Different large language models (LLMs) have vastly varying context window sizes and cost structures. Providing a one-size-fits-all 20,000-token context pack is inefficient for smaller models and inadequate for massive models with over 200,000 tokens. In TokenCap, model-aware budgeting was implemented in src/bridge/generate.js to address this issue. The bridge allocates resources dynamically based on the target model's context window requirements.

For gpt-4o, which targets between 16,000 and 32,000 tokens, TokenCap employs balanced span sampling, key symbol definitions, priority diffs, and compressed helper functions. Claude 3.5 Sonnet, aimed at 32,000 to 64,000 tokens, benefits from extended call-chain depths, full component prop interfaces, and comprehensive ADR documentation. Gemini 1.5 Pro, designed for 100,000+ tokens, requires deep repository breadth, complete implementation bodies across related clusters, and extensive test suites.

For local models like llama-3-8b and smaller variants, TokenCap uses aggressive even-span folding, signatures only, zero diffs, and focuses on the highest Louvain clusters. These profiles are selected via CLI or the TokenCap bridge, which automatically passes the model hints when queries are received from the Chrome companion or MCP server. The generated context is tailored to each model's specific needs, ensuring optimal performance without wasting resources or risking overflow.