Why AI Context Packs Must Adapt Dynamically to Target Model Economics
Different LLMs have wildly different context windows and cost economics. Providing a generic 20k-token context pack is wasteful for smaller models and insufficient for massive 200k+ models. In TokenCap, we implemented model-aware budgeting in src/bridge/generate.js . How Token Allocations Adjust gp
Different LLMs have wildly different context windows and cost economics. Providing a generic 20k-token context pack is wasteful for smaller models and insufficient for massive 200k+ models.
In TokenCap, we implemented model-aware budgeting in src/bridge/generate.js.
# Generate context tailored specifically for Claude 3.5 Sonnet tokencap make --profile claude-3-5-sonnet Enter fullscreen mode Exit fullscreen mode CLI Output:
Loaded model profile: claude-3-5-sonnet Token budget ceiling: 64,000 tokens Allocated: - Architecture and graph overview: 2,400 tokens - Core targeted symbols: 18,200 tokens - Transitive caller bodies: 14,800 tokens - Active ADRs and memory: 1,600 tokens Total Generated: 37,000 tokens (58% of ceiling, 0% overflow risk) Enter fullscreen mode Exit fullscreen mode The bridge passes these model hints automatically when queries arrive from our Chrome companion or MCP server.
Read the model profiling guide at tokencap.vansharora.app