Token Leverage
Getting more useful output per token spent. The inverse of Token Bleed.
Token Leverage measures how hard your tokens are working for you. High Token Leverage means every token you spend is producing value — generating code, answering questions, creating content. Low Token Leverage means your tokens are being consumed by overhead: re-onboarding, processing noise, compensating for ambiguity. Teams that optimize for Token Leverage focus not just on reducing cost but on increasing the value-per-token ratio. A 10,000-token interaction that produces a working feature has high Token Leverage. A 10,000-token interaction that ends with "could you clarify?" has almost none.
More in Reasoning Cost
Reasoning Tax
Extra model cost paid to compensate for context debt.
Token Bleed
Silent budget drain from re-onboarding — tokens spent re-explaining context the system should already hold.
Token Burn
Token waste caused by a client sending the entire session history to the LLM on every call.
Context Compensation
The model using additional reasoning to make sense of poor, bloated, or unstructured context.
Reasoning Inflation
More reasoning is required to extract the same signal from worse context.
Reasoning Load
The amount of interpretive work a model must perform before useful task execution begins.
Cognitive Drag
Friction introduced by poorly structured context.
Reasoning Efficiency
The model producing correct output with minimal interpretive overhead — the reward for clean context.