Reasoning Load
The amount of interpretive work a model must perform before useful task execution begins.
Reasoning Load measures the gap between receiving the prompt and beginning useful work. In an ideal scenario, Reasoning Load is near zero — the model reads the prompt and immediately starts executing the task. In practice, the model often needs to spend significant effort interpreting what it's been asked to do: parsing unstructured instructions, inferring implicit constraints, determining which parts of the context are relevant, and building a mental model of the task. All of this is Reasoning Load — work that precedes the actual work.
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.
Cognitive Drag
Friction introduced by poorly structured context.
Reasoning Efficiency
The model producing correct output with minimal interpretive overhead — the reward for clean context.
Token Leverage
Getting more useful output per token spent. The inverse of Token Bleed.