Context Compensation
The model using additional reasoning to make sense of poor, bloated, or unstructured context.
Context Compensation is the mechanism behind Reasoning Tax. When a model receives unclear or contradictory input, it doesn't give up — it compensates. It generates internal reasoning to resolve ambiguities, hypothesize about intent, and filter signal from noise. This compensation is a testament to model capability, but it's also expensive and unreliable. The model is essentially guessing what you meant, and those guesses cost reasoning tokens. When you see a model produce a long chain of thought before a simple answer, you're often watching Context Compensation in action.
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.
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.
Token Leverage
Getting more useful output per token spent. The inverse of Token Bleed.