Equation 4 · OpenAI Model Systems from First Principles: Weights, Post-Training, and Inference Compute
What does this equation mean?
Read the formula alongside the article passage below. Each part has a deeper page with its role in the equation, the supporting passage and nearby citations.
the number of served requests over the system’s life, and is the mean compute per request. Read the equation part by part below; each part has a contextual explanation and a link to its mathematical background.
Read it piece by piece
Symbol Q
the number of served requests over the system’s life, and is the mean compute per request.
How to interpret it
Read this expression with the definitions, units, and assumptions supplied by the article.
What the article says around this equation
where is pretraining compute, is post-training compute, Q is the number of served requests over the system’s life, and is the mean compute per request. Until roughly 2024 the third term was treated as approximately fixed for a given model, and public discussion of capability collapsed onto the first. That assumption no longer holds. On current OpenAI models, is a parameter the caller sets: as verified on 8 August 2026, the model guidance documents a reasoninffort control taking the values none , low , medium , high , xhigh , and max , alongside a separate text.verbosity setting [ 2 ] .
Sources cited in the surrounding passage
These citations give research context. Read each source to check which claims it supports.
Return to OpenAI Model Systems from First Principles: Weights, Post-Training, and Inference Compute