Equation 1 · Part 6 · OpenAI Model Systems from First Principles: Weights, Post-Training, and Inference Compute

Lifetime inference compute

C_{\mathrm{total}} = C_{\mathrm{pre}} + C_{\mathrm{post}} + Q \cdot \bar{c}_{\mathrm{inf}},

Q \cdot \bar{c}_{\mathrm{inf}}

What this part means

Requests multiplied by average compute per request.

Its job in the formula

Q × bar $c_i$ nf is one of the signed contributions combined to compute the quantity on the left.

Full expressionLifetime inference computeArticle meaning

The passage around this formula

Write the total lifetime computation of a deployed system as $C_{\mathrm{total}} = C_{\mathrm{pre}} + C_{\mathrm{post}} + Q \cdot \bar{c}_{\mathrm{inf}}$ . where $C_{\mathrm{pre}}$ is pretraining compute, $C_{\mathrm{post}}$ is post-training compute, Q is the number of served requests over the system’s life, and $\bar{c}_{\mathrm{inf}}$ 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, $\bar{c}_{\mathrm{inf}}$ is a parameter the caller sets: as verified on 8 August 2026, the model guidance documents a reasonin $g_e$ ffort control taking the values none , low , medium , high , xhigh , and…

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Learn the underlying idea

A subscript is a label attached below a symbol. It often selects a time step, component, category, or member of a sequence.

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Sources cited in the surrounding passage

[2] Model guidance ↗

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