Cost Management & Budgeting

Cloud execution costs are a function of three variables: model selection, task duration, and concurrency. A 10-minute task on codex-1 costs a fraction of a 10-minute task on o3 with high reasoning effort. Running 10 parallel tasks costs 10 times one task. The economics are straightforward but can surprise teams that adopt cloud execution without budgeting: a developer who submits 20 parallel tasks with best-of-3 on o3 has just initiated 60 model invocations.

ChatGPT Pro subscribers get cloud execution included in their subscription at no additional per-task cost. API key users pay per-token for cloud tasks, just like local execution. The subscription model makes cloud execution dramatically more economical for heavy users — the break-even point is typically 2-3 hours of cloud usage per month. For teams, the math strongly favors Pro subscriptions over API keys for cloud-heavy workflows.

# Cost estimation framework

Local Codex:     Model tokens only (input + output)
Cloud Codex:     Model tokens + compute time

# Model cost tiers (approximate, per-token pricing varies)
GPT-4.1:         $ — fast, economical for routine tasks
codex-1:         $$ — optimized for engineering, good balance
o3 (medium):     $$ — reasoning-intensive tasks
o3 (high/xhigh): $$$ — deep analysis, use sparingly

# Cost multipliers
Best-of-N:       N × single task cost
Parallel tasks:  Sum of individual task costs
Retries:         Each retry is a full task cost