Smart AI Token Consumption
One of the easiest ways to burn through tokens unnecessarily is to reach for a powerful model by default, even when the task does not warrant it. A more deliberate approach, matching model capability to task complexity, keeps costs low and latency fast without sacrificing quality where it matters.
The Core Idea
Not all tasks are equal. Adding a timeline entry, running a git commit, following a skill template, or doing a routine push are mechanical operations. They do not need deep reasoning; they need speed and low cost. On the other hand, designing a new system, debugging a subtle concurrency issue, or writing a complex algorithm genuinely benefits from a more capable model with higher reasoning effort.
The practical split:
| Task type | Examples | Model | Reasoning effort |
|---|---|---|---|
| Simple / mechanical | Git commits, file edits, timeline entries, following skills, boilerplate generation | gpt-5.4-mini |
medium |
| Complex / engineering | Architecture decisions, hard debugging, algorithm design, code review | gpt-5.4 |
xhigh |
Codex Config
A reasonable ~/.codex/config.toml setup with a sensible default and two named profiles:
model = "gpt-5.4"
model_reasoning_effort = "medium"
personality = "pragmatic"
[profiles.simple]
model = "gpt-5.4-mini"
model_reasoning_effort = "medium"
[profiles.complex]
model = "gpt-5.4"
model_reasoning_effort = "xhigh"
The default sits in the middle, capable enough for most ad hoc tasks without committing to max cost on everything. Switch to --profile simple for routine operations and --profile complex when you actually need the horsepower.
The Mental Model
Think of it like choosing a tool from a toolbox. You do not reach for a power drill to tighten a loose screw. Routine Codex operations, the ones you run dozens of times a day, should default to the lightest model that gets the job done. Reserve the expensive reasoning budget for problems that genuinely need it.
This matters more than it might seem. High-frequency, low-complexity tasks are exactly where token costs compound quickly. Keeping those on gpt-5.4-mini with medium effort can cut daily usage significantly, while xhigh reasoning on a hard problem is worth the cost because you only pay for it when it counts.