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How Fast Your Codex Limit Hits?
WTF is GPT-5.6 mechanics:
Model choice: Sol, Terra, or Luna
Reasoning options
The model sets the capability and price range.
Reasoning effort sets how much time and token budget it can spend on the task.
Ultra is separate, it uses subagents for work that can be split into independent parts.
GPT-5.6 gives you enough controls to waste tokens without noticing.
Sol can handle serious work, but it is overkill for a clean extraction.
Max reasoning can help on hard debugging, but it can be wasteful for a meeting summary.
A weak answer does not always need a longer prompt. Sometimes the model, reasoning level, and task shape were mismatched before the first word was written.
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Choose the smallest setting that can finish the job correctly.
Use this ladder:
Luna for clear, repeatable work
Terra for ordinary building
Sol Medium for work with uncertainty
Sol High or Extra High for hard decisions
Max for one unusually stubborn problem
Ultra for a large task that can split across agents
Start with the model
Luna: the cheap worker
Use Luna when you can explain the expected result precisely.
Good jobs include extracting information, cleaning a transcript, applying a known edit across many files, and producing a structured summary.
The Artificial Analysis benchmark shows Luna at higher effort reaching roughly the same intelligence range as Sol Low while costing less per measured task. A well-defined task may benefit more from clarity than from the biggest model.
Terra: the daily builder
Use Terra for normal feature work, routine bug fixes, tests, and UI changes.
OpenAI describes Terra as the everyday workhorse. It is the useful middle when Luna needs too much supervision and Sol would spend more quota than the task deserves.
Sol: the judgment model
Use Sol when the task is ambiguous, expensive to get wrong, or difficult to verify.
Sol is the right place for architecture, unfamiliar repositories, hard debugging, deep research, and security review.
The next decision is how much thinking time to give it.
Pick the Sol setting
Sol Medium
Make this your default for serious Codex work.
Use it for building a feature from a clear brief, tracing a bug across a few files, or making changes that need tests. OpenAI uses Sol with Medium reasoning as the default Power setting.
Sol High or Extra High
Move up when the task has competing explanations, several systems, important tradeoffs, or weak evidence.
Use High for a messy multi-step task. Use Extra High when one wrong assumption could waste hours: a difficult migration, an intermittent bug, or an architecture decision.
The graph shows a meaningful intelligence increase from Sol Medium to High and Extra High. It also shows rising cost per task. Use higher effort when it can produce a better decision.
Sol Max
Max keeps one model thinking longer about one problem.
Use it after High or Extra High has failed on one stubborn problem where depth matters more than speed and quota.
Sol Ultra
Max and Ultra solve different problems.
It creates subagents and divides the work. Use it when the task contains separate jobs that can run independently: research the current behavior, inspect the codebase, implement the fix, and verify the result.
Reserve it for work such as a repository-wide migration, full product audit, or research-backed build.
Spend the quota before a reset
Will Codex Reset? watches public signals and estimates the chance of an unexpected quota refill.
The site calls its forecast an unofficial heuristic. Your account's reset timer remains the source of truth.
If your normal reset is close, the site shows a strong signal, and you have unused quota, run a saved difficult task on Sol Extra High, Max, or Ultra.
Spend the remaining quota on work you already needed to finish.
Prompt after choosing the setting
OpenAI's prompting guide is short enough to remember:
Say what should exist when the task is finished.
Give the files or context that can change the result.
Name one or two boundaries that prevent damage.
Tell Codex how to verify the work.
Example:
Fix the failed invoice retry in src/billing. Keep pricing logic unchanged. Add the smallest relevant test, run the billing suite, and report anything you could not verify.
The prompt defines the destination and the guardrails. The model and reasoning setting determine how much intelligence you spend getting there
Now go build something great!
The ShortList
🛠️Cool Tools of the Week:
Sarvam MCP Server: Gives coding agents a standard way to discover and build with Sarvam
Tencent Hy3: A 295-billion-parameter Mixture-of-Experts model available under the Apache 2.0 license.
Cadence: An AI screen recorder that works confidentially and accurately.
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