D1 · Codex Fundamentals and Surfaces

This domain is worth 20% of the mock — roughly 10 of 50 items. It tests whether you can place a coding task on the right Codex surface, pick a model and reasoning effort that fit the work, and reason about the model line-up and its recent changes. Almost every item comes down to one question: given this task, this environment and this deadline, which surface, model and effort do I reach for?

What you need to know

Codex is one agent behind many surfaces: the ChatGPT desktop app, ChatGPT Work on the web, the Codex CLI, the Codex IDE extension, Codex cloud, and Codex Micro, plus integrations into Slack, GitHub, GitLab (beta) and Linear. The surfaces share one configuration file, so the choice between them is about where the work happens, not about capability. Inside Codex you can run several models — gpt-6-astra, gpt-5.6-sol, gpt-5.6-terra, gpt-5.6-luna, and the gpt-5.3-codex-spark research preview — and control them with /model interactively or -m / --model on the command line. A reasoning ladder from Low to Ultra trades thinking time and parallelism for speed and cost. As of 31 August 2026 the gpt-5.4 family retired from Codex under ChatGPT sign-in, so knowing the current replacements matters.

Learning objectives

By the end of this page you should be able to:

1. Select the right Codex surface for a task from the desktop app, ChatGPT Work web, CLI, IDE extension, cloud and Micro.
2. Choose a Codex model and switch it with /model, -m or --model for both interactive and non-interactive runs.
3. Apply the reasoning ladder Low → Medium → High → Extra High → Max → Ultra, and explain the Max-versus-Ultra distinction and subagents.
4. Reason about the model line-up, including the retirement of gpt-5.4 / gpt-5.4-mini from Codex and their replacements.
5. Distinguish interactive, non-interactive and cloud execution and match each to a task shape.

1.1 The Codex surfaces

Codex is the same agent everywhere; the surface decides where it runs and how you interact with it.

                        ONE CODEX AGENT
                        one shared config.toml
   ┌──────────┬───────────┬──────────┬───────────┬─────────┬────────┐
   │ Desktop  │ ChatGPT   │  CLI     │   IDE     │  Cloud  │ Micro  │
   │  app     │ Work web  │ codex(1) │ extension │ sandbox │        │
   └────┬─────┴─────┬─────┴────┬─────┴─────┬─────┴────┬────┴───┬────┘
        │           │          │           │          │        │
     chat on     managed    scripted    tight edit  long /  quick,
     desktop     browser    & CI runs   loop, diff  parallel small

1.2 Interactive, non-interactive and cloud execution

The same task can run three ways, and the exam expects you to match the shape of the work to the mode.

Worked example — the same “fix the failing test” task, three ways:

codex
# then, in the session:
# > fix the failing test in the payments module and show me the diff

codex exec -m gpt-5.6-terra "fix the failing test in the payments module"

Codex cloud → New task → "fix the failing test in the payments module"
Runs in a hosted sandbox; follow progress, come back to the diff and evidence later.

1.3 The models available in Codex

Codex can run the current model line-up plus a Codex-specific research preview. Reach for the lowest-cost model that meets the task.

gpt-5.5 and gpt-5.4 remain listed as “other models”. Use the model lineup appendix as the single source for prices and limits.

1.4 Controlling the model: /model, -m and --model

You choose the model per session, per command, or as a shared default.

# Interactive: switch mid-session
codex
# > /model            (opens the model picker)

# Launch on a specific model
codex --model gpt-5.6
codex -m gpt-5.6-terra

# Non-interactive on a specific model
codex exec -m gpt-5.6 "refactor the auth middleware and run the tests"

The shared default lives in config.toml:

model = "gpt-5.6"

1.5 The reasoning ladder: Low to Ultra

Codex exposes a ladder of reasoning effort in the CLI. Higher effort means more thinking time (and cost); the top rung adds parallelism.

Low ─► Medium ─► High ─► Extra High ─► Max ─► Ultra
(fast,   (default)  (harder)  (deep)      │        │
 cheap)                                   │        │
                                MORE THINKING       DELEGATES TO
                                ON ONE TASK          SUBAGENTS IN
                                (Max)                PARALLEL (Ultra)

• Low — quick, cheap; mechanical edits and simple lookups.
• Medium — the default; most day-to-day work.
• High — harder tasks needing more careful reasoning.
• Extra High — deep, multi-step reasoning on one hard task.
• Max — more thinking time on a single task; use when one hard problem needs the model to reason longer, not when the work splits into pieces.
• Ultra — automatic delegation to subagents in parallel; use when a task genuinely decomposes into independent sub-tasks that can run at once.

The Max-versus-Ultra distinction is a favourite item: Max deepens a single line of reasoning, Ultra spreads work across subagents. In GUI clients the levels read Light / Medium / High / Extra High, and the Astra rollout exposes Power options such as Terra Light, Sol Light, Sol Medium, Astra Light, Astra Medium and Astra Extra High. If Ultra is missing from the picker, enable it via Settings → Configuration → “Ultra in model picker slider”.

1.6 The September 2026 model retirements

Model availability in Codex changed under ChatGPT sign-in, and the exam tests the replacements, not the drama.

Two facts matter for judgment items: the retirement applies to ChatGPT sign-in, and API-key sign-in is unaffected. So a team pinned to gpt-5.4 in a shared config that signs in with ChatGPT must migrate to Terra/Luna; a service that authenticates with an API key does not face the same deadline.

Decision framework

Use SURFACE-MODEL-EFFORT (SME) to place any task in three moves.

Applied: “a scripted overnight batch that renames a deprecated API across many files” → CLI with codex exec (surface), Luna (model — mechanical, high-volume), Low or Medium effort (it is not hard, just large). “redesign the caching layer, one gnarly problem” → IDE extension, Sol or Astra, Max effort.

Common mistakes

Scenario challenge

Scenario. Priya leads a four-person platform team. They have three pieces of work in flight this afternoon: (1) a mechanical rename of a deprecated logging call across ~180 files, (2) a genuinely hard redesign of the request-batching logic that one engineer will pair on, and (3) a set of four independent, small bug fixes in unrelated modules that all need to land today. The team signs in to Codex with their ChatGPT accounts, and their shared config.toml still pins model = "gpt-5.4". One engineer reports Codex “stopped picking up the model” this morning.

Expert reasoning trace.

1. Fix the blocker first. The config pins gpt-5.4, which retired from Codex under ChatGPT sign-in on 31 August. Since the whole team signs in with ChatGPT, the fix is to change the shared default to gpt-5.6-terra (the direct replacement for gpt-5.4). API-key sign-in would be unaffected, but that is not how this team authenticates.
2. Place task 1 (the rename). It is mechanical and high-volume, and it can run unattended. Surface: CLI with codex exec. Model: Luna. Effort: Low. No need to occupy anyone.
3. Place task 2 (the redesign). One hard problem, an engineer pairing. Surface: IDE extension for a tight review loop. Model: Sol or Astra. Effort: Max — more thinking time on one task, not Ultra, because it does not split into independent parts.
4. Place task 3 (four independent fixes). These genuinely decompose. This is the Ultra case — automatic delegation to subagents in parallel — or four cloud tasks running at once. Either way, cloud is a good surface so the fixes do not block the two engineers.
5. Do not over-model. Nothing here needs Astra except possibly the redesign; the rename on Astra would burn budget for no benefit.

Exam-correct decision: migrate the shared config to gpt-5.6-terra; run the rename non-interactively on the CLI with Luna at Low effort; do the redesign in the IDE with Sol/Astra at Max; fan the four independent fixes out with Ultra or as parallel cloud tasks. Not everything on Astra, not Ultra for the single redesign, not the rename in an interactive session.

Assessment traps

Practice questions

Each item states how many responses to select. Attempt before revealing.

Key takeaways

• Codex is one agent behind many surfaces (desktop, ChatGPT Work web, CLI, IDE extension, cloud, Micro) that share a single config.toml.
• Choose the surface by where the work happens: CLI for terminal/CI (with codex exec for unattended runs), IDE for tight review loops, cloud for long-running or parallel work.
• Pick the lowest model that meets the task: Luna (high-volume), Terra (default / GPT-5.5 replacement), Sol (complex), Astra (hardest); Spark is a Pro-only research preview.
• Control the model with /model in-session, -m / --model at launch, or model = '…' in config.toml.
• The reasoning ladder is Low → Medium → High → Extra High → Max → Ultra; Max deepens one task, Ultra delegates to parallel subagents.
• gpt-5.4 / gpt-5.4-mini retired from Codex under ChatGPT sign-in on 31 Aug 2026 → replace with gpt-5.6-terra / gpt-5.6-luna; API-key sign-in is unaffected.