Which model should you run agents on with ChatGPT Plus?

For ChatGPT Plus, start general coding with GPT-6.1 Sol, try Luna on narrow tasks you can check cheaply, and use Terra for writing or visual work. Reserve Astra for problems that need stronger judgment, if your account and app offer it. This is our task-fit starting point, based on current provider guidance, not a benchmark winner or a promise of a particular number of completed jobs.

Checked 14 November 2026. The decision is about the work you get accepted before hitting an allowance, not the biggest model name in the picker. In a current r/OpenAI discussion, people describe very different consumption across agent workflows. Those are anecdotes, not comparable measurements. We use the question to build a controlled selection method, not to turn their results into Plus-plan guarantees.

What do OpenAI's Plus usage estimates actually tell you?

OpenAI's pricing reference gives these estimates for local messages per five-hour period on Plus:

Model	Estimated local messages	Our suggested first trial
GPT-6 Astra	5–45	A difficult decision or failure the cheaper trial cannot resolve
GPT-6.1 Sol	15–160	A bounded coding change with tests
GPT-6.1 Terra	35–285	A source-based draft or visual interpretation
GPT-6 Sol	15–150	An existing workflow that already passes your checks
GPT-6 Luna	350–3,000	Repetitive extraction or a small, verifiable edit

The last column is our recommendation; it is not an OpenAI entitlement table. The ranges are estimates, not fixed message caps, and do not establish that every listed model is available in your particular app. Local and cloud work share allowance, weekly limits may apply, and cloud tasks can use more than local messages. Do not add the rows together or use API token prices to calculate included subscription jobs.

Which model fits the task before you change plans?

OpenAI's model guide positions Astra for demanding workflows, Sol for repeated longer-running work, Terra for writing and visual work, and Luna for focused tasks. Turn that guidance into explicit acceptance criteria.

For a coding change, write down the expected behavior, files in scope and test command before choosing the model. For document extraction, define the fields and require source locations. For a screen-reading task, specify which observations must be checked against the image. A task without a pass condition cannot tell you whether a cheaper model actually saved anything.

A useful escalation rule is: one bounded attempt, inspect the failure, then decide whether the missing ingredient is model judgment or missing information. If a test environment is broken, switching models may simply repeat the same failure. If the model cannot reconcile competing requirements after those requirements are clear, a stronger model is a reasonable next trial.

Why can reasoning effort and speed change the result?

A model name is only part of the configuration. OpenAI says higher reasoning effort uses more tokens and can take longer. Its current model guidance suggests the available default for Sol, High for Luna and Light for Astra as starting points. These are provider suggestions, not a finding that maximum effort always improves your task.

The pricing page's speed table also distinguishes funding modes: Fast uses 2.5 times Standard included subscription usage, but 2 times purchased-credit and Enterprise pay-as-you-go rates. Do not apply the credit multiplier to your included allowance. Availability varies, so use Standard as a controlled baseline when offered, then change speed only when turnaround matters enough to justify another trial.

Keep effort, speed and input size fixed while comparing two models. Changing all three at once leaves you unable to explain what helped. Do not treat a smoother response as acceptance: run the checks and review the output.

Where do you change the model in subscription-powered apps?

In native Codex, the model guide documents /model in an interactive session and the launch option codex --model gpt-6.1-sol. That command is for native Codex, not a universal command for every app on the ChatGPT hub.

OpenClaw has its own setup instructions. Inspect your selected model instead of assuming a fresh setup's default is economical for your job. In OpenCode, the provider guide uses /models and documents built-in context caps intended to keep subscription consumption similar to Codex defaults. Removing those caps is another variable, not a free model upgrade.

For Hermes Agent, the provider reference documents hermes model and separate auxiliary-model routing. Verify the main model, helper overrides and scheduled jobs independently. Selecting Luna in the main chat is not proof that every helper now uses Luna. See the fallback billing audit for that separate task.

How do you compare models without inventing a savings claim?

Use a small task from your own work and keep a record like this. It is a proposed worksheet, not a published experiment:

1. Save the same input files and acceptance criteria for each attempt.
2. Record the exact app version, model, effort, speed and funding route.
3. Use a fresh session with the same relevant context; avoid simultaneous unrelated work if comparing the account's usage meter.
4. Record pass or fail, retries, elapsed time and your review time.
5. Note whether the provider exposes task-level usage. If it does not, do not present the dashboard difference as an exact task cost.

Choose the lowest-consumption configuration that consistently meets your requirements, not the one with the most generous-looking message estimate. We have not run an authenticated comparison or measured a saving here. If you keep hitting limits after narrowing the task, read the per-app capacity guide. If the issue is a stale session or wrong account, use the login diagnosis guide instead of upgrading a plan to solve an authentication problem.