GPT-6 Astra Reasoning Levels: Which Setting Should You Use?

Check the values for your model and product

The API reasoning guide describes several possible values across models. That general list is not a promise that every model accepts all of them. For Astra, use its model-specific list. The guide explicitly says that Astra does not support none and that sending it returns HTTP 400.

If you are using Codex or ChatGPT, use the controls available in that product rather than copying an API parameter into an unrelated setting. Record the model and interface with your results so a future comparison is understandable.

• Astra's supported reasoning values
• OpenAI: reasoning effort and model-specific limits

Match the first trial to the task

A narrow copy edit has a clear result and a small error surface. A campaign plan involving uncertain requirements needs more judgment. A difficult multi-file bug may require deeper reasoning and careful verification. Those differences are more useful than choosing a setting based on the length of the prompt alone.

Treat the table as a starting hypothesis. If a setting repeatedly misses important constraints, increase effort or improve the brief, then compare again. If a higher setting adds time without improving acceptance, a lower one may be sufficient for that specific task.

Keep effort separate from Fast mode

Reasoning effort guides how much the model thinks. Fast mode is a separate speed option where offered by the product. Raising effort and enabling Fast do not cancel each other out in a predictable way, and neither removes the runtime of external tools.

For a creative workflow, image rendering or browser inspection may dominate the time. Measure the accepted artifact, not just the first text response. Review the billing mode as well: ChatGPT credits and API token pricing are different systems.

• Diagnose slow tasks and understand Fast mode

Use a small API example without assuming it ran

For an API integration, the Responses API expresses effort inside the reasoning object. This is an illustrative request shape, not an executed benchmark. Check your account's model access and the current documentation before using it in an application.

Keep the output requirement concrete even when the task is simple. Asking for a short critique of a supplied brief lets you assess whether a lower setting preserves the important constraints. For tool-using Astra workflows, the reasoning guide directs developers to Responses rather than Chat Completions.

{
  "model": "gpt-6-astra",
  "reasoning": { "effort": "medium" },
  "input": "Review this campaign brief for missing audience, asset and delivery requirements. Return the three most consequential gaps, with one concrete fix each: [insert brief]."
}

Count the repair work in your comparison

Save identical inputs and a fixed acceptance checklist. Record the result's defects, necessary follow-ups, elapsed time and reported usage. A setting that produces a shorter initial answer is not automatically cheaper if it requires multiple corrections.

The community question below is a useful example of why this is workload-specific. Splitting a development task into small interface edits, tests and complex changes creates different evaluation cases. A single answer about the ‘best’ setting would hide those differences.

Keep a small team policy and revisit it

After several representative trials, write a short default policy: which setting to start with for each recurring task, which defects justify escalation and what evidence to record. Keep it easy to change as the product or your workflow evolves.

For visual work, pair the policy with an approved brief and an output review. More reasoning cannot compensate for an unknown product specification or an image reference that contradicts the written direction. Resolve those inputs before spending more effort on the same ambiguity.

• Write a focused task contract
• Save a repeatable brief workflow