Free Tools · GPT-5.6 selector

Give routine work a lighter lift.

Answer seven plain questions. You will get one model and one effort level, without a technical matrix or a default to the most expensive option.

Step 1 · Describe the task

Choose the answer that best describes the task, not the answer that feels safest.

1. How repeatable is the task?

Would a clear checklist work most of the time?

2. How much judgment does the task require?

Is the main challenge execution, synthesis, or deciding between tradeoffs?

3. What happens if the answer is wrong?

Think about stakes and how easily a person can reverse the result.

4. How many sources, constraints, or steps are involved?

Count competing inputs, requirements, and dependencies.

5. How easy is it to verify the result?

Can a person quickly tell whether it is good enough?

6. What matters most for this run?

Volume and speed can justify a lighter option when the work is clear.

7. Is this the final critical review?

Reserve this for a difficult final pass where a small reliability gain matters.

Advanced choices are separate from the everyday selector

None, XHigh, and Max are reasoning-effort settings, not model tiers. None is for pure transformations with no meaningful reasoning. XHigh is for rare critical review after it has shown a real benefit. Max is never the default: use it only for the hardest quality-first work after comparing it with XHigh.

Pro and multi-agent are also separate choices. Pro can be used with any GPT-5.6 model, while effort is selected independently. Multi-agent parallel work is a workflow pattern for complex deliverables, not another effort level. For most people, choose one worker and one thinking depth first.

Sources

Official OpenAI references

The selector is a practical routing aid. Test important workflows with your own representative work.

Start with the cheapest option that should succeed. Escalate after a concrete failure, or when the cost of being wrong is clearly higher than the extra model cost. · A free tool from Majestic Labs.