OpenAI GPT-5 introduced two powerful new parameters via the Responses…

OpenAI GPT-5 introduced two powerful new parameters via the Responses API:
→ reasoning.effort – controls how deeply GPT-5 thinks before generating a response
→ text.verbosity – adjusts how detailed and long the final output is

I ran an experiment using a real-world scenario prompt:
🧪 “Should a retail bank build or buy an agentic AI onboarding system?

Across 12 combinations of reasoning effort (minimal, low, medium, high) and verbosity (low, medium, high), I tested how GPT-5 responds.

🔍 Key Discoveries — applicable across domains, not just banking:

✅ Reasoning Effort controls how much thinking happens before answering:
– Minimal: very shallow; fastest, but lacks useful depth
– Low: quick and light; good for getting an initial sense of the topic
– Medium: balanced thinking and speed; great for most use cases
– High: deep analysis; best for complex reasoning and strategic thinking

✅ Verbosity affects how much detail is shared in the answer:
– Low: simple, clear, executive-style summaries
– Medium: balanced content with clear explanations (default)
– High: rich, detailed outputs; best when paired with high reasoning effort

🎯 Effective combinations by purpose:
Initial exploration → low effort, low verbosity
Executive briefing → high effort, low verbosity
Strategy development → high effort, medium verbosity
Documentation/training → high effort, high verbosity
Everyday analysis → medium effort, medium verbosity

💡 Why it matters:
The new Responses API gives you levers to control how deeply GPT-5 reasons and how clearly it communicates.
If you’re migrating to the new responses API, don’t just copy your prompts over.
Tune the reasoning and verbosity levels to match your audience, context, and cost-performance goals. Getting this balance right can make all the difference between surface-level output and strategic insight.