The GPT-5 API includes several customization options that give developers control over how the model reasons, formats, and delivers responses. One key setting is reasoning_effort, which can be set to "minimal", "medium", or "high". This parameter determines how much computation the model invests in producing its answer. For lightweight tasks, "minimal" offers faster results at a lower cost, while "high" is better for complex reasoning, such as multi-step problem solving or deep analysis of technical data.
Another useful parameter is verbosity, which controls the level of detail in the output. You can set it to "low" for short summaries, "medium" for balanced explanations, or "high" for comprehensive detail. This is especially helpful in scenarios where response length directly impacts usability—such as returning concise API responses for mobile apps versus detailed explanations in a developer dashboard. GPT-5 also supports tool integration in the API, where you can register custom functions or APIs that the model can call. Tool-call preambles can be added to guide GPT-5 on when and how to use these tools, improving reliability in automated workflows.
The API also supports progressive response streaming, allowing you to return partial outputs to users while the model is still reasoning—helping reduce perceived latency in high-effort tasks. Developers can choose among multiple GPT-5 variants (gpt-5, gpt-5-mini, gpt-5-nano) to balance cost, speed, and capability. In addition, because GPT-5 works well with retrieval-augmented generation (RAG) setups, developers can customize its context by injecting domain-specific documents before each query, ensuring answers are grounded in the right knowledge. Together, these customization options make GPT-5 adaptable for a wide range of production environments, from real-time chatbots to research-grade analytical tools.