GPT-5 is a highly capable model with advanced features that can be tailored to a wide range of applications. To use it effectively, it's important to understand its key capabilities and how to configure it properly based on your specific needs. One of GPT-5’s standout features is its multimodal input support. You can provide text, images, and audio together in a single prompt. This allows the model to analyze visual content directly—such as identifying objects in an image or interpreting charts—without relying on external OCR tools. Similarly, audio inputs enable analysis of speech patterns, tone, and emotion, offering deeper context than text alone. This makes GPT-5 ideal for tasks like document analysis, customer support, or content summarization involving mixed media. Another powerful feature is tool calling, which transforms GPT-5 into an intelligent agent. You can define custom functions—like retrieving weather data or searching a database—and let the model decide when and how to use them. For example, you can create a function called get_weather with parameters like city. It’s crucial to provide clear, detailed descriptions and parameter definitions so the model uses the tools correctly. This feature is especially useful in dynamic applications where real-time data access is needed. When working with GPT-5, three key parameters influence performance: reasoning effort, verbosity, and structured output. Reasoning effort controls how deeply the model thinks before responding. Options range from minimal to high. Use minimal for simple, fast responses—ideal for chatbots with straightforward queries. For complex tasks like problem-solving or detailed analysis, increase the reasoning level. However, higher reasoning increases cost and latency since it generates more output tokens. A good practice is to start with minimal or low and scale up only if quality suffers. Verbosity determines the length of the final response. Low verbosity produces concise answers, while high verbosity generates detailed, in-depth content. Medium is the default and often the best balance. Choose based on your use case—short summaries may need low verbosity, while reports or research require high. Structured output ensures the model returns results in a predictable format, such as JSON. This is essential for automated data extraction, like pulling dates, names, or key facts from documents. By specifying a JSON object format in the request, you guarantee machine-readable output, which simplifies parsing and integration into downstream systems. File upload is another valuable feature. You can send documents directly—PDFs, Word files, or images—and ask questions about their content. GPT-5 can process the file, extract text, and analyze visuals without requiring prior preprocessing. This saves time and improves workflow efficiency, especially when dealing with unstructured or scanned documents. Despite its strengths, GPT-5 has limitations. The most significant is the lack of access to full reasoning tokens during inference. OpenAI only returns a summary of the model’s internal thought process, which prevents real-time streaming of thinking steps. This makes high-reasoning tasks feel slow and unresponsive in live applications. In contrast, models from providers like Anthropic and Google offer full access to reasoning steps, enabling better user experience. There’s also a perception that GPT-5 is less creative than earlier versions, though this is less of an issue in most API-driven use cases where accuracy and consistency matter more than novelty. In conclusion, GPT-5 is a powerful tool when used with the right settings. Optimize reasoning effort and verbosity for your task, leverage structured output for reliable data extraction, and use multimodal inputs and file uploads to handle diverse data types. However, due to its limitations—especially the missing reasoning tokens—consider having backup models from other providers to ensure reliability and performance across different scenarios.