GPT-5 is OpenAI’s newest flagship AI model, designed to handle both quick answers and complex reasoning tasks in one system. It improves on earlier versions by using an internal routing system that decides whether to respond in a fast “chat” style or engage in deeper, slower “thinking” mode. This means you don’t have to pick different models for different jobs—the same endpoint can shift between lightweight and heavy reasoning depending on your request. For example, a short factual query like “What’s the capital of Japan?” will be answered instantly, while a multi-step coding problem might trigger the thinking mode to ensure accuracy. GPT-5 is available in multiple forms for developers, including gpt-5 for full capabilities, gpt-5-mini for faster and cheaper responses, and gpt-5-nano for highly efficient use cases. There’s also a gpt-5-chat-latest version tuned specifically for conversational applications.
The biggest differences from previous versions, like GPT-4o or o3, come down to accuracy, efficiency, and flexibility. GPT-5 is significantly better at reasoning and fact-checking—it’s up to 80% less likely to give incorrect information compared to o3 and produces far fewer hallucinations in long responses. In testing, it scored 94.6% on the AIME 2025 math exam, 74.9% on the SWE-bench Verified coding benchmark, and 84.2% on the MMMU multimodal benchmark, all of which are higher than earlier models. It also uses tokens more efficiently, often needing 50–80% fewer tokens for the same answer. In coding scenarios, GPT-5 can work with large codebases, generate cleaner and more functional UI components, and follow multi-step instructions more reliably. This makes it especially useful for tasks like debugging across multiple files or generating production-ready frontend code.
From a developer’s point of view, GPT-5 adds more ways to control responses. You can set a verbosity level to control the detail in answers, and use a reasoning_effort parameter to choose how much processing time the model should spend. In the API, it supports advanced tool use, progressive result streaming, and custom preambles that explain tool behavior to end users. For example, you could connect GPT-5 to a retrieval system and configure it to narrate each step of the reasoning process while fetching relevant documents. In ChatGPT, GPT-5 runs all existing tools—like file handling, browsing, and image generation—and can be manually switched to “Thinking” mode if you want deeper reasoning. Overall, the combination of better accuracy, efficiency, and developer control makes GPT-5 a more capable and predictable tool than its predecessors.