GPT-5 improves reasoning by combining better internal logic with a more flexible way of deciding how much time and computation to spend on each request. Unlike earlier models that handled all queries with a single fixed reasoning style, GPT-5 uses a built-in “thinking” mode for complex problems. This mode allows the model to break a question into steps, check intermediate results, and avoid rushing to an answer. For example, if you ask it to write a SQL query based on a vague dataset description, it will reason through the requirements, infer missing details, and produce a working query with fewer mistakes than GPT-4o or o3. Benchmark data backs this up—GPT-5 scored 94.6% on AIME 2025 (math), 74.9% on SWE-bench Verified (coding), and achieved six times fewer hallucinations in long-form responses compared to o3.
A key part of this improvement is honesty in uncertainty handling. GPT-5 is more likely to say “I don’t know” rather than guess when it lacks enough information. In a missing-image test, it only gave an incorrect confident answer 9% of the time versus 86.7% for o3. This matters in real development work where a wrong assumption can break a system. Another factor is token efficiency—by using 50–80% fewer tokens for many answers, GPT-5 can allocate more capacity to actual reasoning steps instead of verbose filler, making it both faster and more accurate in complex problem-solving.
Developers using the API can tune reasoning behavior directly with the reasoning_effort parameter, which can be set to “minimal,” “medium,” or “high.” This means you can optimize for speed in quick checks or prioritize depth in critical reasoning tasks. When combined with tool use, GPT-5 can chain multi-step logic across different APIs—for instance, fetching external data, filtering it based on custom rules, and then generating insights in one flow. This combination of better default reasoning, configurable effort, and improved honesty makes GPT-5 a stronger fit for scenarios where correctness matters as much as creativity.