GPT-5 incorporates multiple safeguards aimed at improving reliability and reducing harmful or biased outputs. One major improvement is its ability to recognize when it does not have enough information to answer a question, choosing to say so instead of fabricating an answer. In testing, GPT-5 gave an incorrect confident response in only 9% of missing-image cases, compared to 86.7% for the earlier o3 model. This is especially important in professional and high-stakes environments, where incorrect information can lead to costly errors.
The model’s reasoning process has been refined to lower hallucination rates by up to six times in long-form responses compared to o3, and it is up to 80% less likely to produce factual errors. These gains come from both architectural improvements and better alignment techniques that guide the model toward more accurate and honest answers. Additionally, GPT-5’s reduced token usage for equivalent responses (50–80% fewer tokens in many cases) not only improves efficiency but also limits opportunities for unnecessary or speculative content to appear in answers.
Bias mitigation is also built into GPT-5’s training and deployment. OpenAI applies safety filters, human feedback processes, and ongoing evaluations to reduce harmful biases in generated content. The company’s deployment strategy includes continuous monitoring, user feedback channels, and prompt-level control so developers can guide GPT-5 toward safe and relevant outputs in their specific domains. For example, through the API, you can set system messages and tool-use constraints to keep outputs within policy and task-relevant boundaries. Combined, these measures make GPT-5 not only more accurate but also more dependable in diverse real-world settings.