OpenAI's recently launched GPT-5 model is showcasing advanced capabilities in scientific reasoning and experimental design, marking a significant leap in artificial intelligence's ability to tackle complex research challenges. The model, officially released on August 7, 2025, is designed to infer mechanisms and devise experiments from intricate scientific figures, a task previously requiring extensive human expertise. This development promises to accelerate discovery across various scientific disciplines.
According to Rohan Paul, GPT-5's power is evident in its capacity to "infer mechanism and design experiments from a single expert level scientific figures." He further elaborated in a tweet, stating that "Solving this needed expert-level reasoning and actionable experimental planning," and emphasized the necessity to "find the real cause from one complex scientific figure, separate many overlapping possibilities, and design exact experiments to confirm it." This highlights the model's sophisticated analytical and planning functions.
The enhanced reasoning stems from GPT-5's unified system, which integrates a dual-model architecture featuring both a fast-response unit and a dedicated deep-reasoning "Thinking" model. This allows the AI to dynamically allocate computational effort based on the complexity of the query, leading to more accurate and comprehensive outputs. Benchmarks indicate that GPT-5 (with thinking enabled) achieves 81.1% accuracy on CharXiv Reasoning, a test for scientific figure interpretation, and outperforms previous models like GPT-4o by 15-30% on complex reasoning tasks.
These advancements are already impacting real-world scientific workflows. Dr. Daria Unutas, a human immunologist, noted that GPT-5 "not only suggested things that we later on performed but also remarkable new insights," even predicting experiment outcomes and suggesting follow-up experiments. This capability can save "weeks sometimes months" in research, shifting scientific methodology from extensive trial-and-error to more targeted experimentation. The model's ability to process and understand complex instructions positions it as a transformative tool for accelerating innovation in research and development.