Artificial intelligence has rapidly become a key tool in scientific workflows. Researchers use tools such as large language models (LLMs) to summarize complex literature, propose hypotheses, and solve structured problems. The use of AI has increased rapidly since it became publicly available. But with increasing reliance comes a vital question: How accurate is AI for scientific research, and how often does it produce incorrect answers?
This article explains current performance with real numbers based on benchmark results and published analyses based on a publication released by OpenAI. It highlights where AI excels, where it struggles, and why accuracy matters for science.
Why Accuracy Matters in Science
Science depends on evidence-based conclusions. An AI system that produces incorrect information can mislead research, waste time, or propagate misinformation. Therefore, understanding the accuracy of AI in scientific research is essential for responsible use. If researchers know what AI can and cannot do reliably, they can integrate it into workflows in ways that increase productivity without compromising scientific integrity.
OpenAI’s FrontierScience Benchmark: A New Standard
OpenAI recently introduced the OpenAI FrontierScience benchmark to evaluate scientific reasoning in physics, chemistry, and biology. Instead of focusing on multiple-choice questions or factual recall, FrontierScience measures expert-level scientific reasoning. The benchmark contains two types of tasks, each representing a different mode of scientific thinking:
Before describing what the latest results show, it helps to understand the structure of the test itself:
- Olympiad-style problems, which are structured questions with well-defined answers.
- Research-style problems, which are complex, open-ended tasks aimed at mimicking real scientific reasoning.
These two formats reveal very different performance levels. Olympiad tasks resemble traditional science exams with clear paths to correct answers. Research tasks are closer to the uncertainty and complexity of real scientific work, where answers may not be obvious or singular.
According to the latest FrontierScience results:
- On Olympiad-style tasks, the most advanced model tested (GPT-5.2) correctly answered about 77% of questions.
- On the more open-ended Research track, accuracy dropped to around 25%.
This contrast is key: AI can be reasonably accurate on structured tasks but struggles with open-ended research-level reasoning—requiring human interpretation and judgment. These numbers also demonstrate how accuracy depends heavily on task type, not just model strength.
How Often AI Goes Wrong: Real-World Error Rates
Even when AI provides scientifically grounded answers, it can still produce plausible but incorrect information—commonly referred to as hallucinations. Hallucinations occur because large language models generate responses based on patterns in text, not on direct access to verified knowledge. Understanding how frequently these errors occur gives users a clearer sense of risk.
General AI Accuracy Metrics
Across published testing environments, researchers have observed error rates that vary depending on task complexity and scientific depth. Studies show that average hallucination rates for large language models can range from:
- about 2% to 30% or more, depending on the task and model version.
In specialized scientific domains, where accuracy expectations are higher, error rates concentrate around:
- 3.7% to 16.9%, with lower rates on straightforward tasks and higher rates on complex reasoning challenges.
These findings reinforce that AI can be very accurate in defined technical tasks but demonstrates variability when domain complexity increases.
Scientific Reference Accuracy Studies
Accuracy issues also appear in how models cite academic sources, which the AI seems to have increased difficulty with. This seems to hold true One study testing reference retrieval found:
- hallucination rates of around 39.6% for GPT-3.5 and
- 28.6% for GPT-4,
when the task involved generating reference lists tied to scientific claims. The majority of errors involved citations that did not exist or that misrepresented real papers—critical flaws for scientific research. It is important to note the generic hallucination rate for GPT-5 is under 12%, with better prompt strategies often leading to improved results.
Exaggeration and Misinterpretation
A different category of error appears when AI summarizes scientific papers. Even when facts are correct, conclusions may not accurately reflect the original source. Studies have found:
- overgeneralizations or interpretive errors appearing in up to 26% to 73% of AI-generated summaries, depending on the prompting approach.
This demonstrates that accuracy goes beyond factual correctness—it includes how well meaning and nuance are preserved.
Taken together, these numbers show that while AI produces correct scientific answers frequently, error rates are non-negligible, especially when precision, citations, or research framing matter.
Where AI Is Most Accurate
AI systems today tend to be most reliable in tasks that share several qualities. These areas are important not only because accuracy is higher, but because they represent practical use cases researchers commonly apply today. AI is strongest when tasks are:
- Structured and well defined: clear prompts and known answer formats reduce ambiguity and improve reasoning performance.
- Based on existing knowledge: summarizing established concepts or solving known problem types leads to higher accuracy.
- Technical but constrained: questions grounded in formal logic, mathematics, or classical physics play to the strengths of model-based reasoning.
In these contexts—such as answering Olympiad questions or solving defined equations—accuracy commonly reaches 70–80% or higher, making AI a dependable assistant for analytical and computational tasks.
Where AI Is Less Reliable
Despite progress, researchers must remain alert to limitations. These weaknesses explain why accuracy varies so widely across scientific task types and why open-ended research still requires human leadership.
Hallucinations Remain a Challenge
Models sometimes assert false statements with complete confidence. This happens because they generate text by predicting patterns rather than checking truth. As a result, even outputs that appear polished or authoritative may conceal errors.
Citation Errors Are Common in Research Outputs
Reference generation is an area where accuracy remains particularly fragile. As noted above, observed rates of incorrect citations often exceed 25% to 40%. This makes unsupervised use of AI-generated references unsuitable for formal research environments.
Open-Ended Reasoning Has Lower Accuracy
FrontierScience results show that AI’s ability to complete research-like tasks requiring judgment and hypothesis-level reasoning is significantly lower than performance on structured test-style problems. With an accuracy of around 25% in this domain, these tasks require human expertise to guide, verify, and complete.
These limitations emphasize that while AI can accelerate parts of the research process, it cannot replace human reasoning
Interpreting the Numbers: What They Mean for Scientists
Accuracy statistics are useful only when placed in real scientific context. These numbers highlight three important takeaways for scientists:
- AI is a tool — not a scientist. High performance on structured tasks does not mean AI can independently generate valid research findings.
- Human oversight is crucial. Peer review, experimental design, data interpretation, and verification remain human responsibilities.
- Context matters. Fact-based questions may produce low error rates, while research-style problems introduce uncertainty that increases error frequency.
Understanding these distinctions helps researchers use AI strategically—capitalizing on strengths and managing risks.
The Bottom Line: Valuable Aid, Not a Replacement
Current evidence suggests that the accuracy of AI in scientific research is mixed. AI can be highly accurate on structured scientific tasks and can dramatically accelerate early research workflows. But models also generate incorrect or misleading outputs frequently enough that human validation remains essential.
As tools like OpenAI’s FrontierScience continue developing, accuracy will improve, and new capabilities will emerge. For now, the best scientific outcomes come from combining AI’s speed and pattern recognition with human expertise, skepticism, and judgment.
AI is not a replacement for researchers. It is a powerful new instrument, one that can enhance scientific inquiry, provided it is used carefully, critically, and collaboratively.
