Editorial illustration for GPT-5 Struggles with Research Tasks Despite Strong Test Performance
GPT-5 Falters in Research Tasks Despite High Test Scores
GPT-5.2 leads FrontierScience test, but falters on real research tasks
Artificial intelligence's latest frontier isn't just about passing tests, it's about tackling complex, open-ended research challenges. OpenAI's newest language model, GPT-5.2, recently underwent rigorous scientific benchmarking that revealed a stark gap between standardized performance and real-world problem-solving.
Initial test results suggest impressive capabilities, but researchers are digging deeper into how AI handles nuanced scientific investigations. The challenge isn't just answering questions, but generating meaningful, multifaceted research insights that go beyond simple computational tasks.
While machine learning models have excelled at structured evaluations, research demands something more: sustained reasoning, critical analysis, and the ability to navigate ambiguous intellectual terrain. GPT-5.2's performance hints at both remarkable potential and significant limitations in how artificial intelligence approaches scholarly work.
The stakes are high. As scientific communities increasingly explore AI's research capabilities, each benchmark becomes a critical window into understanding these powerful yet still-evolving systems.
OpenAI says each task should take at least three to five hours to solve. Instead of a single correct answer, research tasks are scored on a ten-point rubric, with GPT-5 handling the automated grading at high reasoning intensity. GPT-5.2 leads, but research tasks remain tough All reasoning models were tested at "high" reasoning intensity, with GPT-5.2 also tested at "xhigh" - and without browsing enabled.
GPT-5.2 scores 77 percent on the Olympiad set and 25 percent on Research. Gemini 3 Pro trails close behind on Olympiad at 76 percent. On Research, GPT-5.2 and GPT-5 tie for first place, and in a "surprising" (OpenAI) twist, GPT-5 significantly outperforms the newer GPT-5.1, which manages just about 19 percent.
Claude Opus 4.5 hits 71 percent on Olympiad and 18 percent on Research. Grok 4 scores 66.2 percent and 16 percent respectively. The older GPT-4o lags far behind at 12 percent on Olympiad and under one percent on Research.
OpenAI's first reasoning model o1, released last September, marked a major leap forward. More compute means better results Performance scales with compute time. GPT-5.2 jumps from 67.5 percent at low reasoning intensity to 77 percent at the highest setting on the Olympiad set.
On Research, scores climb from 18 to 25 percent. OpenAI's o3 model bucks the trend on Research: it actually does slightly worse at high reasoning intensity than at medium. The company calls this "surprising" but doesn't explain why.
OpenAI says the results show real progress on expert-level questions but leave plenty of room for improvement, especially on open research tasks.
OpenAI's latest GPT-5.2 reveals the complex landscape of AI research capabilities. The model demonstrated impressive performance on certain benchmarks, scoring 77 percent on Olympiad tasks, yet struggled significantly with more nuanced research challenges.
The testing approach highlights the intricate nature of AI reasoning. Research tasks aren't simply about finding a single correct answer, but require sophisticated evaluation across a ten-point rubric - a nuance that challenges current AI systems.
Despite leading in test performance, GPT-5.2 could only manage a 25 percent success rate on research-oriented challenges. This suggests that while AI continues to advance, true research-level comprehension remains elusive.
OpenAI's methodology emphasizes the depth required for research tasks, noting that each challenge should take human researchers three to five hours to solve. The automated grading system, run at high reasoning intensity, provides a structured way to assess these complex interactions.
The results underscore a critical point: technological progress isn't linear. AI's potential is growing, but the gap between test performance and real-world research complexity remains significant.
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Common Questions Answered
How did GPT-5.2 perform on different types of scientific benchmarks?
GPT-5.2 demonstrated impressive performance on Olympiad tasks, scoring 77 percent, but struggled significantly with more complex research challenges, only achieving a 25 percent success rate. The model's performance varied dramatically depending on the task complexity and reasoning intensity.
What makes research tasks challenging for AI models like GPT-5.2?
Research tasks are not about finding a single correct answer, but require sophisticated evaluation across a ten-point rubric that tests nuanced problem-solving capabilities. OpenAI suggests these tasks should take three to five hours to solve, highlighting the depth and complexity beyond standard testing metrics.
How does OpenAI evaluate the reasoning capabilities of GPT-5.2?
OpenAI tests reasoning models at different intensity levels, including 'high' and 'xhigh' reasoning modes, with tests conducted without browsing capabilities to assess the model's intrinsic problem-solving skills. The evaluation process goes beyond simple test scores to examine the model's ability to handle complex, open-ended scientific investigations.