What just happened? OpenAI and DeepMind have tested their systems in many high-level contests. DeepMind's AI has defeated world champions in Go and secured gold medals in the International Mathematical Olympiad, while OpenAI reported a win in mathematics at the same Olympiad this summer.
The results, together with their recent achievements in coding's most recognized competition, show how these settings are becoming proving grounds for technologies approaching the frontier of human-level reasoning.
AI models from Google DeepMind and OpenAI have reached a new benchmark in competitive programming, with both groups reporting that their latest models would have placed at the top of this year's International Collegiate Programming Contest World Finals. While neither company officially entered the event, held in early September, internal tests suggest that OpenAI's GPT-5 model would have finished first, while DeepMind's newly trained Gemini 2.5 Deep Think system would have ranked second.
The ICPC has produced some of the most influential figures in the technology industry, including Google co-founder Sergey Brin and OpenAI Chief Scientist Jakub Pachocki. Teams of three students, working at a single computer within a five-hour window, must solve twelve programming problems that require abstract reasoning, creative problem-solving, and error-free execution.
This year, the strongest human competitors managed to solve ten questions; OpenAI reported that GPT-5 completed all twelve, with eleven correct on its first attempt. DeepMind's Gemini 2.5 also matched and outperformed many human participants, solving one task that no team of students could complete.
The achievement underscores how closely artificial intelligence systems are now competing with elite human programmers in areas once thought beyond reach. "This is a historic moment towards AGI," Quoc Le, vice president of Google DeepMind and Google Fellow, told The Financial Times.
For OpenAI, the result highlights the increasing sophistication of its GPT-5 system, which the company used on all problems except the final, most complex one. That last problem was solved using GPT-5 in tandem with an experimental reasoning model still under development.
London-based DeepMind, founded by British neuroscientist and chess prodigy Sir Demis Hassabis, selected a different path. It combined reinforcement learning – an approach that rewards systems for producing correct results – with intensive exposure to difficult mathematics, reasoning exercises, and coding challenges to train Gemini 2.5 Deep Think.
Experts in the programming contest community were struck by the demonstration. "It's impressive for a purely AI system with no human in the loop to be able to get the performance that they did," Jelani Nelson, chair of electrical engineering and computer science at the University of California, Berkeley, said.
He noted that such capabilities seemed impossible until recently. "If someone had told me just a few years ago that we would have new technology able to perform at this level in math and computer science, I would not have believed them," Nelson, who has coached ICPC teams at Berkeley, Harvard, and MIT, added.
Still, observers cautioned against drawing broad conclusions about AI's ability to write production-ready software. Bartek Klin, associate professor of computer science at the University of Oxford and an ICPC coach, said the competition rewards speed in high-pressure situations, a skill that does not necessarily translate into practical engineering success. "In real life, the hardest problems are the ones that take half a year to think about," Klin said. He noted that human teams must also develop strategies for collaboration, a challenge that AI systems do not face.
DeepMind emphasized that Gemini 2.5's performance had not perfectly matched that of the leading human teams; in some cases, the AI did not solve problems that competitors completed successfully. Nonetheless, the laboratory highlighted the model's ability to produce unique solutions that no human team attempted. The company argued that this points to a future in which AI systems augment human intelligence by contributing original approaches to intractable problems.
The companies see potential beyond contests. Le of DeepMind said that progress in mathematical reasoning and competitive coding could translate into breakthroughs in science and engineering.
Disciplines such as drug design and semiconductor development, which require both algorithmic rigor and mathematical innovation, could ultimately benefit from models with demonstrated capacity to solve complex, abstract challenges. Heng-Tze Cheng, research director and principal scientist at DeepMind, described competitive programming as "the ultimate thinking game," since it requires developing new approaches rather than relying on memorized answers.