A new benchmark assembled by a team of International Olympiad medalists suggests the hype about large language models beating elite human coders is premature. LiveCodeBench Pro, unveiled in a 584-problem study [PDF] drawn from Codeforces, ICPC and IOI contests, shows the best frontier model clears just 53% of medium-difficulty tasks on its first attempt and none of the hard ones, while grandmaster-level humans routinely solve at least some of those highest-tier problems.
The researchers measured models and humans on the same Elo scale used by Codeforces and found that OpenAI’s o4-mini-high, when stripped of terminal tools and limited to one try per task, lands at an Elo rating of 2,116 — hundreds of points below the grandmaster cutoff and roughly the 1.5 percentile among human contestants. A granular tag-by-tag autopsy identified implementation-friendly, knowledge-heavy problems — segment trees, graph templates, classic dynamic programming — as the models’ comfort zone; observation-driven puzzles such as game-theory endgames and trick-greedy constructs remain stubborn roadblocks.
Because the dataset is harvested in real time as contests conclude, the authors argue it minimizes training-data leakage and offers a moving target for future systems. The broader takeaway is that impressive leaderboard jumps often reflect tool use, multiple retries or easier benchmarks rather than genuine algorithmic reasoning, leaving a conspicuous gap between today’s models and top human problem-solvers.
