OJBench: A Competition Level Code Benchmark For Large Language Models
This addresses the need for better benchmarks to assess code reasoning in AI models, though it is incremental as it builds on existing evaluation frameworks.
The authors tackled the problem of evaluating large language models' competitive-level code reasoning by introducing OJBench, a benchmark with 232 programming competition problems, and found that even state-of-the-art models like o4-mini and Gemini-2.5-pro-exp struggle with these challenges.
Recent advancements in large language models (LLMs) have demonstrated significant progress in math and code reasoning capabilities. However, existing code benchmark are limited in their ability to evaluate the full spectrum of these capabilities, particularly at the competitive level. To bridge this gap, we introduce OJBench, a novel and challenging benchmark designed to assess the competitive-level code reasoning abilities of LLMs. OJBench comprises 232 programming competition problems from NOI and ICPC, providing a more rigorous test of models' reasoning skills. We conducted a comprehensive evaluation using OJBench on 37 models, including both closed-source and open-source models, reasoning-oriented and non-reasoning-oriented models. Our results indicate that even state-of-the-art reasoning-oriented models, such as o4-mini and Gemini-2.5-pro-exp, struggle with highly challenging competition-level problems. This highlights the significant challenges that models face in competitive-level code reasoning.