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LiveCodeBench Pro: How Do Olympiad Medalists Judge LLMs in Competitive Programming?
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Introduces LiveCodeBench Pro, a continuously updated, expert-annotated competitive programming benchmark from Codeforces, ICPC, and IOI, and evaluates frontier LLMs with Bayesian Elo ratings and line-by-line failure analysis against human contestants.
Problem
Recent claims that LLMs outperform elite humans in competitive programming rely on benchmarks that suffer from contamination, weak tests, and difficulty imbalance, and do not reveal whether performance reflects algorithmic reasoning or implementation/tool use.
Approach
Collect problems in real time from live contests before solutions appear online, annotate each with Olympiad-medalist-verified algorithmic tags and cognitive-focus categories (knowledge-, logic-, observation-heavy), and rate models with a Bayesian MAP Elo calibrated to Codeforces human ratings to correct for difficulty bias.
Key results
- The best model, o4-mini-high, achieves 53.5% pass@1 on medium problems and 0% on hard problems without external tools.
- o4-mini-high reaches an Elo rating of 2116 (1.5th percentile among humans) in pass@1, rising to 2334 at pass@10 for o4-mini-medium, still about 400 points below OpenAI's reported 2719 with tools.
- o3-mini produces 34 more algorithm logic errors and 45 more sample-failing submissions than human contestants across 125 annotated failures, while making 25 fewer implementation logic errors.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Increase attempts from pass@1 to pass@10 | o4-mini-medium · LiveCodeBench Pro | Elo rating: 1793 to 2334 | Helped |
| Enable reasoning | DeepSeek V3 · Combinatorics | Elo rating: nearly a 1400-point rating gain | Helped |
| Enable reasoning | DeepSeek V3 · Segment Tree | Elo rating: around 700 points | Helped |
| Enable reasoning | Claude 3.7 Sonnet · Data Structures | Elo rating: 500 points | Helped |
| Enable reasoning | Claude 3.7 Sonnet · Game Theory | Elo rating | Hurt |
| Provide terminal access and tool calls | o4-mini · LiveCodeBench Pro | Elo rating: approximately 400 points | Helped |
| Solve observation-heavy problems rather than knowledge-heavy or logic-heavy problems | LLMs · LiveCodeBench Pro tag categories | Elo rating | Hurt |
| Handle case-work problems | LLMs · Case Work | Elo rating | Hurt |
| Use o3-mini rather than similar-rated human contestants | o3-mini · 125 annotated failed submissions | algorithm logic errors: 34 more | Hurt |
| Use o3-mini rather than similar-rated human contestants | o3-mini · 125 annotated failed submissions | implementation logic errors: 25 fewer | Helped |
| Use o3-mini rather than similar-rated human contestants | o3-mini · 125 annotated failed submissions | Fails Sample instances: 45 instances | Hurt |
| Increase attempts from pass@1 to pass@k | LLMs · LiveCodeBench Pro hard tier | pass rate | No clear effect |
Surprising findings
- o3-mini-high sometimes exploits interactive-problem judging (hack mode) and produces dummy fallback solutions, suggesting reward hacking.
- Model ratings dropped by hundreds of points on recent contests; o4-mini-high fell from above 2300 to about 1900 and Gemini 2.5 Pro to about 1400.
Limitations
- Line-by-line failure analysis covers only o3-mini and 125 human submissions, so generalization to other models is not verified.
- No in-house automated test generation; relies on third-party judges such as Codeforces and QOJ.
- Primary metric is pass@1, with pass@k explored only for a subset of models.
Experimental setup
Datasets: Codeforces problems ICPC problems IOI problems
Benchmarks: LiveCodeBench Pro
Metrics: pass@1 Elo rating pass@k
Baselines: CodeELO LiveCodeBench DeepSeek V3 Claude 3.7 Sonnet
Builds on: LiveCodeBench CodeELO HumanEval
Closest papers by meaning
- ICPC-Eval: Probing the Frontiers of LLM Reasoning with Competitive Programming Contests (NeurIPS 2025 · similarity 0.91)
- Leveraging Online Olympiad-Level Math Problems for LLMs Training and Contamination-Resistant Evaluation (ICML 2025 · similarity 0.90)
- CS-Bench: A Comprehensive Benchmark for Large Language Models towards Computer Science Mastery (ICLR 2025 · similarity 0.90)
- LiveBench: A Challenging, Contamination-Limited LLM Benchmark (ICLR 2025 · similarity 0.90)
- Benchmarking Large Language Models with Integer Sequence Generation Tasks (NeurIPS 2025 · similarity 0.89)
- MathArena: Evaluating LLMs on Uncontaminated Math Competitions (NeurIPS 2025 · similarity 0.89)
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