reasoning benchmark
LiveCodeBench is a holistic and contamination-free evaluation benchmark for large language models for code. It continuously collects new problems from programming contests (LeetCode, AtCoder, CodeForces) and evaluates four different scenarios: code generation, self-repair, code execution, and test output prediction. Problems are annotated with release dates to enable evaluation on unseen problems released after a model's training cutoff.
Updated Aug 11, 2026
Higher score ranks better on this benchmark.
| 01 | GO | 75.6% | 100.0% | 9 | C | |
| 02 | GO | 63.9% | 87.5% | 9 | C | |
| 03 | AC | 61.4% | 75.0% | 9 | C | |
| 04 | OP | 60.5% | 62.5% | 9 | C | |
| 05 | GO | 28.9% | 50.0% | 9 | C | |
| 06 | GO | 25.7% | 37.5% | 9 | C | |
| 07 | GO | 25.7% | 25.0% | 9 | C | |
| 08 | GO | 18.6% | 12.5% | 9 | C | |
| 09 | GO | 18.6% | 0.0% | 9 | C |
A closer view of the leading scores on this benchmark.
The leading models and scores on this benchmark.
What LiveCodeBench v5 measures and how its scores work.
LiveCodeBench is a holistic and contamination-free evaluation benchmark for large language models for code. It continuously collects new problems from programming contests (LeetCode, AtCoder, CodeForces) and evaluates four different scenarios: code generation, self-repair, code execution, and test output prediction. Problems are annotated with release dates to enable evaluation on unseen problems released after a model's training cutoff.
Scores are shown in ratio. This benchmark is not independently verified and has an evidence level of B.
Benchmark scores retain their original unit. Overall score eligibility is shown separately.
Common questions about LiveCodeBench v5.
Gemini 2.5 Pro is currently ranked first with 75.6%.
LiveCodeBench is a holistic and contamination-free evaluation benchmark for large language models for code. It continuously collects new problems from programming contests (LeetCode, AtCoder, CodeForces) and evaluates four different scenarios: code generation, self-repair, code execution, and test output prediction. Problems are annotated with release dates to enable evaluation on unseen problems released after a model's training cutoff.
Yes. Higher values rank better for this benchmark.
9 model results are currently shown.
Yes. This benchmark can contribute to the current LLMBoard capability score.