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 7, 2026
Sorted by the source-provided rank. Higher score is better according to the registry.
| 1 | GO | 75.6% | 100.0% | 9 | C | |
| 2 | GO | 63.9% | 87.5% | 9 | C | |
| 3 | AC | 61.4% | 75.0% | 9 | C | |
| 4 | OP | 60.5% | 62.5% | 9 | C | |
| 5 | GO | 28.9% | 50.0% | 9 | C | |
| 6 | GO | 25.7% | 37.5% | 9 | C | |
| 7 | GO | 25.7% | 25.0% | 9 | C | |
| 8 | GO | 18.6% | 12.5% | 9 | C | |
| 9 | GO | 18.6% | 0.0% | 9 | C |
Top published rows on the benchmark's original scale.
The top published results on this benchmark's own scale.
Definition and scoring fields from the benchmark registry.
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. The current registry marks this benchmark as not independently verified with evidence level B.
Source-native results are preserved. Eligibility for the overall LLMBoard score is a separate policy decision.
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 unique published model results are currently shown.
This benchmark is preserved as source-native evidence but is not eligible for the current overall score.