reasoning benchmark
LiveCodeBench(01-09)
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
Models1
Model coverage1
MetricScore
EvidenceB
LiveCodeBench(01-09) Ranking
Higher score ranks better on this benchmark.
| 01 | DE | 41.8% | 100.0% | 1 | C |
LiveCodeBench(01-09) Highlights
The leading models and scores on this benchmark.
What is LiveCodeBench(01-09)?
What LiveCodeBench(01-09) 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.
- Family
- LiveCodeBench(01-09)
- Modality
- text
- Primary category
- reasoning
- Score direction
- higher
- LLMBoard eligible
- No
- Evaluation key
- livecodebench(01-09)|llm-stats-current
Benchmark scores retain their original unit. Overall score eligibility is shown separately.
FAQ
Common questions about LiveCodeBench(01-09).
Which model scores highest on LiveCodeBench(01-09)?
DeepSeek-V2.5 is currently ranked first with 41.8%.
What does LiveCodeBench(01-09) measure?
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.
Is a higher score better?
Yes. Higher values rank better for this benchmark.
How many models are compared?
1 model results are currently shown.
Does this benchmark affect the overall score?
No. This benchmark is shown for reference but does not contribute to the overall score.