Unverified60% confidenceFactExact time
LiveCodeBench continuously crawls new problems from competitive programming platforms such as LeetCode, Codeforces, and AtCoder to prevent models from gaming the benchmark, and its evaluation covers four dimensions: code generation, self-repair, code execution reasoning, and test output prediction.
2
Sources
60%
Confidence
Long-term
Relevance
9/6/2026
First Seen
Sources
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VerifiedLiveCodeBench introduces a timestamp filtering strategy that selects problems published after a model's training cutoff date to ensure that the evaluation data is entirely new to the model being tested.75% similarUnverifiedSWE-bench and LiveCodeBench are relatively well-recognized industry benchmarks for evaluating code large language models74% similarVerifiedMainstream benchmarks for evaluating programming large models include HumanEval, MBPP, SWE-bench, and LiveCodeBench71% similarVerifiedThe training data sources for specialized programming models include open-source code from GitHub, competitive programming solutions, technical documentation, and code review records, and the models are optimized for benchmarks such as HumanEval, MBPP, and SWE-bench.66% similar
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