Verified75% confidenceBenchmarkExact time
Code large language models are evaluated using executable benchmarks such as HumanEval and MBPP, meaning the generated code is directly executed and the output is checked for correctness.
3
Sources
75%
Confidence
Long-term
Relevance
7/6/2026
First Seen
Sources
Related Claims
UnverifiedCodex模型的评估通常使用HumanEval和MBPP等编码基准测试79% similarUnverified传统代码评测基准（如HumanEval、MBPP）主要测试模型在孤立函数级别的生成准确率79% similarUnverifiedHumanEval and MBPP primarily measure a model's code generation capability, while code review is an essentially different task, and the industry currently lacks standardized code review benchmarks.78% similarVerifiedMainstream benchmarks for evaluating programming large models include HumanEval, MBPP, SWE-bench, and LiveCodeBench78% similarUnverifiedHumanEval tests code generation capabilities by requiring models to generate correct Python code based on function signatures and docstrings.77% similar
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