Unverified50% confidenceTradeoffExact time
Benchmark overfitting is a core problem in current large model evaluation; some models achieve pass rates above 90% on HumanEval but perform far below expectations in real codebases.
1
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50%
Confidence
Long-term
Relevance
8/16/2026
First Seen
Sources
Related Claims
UnverifiedOn code capability benchmarks such as HumanEval, top large language models have already achieved pass rates exceeding 90%.77% similarVerifiedPass rates of code generation models on benchmarks such as HumanEval jumped from 60% to over 90% between 2024 and 202577% similarUnverifiedWhen multiple models all achieve accuracy rates above 90% on benchmarks such as MMLU and HumanEval, user attention shifts to practical metrics such as inference latency, service availability, and total cost of ownership.73% similarUnverifiedStandardized code benchmark tests such as HumanEval show that top models have already exceeded a pass@1 score of 90%.73% similarUnverifiedEven the most advanced code generation models achieve a functional correctness rate of only 60%–80% for generated code, while the models' confidence in the correctness of their own output is often as high as 90% or more.73% similar
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