CRUXEval-output-T0.8: by models

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SE predicted by accuracy

The typical standard errors between pairs of models on this dataset as a function of the absolute accuracy.

SE vs accuracy difference

The standard error of each model pair against their observed accuracy difference. Pairs below a reference line differ by more than the corresponding number of standard errors, i.e. they are statistically distinguishable at that level.

CDF of question level accuracy

Results table by model

model pass1 pass@count win_rate count SE(A) SE_x(A) SE_pred(A)
gpt-4-0613+cot 77.2 90.8 42 10 1.5 1.2 0.86
gpt-4-0613 68 75.1 33.8 10 1.6 1.6 0.53
gpt-3.5-turbo-0613+cot 56.5 82.2 25.7 10 1.8 1.3 1.1
deepseek-instruct-33b 48.4 66.1 18.3 10 1.8 1.5 0.88
gpt-3.5-turbo-0613 47.5 63.6 18 10 1.8 1.6 0.82
deepseek-base-33b 45.5 67.1 16.1 10 1.8 1.5 0.96
codetulu-2-34b 43.8 64 14.9 10 1.8 1.5 0.92
codellama-34b+cot 42.8 77 16.5 10 1.7 1.2 1.3
magicoder-ds-7b 41.7 63.1 13.8 10 1.7 1.5 0.94
wizard-34b 41.4 58.5 13.3 10 1.7 1.5 0.84
deepseek-instruct-6.7b 40.5 57 12.9 10 1.7 1.5 0.83
codellama-python-34b 39.7 57.9 12.3 10 1.7 1.5 0.87
codellama-34b 39.3 61.6 12.3 10 1.7 1.4 0.97
phind 38.9 57.4 12.2 10 1.7 1.5 0.89
deepseek-base-6.7b 38.3 60.5 11.8 10 1.7 1.4 0.97
wizard-13b 36.9 58 10.9 10 1.7 1.4 0.93
codellama-python-13b 36.4 58.1 10.7 10 1.7 1.4 0.96
mixtral-8x7b 36.3 60.9 10.8 10 1.7 1.4 0.99
codellama-13b 36.1 61 10.5 10 1.7 1.4 0.98
codellama-13b+cot 34.9 70.1 12.1 10 1.7 1.1 1.3
codellama-python-7b 32.4 55.1 8.91 10 1.7 1.4 0.95
codellama-7b 30.9 55.2 8.22 10 1.6 1.3 0.98
starcoderbase-16b 30.7 54 8.17 10 1.6 1.3 0.94
mistral-7b 30.1 55.5 8.19 10 1.6 1.3 1
phi-2 29.7 52.9 8.18 10 1.6 1.3 0.96
codellama-7b+cot 29.1 64.6 8.99 10 1.6 1.1 1.2
starcoderbase-7b 28.9 51.2 7.3 10 1.6 1.3 0.93
deepseek-instruct-1.3b 27.4 45.1 7.41 10 1.6 1.3 0.83
deepseek-base-1.3b 25.9 49.4 6.94 10 1.5 1.2 0.99
phi-1.5 21.7 45.9 6.39 10 1.5 1.1 0.96
phi-1 19.3 37.4 5.43 10 1.4 1.1 0.82