swebench-multimodal: 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)
20251117_codefuse_pycfuse_svr 36.5 36.5 7.5 1 2.1 NaN NaN
20250701_GUIRepair_o3 36.5 36.5 7.59 1 2.1 NaN NaN
20250611_Refact_Agent_claude-4-sonnet 36.1 36.1 7.17 1 2.1 NaN NaN
20250528_OpenHands-Versa-claude4 34.9 34.9 6.63 1 2.1 NaN NaN
20250531_GUIRepair_o4mini 34.3 34.3 6.45 1 2.1 NaN NaN
20250509_OpenHands-Versa_claude3.7 31.8 31.8 4.81 1 2.1 NaN NaN
20250531_GUIRepair_gpt41 31.6 31.6 4.26 1 2.1 NaN NaN
20250401_zencoder 31 31 3.67 1 2 NaN NaN
20250531_GUIRepair_gpt4o 30.8 30.8 3.67 1 2 NaN NaN
20250325_globant_codefixer_agent 30 30 3.53 1 2 NaN NaN
20250311_zencoder 27.5 27.5 3.08 1 2 NaN NaN
20250226_agentless_lite_claude3.5 25.7 25.7 1.64 1 1.9 NaN NaN