LLM Evaluation Report
claude-opus-4-20250514
2025-05-27
682.341
45
0.373498
3.68902
3.71951
claude-sonnet-4-20250514
2025-05-27
685.546
112
0.317174
3.7378
3.65854
claude-3-7-sonnet-20250219
2025-05-27
746.497
108
0.319258
3.65244
3.65244
claude-3-5-sonnet-20241022
2025-05-27
445.549
114
0.332094
3.65244
3.72561
gpt-4.1
2025-05-27
340.45
114
0.345565
3.71951
3.79878
o4-mini
2025-05-27
1380.26
128
0.322408
3.70122
3.7439
o3
2025-05-27
1592.45
141
0.314449
3.71341
3.85366
gpt-4o
2025-05-27
254.478
123
0.305002
3.70732
3.7378
gemini_gemini-2.0-flash
2025-05-27
428.324
102
0.304022
3.65244
3.60976
gemini_gemini-2.5-pro-preview-05-06
2025-05-27
1317.42
71
0.319577
2.45732
2.67683
gemini_gemini-2.5-flash-preview-05-20
2025-05-27
1042.03
108
0.32728
3.39024
3.46341
Total Response Time (s): The total time taken by the model to generate all the outputs.
Tests passed: The number of unit tests that the model has passed during evaluation, out of a total of 164 tests.
Mean CodeBLEU: Average CodeBLEU score, a metric for evaluating code generation quality based on both syntactic and semantic correctness.
Mean Usefulness Score: Average rating of the model's output usefulness as rated by a LLM model.
0: Snippet is not at all helpful, it is irrelevant to the problem.
1: Snippet is slightly helpful, it contains information relevant to the problem, but it is easier to write the solution from scratch.
2: Snippet is somewhat helpful, it requires significant changes (compared to the size of the snippet), but is still useful.
3: Snippet is helpful, but needs to be slightly changed to solve the problem.
4: Snippet is very helpful, it solves the problem.
Mean Functional Correctness Score: Average score of the functional correctness of the model's outputs, assessing how well the outputs meet the functional requirements, rated by a LLM model.
0 (failing all possible tests): The code snippet is totally incorrect and meaningless.
4 (passing all possible tests): The code snippet is totally correct and can handle all cases.
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