LLM Evaluation Report
o1-preview
2025-03-25
2615.06
132
0.317859
3.53659
3.65854
o1-mini
2025-03-25
886.248
129
0.333311
3.71341
3.73171
gpt-4o
2025-03-25
205.895
130
0.312619
3.69512
3.75
gpt-4o-mini
2025-03-25
189.939
119
0.332049
3.60976
3.60366
claude-3-5-sonnet-20240620
2025-03-25
274.408
111
0.303585
3.64024
3.64024
claude-3-5-sonnet-20241022
2025-03-25
345.555
111
0.318958
3.66463
3.68902
gemini-1.5-pro
2025-03-25
502.629
99
0.340841
3.45122
3.36585
gemini-1.5-flash
2025-03-25
760.225
0
0.264967
0.823171
0.914634
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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