LLM Evaluation Report
o1-preview
2025-03-26
3050.82
133
0.320163
3.57317
3.56707
o1-mini
2025-03-26
772.183
125
0.341362
3.7622
3.78049
gpt-4o
2025-03-26
203.529
129
0.319673
3.71951
3.79878
gpt-4o-mini
2025-03-26
241.823
117
0.33884
3.64024
3.66463
claude-3-5-sonnet-20240620
2025-03-26
283.79
108
0.301366
3.60976
3.60366
claude-3-5-sonnet-20241022
2025-03-26
375.616
110
0.327685
3.68902
3.70732
gemini-1.5-pro
2025-03-26
521.169
109
0.336873
3.56707
3.53049
gemini-1.5-flash
2025-03-26
764.637
0
0.264605
0.768293
1.03659
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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