LLM Evaluation Report
o1-preview
2025-02-21
2688.52
126
0.312426
3.57317
3.60976
o1-mini
2025-02-21
999.934
128
0.353161
3.68293
3.7439
gpt-4o
2025-02-21
211.039
124
0.315859
3.67073
3.75
gpt-4o-mini
2025-02-21
228.654
118
0.33717
3.63415
3.68293
claude-3-5-sonnet-20240620
2025-02-21
279.791
110
0.302947
3.68902
3.70732
claude-3-5-sonnet-20241022
2025-02-21
578.256
111
0.325341
3.64634
3.63415
gemini-1.5-pro
2025-02-21
563.298
100
0.329829
3.48171
4.07317
gemini-1.5-flash
2025-02-21
772.702
0
0.264112
0.780488
1.2561
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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