LLM Evaluation Report
o1-preview
2025-01-21
2379.88
131
0.317852
3.62805
3.62805
o1-mini
2025-01-21
933.915
128
0.326939
3.68293
3.77439
gpt-4o
2025-01-21
317.122
121
0.321377
3.75
3.7622
gpt-4o-mini
2025-01-21
309.799
117
0.338521
3.68902
3.75
claude-3-5-sonnet-20240620
2025-01-21
244.255
111
0.298804
3.62805
3.65244
claude-3-5-sonnet-20241022
2025-01-21
254.239
115
0.312278
3.70732
3.66463
gemini-1.5-pro
2025-01-21
507.246
101
0.335308
3.48171
3.47561
gemini-1.5-flash
2025-01-21
764.864
2
0.267744
0.689024
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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