LLM Evaluation Report

Model
Date
Total Response Time (s)
Tests Passed
Mean CodeBLEU (0-1)
Mean Usefulness Score (0-4)
Mean Functional Correctness Score (0-4)

gpt-5.4

2026-03-18

371.302

151

0.30016

3.85976

3.88415

gpt-5

2026-03-18

3307.15

160

0.312013

3.71951

3.82927

gpt-5-mini

2026-03-18

2223.12

161

0.305418

3.79268

3.93293

claude-opus-4-6

2026-03-18

630.643

164

0.38829

3.87195

3.90854

claude-sonnet-4-6

2026-03-18

604.89

161

0.379059

3.85366

3.90854

claude-opus-4-1

2026-03-18

635.166

157

0.349491

3.85366

3.92683

claude-sonnet-4-5

2026-03-18

546.74

162

0.331766

3.89024

3.95732

claude-haiku-4-5

2026-03-18

280.497

154

0.317284

3.84756

3.92073

gemini-3.1-pro-preview

2026-03-18

3339.78

162

0.395161

3.73171

3.82317

gemini-3.1-flash-lite-preview

2026-03-18

176.493

148

0.370935

3.77439

3.87805

gemini-3-flash-preview

2026-03-18

2146.97

142

0.395257

3.59146

3.60366

gemini-2.5-pro

2026-03-18

2788.94

118

0.373488

3.2561

3.38415

gemini-2.5-flash

2026-03-18

952.543

148

0.338621

3.7439

3.83537

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 CodeBLEUarrow-up-right: Average CodeBLEU score, a metric for evaluating code generation quality based on both syntactic and semantic correctness.

Mean Usefulness Scorearrow-up-right: 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 Scorearrow-up-right: 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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