This is a submission for the Kaggle Benchmarking Challenge
What I Benchmarked
I built a benchmark to evaluate how reliably large language models solve practical Python programming tasks. Rather than measuring only whether a model can generate code that looks correct, I focused on three dimensions: functional correctness, debugging ability, and instruction following.
The benchmark includes Python problems involving data manipulation, edge cases, bug fixing, and code generation under explicit constraints. Each task is evaluated against predefined expected outputs or test cases wherever possible.
I chose this capability because code that appears convincing can still fail in real applications. A useful coding assistant must handle unusual inputs, follow requirements precisely, and produce solutions that work—not just explanations that sound plausible.
Models Tested
All models were evaluated on the same task set and under consistent prompting and scoring conditions. I used automated test cases where possible to reduce subjective judgments and make the results reproducible.
Findings
The benchmark measured functional correctness, edge-case handling, debugging success, and instruction compliance.
Key results:
One important aspect of this evaluation was separating plausible-looking code from code that actually passes tests. This distinction helps reveal whether a model is dependable on practical programming tasks, particularly when inputs are unusual or requirements are restrictive.
The results also highlight opportunities for further investigation. I would next expand the benchmark with more adversarial edge cases, multi-step debugging tasks, and repeated runs to measure consistency. I would also compare performance with and without additional reasoning or tool access to understand which conditions improve reliability.
These findings should be interpreted within the scope of the benchmark's task set, model versions, and evaluation settings rather than as a universal ranking of coding ability.
My Benchmark
python -m venv venv venv\Scripts\activate pip install -r requirements.txt
The benchmark, evaluation procedure, and results are available at the link above so that others can inspect the methodology, reproduce the comparison, and extend the task set.
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