arXiv:2606.08840v1 Announce Type: new Abstract: Code generation models are typically compared using compact execution benchmarks and aggregate pass rates, but such summaries obscure how performance varies across programming languages, problem families, and failure modes. We present a large-scale, execution-grounded evaluation of 9 openly accessible LLMs specialized for coding on 2,707 free LeetCode problems across 12 programming languages. Our corpus contains 325,343 problem-model-language jobs, each linked to prompt metadata, extracted code, LeetCode execution outcomes, and static-analysis si
The proliferation of open-source LLMs for coding necessitates more rigorous, execution-grounded evaluation metrics to move beyond basic pass rates, especially as these models become more capable and diversified.
A robust, multilingual evaluation framework directly impacts the development and application of code generation AI, influencing which models gain traction and how quickly they are adopted in real-world scenarios.
The standard for comparing code generation LLMs is shifting from rudimentary pass rates to comprehensive, execution-grounded metrics that account for linguistic diversity, problem families, and failure modes.
- · Developers needing reliable code generation
- · Open-source AI research
- · Companies building on open-source code LLMs
- · Benchmarking platforms and services
- · Code LLMs with poor performance across diverse languages/tasks
- · Evaluations relying solely on basic pass rates
- · Companies relying on opaque, non-standardized model comparisons
The adoption rate of specific open code LLMs will be heavily influenced by their performance on this new, detailed evaluation framework.
Improved benchmarking will accelerate the development of more robust, language-agnostic code generation models, potentially leading to 'smarter' AI developers.
As code generation becomes more reliable and multilingual, it could significantly lower the barrier to entry for programming in various languages, potentially reshaping global software development landscapes.
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Read at arXiv cs.AI