Evaluating the Limitations of Local LLMs in Solving Complex Programming Challenges

• URL: http://arxiv.org/abs/2509.15283v1
• Date: Thu, 18 Sep 2025 14:13:30 GMT
• Title: Evaluating the Limitations of Local LLMs in Solving Complex Programming Challenges
• Authors: Kadin Matotek, Heather Cassel, Md Amiruzzaman, Linh B. Ngo,
• Abstract summary: This study examines the performance of open-source, locally hosted large-language models (LLMs) in handling complex programming tasks.<n>Building on the original Framework for AI-driven Code Generation Evaluation (FACE), the authors retrofit the pipeline to work entirely offline.<n>Results show that the overall pass@1 accuracy is modest for the local models, with the best models performing at approximately half the acceptance rate of the proprietary models.
• Score: 0.31498833540989407
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This study examines the performance of today's open-source, locally hosted large-language models (LLMs) in handling complex competitive programming tasks with extended problem descriptions and contexts. Building on the original Framework for AI-driven Code Generation Evaluation (FACE), the authors retrofit the pipeline to work entirely offline through the Ollama runtime, collapsing FACE's sprawling per-problem directory tree into a handful of consolidated JSON files, and adding robust checkpointing so multi-day runs can resume after failures. The enhanced framework generates, submits, and records solutions for the full Kattis corpus of 3,589 problems across eight code-oriented models ranging from 6.7-9 billion parameters. The submission results show that the overall pass@1 accuracy is modest for the local models, with the best models performing at approximately half the acceptance rate of the proprietary models, Gemini 1.5 and ChatGPT-4. These findings expose a persistent gap between private, cost-controlled LLM deployments and state-of-the-art proprietary services, yet also highlight the rapid progress of open models and the practical benefits of an evaluation workflow that organizations can replicate on in-house hardware.

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