An Empirical Evaluation of Cost-Efficient Large Language Models on Algorithmic Programming Tasks

arXiv:2609.18052 · cs.SE, cs.AI · Submitted 2026-09-16 · Read on arXiv

cs.SE, cs.AI

Submitted: 2026-09-16

Updated: 2026-09-16

Comments: 7 pages, 7 figures. Accepted for publication in the IEEE Proceedings of the 2026 International Conference on Advanced Computing Technologies (ICACT)

Code: https://github.com/researchartifacts/ProjectEulerAIEvaluation

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Benchmark — 9× in this paper · explained in 2 episodes
• Agentic Coding — 3× in this paper · explained in 1 episode
• Iterative Refinement — 3× in this paper · explained in 3 episodes
• Accuracy — 2× in this paper · explained in 1 episode
• Hallucination — 2× in this paper · explained in 3 episodes
• Large Language Models (LLMs) — 2× in this paper · explained in 6 episodes
• Benchmarking — 1× in this paper · explained in 3 episodes
• Instruction Tuning — 1× in this paper · explained in 2 episodes
• Prompt Engineering — 1× in this paper · explained in 4 episodes
• Provenance — 1× in this paper · explained in 3 episodes

Sources

• Human vs Machine Mathematical Difficulty on Project Euler: An Experimental Analysis
• MathArena: Evaluating LLMs on Uncontaminated Math Competitions
• Code Generation with AlphaCodium: From Prompt Engineering to Flow Engineering

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