SOURCE-LINKED INTELLIGENCE
An Empirical Evaluation of Cost-Efficient Large Language Models on Algorithmic Programming Tasks
This study empirically evaluates whether cost-efficient Large Language Models (LLMs) can be trusted to generate enterprise code to a written specification. Three models (Gemini Flash 3, GPT-5.4 mini and Claude Haiku 4.5) were asked to solve 992 algorithmic problems as Java Spring Boot service methods conforming to a mandated signature and data-transfer-object specification, crossing four model and agentic coding tool combinations with two prompt variants to yield eight configurations, with iteration forbidden and hardcoded answers explicitly prohibited. Eight problem statements were withheld t
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-16T02:55:03.000Z
First collected: 2026-09-20T08:20:57.646Z. This is not the publication date.