Rethinking Scale: Deployment Trade-offs of Small Language Models under Agent Paradigms

• URL: http://arxiv.org/abs/2604.19299v1
• Date: Tue, 21 Apr 2026 10:05:10 GMT
• Title: Rethinking Scale: Deployment Trade-offs of Small Language Models under Agent Paradigms
• Abstract summary: Small Language Models (SLMs) with fewer than 10 billion parameters present a promising alternative.<n>Existing research primarily focuses on enhancing SLMs through scaling laws or fine-tuning strategies.<n>This paper presents the first large-scale, comprehensive study of 10B open-source models under three paradigms.
• Score: 2.8207195763355712
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Despite the impressive capabilities of large language models, their substantial computational costs, latency, and privacy risks hinder their widespread deployment in real-world applications. Small Language Models (SLMs) with fewer than 10 billion parameters present a promising alternative; however, their inherent limitations in knowledge and reasoning curtail their effectiveness. Existing research primarily focuses on enhancing SLMs through scaling laws or fine-tuning strategies while overlooking the potential of using agent paradigms, such as tool use and multi-agent collaboration, to systematically compensate for the inherent weaknesses of small models. To address this gap, this paper presents the first large-scale, comprehensive study of <10B open-source models under three paradigms: (1) the base model, (2) a single agent equipped with tools, and (3) a multi-agent system with collaborative capabilities. Our results show that single-agent systems achieve the best balance between performance and cost, while multi-agent setups add overhead with limited gains. Our findings highlight the importance of agent-centric design for efficient and trustworthy deployment in resource-constrained settings.

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