Small Language Models (SLMs) Can Still Pack a Punch: A survey (updated 2026)

Akanksha Gupta; Bijo Thomas; Harshita Asnani; Phanindra Reddy Madduru; Samia Feroze; Shreyas Subramanian; Vikram Elango; Mecit Gungor

arXiv · 2501.05465

Small Language Models (SLMs) Can Still Pack a Punch: A survey (updated 2026)

Akanksha Gupta·Bijo Thomas·Harshita Asnani·Phanindra Reddy Madduru·Samia Feroze·Shreyas Subramanian·Vikram Elango·Mecit Gungor

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Abstract

As foundation AI models continue to increase in size, an important question arises - is massive scale the only path forward? This survey of about 160 papers presents a family of Small Language Models (SLMs) in the 1 to 8 billion parameter range that demonstrate smaller models can perform as well, or even outperform large models. We explore task agnostic, general purpose SLMs, task-specific SLMs and techniques to create SLMs that can guide the community to build models while balancing performance, efficiency, scalability and cost. Furthermore we define and characterize SLMs' effective sizes, representing increased capability with respect to LLMs.

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Akanksha Gupta, Bijo Thomas, Harshita Asnani, Phanindra Reddy Madduru, Samia Feroze, Shreyas Subramanian, Vikram Elango, Mecit Gungor. 2025-01-03. Small Language Models (SLMs) Can Still Pack a Punch: A survey (updated 2026). https://arxiv.org/abs/2501.05465

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