Small vs. Large Language Models: Choosing the Right AI Tool for the Job

Language models have taken center stage in today’s technology landscape, with headlines dominated by the latest breakthroughs in Large Language Models (LLMs) such as GPT, LLaMa, Gemini, and more. Yet, while LLMs often steal the limelight with their broad, generative capabilities, Small Language Models (SLMs) have been quietly advancing—and they’re a critical part of the natural language processing (NLP) toolbox.

In this post, we’ll explore the key differences and similarities between SLMs and LLMs. Our aim is to help you understand when to harness the versatility of an LLM versus when the speed, efficiency, and specialized performance of an SLM might be the best fit for your project.

Defining the Landscape
There’s no strict rule that separates “small” from “large” language models. In practice, LLMs are generally characterized by billions (or even hundreds of billions) of parameters. Think of models like GPT-4, various LLaMa2 configurations, or even emerging models like Mistral. SLMs, on the other hand, usually contain up to a few hundred million parameters and are predominantly geared toward language understanding tasks—think BERT, RoBERTa, or BigBird.

While LLMs are designed to generate text across countless applications—from poetry to code—SLMs excel at precision tasks like classification and named entity recognition. This tradeoff between breadth and specialization is at the heart of choosing the right model for your project.

Breadth Versus Specialization
LLMs are true “jack-of-all-trades” models. Their ability to perform across a wide range of languages and tasks makes them ideal for exploratory phases in AI projects. Need a quick sentiment analysis or an on-the-fly translation? An LLM can handle that—and much more. However, this versatility often comes at a cost. The all-encompassing nature of LLMs means they may not be as finely tuned for specific tasks as a dedicated model might be.

SLMs, in contrast, are built for specific challenges. When you have abundant data for a narrowly defined problem, a well-trained SLM can deliver faster, more cost-effective, and sometimes more accurate results. In scenarios where transparency and explainability are key—especially in sensitive industries like healthcare—SLMs can be fine-tuned to offer greater insight into their decision-making processes.

Transparency, Trust, and Deployment
One significant advantage of SLMs is their relative openness. Models like BERT, for instance, have been extensively studied and can be run on standard hardware, making them more accessible for research and rigorous auditing. This transparency is essential when trust is paramount—say, in healthcare applications where model decisions must be carefully validated.

LLMs, often proprietary and resource-hungry, are typically hosted in the cloud by large providers. Their immense computational needs and the inherent “black box” nature of their design can sometimes complicate efforts to pinpoint why they generate certain outputs. Still, for many applications, the ability of LLMs to “think out loud” and iteratively improve results justifies their use.

Cost, Efficiency, and Sustainability
Another key consideration is resource consumption. Training an LLM can cost tens or even hundreds of millions of euros, while training an SLM like BERT is considerably more accessible—with estimates in the ballpark of 10,000 euros. Moreover, fine-tuning an SLM for a specific task can be done in less than a day of GPU time. In an era where sustainability and operational efficiency are crucial, these differences can play a major role in deciding the right approach.

The Hallucination Challenge
No AI system is perfect. One of the more puzzling challenges with LLMs is the phenomenon of “hallucination”—instances where the model fabricates information. Although all AI systems make mistakes, the broad generative capacity of LLMs means their errors can be unexpected and diverse. With SLMs, errors are typically more straightforward (such as misclassifying text), making them easier to audit and rectify.

When to Go Big—and When to Go Small

Not an Either/Or Proposition
In many real-world applications, the best solution may not be to choose one over the other, but rather to combine both. An effective AI strategy might involve using a lightweight SLM for routine tasks while leveraging the expansive capabilities of an LLM when more complex, nuanced understanding is required. This layered approach can maximize efficiency while still delivering top-tier performance where it matters most.

In Summary
Choosing between a small and a large language model comes down to tradeoffs in performance, cost, and flexibility. By understanding the strengths and limitations of each, you can make a more informed decision that aligns with the specific needs of your project. Whether you need the all-encompassing versatility of an LLM or the specialized focus of an SLM, the key is to match the tool to the task at hand.

By Rami Luisto, PhD – Healthcare AI Lead Data Scientist at Digital Workforce