Bigger is not always better: comparing LLMs and SLMs
Discover the differences and similarities as we compare LLMs and SLMs, exploring their strengths, use cases.
I still remember the first time I encountered the power of Large Language Models (LLMs), especially ChatGPT. As a Developer, I loved how it could help me prototype my idea and even help me set up the boilerplate code within minutes. I wanted to bring this functionality to my side projects and started my exploration.
Behind the scenes, however, the truth was less ideal.
Expensive costs, slow response times, infrastructure needs, and API costs eventually found me searching for alternatives. It was during this time that I discovered Small Language Models (SLMs) and changed my perspective about the use of Large Language Models. Models don’t need to be large for small tasks or rather one task well instead of completing 1000 at a time.
In this blog, I’ll share my journey navigating the trade-offs between LLMs and SLMs. Whether you’re a startup founder, developer, or AI enthusiast, this guide will help you make a choice on when to use an LLM, when an SLM is the better choice, and how each one of them fits into the Generative AI ecosystem we have today.
My journey to LLM vs. SLM
LLMs, such as OpenAI’s GPT-4, are vast neural networks containing hundreds of millions to billions of parameters. These models excel at generating original output based on their training data patterns, and they are capable of handling open-ended tasks with remarkable fluency; they are heavy, though means they can’t run on-device efficiently with limited power.
On the other hand, SLMs – like DistilBERT or MiniLM – are lighter, with fewer parameters (typically tens to hundreds of millions). This makes them more efficient, faster, and significantly cheaper to deploy and run on-device.
At first, I experimented with building a chatbot as a side project and implementing large language model (LLM) APIs. It was impressive in its ability to understand a wide range of topics and provide in-depth responses.
However, I experienced a mini heart attack when I saw the API and cloud billing. The costs of hosting and running the model proved to be unsustainable, and latency issues frustrated users. That’s when I began exploring smaller language models (SLMs) and discovered ways to reduce costs without significantly impacting functionality.
SLMs are also listed on 10 Breakthrough Technologies 2025 by MIT Technology Review.
What is the difference between SLM and LLM?
The trade-off is clear: LLMs bring unparalleled power but demand significant infrastructure and also come with privacy concerns. SLMs, while narrower in scope, offer practical use cases for targeted use cases and can be deployed on edge.
In which scenario might a SLM be a more appropriate solution than an LLM?
An SLM might be more appropriate when resource constraints, such as limited computational power or storage, are a concern, or when the task requires focused expertise rather than the wider range of capabilities of an LLM.
Let’s review each scenario.
Lower computational costs
Hosting an LLM-based system in production comes with a hefty price tag. With thousands of daily user queries, you have to invest in expensive GPU instances, driving up your cloud bills. Switching to an SLM for targeted use cases cut those costs dramatically. Like in Pieces, we do the pre-processing on-device using SLMs.
Easier fine-tuning
Fine-tuning an SLM is not only feasible but also gives a high-performing, domain-specific model.
Fine-tuning an LLM would have been overkill, requiring more data and computing resources to on-device. For tasks requiring expertise in a specific domain (e.g., legal contracts or technical documentation), SLMs are easier to fine-tune and maintain focus, often outperforming LLMs in these narrow scenarios.
Privacy and on-device deployment
SLMs’ smaller size allows them to run on local devices or edge servers. This capability is invaluable for projects in industries like healthcare and finance, where data security is non-negotiable. Instead of transmitting sensitive data to the cloud, we could process it locally, ensuring compliance with privacy regulations.
Leading small language models
GPT-4o Mini by OpenAI
A compact version of OpenAI's flagship model, GPT-4o Mini offers substantial performance improvements over previous models, and is 60% cheaper than GPT-3.5 Turbo.
Applications: Suitable for tasks requiring advanced language understanding with limited computational resources.
Phi-4 by Microsoft
Phi-4 is a 14-billion-parameter model optimised for complex reasoning, particularly in mathematical domains.
Applications: Ideal for applications involving mathematical problem-solving and advanced language processing.
Mistral 7B by Mistral AI
A 7-billion-parameter model known for its efficiency, delivering performance comparable to larger models.
Applications: Useful for tasks like text summarization, translation, and other natural language processing activities.
Claude Haiku by Anthropic
The smallest variant in Anthropic's Claude series, delivering advanced coding, tool use, and reasoning.
Applications: Suitable for applications where responsible AI usage is paramount, such as content moderation and ethical AI deployments.
Hands-on with Pieces
Let’s try out some of these models in Pieces. But what is Pieces? Pieces is your AI companion that captures live context from browsers to IDEs and collaboration tools and supports multiple LLMs – all while processing data locally for maximum control.
Yes, we use SLMs and enable you to use state-of-the-art models offline.
Step 1: To get started Download and Install Pieces. Pieces uses Ollama to download and manage on-device models, don’t worry you don’t need to install it separately.
Step 2: Download offline models by clicking on models -> on-device -> select the model to download
As you can see I have downloaded Mistral 7B and Phi-e Mini 4K. You can download more models based on your use case or system capacity.
Step 3: Try the local mode by selecting them from the dropdown and giving it a prompt
Conclusion
LLMs dazzled me with their capabilities but put a heavy strain on the resources and API cost for very niche use cases.
SLMs, on the other hand, provide practical solutions for on-device, privacy, and task-specific use cases.
Understanding your project's constraints and goals is the key to deciding which model to choose and deploy for your project. Don’t bring a tank (LLMs) to a water fight.
My journey with LLMs and SLMs has taught me that neither is inherently better – it's all about context.
If you can manage context better you will get a better outcome.
So, whether you're building the next-gen chatbot, designing an edge-based agent, or automating user workflows, remember this: sometimes, smaller truly is better.
