When thinking about whether a small language model (SLM) or large language model (LLM) is right for your business, the answer will depend, in part, on what you want to accomplish and the resources you have available to get there.
An SLM focuses on specific AI tasks that are less resource-intensive, making them more accessible and cost-effective.1 SLMs can respond to the same queries as LLMs, sometimes with deeper expertise for domain-specific tasks and at a much lower latency, but they can be less accurate with broad queries.2 LLMs are an excellent choice for building your own enterprise custom agent or generative AI applications because of how capable they are.
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Compare SLMs versus LLMs
Here are some criteria for each model type shown side-by-side to help you evaluate at a glance before diving deep into your due diligence and choosing one approach over another.
SLM and LLM functions
When comparing functions for small versus large language models, you should consider the balance between cost and performance. Smaller models typically require less computational power, reducing costs, but might not be well-suited for more complex tasks. Larger models offer superior accuracy and versatility but come with higher infrastructure and operational expenses. Evaluate your specific needs, like real-time processing, task complexity, and budget constraints, to make an informed choice.
Customize fine-tuning
You should also consider that SLMs can be fine-tuned to perform well in required tasks. Fine-tuning is a powerful tool to tailor advanced SLMs to your specific needs, using your own proprietary data. By fine-tuning an SLM, you can achieve a high level of accuracy for the particular use cases you require without needing to deploy an LLM that could be more expensive.
For more complex tasks with a lot of edge cases, such as natural language queries or teaching a model to speak in a specific voice or tone, fine-tuning LLMs is a better solution.
| SLMs | LLMs |
|---|---|
| Handling basic customer queries or frequently asked questions (FAQs) | Generating and analyzing code |
| Translating common phrases or short sentences | Retrieving complex information for answering complex questions |
| Identifying emotions or opinions in text | Synthesizing text-to-speech with natural intonation and emphasis |
| Summarizing text for short documents | Generating long scripts, stories, articles, and more |
| Suggesting words as users type them | Managing open-ended conversation |
SLM and LLM features
Also be sure to consider features like computational efficiency, scalability, and accuracy. Smaller models often offer faster processing and lower costs, while larger models provide enhanced understanding and performance on complex tasks but require more resources. Evaluate your specific use cases and resource availability to help make an informed decision.
| Features | SLMs | LLMs |
|---|---|---|
| Number of parameters | Millions to tens of millions | Billions to trillions |
| Training data | Smaller, more specific domains | Larger, more varied datasets |
| Computational requirements | Lower (faster and less memory power) | Higher (slower and more memory power) |
| Customization | Can be fine-tuned with proprietary data for specific tasks | Can be fine-tuned for complex tasks |
| Cost | Lower cost to train and operate | Higher cost to train and operate |
| Domain expertise | Can be fine-tuned for specialized tasks | More general knowledge across domains |
| Simple task performance | Satisfactory performance | Good to excellent performance |
| Complex task performance | Lower capability | Higher capability |
| Generalization | Limited extrapolation | Exceptional across domains and tasks |
| Transparency3 | More interpretability and transparency | Less interpretability and transparency |
| Example use cases | Chatbots, plain text generation, domain-specific natural language processing (NLP) | Open-ended dialogue, creative writing, question answering, general NLP |
| Models | Phi-3, GPT-4o mini | OpenAI, Mistral, Meta, and Cohere |
SLM and LLM use cases
Carefully consider your specific use cases when comparing language models. Smaller models are ideal for tasks that require quick responses and lower computational costs, such as basic customer service chatbots or simple data extraction. On the other hand, large language models excel in more complex tasks requiring deep comprehension and nuanced responses, like advanced content generation or sophisticated data analysis. Aligning the model size with your specific business needs ensures you achieve both efficiency and effectiveness.
| SLM use cases | LLM use cases |
|---|---|
| Automate responses to routine customer queries using a closed custom agent | Analyze trends and consumer behavior from vast datasets, providing insights that inform business strategies and product recommendations |
| Identify and extract keywords from text, aiding in SEO and content categorization | Translate technical white papers from one language to another |
| Classify emails into categories like spam, important, or promotional | Generate boilerplate code or assist in debugging |
| Build a set of FAQs | Extract treatment options from a large dataset for a complex medical condition |
| Tag and organize data for easier retrieval and analysis | Process and interpret financial reports and provide insights that aid in investment decisions |
| Translate simple translations for common phrases or terms | Automate the generation and scheduling of social media posts, helping brands maintain active audience engagement |
| Guide users to complete forms by suggesting relevant information based on context | Generate high-quality articles, reports, or creative writing pieces |
| Run a sentiment analysis on a social media or short blog post | Condense lengthy documents such as case studies, legal briefs, or medical journal articles into concise summaries, helping users quickly grasp essential information |
| Categorize data, such as support tickets, emails, or social media posts | Power virtual assistants that understand and respond to voice commands, improving user interaction with technology |
| Generate quick replies to social media posts | Review contracts and other legal documents, highlighting key clauses and potential issues |
| Analyze survey responses and summarize key findings and trends | Analyze patient data and assist in generating reports |
| Summarize meeting notes and highlight key points and action items for participants | Analyze communication patterns in times of crisis and suggest responses to mitigate public relations (PR) issues |
SLM and LLM limitations
It’s also essential to consider limitations like computational requirements and scalability. Smaller models can be cost-effective and faster, but might not have the same nuanced understanding and depth of larger models. Larger models require significant computational resources, which can lead to higher costs and longer processing times. Balance these limitations against your specific use cases and available resources.
| SLM limitations | LLM limitations |
|---|---|
| Does not have the capability to manage multiple models | Requires extensive resources and costs for training |
| Limited abilities for nuanced understanding and complex reasoning | Not optimized for specific tasks |
| Less contextual understanding outside their specific domain | More complexity requires additional maintenance |
| Deals with smaller datasets | More computational power and memory |
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This article touches on at-a-glance comparative information demonstrating the power and benefits of both SLMs and LLMs. With AI innovation accelerating at an intense pace involving different languages and scenarios, this rapid development will be sure to push the limits of both types of models—resulting in better, cheaper, and faster versions of current AI systems. This is particularly true for startups with limited resources where SLMs like Phi-3 open models will likely be the preferred, practical choice to leverage AI for their use cases.
Explore more resources on SLMs and LLMs
- Watch our AI in a Minute video about LLMs
- Explore our training: Introduction to large language models
- Read about the benefits of using SLMs in certain scenarios
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1Small Language Models (SLMs): The Next Frontier For The Enterprise, Forbes.
2Small Language Models vs. Large Language Models: How to Balance Performance and Cost-effectiveness, instinctools.
3Big is Not Always Better: Why Small Language Models Might Be the Right Fit, Intel.
