Discover AI models: key differences between small language models and large language models

When you think 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 achieve and the resources you have available to achieve it.

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 with much lower latency, but they can be less accurate on broad queries.² LLMs are an excellent choice for building your own custom agent for enterprises or generative AI applications because of their capabilities.

Compare SLMs vs. LLMs

Here are some criteria for each model type side by side so you can assess them at a glance before diving deep into your due diligence and choosing one approach over the other.

SLM and LLM positions

When comparing small and large language model features, consider the balance between cost and performance. Smaller models typically require less computing power, reducing costs, but may not be well suited to more complex tasks. Larger models offer superior accuracy and versatility, but come with higher infrastructure and operational costs. Evaluate your specific needs, such as real-time processing, task complexity, and budget constraints, to make an informed choice.

Also note that SLMs can be tuned to perform well at the 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 specific use cases you need, without having to deploy an LLM which can be more expensive.

For more complex tasks with many edge cases, such as natural language queries or teaching a model to speak in a specific voice or tone, refining LLMs is a better solution.

SLMs	LLMs
Handling basic customer questions or frequently asked questions (FAQs)	Generate and analyze code
Translating common sentences or short sentences	Retrieving complex information to answer complex questions
Identify emotions or opinions in text	Synthesize text-to-speech with natural intonation and emphasis
Summary text for short documents	Generate long scripts, stories, articles and more
Suggest words as users type them	Manage open-ended conversations

SLM and LLM positions

Also consider features such as computational efficiency, scalability, and accuracy. Smaller models often provide faster processing and lower costs, while larger models provide better 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 data sets
Computational requirements	Lower (faster and less memory power)	Higher (slower and more memory power)
Customization	Can be refined with proprietary data for specific tasks	Can be refined for complex tasks
Costs	Lower training and operation costs	Higher costs to train and operate
Domain expertise	Can be refined for specialized tasks	More general knowledge across domains
Easy task execution	Satisfactory performance	Good to excellent performance
Complex task execution	Lower power	Higher power
Generalization	Limited extrapolation	Exceptional for all domains and tasks
Transparency3	More interpretability and transparency	Less interpretability and transparency
Examples of usage scenarios	Chatbots, plain text generation, domain-specific natural language processing (NLP)	Open dialogue, creative writing, answering questions, general NLP
Models	Phi-3, GPT-4o mini	Open AI, Mistral, MetaAnd Coherence

SLM and LLM use cases

When comparing language models, carefully consider your specific use cases. Smaller models are ideal for tasks that require fast responses and lower computing costs, such as simple customer service chatbots or simple data extraction. On the other hand, large language models excel at more complex tasks that require deep understanding and nuanced answers, such as advanced content generation or advanced data analysis. By tailoring model size to your specific business needs, 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 massive data sets and provide insights that inform business strategies and product recommendations
Identify and extract keywords from text, which helps with SEO and content categorization	Translate technical whitepapers from one language to another
Classify emails into categories such as spam, important or promotional	Generate standard code or help debug
Build a series of frequently asked questions	Extract treatment options from a large data set for a complex medical condition
Label and organize data to make it easier to retrieve and analyze	Process and interpret financial reports and provide insights that help with investment decisions
Translate simple translations of commonly used sentences or terms	Automate the generation and scheduling of social media posts so brands can maintain active audience engagement
Guide users through form filling by suggesting relevant information based on context	Generate high-quality articles, reports or creative writing pieces
Conduct sentiment analysis on a social media or short blog post	Shorten long documents, such as case studies, legal briefs, or medical journal articles, into concise summaries so users can quickly understand essential information
Categorize data, such as support tickets, emails, or social media posts	Empower virtual assistants to understand and respond to voice commands, improving user interaction with technology
Generate quick replies to social media posts	Review contracts and other legal documents and highlight important clauses and potential issues
Analyze survey responses and summarize key findings and trends	Analyze patient data and assist in generating reports
Summarize the minutes of the meeting and highlight key points and action items for participants	Analyze communication patterns in times of crisis and propose responses to mitigate public relations (PR) problems

SLM and LLM restrictions

It is also essential to consider limitations such as computational requirements and scalability. Smaller models can be cost-effective and faster, but may not have the same nuanced insight and depth as larger models. Larger models require significant computing resources, which can lead to higher costs and longer processing times. Weigh these limitations against your specific use cases and available resources.

SLM restrictions	LLM Restrictions
Does not have the ability to manage multiple models	Requires extensive resources and costs for training
Limited capacities for nuanced understanding and complex reasoning	Not optimized for specific tasks
Less contextual understanding outside their specific domain	More complexity requires additional maintenance
Handles smaller data sets	More computing power and memory

This article covers at-a-glance comparative information demonstrating the power and benefits of both SLMs and LLMs. With AI innovation accelerating at an intense pace and involving different languages ​​and scenarios, this rapid development will certainly push the boundaries of both types of models, resulting in better, cheaper and faster versions of current AI systems. This is especially true for resource-constrained startups that SLMs like Phi-3 open models will likely be the practical choice of choice to deploy AI for their use cases.

Discover more resources about SLMs and LLMs

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¹Small Language Models (SLMs): The Next Frontier for the EnterpriseForbes.

²Small Language Models vs. Large Language Models: How to Balance Performance and Cost-Effectivenessinstincttools.

³Big isn’t always better: why small language models can be the right choiceIntel.