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Small Language Models (SLMs) are compact yet powerful AI systems designed to process and generate human language with remarkable efficiency.

We all know about LLMs – the large language models that have disrupted our lives since 2023. ChatGPT, Gemini, DeepSeek, and so many popular LLMs have taken all the limelight. The discussions have gone as high as ‘AGI’ conspiracy theories.

We have come a long way in artificial intelligence. We moved from simple statistical approaches to complex neural networks. For example, early versions used simple math to guess the next word in a sentence. Older language models used statistics. They counted how often words appeared together. For example, they’d learn that “peanut” often comes before “butter.”

Thus, early models relied on probability distributions of word sequences.

Modern approaches use deep learning techniques to capture intricate patterns in language. These are neural networks, or complex computer systems that learn patterns from huge amounts of text data. This lets them understand language in a more nuanced way. The evolution of these models has been marked by increasing size and have become more complex.

As a result, Large Language Models containing hundreds of billions of parameters are now the norm. This has led to birth of AI models on crack like the latest free Gemini 2.5 Pro churning SaaS apps in seconds.

However, they also have significant limitations. These include enormous computational requirements and considerable energy consumption. There are also deployment challenges, particularly on edge devices where resources are constrained.

We, as in the AI engineers, must fill this gap – which is what ‘small language models’ do. SLMs offer comparable functionality at a fraction of the size. This makes AI more accessible and practical across a wider range of scenarios.

By the end of this guide, you will learn:

• What are small language models (SLMs)
• Benefits of small language models (SLMs)
• Examples of Small Language Models across use cases and AI companies
• Frequently Asked Questions (FAQs) answered on small language models

Why Small Language Models are the next big thing in AI?

A small language model (SLM) is a type of artificial intelligence (AI) that can understand and create human language. We also call them “mini models”. These models are designed to be smaller and more efficient than very large language models.

Think of SLMs as the agile sprinters of the AI world, compared to the marathon runners of LLMs. They can still carry out language tasks, but they do it with fewer computational resources.

The key difference between LLM vs SLMs is that the SLMs focus on specific tasks.

For example, an SLM might be really good at understanding customer service questions or summarizing short articles.

This is an important concept to learn. At present, Generative AI biggest practical use case is automating specific tasks. We are already creating AI agents to replace vertical SaaS, and SLMs are what make it happen.

Understanding limitations of LLMs and how SLM fill the gap

Can’t we just use large language models for specific tasks too?

Well, LLMs need a lot of computer power. Training them requires specialized hardware, like powerful graphics processing units (GPUs). That’s the reason NVIDIA’s share surged so much, because everyone thought their advanced GPUs are ‘essential’ to keep LLMs working. Think of it like running a super-fast race car; you need a lot of fuel and a big team.

All this compute powere requires lots of energy. This raises concerns about their environmental impact. It’s like the race car needing a large amount of fuel, contributing to pollution.

Also, LLM deployment is hard. You often can’t run LLMs on your phone or laptop. You usually need to access them through the internet.

Because of this, LLMs can be slow for some tasks. It can take time for them to process information and give an answer.

Enters: Small Language Models or SLMs.

4 key advantages of small language models

On the other hand, here’s how small language models fill the gap for LLMs:

• SLMs are efficient. They can run on less powerful hardware, like mobile phones and smaller computers.
• SLMs are accessible. More people and businesses can use them because they don’t need huge resources.
• SLMs are practical. They are well-suited for specific tasks and real-world applications.
• SLMs are secure: They promote privacy by allowing more processing to occur locally rather than in the cloud

The small language model (SLM) market size:

According to MarketsAndMarkets, the small language model market is presently valued at approximately $5.45 billion. This is fueled by demand for energy-efficiency AI model solutions that is practical and environmental friendly.

Major players like Microsoft and IBM in the US are leading the market. Innovative firms like Mistral AI in France are also at the forefront. These models are becoming popular due to their

• Lower operational costs
• Adaptable for on-device applications
• Improved multimodal capabilities. These capabilities allow them to process and integrate diverse types of data.

The market is expected to continue expanding as organizations are seeking scalable and cost-effective AI tools. These tools are tailored to specific applications, from customer service bots to personalized digital assistants.

This infographic illustrates the fascinating evolution of language models over the past two decades. It reveals two distinct trends that have shaped AI development:

1. The size race:

From 2018 onward, we see a dramatic increase in model size, with parameters scaling from millions to potentially trillions. This “bigger is better” approach culminated in massive models like GPT-4, which demonstrated remarkable capabilities but at significant computational cost.

2. The efficiency revolution:

Around 2019-2020, a counter-trend emerged with Small Language Models (SLMs). These models focus on efficiency over size, seeking to maximize performance per parameter rather than raw scale. Models like DistilBERT, MobileBERT, and the Phi series show that impressive capabilities can be achieved with significantly fewer resources.

Practical implications of LLM vs SLM

The divergence between these trends shows a crucial development in AI. While large models pushed boundaries of what’s possible, smaller models made AI more practical, accessible, and sustainable. The SLM trend line’s relatively flat trajectory indicates that researchers are improving performance. They are achieving this without proportionally increasing size.

Let’s understand more about how small language models work:

What is different about small language models?

I will help you understand the difference between LLM and SLM considering below key areas:

Size and complexity

SLMs have fewer parameters than LLMs. SLMs usually have millions to a few billion parameters.

Parameters are the values a model learns during training to understand and generate language. You can think of parameters as the “knobs” the model can tweak to get better at its job. A model with more knobs can potentially learn more, but it also becomes more complex. SLMs intentionally have fewer of these knobs.

In comparison, LLMs can have tens or hundreds of billions of parameters.

Model architecture

SLMs often use simpler designs. They may have fewer layers or a more streamlined way of processing information. This makes them more efficient.

Techniques like distillation help create SLMs. In distillation, we train a small model to mimic a large one. It is like a student learning from a teacher. The student (SLM) becomes good at the important stuff without needing to learn everything the teacher (LLM) knows.

Inference optimization

Beyond model design, various runtime optimizations help SLMs execute efficiently during inference:

• Caching intermediate results to avoid redundant computation
• Optimizing matrix operations for specific hardware architectures
• Batching inputs for parallel processing
• Early termination for tasks that don’t need full model traversal

Computational efficiency

SLMs need less computing power. They can process information and give answers faster than LLMs. Because they are smaller, SLMs can do calculations more quickly. Imagine searching for a word in a small book versus a massive library; the small book is much faster.

Further, SLMs need less memory to operate. As a result, SLMs can run on less powerful devices. These include smartphones and laptops. They can also operate on small embedded systems, like those in smart devices.

Energy efficiency

SLMs use less energy. This is good for the environment and reduces operating costs.

Here’s a good panel discussion I found which covers the AI sustainability aspect:

How does small language model work? – strategies for maximizing SLM performance

Small language models, like their larger counterparts, learn to understand and generate human language from vast amounts of text data.

However, they use specific techniques to achieve this with greater efficiency.

Researchers are developing new ways to train and improve SLMs. This helps them get the best possible performance from a smaller size. Here are some key strategies:

Distillation:

As mentioned earlier, knowledge distillation is a core technique. This involves training a smaller “student” model to replicate the behavior of a larger, more powerful “teacher” model. The student learns to mimic the teacher’s soft outputs, which contain more information than just the final prediction.

For example, imagine the teacher model is deciding between “happy,” “joyful,” and “sad.” Instead of just saying “happy” is correct, it might say “happy: 0.8, joyful: 0.15, sad: 0.05.” The student learns that “happy” is the best answer, but “joyful” is also related.

This technique was also used to develop Stanford’s cost-effective S1 model – read to learn more.

Pruning:

Neural networks have connections between their artificial neurons. Pruning is the process of removing some of these connections that are not very important for the model’s performance.

For this, first, AI engineers use algorithms to figure out which connections have the least impact on the model’s output. Once identified, they remove these connections.

Doing so can significantly reduce model size, making it faster.

Note that pruning can reduce accuracy if we remove too many connections. The goal is to find the right balance. Usually, most of the original performance is maintained. Various approaches exist, from simple size-based pruning to more sophisticated techniques that consider the impact on overall model behavior.

Quantization:

This technique reduces the precision of the numerical values used to represent the model’s parameters. For example, instead of using 32-bit floating-point numbers, we might use 8-bit integers. This significantly reduces the model’s size and speeds up computation, with minimal loss in accuracy.

Note that quantization makes the model smaller and faster, but there might be a small decrease in accuracy.

I have explained quantization in detail done for DeepSeek’s game-changing R1 model making it cost-effective compared to OpenAI models.

Efficient attention mechanisms:

The attention mechanism is a key part of transformer-based language models. However, it can be computationally expensive, especially for long sequences. Researchers are exploring more efficient alternatives. These include sparse attention, low-rank approximations, and linear attention. Their goal is to reduce this cost without sacrificing too much performance.

I came across this detailed explanation of attention mechanism based optimization: Attention Mechanism in Deep Learning

Parameter sharing:

This involves sharing parameters across different parts of the model. For example, some layers might use the same set of weights. This reduces the overall number of unique parameters that need to be learned.

Performance trade-offs for SLM vs LLM

While the above characteristics of SLM make it efficient, there are definitely some trade-offs:

The balance between size and performance for SLM vs LLM

SLMs may not execute as well as LLMs on very complex tasks. There is often a trade-off: smaller size means less capacity for handling extremely difficult problems.

For example, SLMs might struggle with tasks that need a deep understanding of context or a lot of world knowledge. They can’t pull-off features like ‘Deep Research’ by ChatGPT or Gemini 2.5 Pro.

Context window difference between LLM and SLM

SLMs generally have a smaller context window than LLMs. The context window is the amount of text the model can consider when processing information.

If a model has a context window of 512 tokens, it can only look at the previous 512 words/pieces of words. LLMs can have context windows of 8k, 32k, or even 100k.

This means SLMs might not be as good at tasks where understanding long-range dependencies is crucial.

Small Language Models vs. Large Language Models comparison table

Now that we understand where SLM stand in terms of benefits and performance, I have created a quick reference table to track the difference between LLMs and SLMs:

Characteristic	Small Language Models	Large Language Models
Parameters	<10 billion	10 billion to 1+ trillion
Memory Requirements	Megabytes to a few gigabytes	Tens to hundreds of gigabytes
Inference Speed	Fast	Relatively slower
Energy Consumption	Low	High
Deployment	Can run on edge devices	Typically requires cloud infrastructure
Generalization	Good for specific domains	Better for general knowledge
Context Window	Smaller	Larger
Cost	Lower	Higher

When to use small language models?

SLMs shine in scenarios where efficiency, speed, and accessibility take priority. For example:

• On-device processing: Running AI directly on devices like phones or smart speakers.
• Real-time applications: Where speed is critical, such as chatbots or interactive systems.
• Resource-constrained environments: Where computing power and energy are limited.
• Specific, well-defined tasks: Where the model’s focus can be narrow and precise.

When to use large language models?

LLMs stay the preferred choice for applications demanding breadth and depth:

• Complex reasoning and understanding: Tasks that need deep language comprehension.
• General-purpose language tasks: A wide variety of language-related activities like search, translations, deep research, etc.
• Handling nuanced or ambiguous inputs: Situations where the meaning is subtle or unclear.
• Tasks requiring extensive world knowledge: When the model needs to draw on a broad range of information.

Differences between mini models

Not all SLMs are created equal.

Models of similar size can showcase significant differences in capabilities based on their architecture, training methodology, and optimization techniques. Some excel at specific tasks while offering mediocre performance on others, reflecting their training focus and design choices.

Small language model examples

Several notable SLMs have demonstrated impressive capabilities despite their compact size:

BERT and its smaller variants

BERT stands for Bidirectional Encoder Representations from Transformers. Google developed it. It is not a single model but a family of models. The original BERT is considered large, but researchers have created smaller versions of BERT.

BERT uses a transformer architecture. A key innovation is that it looks at words in a sentence in both directions. This bidirectional approach helps to better understand their meaning. Smaller BERT variants have fewer layers and parameters than the original.

People use BERT and its variants for various language tasks, including:

• Understanding the meaning of words in context.
• Answering questions.
• Analyzing sentiment (whether a piece of text is positive, negative, or neutral).

For example, Imagine you have the sentence “The cat sat on the mat.” A BERT model can understand that “cat” is the subject. It identifies “mat” as the object. It also comprehends how the word “sat” relates them.

BERT revolutionized natural language processing upon its introduction, and smaller variants quickly followed:

• DistilBERT: Researchers at Hugging Face created DistilBERT. They “distilled” a smaller model from the larger BERT. A distilled version holds 40% fewer parameters while retaining 97% of BERT’s performance on benchmarks.
• MobileBERT: Developed by Google, MobileBERT is specifically designed for mobile devices. It is highly optimized to run efficiently on phones and other devices with limited resources. This enables AI-powered features directly on your phone, like smart replies, text summarization, or language translation.
• TinyBERT: An extremely compact version suitable for resource-constrained environments – explore TinyBERT paper.

Small language models by OpenAI

GPT stands for Generative Pre-trained Transformer. OpenAI developed this family of models. While the most famous GPT models are very large, smaller versions also exist. Smaller GPT models have fewer layers and parameters, making them more efficient.

For example, although it is not a small language model, GPT-4o mini is an affordable smaller version of GPT-4o LLM. It is designed for tasks like customer support chatbots and applications that chain multiple model calls.

Small language models by Meta

Meta has developed open-source small language models (SLMs) like Llama 3.2.

Being open-source means that anyone can use and change these models. Meta designs Llama 3.2 for both efficiency and wide use, creating smaller versions (with 1B and 3B parameters). These smaller models can generate text in multiple languages and work on devices with limited resources, like smartphones.

Meta AI researchers also introduced MobileLLM (links to the published paper). It is a method to build efficient language models specifically for these kinds of devices. MobileLLM uses techniques like prioritizing model depth, sharing certain parts of the model, and a new way of sharing model information to improve how well it works on phones.

Small language models by Microsoft: Phi Models

Microsoft’s Phi models represent leading examples of SLMs that achieve remarkable performance despite their size:

• Phi-1 (1.3B parameters): Demonstrated strong capabilities in coding and reasoning tasks.
• Phi-2 (2.7B parameters): Showed performance competitive with much larger models.
• Phi-3 (3.8B parameters): Further refined architecture and training methodology.

They have recently released the Phi-4 model which beats many other popular small language models as shown here:

Phi models are known for achieving strong performance with a relatively small number of parameters. They are trained on a dataset of “textbook quality”. They are good for use cases where efficiency and performance are both important.

Open source small language models by Mistral

Mistral AI provides a selection of small language models designed for efficiency. Models like “Mistral Small” and “Les Ministraux” (which includes Mistral 3B and Mistral 8B) deliver strong performance while using fewer resources.

Mistral Small excels at tasks that need quick responses. It handles lots of data, like Retrieval-Augmented Generation (RAG) and coding. It supports many languages, like English, French, German, Spanish, and Italian, and includes safety features.

Mistral AI also released Mistral Small 3.1 in March 2025, an upgraded version with better text understanding, multimodal capabilities, and a larger context window. The “Les Ministraux” models are built for local use.

Mistral’s models are designed to be cost-effective. They work well in various settings. The models are open-source, allowing for flexible use and modification.

Open-source small language models list

I found this small language model leaderboard that you can explore: Open-Source Small Language Models.

Here’s a brief of the best open-source small language models to get started with:

Qwen 2.5 by Alibaba:

Available in 0.5B and 1.5B parameter versions, Qwen 2.5 is designed for tasks like text generation, summarization, and translation, offering versatility in resource-constrained environments.

Gemma-2 by Google

A 2B parameter model focuses on high performance in a compact form. It excels at various NLP tasks. In these tasks, computational efficiency is key.

BLOOM by BigScience

A multilingual model developed by over 1,000 AI researchers, aiming to allow public research on large language models.

StableLM Zephyr 3B by Stability AI

A 3B parameter model is optimized for chat-based applications. It excels in tasks like copywriting, summarization, and content personalization. The model operates without requiring high-end hardware.

Granite 3.2 by IBM

Granite 3.2 is a small language model by IBM with 2B parameters size. It is a family of AI models featuring experimental chain-of-thought reasoning and vision-language capabilities, optimized for enterprise applications. ​

Challenges and limitations of small language models

There are many benefits of small language models we discussed in earlier sections. It is also good to know its limitations to avoid using in these scenarios:

Potential for reduced accuracy on complex tasks

Despite improvements in efficiency, SLMs still face limitations in handling complex tasks. They could struggle with tasks that need a deep understanding of context. They might also be challenged by subtle nuances or need a lot of world knowledge. It’s like trying to use a smaller tool for a job that requires a larger, more powerful one.

Risk of overfitting to specific datasets

SLMs may not adapt as well as LLMs to new situations. They struggle with data very different from what they learned during training. If they face something unexpected, they might not manage to handle it correctly.

Due to this, SLMs have a higher risk of overfitting.

Overfitting occurs when a model learns the training data too well. This includes its noise and quirks. As a result, it performs poorly on new, real-world data. Because SLMs have less capacity, they can memorize the training data instead of learning general patterns.

Reduced ability to store and retrieve information

The reduced parameter count of SLMs constrains their ability to store and retrieve factual information. This means they might struggle with tasks that require extensive background knowledge. They might also struggle with the ability to remember many facts.

Because of this, tasks requiring a broad understanding of the world can be more difficult for SLMs. For example, do not use SLMs for tasks like answering complex questions or making inferences or relationship between data points.

Hallucination

Like their larger counterparts, SLMs can produce incorrect or fabricated information.

Their limited knowledge capacity can sometimes make this tendency worse. This happens particularly when they face queries that extend beyond their training or fine-tuning.

Frequently asked questions (FAQs) on Small Language Models

How are SLMs different from LLMs?

SLMs differ from LLMs primarily in size, computational requirements, and deployment options. While LLMs prioritize maximum performance through scale, SLMs emphasize efficiency and practical deployment across a wider range of devices.

How many parameters does a small language model have?

SLM definitions can differ. However, they typically contain fewer than 10 billion parameters. Many successful models operate in the 1-7 billion parameter range. Some specialized SLMs may have as few as a hundred million parameters.

Is BERT a small language model?

The original BERT model has 110 million to 340 million parameters. It would be considered a small language model by current standards. However, it was relatively large when introduced. Its smaller variants like DistilBERT are definitely SLMs.

Is Mistral 7B a small language model?

Mistral 7B sits at the upper boundary of what might be considered an SLM. At 7 billion parameters, it’s larger than many SLMs. However, it’s still substantially smaller than leading LLMs with hundreds of billions of parameters.

Is GPT-4 a small language model?

No, GPT-4 is definitely not a small language model.

How small can language models be and still speak coherent English?

The size at which a language model can still produce coherent English is an area of active research. Models with even a few hundred million parameters can demonstrate reasonable coherence, especially for specific tasks. Here’s a research paper I found that covers this topic: TinyStories

Learn more about small language models

Research in SLMs continues, with significant efforts focused on designing novel architectures that maximize performance per parameter. We are already deploying specialized SLM models for specific domains and use cases.

One area to think about – using hybrid approaches that combine local SLMs with cloud-based LLMs when needed

For example, in healthcare, SLMs could help with tasks like medical summarization or patient communication. In finance, they could help with fraud detection or risk assessment.

Further, SLMs can bring the benefits of AI to areas with limited internet connectivity. They can also help regions with limited computing infrastructure like developing regions or remote locations.

For example, this paper published on ScienceDirect speaks about using SLMs for personal medical assistant chatbots.

Nevertheless, this is one hot area that is developing which will make our smartphones much more intelligent.

Learn more about AI models to understand their use case and science behind their optimizations in easy language:

• Vertical AI Agents Will Replace SaaS – Experts Warn On Future Of Workflow Automation
• Stanford’s S1 model – How Stanford built low-cost open source rival to OpenAI’s o1
• What is OpenAI o3-mini – technical features, performance benchmarks, and applications
• What is Alibaba’s Qwen2.5-Max – technical features, performance benchmarks, and applications
• Gemini 2.5 Pro is now free – learn how to make the most of it for daily productivity

I will cover more topics that dive deeper into small language models – subscribe to stay tuned:

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