Some readers have written in asking about the difference between "LLMs (large language models)" and "SLMs (small language models)". So today, let’s explore how they differ—and some key concepts around them.
Before large language models (LLMs) appeared, there were already various models in use. But the “small language models” (SLMs) we talk about today are fundamentally different from those older models in terms of technical architecture, design philosophy, and capabilities. Modern SLMs—such as Mistral 7B, Phi-3, Llama 3 8B, and others—emerged after LLMs as their smaller-scale counterparts. In a sense, they are the direct descendants of LLMs.
That said, there is no universally recognized or precisely defined cutoff between LLMs and SLMs. Rather than a clear-cut binary, it's more of a continuous spectrum. However, we can understand the commonly accepted differences from several angles:
1. Parameter Count: The Most Intuitive but Fuzziest Boundary
This is the most frequently used and straightforward distinction, but the specific numbers are constantly evolving. As of 2025, the general consensus is:
SLMs typically have tens of billions up to around 15 billion parameters. Examples include Mistral 7B (7 billion), Llama 3 8B (8 billion), Gemma 2 9B (9 billion), and Phi-3 Medium (14 billion). Some models are even smaller, with just a few hundred million parameters.
LLMs usually start in the tens of billions and scale up to hundreds of billions—or even trillions. For instance, GPT-3 has 175 billion parameters. Frontier models like GPT-4o and Claude 3 Opus haven’t publicly disclosed their architectures or parameter sizes, but it's widely believed they use advanced techniques such as mixture-of-experts (MoE), achieving massive model sizes (possibly in the trillions) and efficient computation through dynamic routing.
⚠️ Note: These numbers aren’t absolute. What counts as “large” today may be seen as “medium” in the future as the field evolves.
2. Design Philosophy & Goals: The More Fundamental Divide
This is a more essential difference than just parameter size.
LLMs aim for the shadow of artificial general intelligence (AGI). They strive to be “universal polymaths,” absorbing vast, diverse internet data in order to understand and emulate all aspects of human knowledge and reasoning. Their goal is to tackle any open-ended, complex task.
SLMs, on the other hand, aim to be the ultimate “specialist tools.” Their design is focused on achieving peak performance in specific domains, balancing intelligence with efficiency, cost, and performance. They are finely tuned instruments built for specialized tasks.
3. Capabilities & Emergent Abilities
LLMs exhibit “emergent abilities” once they reach a certain scale—skills that weren’t explicitly trained but arise spontaneously, such as advanced reasoning, theory of mind (understanding others’ intentions), and creative thinking. This is the magic behind LLMs.
SLMs typically do not show—or only show very limited—emergent abilities. They excel at producing high-quality results in trained domains, but their performance drops when facing open-ended, creative problems that require deep reasoning. That said, with advances in optimization techniques, today’s top-tier SLMs (in the 7–14B range) can sometimes exhibit complex behaviors that approach early-stage emergence in specific areas.
4. Resource Needs & Deployment Environment
This is the most practical difference—it determines who can use these models, and where.
LLMs are “data center beasts.” They require massive GPU clusters to train and run, are only accessible via cloud APIs, and come with high costs and network latency.
SLMs are born to be “everywhere.” They’re compact and efficient enough to run locally on laptops, smartphones, or even cars. This brings advantages like low latency, high privacy, and reduced costs.
In today’s (2025) mainstream AI landscape, when you hear names like GPT-4o, Claude 3 Opus, or Gemini, you’re looking at state-of-the-art large language models. When you hear Llama 3 8B, Phi-3, Gemma 2, or Mistral, you’re seeing the leading examples of small language models.
Now that we’ve clarified the difference between large and small language models, let’s turn to a recent hot topic: “SLMs are the future of AI agents.” To be honest, I don’t entirely agree with that statement—it’s not entirely accurate and feels more like a business catchphrase designed to grab attention. A more rigorous formulation would be: Small language models are the foundation for making AI agents scalable, real-time, and cost-effective. They will work alongside large models to form a collaborative, distributed, and highly efficient hybrid intelligence system.
Why are SLMs getting so much attention? Because they directly address three unavoidable bottlenecks that LLMs face when transitioning from “tech demos” to real-world, large-scale applications: Cost, Latency, and Privacy.
Training and running top-tier models like GPT-4o is extremely expensive. For companies, every API call incurs a cost. When an application requires frequent, large-scale interaction with AI, relying entirely on an LLM-based API can quickly become financially unsustainable—hindering the widespread adoption of AI. In contrast, SLMs cost several orders of magnitude less to train and operate. A company can fine-tune an SLM specifically for its own business needs on a manageable budget and deploy it on its own infrastructure, with almost negligible marginal cost.
Calling a cloud-based LLM inevitably introduces network latency. For real-time applications—like voice assistants in smart cars, live translation in AR glasses, or intelligent NPCs in video games—even half a second of delay can ruin the user experience. SLMs are compact enough to run directly on local devices such as phones, laptops, cars, or even smartwatches. This means both input and response stay entirely on-device, delivering near-instantaneous interaction—without needing any internet connection. That’s the key to creating truly seamless, intelligent experiences.
When using third-party LLM services, all your data—whether it’s a personal conversation or a confidential business plan—must be sent to external servers. This presents serious compliance risks and trust issues, especially in sensitive industries like finance, healthcare, and law. Deploying SLMs locally or on private servers ensures that sensitive data remains entirely within a secure, controlled environment, fully addressing privacy concerns.
The rising interest in SLMs marks a shift in the AI industry—from a tech arms race centered on “who has the biggest, most powerful model” to a more pragmatic phase focused on “how to make AI more useful, accessible, and affordable.” It’s a clear sign that the field is maturing, preparing to unlock broader economic value at scale. But this doesn’t mean SLMs will replace LLMs—instead, it means that AI’s core capabilities are being liberated from the cloud-based ‘mainframes’ and brought closer to everyone, in the form of a “personal computer” model for AI.
In a hybrid intelligence system, large language models (LLMs) serve as strategic planners, tasked with understanding users’ high-level goals (such as “design a product launch campaign”) and breaking them down into actionable sub-tasks. Small language models (SLMs), then, take on the role of highly efficient, specialized executors—generating marketing copy, analyzing market data, creating social media content, and more. Once the tasks are complete, the LLM steps back in to integrate the results and deliver a cohesive solution.
This layered structure balances LLMs’ broad reasoning and planning power with SLMs’ speed and cost-efficiency in specific domains, forming a synergy between intelligence and execution. For simpler tasks, the system can skip the orchestration entirely and let an SLM handle everything directly—optimizing resource usage even further.
In the end, bigger isn’t always better, and smaller doesn’t mean weaker. The real difference between large and small language models lies not in size alone, but in how wisely they’re applied. As technology continues to evolve, the line between “large” and “small” will keep shifting—what matters most is finding the right tool for the right job. Whether it’s the strategic might of LLMs or the agile precision of SLMs, both are essential gears in the fast-turning machine of AI’s future.
Some readers have written in asking about the difference between "LLMs (large language models)" and "SLMs (small language models)". So today, let’s explore how they differ—and some key concepts around them.
Before large language models (LLMs) appeared, there were already various models in use. But the “small language models” (SLMs) we talk about today are fundamentally different from those older models in terms of technical architecture, design philosophy, and capabilities. Modern SLMs—such as Mistral 7B, Phi-3, Llama 3 8B, and others—emerged after LLMs as their smaller-scale counterparts. In a sense, they are the direct descendants of LLMs.
That said, there is no universally recognized or precisely defined cutoff between LLMs and SLMs. Rather than a clear-cut binary, it's more of a continuous spectrum. However, we can understand the commonly accepted differences from several angles:
1. Parameter Count: The Most Intuitive but Fuzziest Boundary
This is the most frequently used and straightforward distinction, but the specific numbers are constantly evolving. As of 2025, the general consensus is:
SLMs typically have tens of billions up to around 15 billion parameters. Examples include Mistral 7B (7 billion), Llama 3 8B (8 billion), Gemma 2 9B (9 billion), and Phi-3 Medium (14 billion). Some models are even smaller, with just a few hundred million parameters.
LLMs usually start in the tens of billions and scale up to hundreds of billions—or even trillions. For instance, GPT-3 has 175 billion parameters. Frontier models like GPT-4o and Claude 3 Opus haven’t publicly disclosed their architectures or parameter sizes, but it's widely believed they use advanced techniques such as mixture-of-experts (MoE), achieving massive model sizes (possibly in the trillions) and efficient computation through dynamic routing.
⚠️ Note: These numbers aren’t absolute. What counts as “large” today may be seen as “medium” in the future as the field evolves.
2. Design Philosophy & Goals: The More Fundamental Divide
This is a more essential difference than just parameter size.
LLMs aim for the shadow of artificial general intelligence (AGI). They strive to be “universal polymaths,” absorbing vast, diverse internet data in order to understand and emulate all aspects of human knowledge and reasoning. Their goal is to tackle any open-ended, complex task.
SLMs, on the other hand, aim to be the ultimate “specialist tools.” Their design is focused on achieving peak performance in specific domains, balancing intelligence with efficiency, cost, and performance. They are finely tuned instruments built for specialized tasks.
3. Capabilities & Emergent Abilities
LLMs exhibit “emergent abilities” once they reach a certain scale—skills that weren’t explicitly trained but arise spontaneously, such as advanced reasoning, theory of mind (understanding others’ intentions), and creative thinking. This is the magic behind LLMs.
SLMs typically do not show—or only show very limited—emergent abilities. They excel at producing high-quality results in trained domains, but their performance drops when facing open-ended, creative problems that require deep reasoning. That said, with advances in optimization techniques, today’s top-tier SLMs (in the 7–14B range) can sometimes exhibit complex behaviors that approach early-stage emergence in specific areas.
4. Resource Needs & Deployment Environment
This is the most practical difference—it determines who can use these models, and where.
LLMs are “data center beasts.” They require massive GPU clusters to train and run, are only accessible via cloud APIs, and come with high costs and network latency.
SLMs are born to be “everywhere.” They’re compact and efficient enough to run locally on laptops, smartphones, or even cars. This brings advantages like low latency, high privacy, and reduced costs.
In today’s (2025) mainstream AI landscape, when you hear names like GPT-4o, Claude 3 Opus, or Gemini, you’re looking at state-of-the-art large language models. When you hear Llama 3 8B, Phi-3, Gemma 2, or Mistral, you’re seeing the leading examples of small language models.
Now that we’ve clarified the difference between large and small language models, let’s turn to a recent hot topic: “SLMs are the future of AI agents.” To be honest, I don’t entirely agree with that statement—it’s not entirely accurate and feels more like a business catchphrase designed to grab attention. A more rigorous formulation would be: Small language models are the foundation for making AI agents scalable, real-time, and cost-effective. They will work alongside large models to form a collaborative, distributed, and highly efficient hybrid intelligence system.
Why are SLMs getting so much attention? Because they directly address three unavoidable bottlenecks that LLMs face when transitioning from “tech demos” to real-world, large-scale applications: Cost, Latency, and Privacy.
Training and running top-tier models like GPT-4o is extremely expensive. For companies, every API call incurs a cost. When an application requires frequent, large-scale interaction with AI, relying entirely on an LLM-based API can quickly become financially unsustainable—hindering the widespread adoption of AI. In contrast, SLMs cost several orders of magnitude less to train and operate. A company can fine-tune an SLM specifically for its own business needs on a manageable budget and deploy it on its own infrastructure, with almost negligible marginal cost.
Calling a cloud-based LLM inevitably introduces network latency. For real-time applications—like voice assistants in smart cars, live translation in AR glasses, or intelligent NPCs in video games—even half a second of delay can ruin the user experience. SLMs are compact enough to run directly on local devices such as phones, laptops, cars, or even smartwatches. This means both input and response stay entirely on-device, delivering near-instantaneous interaction—without needing any internet connection. That’s the key to creating truly seamless, intelligent experiences.
When using third-party LLM services, all your data—whether it’s a personal conversation or a confidential business plan—must be sent to external servers. This presents serious compliance risks and trust issues, especially in sensitive industries like finance, healthcare, and law. Deploying SLMs locally or on private servers ensures that sensitive data remains entirely within a secure, controlled environment, fully addressing privacy concerns.
The rising interest in SLMs marks a shift in the AI industry—from a tech arms race centered on “who has the biggest, most powerful model” to a more pragmatic phase focused on “how to make AI more useful, accessible, and affordable.” It’s a clear sign that the field is maturing, preparing to unlock broader economic value at scale. But this doesn’t mean SLMs will replace LLMs—instead, it means that AI’s core capabilities are being liberated from the cloud-based ‘mainframes’ and brought closer to everyone, in the form of a “personal computer” model for AI.
In a hybrid intelligence system, large language models (LLMs) serve as strategic planners, tasked with understanding users’ high-level goals (such as “design a product launch campaign”) and breaking them down into actionable sub-tasks. Small language models (SLMs), then, take on the role of highly efficient, specialized executors—generating marketing copy, analyzing market data, creating social media content, and more. Once the tasks are complete, the LLM steps back in to integrate the results and deliver a cohesive solution.
This layered structure balances LLMs’ broad reasoning and planning power with SLMs’ speed and cost-efficiency in specific domains, forming a synergy between intelligence and execution. For simpler tasks, the system can skip the orchestration entirely and let an SLM handle everything directly—optimizing resource usage even further.
In the end, bigger isn’t always better, and smaller doesn’t mean weaker. The real difference between large and small language models lies not in size alone, but in how wisely they’re applied. As technology continues to evolve, the line between “large” and “small” will keep shifting—what matters most is finding the right tool for the right job. Whether it’s the strategic might of LLMs or the agile precision of SLMs, both are essential gears in the fast-turning machine of AI’s future.
Some readers have written in asking about the difference between "LLMs (large language models)" and "SLMs (small language models)". So today, let’s explore how they differ—and some key concepts around them.
Before large language models (LLMs) appeared, there were already various models in use. But the “small language models” (SLMs) we talk about today are fundamentally different from those older models in terms of technical architecture, design philosophy, and capabilities. Modern SLMs—such as Mistral 7B, Phi-3, Llama 3 8B, and others—emerged after LLMs as their smaller-scale counterparts. In a sense, they are the direct descendants of LLMs.
That said, there is no universally recognized or precisely defined cutoff between LLMs and SLMs. Rather than a clear-cut binary, it's more of a continuous spectrum. However, we can understand the commonly accepted differences from several angles:
1. Parameter Count: The Most Intuitive but Fuzziest Boundary
This is the most frequently used and straightforward distinction, but the specific numbers are constantly evolving. As of 2025, the general consensus is:
SLMs typically have tens of billions up to around 15 billion parameters. Examples include Mistral 7B (7 billion), Llama 3 8B (8 billion), Gemma 2 9B (9 billion), and Phi-3 Medium (14 billion). Some models are even smaller, with just a few hundred million parameters.
LLMs usually start in the tens of billions and scale up to hundreds of billions—or even trillions. For instance, GPT-3 has 175 billion parameters. Frontier models like GPT-4o and Claude 3 Opus haven’t publicly disclosed their architectures or parameter sizes, but it's widely believed they use advanced techniques such as mixture-of-experts (MoE), achieving massive model sizes (possibly in the trillions) and efficient computation through dynamic routing.
⚠️ Note: These numbers aren’t absolute. What counts as “large” today may be seen as “medium” in the future as the field evolves.
2. Design Philosophy & Goals: The More Fundamental Divide
This is a more essential difference than just parameter size.
LLMs aim for the shadow of artificial general intelligence (AGI). They strive to be “universal polymaths,” absorbing vast, diverse internet data in order to understand and emulate all aspects of human knowledge and reasoning. Their goal is to tackle any open-ended, complex task.
SLMs, on the other hand, aim to be the ultimate “specialist tools.” Their design is focused on achieving peak performance in specific domains, balancing intelligence with efficiency, cost, and performance. They are finely tuned instruments built for specialized tasks.
3. Capabilities & Emergent Abilities
LLMs exhibit “emergent abilities” once they reach a certain scale—skills that weren’t explicitly trained but arise spontaneously, such as advanced reasoning, theory of mind (understanding others’ intentions), and creative thinking. This is the magic behind LLMs.
SLMs typically do not show—or only show very limited—emergent abilities. They excel at producing high-quality results in trained domains, but their performance drops when facing open-ended, creative problems that require deep reasoning. That said, with advances in optimization techniques, today’s top-tier SLMs (in the 7–14B range) can sometimes exhibit complex behaviors that approach early-stage emergence in specific areas.
4. Resource Needs & Deployment Environment
This is the most practical difference—it determines who can use these models, and where.
LLMs are “data center beasts.” They require massive GPU clusters to train and run, are only accessible via cloud APIs, and come with high costs and network latency.
SLMs are born to be “everywhere.” They’re compact and efficient enough to run locally on laptops, smartphones, or even cars. This brings advantages like low latency, high privacy, and reduced costs.
In today’s (2025) mainstream AI landscape, when you hear names like GPT-4o, Claude 3 Opus, or Gemini, you’re looking at state-of-the-art large language models. When you hear Llama 3 8B, Phi-3, Gemma 2, or Mistral, you’re seeing the leading examples of small language models.
Now that we’ve clarified the difference between large and small language models, let’s turn to a recent hot topic: “SLMs are the future of AI agents.” To be honest, I don’t entirely agree with that statement—it’s not entirely accurate and feels more like a business catchphrase designed to grab attention. A more rigorous formulation would be: Small language models are the foundation for making AI agents scalable, real-time, and cost-effective. They will work alongside large models to form a collaborative, distributed, and highly efficient hybrid intelligence system.
Why are SLMs getting so much attention? Because they directly address three unavoidable bottlenecks that LLMs face when transitioning from “tech demos” to real-world, large-scale applications: Cost, Latency, and Privacy.
Training and running top-tier models like GPT-4o is extremely expensive. For companies, every API call incurs a cost. When an application requires frequent, large-scale interaction with AI, relying entirely on an LLM-based API can quickly become financially unsustainable—hindering the widespread adoption of AI. In contrast, SLMs cost several orders of magnitude less to train and operate. A company can fine-tune an SLM specifically for its own business needs on a manageable budget and deploy it on its own infrastructure, with almost negligible marginal cost.
Calling a cloud-based LLM inevitably introduces network latency. For real-time applications—like voice assistants in smart cars, live translation in AR glasses, or intelligent NPCs in video games—even half a second of delay can ruin the user experience. SLMs are compact enough to run directly on local devices such as phones, laptops, cars, or even smartwatches. This means both input and response stay entirely on-device, delivering near-instantaneous interaction—without needing any internet connection. That’s the key to creating truly seamless, intelligent experiences.
When using third-party LLM services, all your data—whether it’s a personal conversation or a confidential business plan—must be sent to external servers. This presents serious compliance risks and trust issues, especially in sensitive industries like finance, healthcare, and law. Deploying SLMs locally or on private servers ensures that sensitive data remains entirely within a secure, controlled environment, fully addressing privacy concerns.
The rising interest in SLMs marks a shift in the AI industry—from a tech arms race centered on “who has the biggest, most powerful model” to a more pragmatic phase focused on “how to make AI more useful, accessible, and affordable.” It’s a clear sign that the field is maturing, preparing to unlock broader economic value at scale. But this doesn’t mean SLMs will replace LLMs—instead, it means that AI’s core capabilities are being liberated from the cloud-based ‘mainframes’ and brought closer to everyone, in the form of a “personal computer” model for AI.
In a hybrid intelligence system, large language models (LLMs) serve as strategic planners, tasked with understanding users’ high-level goals (such as “design a product launch campaign”) and breaking them down into actionable sub-tasks. Small language models (SLMs), then, take on the role of highly efficient, specialized executors—generating marketing copy, analyzing market data, creating social media content, and more. Once the tasks are complete, the LLM steps back in to integrate the results and deliver a cohesive solution.
This layered structure balances LLMs’ broad reasoning and planning power with SLMs’ speed and cost-efficiency in specific domains, forming a synergy between intelligence and execution. For simpler tasks, the system can skip the orchestration entirely and let an SLM handle everything directly—optimizing resource usage even further.
In the end, bigger isn’t always better, and smaller doesn’t mean weaker. The real difference between large and small language models lies not in size alone, but in how wisely they’re applied. As technology continues to evolve, the line between “large” and “small” will keep shifting—what matters most is finding the right tool for the right job. Whether it’s the strategic might of LLMs or the agile precision of SLMs, both are essential gears in the fast-turning machine of AI’s future.
Fast Take
As the AI landscape evolves, the conversation is shifting from sheer model size to strategic application. This article explores the distinctions between large and small language models—and why their roles are increasingly complementary in building scalable, real-world AI systems.
Share Now
Fast Take
As the AI landscape evolves, the conversation is shifting from sheer model size to strategic application. This article explores the distinctions between large and small language models—and why their roles are increasingly complementary in building scalable, real-world AI systems.
Share Now
Fast Take
As the AI landscape evolves, the conversation is shifting from sheer model size to strategic application. This article explores the distinctions between large and small language models—and why their roles are increasingly complementary in building scalable, real-world AI systems.
Share Now