The idea that a small language model (SLM) can perform better than a large language model (LLM) for generative artificial intelligence (AI) applications is counterintuitive. Larger is better in many disciplines and products. More information technology capacity is better for search, data analytics, and transaction processing.
However, while LLMs are more effective for large, general-purpose AI applications, SLMs offer many advantages for small, specialized AI applications.
Language models are AI computational models that can generate natural human language. That is much easier said than done. This goal of generating natural human language has consumed the attention of countless bright AI researchers and data scientists for decades.
SLM vs LLM
SLMs are efficient, domain-specific AI models optimized for tasks that can run on smaller devices using limited resources. LLMs are powerful, general-purpose AI models that excel at complex tasks but require substantial computing resources.
SLMs are explicitly designed for small domain-specific tasks, offering high accuracy for niche AI applications. LLMs are trained on enormous datasets to enable them to respond to a wide range of general-purpose tasks. LLMs sacrifice accuracy and efficiency to achieve general applicability.
Comparing language model characteristics
SLMs are much more different from LLMs than their similar names would suggest.
| Language model characteristic | SLM | LLM |
| Number of knowledge domains[1] of understanding | One – specialized | Many – general purpose |
| Capability | Narrow but detailed | Vast but general |
| Ideal AI application | Low complexity | High complexity |
| Number of parameters[2] – a measure of language model size | A few million to a few billion | A few billion to hundreds of billions |
| Ability to handle contextual relevance[3] | None or limited | Significant |
| Data source | Curated proprietary domain-specific data[4] | Public web data |
| Accuracy of AI output[5] | High | Variable[6] |
| Potential for bias[7] and hallucinations[8] | Low | Higher |
| Ability to handle a wide variety of complex prompts[9] | Low | High |
Figure 1 – Artificial analysis intelligence (higher is better).
Source: Independent analysis of AI
Support for data privacy depends on where the SLM or LLM is deployed. If the AI model is deployed on-premise, data privacy can be high if appropriate cybersecurity defences are in place. If the SLM or LLM is deployed at a data centre in the cloud, data privacy varies depending on statements in the cloud service agreement. Some AI service vendors state that all end-user prompts will be used to train the AI model further. Other vendors commit to not using the provided data. If a customer is unsure if the vendor can meet its stated data-privacy practices, implementing the AI application on-premises is the only course of action.
Comparing language model construction
SLMs are much cheaper to construct than LLMs because they build a model from much less data.
| Language model construction | SLM | LLM |
| Capital investment to build | Modest | Huge |
| Resources to train – compute, energy, and oversight |
Low | High[10] |
| Number of GPUs[11] required – a significant cost component | None or a small number | Several thousand |
| Data volume | Modest | Vast |
| Ability to fine-tune[12] AI model | Significant | Difficult |
| Examples of AI models | DeepSeek-V3, DistilBERT, ELECTRA-Small, Llama 4 | ChatGPT 4, Claude 3, CoPilot, DeepSeek, Gemini 2.5, Grok-3 |
Source: Is Bigger Really Better? Rethinking AI with SLMs – Why Smaller Models Are Leading the Next Wave of Innovation
Comparing language model operation
SLMs are much cheaper to operate and perform faster than LLMs because they need to process much less data volume to create inferences.
| Language model operation | SLM | LLM |
| Performance[13] – Inference[14] speed | Faster | Slower |
| Cost per inference | Low | Significant10 |
| Execution computing environment | Smartphones Laptops[15] |
Large data centre – often in the cloud |
| Scalability – increasing the number of concurrent end-users | Easy | More complex |
| Energy consumption | Modest | Substantial |
Source: Independent analysis of AI
Impediments to implementing an SLM
What is keeping organizations from implementing an SLM for an AI application?
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- Poor internal digital data quality and accessibility.
- Insufficient subject matter expertise to curate the specialized data required for the contemplated SLM.
- Incomplete digital transformation or too much unstructured paper data.
- Insufficient AI technical skills.
- Uncertain business case for the AI application.
- Immature AI tools and vendor solutions.
- Immature project management practices.
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How will SLMs and LLMs evolve?
The most likely trends for the foreseeable future of SLMs and LLMs include:
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- Increasing numbers of organizations will use both SLMs and LLMs as the benefits of AI applications become clearer and more organizations acquire the skills to implement and operate the applications.
- Both SLMs and LLMs will grow in size and sophistication as software improves and data quality increases.
- Both SLMs and LLMs will improve in performance as software for inference processing improves and incorporates reasoning.
- The training costs for SLMs and LLMs will decrease as training algorithms are optimized.
- The limits on the number of words in a prompt will increase.
- Integration of AI models with enterprise applications will become more widespread.
- Hosting SLM-based AI applications internally will appeal to more organizations as the price point is achievable and because it mitigates the risk of losing control over proprietary information.
- Hosting an LLM internally will remain too costly and unnecessary when the organization has published and is enforcing an AI usage policy, as described in this article: Why You Need a Generative AI Policy.
- The clear distinction between SLMs and LLMs will blur as medium language models (MLMs) or small Large Language Models (sLLMs) are built and deployed.
- LLMs will reduce hallucinations[16] by fact-checking external sources and providing references for inferences.
The AI landscape is changing rapidly due to the enormous research and development investments tech giants and startups are making. But, it is safe to say that SLMs will play an essential role as consumers and businesses increasingly recognize the benefits of specialized AI applications that use domain-specific data.
[1] Domain knowledge is knowledge of a specific discipline in contrast to general knowledge.
[2] Parameters are the variables that the AI model learns during its training process.
[3] Contextual relevance is the ability of the AI model to understand the broader context of the prompt text to which it is responding.
[4] Curated proprietary domain-specific data is typically data that is internal to the organization. Internal data is often uneven or poor in quality. Improving this data’s quality is often a constraint on the value AI applications based on an SLM can achieve.
[5] Accurate output is essential to build confidence and trust in the AI model.
[6] The accuracy of LLM output is undermined by the contradictions, ambiguity, incompleteness and deliberately false statements found in public web data. LLM AI output is more aligned with English and western societies because that is where most of the web data originates.
[7] Bias refers to incidents of biased AI model output caused by human biases that skew the training data. The bias leads to distorted outputs and potentially harmful outcomes.
[8] Hallucinations are false or misleading AI model outputs that are presented as factual. They can mislead or embarrass. They occur when an AI model has been trained with insufficient or erroneous data.
[9] Prompts are the text that end-users provide to AI models to interpret and generate the requested output.
[10] This high consumption of resources is driving the many announcements about building large, new data centres and related electricity generation capacity. Training an LLM can cost more than $10 million each time.
[11] GPU stands for graphics processing unit. GPU chips are particularly well-suited for the types of calculations that AI models perform in great quantity.
[12] Fine-tuning is a manual process with automated support where a trained AI model is further refined to improve its accuracy and performance.
[13] Performance is also called latency. In either case, it refers to the elapsed from when the end-user completes the prompt, and the AI application output appears on the monitor.
[14] Inference is the term that refers to the process that the AI model performs to generate text in response to the prompt it receives.
[15] Sometimes called edge devices or edge computing.
[16] Techniques for reducing hallucinations are described in this article: How can engineers reduce AI model hallucinations.
(Yogi Schulz – BIG Media Ltd., 2025)
