Small vs. Large Language Models: Exploring the right fit

Choosing the right language model—whether a Small Language Model (SLM) or a large language model (LLM) is contingent on a range of factors, including business goals, customer context, and operational constraints. While SLMs offer advantages such as lower latency, lighter footprint, and easier fine-tuning, they may not always be the most suitable choice, depending on task complexity and reasoning needs.

As we experiment with SLMs (10M to 3B parameters) and LLMs (6B to 180 B+), we observe meaningful trade-offs across dimensions like performance, accuracy, cost, and adaptability. This article shares a structured perspective on how organizations can evaluate these models for their use cases. The insights and examples provided are based on our ongoing work and may serve as useful reference points for others navigating similar decisions.

 Read on to learn from Movate’s ongoing exploration of model selection strategies.

Putting the models to the test

The landscape of SLMs and LLMs is rapidly evolving, with a growing variety of models now available. At Movate AI Labs, we’ve been actively experimenting with several of these to better understand their real-world performance, trade-offs, and where each model tends to be most effective.

Model selection process

In many client engagements, model selection often begins with their existing technology stack or preferred LLM providers. Unless there’s a strong motivation to explore alternatives, these choices typically guide the initial direction.

Based on our PoC work across different client environments, we’ve observed that selecting the right model is an iterative process—shaped by context, constraints, and evolving goals. Below is a simplified view of the typical activities, decision points, and deliverables that support the journey from model evaluation to adoption.

Validation: Movate proposed 8 parameters

Beyond selection, model validation is crucial. Clients often weigh parameters like cost, accuracy, scalability, and robustness differently based on their business priorities. Movate proposes a validation framework across eight major dimensions (and sub-dimensions). Clients may choose to assign weightages to all, or a subset of these, based on their use case.

Training models for specific requirements

Here are some sample illustrative scenarios (iPhone as a use case) taken at random for the previously mentioned themes and the contextual and factually accurate queries and responses that need to be built into the model to see if the model is responding appropriately to the questions.

SCENARIO	QUERY	COMMENTS
Factual retrieval	What is the screen size of the iPhone 15?	Direct, fact-based answer required.
Complex queries	Compare the environmental impact of manufacturing an iPhone vs a Pixel phone.	Requires synthesis of multiple dimensions.
Ambiguous queries	Is it better?	Unclear what ‘it’ refers to – needs context.
Out-of-scope questions	Can you diagnose a hardware issue with my Pixel based on this photo?	Assumes technical diagnostic capabilities not available.
Domain-specific terminology	How does the Tensor chip in Pixel improve on-device AI performance?	Involves specialized tech terms.
Temporal reasoning	When was the iPhone X released, and how does it differ from the current model?	Requires understanding of time-based product evolution.
Numerical analysis	If the iPhone battery lasts 20% longer per generation, how much longer does the 15 last compared to the 12?	Involves multi-step math reasoning.
Document length impact	Summarize this 300-page iPhone user manual.	Tests summarization on long input.
Cross-document references	Based on Apple’s keynote and the specs sheet, what are the top improvements in the iPhone 15?	Requires combining info from multiple sources.
Format variations	Extract purchase dates from receipts in PDF, Excel, and text formats for Pixel phones.	Input spans various data formats.
Incomplete info queries	Tell me about the recent Pixel event.	Lacks detail—model must infer likely intent.
Inference requirements	If Pixel 6 is faster than Pixel 5, and Pixel 7 is faster than Pixel 6, which is the fastest?	Requires logical inference.
Contradictory information	Specs on one site say Pixel 8 has 12GB RAM, another says 16GB. What’s correct?	Requires conflict resolution across sources.
Long-tail queries	What are niche apps that best leverage iPhone ProMotion technology?	Uncommon and specialized user query.
Paraphrased queries	How can I make my iPhone animations smoother?	Rephrased version of a performance optimization query.
Multi-turn conversations	User: Tell me about Pixel 8 Pro. User: How does its camera compare to the iPhone 15 Pro?	Requires context carryover across turns.
Document structure comprehension	Based on the introduction and conclusion of this iPhone market report, what are the key insights?	Tests understanding of structured document layout.
Response time utilization	Give me quick tips now on using Pixel camera, and send a full guide later.	Requests output in stages.
Resource utilization	Use reviews and support forums to summarize iPhone 15 battery issues.	Combines multiple resource types for response.
Degradation with Content	Analyze volumes of documents ( Knowledge base) and feedback to identify recurring camera complaints.	Tests performance under large input load.

Evaluating model responses

After defining evaluation criteria and sample queries, it’s essential to assess how different models perform. Outputs or results across various LLMs and SLMs may vary significantly.

Evaluations should consider multiple dimensions such as:

• Accuracy
• Response Time
• Cost
• Scalability
• Maintenance Complexity, and more.

Performance should be graphed across Movate’s proposed dimensions for a representative set of 15 sample (illustrative) queries for the “Model under test.”

Model Metrics

To benchmark performance, Movate’s Innovation Labs tested four anonymized models (both large and small) across 15 representative scenarios. Using a consistent prompt framework and scoring method, the models were evaluated for both response time and accuracy.

Key findings and trade offs

The box plot results highlight clear performance differences between the models being assessed:

• Response Time: Only two models showed fast and consistent latency, making them ideal for high-frequency enterprise use cases;
• Accuracy: A wider spread in three models indicated inconsistencies in domain understanding and reasoning depth; and
• Stability: One model stands out by delivering consistent and reliable outputs across all fifteen scenarios.

The regression suite (used for repeated sample queries) offers critical insight into trade-offs between speed and quality under enterprise operating conditions. Consistency in this suite suggests a model’s ability to scale successfully.

Final thoughts

Movate’s methodological approach emphasizes that model selection should be requirement-driven, context-aware, and customized—rather than relying solely on generic performance benchmarks. While LLMs currently lead in areas like reasoning and complexity handling, SLMs present compelling advantages in terms of cost-efficiency (often 1/10th the cost of LLMs), faster training cycles, and lower data demands.

That said, our exploration into SLMs is still in its early stages, and the field itself is rapidly evolving. We view this work as an ongoing journey—experimenting, learning, and adapting as the capabilities and use cases of both SLMs and LLMs continue to mature. Practitioners should approach model selection as a dynamic process, informed by emerging evidence, evolving tools, and real-world feedback.

Articles by Kiran Marri

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