AI Model Selection: Small, Large, Multimodal & Model Routing

When we build an AI application, one of the most important decisions is choosing the right AI model.

In simple terms, a model can be thought of as the brain of an AI system. It takes an input, processes it based on what it has learned, and produces an output.

Input
  ↓
AI Model
  ↓
Output

The important point is that not every model is suitable for every task. Some tasks are simple and repetitive, while others need deeper reasoning. Some applications work only with text, while others need to understand images, documents, audio, or video.

So instead of asking, “Which is the most powerful model?”, a better question is: “Which model is the right fit for this business requirement?”

What is an AI model?

An AI model is a trained system that accepts input and produces an output. Different models are suited to different kinds of workloads, which makes model selection an architecture decision rather than just an AI configuration choice.

1. Small language model

A small language model is useful when the task is relatively simple, repetitive, and does not require deep reasoning.

Typical examples include intent classification, request routing, simple data extraction, short summaries, and basic question answering.

For example, suppose a user says:

I want information about old-age pension.

If the application only needs to identify that the request is related to pension services, a smaller model may be enough.

Intent = Pension

A smaller model can be a better fit because it may provide lower cost, faster response time, and higher throughput for high-volume workloads.

2. Large or more capable language model

A larger or more capable language model is useful when the task requires deeper reasoning.

For example:

Compare these three government schemes and explain which one applies based on age, income, disability status, and other eligibility rules.

Here the model needs to understand several pieces of information, compare conditions, reason about them, and explain the result.

A stronger model may be a better choice for this kind of workload. But this does not mean that a larger model should be used for every request. More capable models can also introduce higher cost and latency.

3. Multimodal model

A multimodal model can work with more than one type of input, such as text, images, documents, audio, or video.

Suppose a user uploads an image of a certificate and asks:

Can you tell me what information is available in this document?

A multimodal model may be useful because the system needs to understand both the visual content and the user’s question.

However, if the requirement is to extract the same 20 fields from 100,000 invoices every day, a specialized document-processing or information-extraction capability may be more predictable and efficient.

The correct choice depends on the actual business problem.

4. Embedding model

An embedding model is different from a normal chat model.

A chat model generates language. An embedding model converts meaning into a numerical representation called a vector.

"How can I apply for pension?"
          ↓
   Embedding Model
          ↓
[0.21, -0.57, 0.81, ...]

Another sentence such as Old-age pension application process can produce a mathematically similar vector because the meaning is similar even though the words are different.

Embedding models are commonly used in vector search, semantic search, Retrieval-Augmented Generation (RAG), similarity matching, and recommendation systems.

A simple way to remember the difference is:

Chat Model → Generates language
Embedding Model → Represents meaning as numbers

How do we select the right AI model?

Model selection should always start with the business requirement.

Before choosing a model, I think about:

• What type of task are we solving?
• How complex is the reasoning?
• How many requests will the system receive?
• What response time is acceptable?
• What will the model cost at that volume?
• What type of input does the model need to understand?

For a simple classification workload, a small language model may be enough. For complex reasoning, a more capable model may be required. For image understanding, a multimodal model may be appropriate.

The key is that the model should match the workload.

What is model routing?

Model routing is a useful architecture approach when an application receives different types of requests.

Suppose an AI application receives 500,000 requests per day, but only 10,000 of them require complex reasoning.

If every request is sent to the most capable model, the system may become unnecessarily expensive.

A better design is:

                  User Request
                       ↓
                 Request Router
                  ↙          ↘
          Simple Request   Complex Request
                ↓                ↓
          Small Model      Capable Model

This is model routing. It can help improve cost efficiency, response time, resource utilization, and scalability.

A simple real-world example

Consider a citizen-service AI application.

If a user asks, “Which department handles pension applications?”, the requirement may only need classification, so a smaller model could be sufficient.

If another user asks, “Explain the eligibility criteria from this government policy”, the application may eventually use a language model together with RAG so the response is grounded in current source information.

If someone asks, “Compare four policy circulars and explain which rule currently takes priority”, that may require a more capable reasoning model.

And if someone uploads an image of a certificate, the system may need a multimodal or specialized document-processing capability.

The model is selected based on the task, not because one model is considered the best overall.

An important AI architecture principle

One of the most useful principles I learned while studying AI model selection is:

Use the smallest model that can reliably meet the business requirement.

Using a powerful model everywhere may work technically, but it may not be the best architecture.

A good AI architecture should balance quality, cost, latency, scale, and the business requirement.

Model selection is therefore not only an AI decision. It is an overall system architecture decision.

Final thoughts

AI model selection is not about choosing the newest or most powerful model available. It starts with understanding the problem.

A simple task may work perfectly with a smaller model, while a complex reasoning problem may need a more capable one. Visual or document-based workloads may require multimodal capabilities, while semantic search and RAG commonly depend on embedding models.

As AI applications grow in scale, techniques such as model routing can help balance quality, cost, and performance.

The right model is the one that meets the business requirement reliably without adding unnecessary complexity or cost.