Choosing the Right AI Model

Why model choice matters

Model categories

Category	What They Do	Examples	Best For
Large Language Models (LLMs)	Generate and understand text	GPT-4o, GPT-4, Phi-4	Chat, summarisation, translation, code
Small Language Models (SLMs)	Text tasks with lower cost/latency	Phi-4-mini, Phi-3-small	Simple tasks, edge devices, cost-sensitive apps
Image generation models	Create images from text descriptions	GPT-image-1.5	Marketing visuals, concept art, design
Vision models	Analyse and understand images	GPT-4o (vision), Florence	Image classification, object detection, OCR
Speech models	Convert speech ↔ text	Azure Speech Service	Transcription, voice assistants, TTS
Embedding models	Convert text to numerical vectors	text-embedding-ada-002	Search, similarity, RAG retrieval

How to choose: the decision framework

When the exam gives you a scenario, use this framework:

Step 1: What type of input/output do you need?

• Text in, text out → LLM
• Text in, image out → Image generation
• Image in, text out → Vision model
• Audio in, text out → Speech model
• Multiple types → Multimodal model (GPT-4o)

Step 2: What’s the complexity?

• Simple task (classify, extract) → Smaller, cheaper model
• Complex task (reason, create) → Larger, more capable model

Step 3: What are your constraints?

• Low budget → SLM (Phi-4-mini)
• Low latency → SLM or smaller LLM
• Highest quality → GPT-4o or GPT-4
• Privacy-sensitive → On-device or edge model

Model selection guide — matching scenarios to models

Feature	When to Use	Model Choice
Chat assistant for customers	Need natural conversation, reasoning	GPT-4o or GPT-4
Summarise meeting notes	Text in, text out, moderate complexity	GPT-4o-mini or Phi-4
Generate product images	Text description → new image	GPT-image-1.5
Classify support tickets	Simple text classification	Phi-4-mini (cost-efficient)
Transcribe phone calls	Audio → text	Azure Speech Service
Analyse medical X-rays	Image understanding + reasoning	GPT-4o (multimodal)
Search company documents	Need to find relevant passages	Embedding model + RAG

Large vs small models

Large language models vs small language models

Feature	Large Models (GPT-4o)	Small Models (Phi-4-mini)
Parameters	Hundreds of billions	Billions (10x-100x smaller)
Capability	Broad, complex reasoning	Focused, specific tasks
Cost	Higher per-token pricing	Significantly cheaper
Latency	Slower (more computation)	Faster responses
Best for	Complex tasks, multimodal, creative	Classification, extraction, simple chat
Can run on edge?	No — cloud only	Yes — can run on devices

ℹ️ Microsoft's Phi family — small but mighty

Microsoft developed the Phi family of small language models specifically for scenarios where cost, latency, or deployment location matters more than maximum capability.

• Phi-4 — latest, most capable small model
• Phi-4-mini — even smaller, great for classification and extraction
• Phi-3 — previous generation, still widely deployed

The key insight: for many business tasks (email classification, FAQ answers, data extraction), a small model performs nearly as well as GPT-4o at a fraction of the cost.

Exam relevance: When a scenario mentions “cost-effective” or “edge deployment” or “low latency” → think Phi or other SLMs.

The Foundry model catalog

Microsoft Foundry includes a model catalog — a library of models from multiple providers that you can deploy directly:

Provider	Models	Strengths
OpenAI	GPT-4o, GPT-4, GPT-image-1.5	Best general-purpose, multimodal
Microsoft	Phi-4, Phi-4-mini	Cost-efficient, edge-friendly
Meta	Llama 3	Open-source, customisable
Mistral	Mistral Large, Mistral Small	European alternative, efficient
Cohere	Command R	Strong at RAG and retrieval

Key exam concept: You don’t need to memorise every model. You need to understand the categories (LLM, SLM, vision, speech, embedding) and know how to choose based on task requirements.

🎬 Video walkthrough

Flashcards

Question

What is the difference between a Large Language Model (LLM) and a Small Language Model (SLM)?

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Answer

LLMs have hundreds of billions of parameters and excel at complex reasoning and multimodal tasks but cost more and are slower. SLMs have billions of parameters (10-100x smaller), are cheaper and faster, and work well for focused tasks like classification and extraction.

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Question

When should you choose a small model (like Phi-4-mini) over GPT-4o?

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Answer

When the task is simple (classification, extraction, FAQ), when cost is a concern, when low latency is required, or when you need to run the model on edge devices.

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Show hint

Question

What is an embedding model used for?

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Answer

Converting text into numerical vectors (lists of numbers) that capture semantic meaning. Used for search, document similarity, and RAG retrieval — finding relevant documents to feed to an LLM.

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Question

What model would you use to generate images from text descriptions?

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Answer

GPT-image-1.5 — an image generation model available in Microsoft Foundry. You provide a text prompt, and it creates a new image matching that description.

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Knowledge Check

Knowledge Check

GreenLeaf wants to automatically classify incoming support emails into categories: billing, technical, general inquiry. They process 50,000 emails per day and need to keep costs low. Which model approach is most appropriate?

AGPT-4o — the most capable model ensures highest accuracyBGPT-image-1.5 — it can process text and classify it visuallyCPhi-4-mini — a small model is cost-efficient for simple classification tasksDAzure Speech Service — to convert emails to audio for processing

Check Answer

Knowledge Check

MediSpark needs an AI model that can accept both a medical image (X-ray) and a text question ('What abnormalities are visible?') and return a text response. Which type of model do they need?

AAn embedding model — to convert the image to numerical vectorsBA small language model — for cost-efficient text generationCA multimodal model — it can process both image and text inputsDAn image generation model — it works with images

Check Answer

Knowledge Check

Priya needs to build a search feature that finds the most relevant company documents when a user types a question. Which combination of models should she use?

AGPT-4o alone — it can search through all documentsBAn embedding model to convert documents to vectors, then an LLM to generate answers from the retrieved documentsCGPT-image-1.5 to visualise the search resultsDAzure Speech Service to read out the search results

Check Answer

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Next up: Deploying AI Models — configuration parameters like temperature, top-p, and max tokens that control how your model behaves.