General-Purpose vs Reasoning Models in Azure OpenAI

1. Introduction

Since Large Language Models (LLMs) have become mainstream, a wide range of models have emerged to serve different types of tasks—from casual chatbot interactions to advanced scientific reasoning. If you're familiar with GPT-3.5 and GPT-4, you'll know that these models set a high standard for general-purpose AI. But as the field evolves, the distinction between model types has become more pronounced.

In this blog, we'll explore the differences between two major categories of LLMs:

• General-Purpose Models – Designed for broad tasks like conversation, content generation, and multimodal input processing.
• Reasoning Models – Optimized for tasks requiring logic, problem-solving, and step-by-step breakdowns.

We'll use specific models available in Azure OpenAI as examples to illustrate these differences:

• General-Purpose: GPT-4o, GPT-4o-mini
• Reasoning: o1, o3-mini

These models differ not just in capabilities, but in speed, accuracy, and cost. We’ll compare them across four key dimensions to help you determine which best fits your use case:

• Capabilities
• Accuracy
• Latency
• Cost

2. What does it mean to "reason"

In the context of large language models, "reasoning" refers to the model’s ability to systematically solve problems, apply logical thinking, and explicitly work through multi-step tasks. Unlike general text generation, which primarily involves producing coherent and contextually relevant content, reasoning requires structured thought processes similar to human problem-solving.

Reasoning in language models can manifest in a few ways:

• Logical Deduction – Drawing accurate conclusions based on given premises.
• Step-by-Step Problem-Solving – Clearly breaking down complex problems into simpler, manageable steps.
• Mathematical Computation – Solving arithmetic, algebraic, and calculus problems accurately.
• Structured Decision-Making – Evaluating scenarios methodically and proposing rational solutions.
• Coding and Debugging – Writing syntactically correct, logically consistent, and functional code.

Reasoning models utilize intermediate "thinking" steps—often referred to as "chain-of-thought" reasoning. By deliberately thinking through each stage of a problem, reasoning models provide more transparent, accurate, and reliable outputs. This structured approach makes them especially valuable for tasks where correctness and logical clarity are crucial, such as:

• Advanced scientific or mathematical computation
• Complex business analysis and strategic planning
• Legal document interpretation and drafting
• Detailed programming assistance and software debugging

3. Key Factors for Model Comparison

When choosing between a general-purpose or reasoning LLM, it’s crucial to understand what you’re optimizing for. Are you focused on raw capability? Speed and cost-efficiency? Or striking a balance between all three?

Capabilities

Capabilities refer to the kinds of tasks a model performs well, such as:

• Text Generation – Can the model create coherent, creative, or structured content?
• Multi-modality – Can it handle inputs beyond plain text (e.g., images or audio)?
• Context Understanding – Does it maintain consistency in long conversations?
• Complex Reasoning – Can it tackle multi-step logical problems?
• Function Calling – Is it good at structured tool use via APIs or function calls?

General-purpose models excel at generating text, holding multi-turn conversations, and handling multimodal inputs like images and audio. Reasoning models, on the other hand, are more effective at handling tasks that require complex logic, such as math, science, and structured problem-solving.

Accuracy

At a high level, accuracy is about producing correct, relevant, and contextually appropriate responses. Generally, larger models with more recent training data and thoughtful prompting techniques perform better. That said, smaller, well-optimized models can still be impressively accurate in specific domains.

General-purpose models are highly accurate for broad, creative, or conversational tasks. However, reasoning models tend to outperform them in structured domains like code generation and mathematical proofs.

Latency

Latency is how quickly a model responds. For some applications (like real-time chat), even a one-second delay can feel disruptive. Others (like batch processing or data analysis) can tolerate more delay in exchange for accuracy.

Two metrics to keep in mind when discussing latency are:

• Time to First Token (TTFT) – How quickly the model begins generating output.
• End-to-End Latency – Total time from prompt to final output.

General-purpose models—especially the mini variants—are optimized for speed. Reasoning models often have longer latency due to more complex internal processing designed to "think" through problems.

Cost

LLM usage is typically priced by tokens—small chunks of text. Costs depend on the number of input and output tokens and the model used. Larger models generally cost more, so cost-efficiency may involve:

• Using smaller models for simpler tasks.
• Limiting output length.
• Finding the right balance between performance and price.

General-purpose models offer a range of pricing tiers, with mini versions being the most affordable. When reasoning models "think", they generate "thinking" tokens, this tends to cause these models to be more expensive. 

4. Meet the GPT Models

Before we compare them in detail, here’s a quick snapshot of each model in Azure OpenAI:

General Purpose Models

GPT-4o

• Best for: Complex tasks involving multiple modalities (text, image, audio).
• Strengths: High accuracy, 128k context window, multilingual support.
• Trade-offs: Higher cost and latency than smaller models.

GPT-4o-mini

• Best for: Chatbots, general text processing, lightweight automation.
• Strengths: Faster responses, lower cost.
• Trade-offs: Less effective in deep reasoning tasks.

Reasoning Models

o1

• Best for: Complex reasoning, problem-solving, advanced logic.
• Strengths: Step-by-step explanations, strong in math, science, and programming.
• Trade-offs: Slower and more expensive than GPT-4o.

o3-mini

• Best for: Faster reasoning at lower cost.
• Strengths: Good latency, balanced logic and performance.
• Trade-offs: Less capable for long-form problem-solving.

5. Model Performance Observations

Here, we delve into some observations which focus on latency, accuracy, and cost. 

Latency 

Ongoing and detailed latency comparisons for each model type are hard to find. After combing through dozens of third-party comparisons from one-off blogs, GitHub repos and other forum threads, we can make broad generalizations, but it is always best to run your own tests with data from your enterprise. 

You can gather real-time results using some basic benchmarking code similar to what is outlined below.

 

import time
import openai

def benchmark_model(model_name, prompt, n_runs=10):
    times = []
    for _ in range(n_runs):
        start = time.time()
        response = openai.ChatCompletion.create(
            model=model_name,
            messages=[{"role": "user", "content": prompt}]
        )
        times.append(time.time() - start)
    return sum(times)/len(times), min(times), max(times)

 

A sample benchmark might look like this: 

• Prompt Length Average: 300 tokens (Max: 400) 

• Method: Synchronous requests repeated 10 times each, the average, no prompt caching turned on 

 

Takeaways:

• Mini models are fastest overall.
• GPT-4o latency is manageable, especially given it's varying capabilities
• o1 has the longest latency but excels in quality.

Accuracy

Accuracy varies by use case, but in general:

1. o1 – Best for complex reasoning and logic-heavy tasks.
2. GPT-4o – Excellent all-around performance, strong in multi-modal tasks.
3. GPT-4o-mini – Moderate accuracy, good for general tasks.
4. o3-mini – Good reasoning, but more prone to hallucinations.

Cost 

Cost is constantly fluctuating but prices relative to one another typically look like this:

These relative costs are illustrative only. For the most up to date information please refer to the AOAI pricing page.

6. Practical Usage Scenarios

Let’s translate these metrics into real-world choices. Where do these models shine, and which ones might be overkill? 

GPT-4o: The Flagship Model for Multimodal AI 

• Advanced Conversational AI – Virtual assistants with nuanced conversations. 

• Multimodal Applications – Text, image, and audio processing. 

• Complex Content Creation – Automated writing, blog generation, and creative storytelling. 

• Scientific Research Assistance – Summarizing research papers, hypothesis testing. 

• Legal & Financial Analysis – Reviewing contracts, drafting reports, financial forecasting. 

• Medical Text Processing – Analyzing medical reports, summarizing patient data. 

GPT-4o-mini: Fast & Cost-Effective Language Processing 

• Customer Support Chatbots – AI-powered assistants with lower latency. 

• SEO & Marketing Content Generation – Quick blog writing and social media content. 

• Automated Translations – Fast, cost-efficient multilingual support. 

• FAQ Automation – Interactive knowledge base queries. 

• Summarization Tools – Condensing large texts for faster insights. 

• Lightweight Coding Help – Code snippets and syntax correction. 

o1: Advanced Reasoning & Problem-Solving 

• Code Generation & Debugging – Writing complex programs, debugging large codebases. 

• Agentic Orchestration – Planning across multiple AI agents in complex workflows. 

• Mathematical & Scientific Computation – Solving algebra, calculus, and physics equations. 

• Business & Strategic Planning – Generating multi-step business models. 

• Legal Contract Review & Drafting – Identifying clauses and legal loopholes. 

• High-Accuracy Data Analysis – Extracting insights from structured/unstructured data. 

o3-mini: Balanced Cost & Reasoning 

• Automated Report Writing – Financial, legal, and academic reports. 

• E-commerce Product Recommendations – Personalized shopping assistants. 

• Automated Coding Assistant – Debugging, refactoring, and optimizing code. 

• Workflow Automation – Simplifying internal business processes. 

• Enhanced Search & Retrieval – Intelligent document search and summarization. 

 

7. Which Model Should You Use?

 

• Need top-tier logic and coding help? → o1
• Need general-purpose multimodal support? → GPT-4o
• Need fast and cheap results? → GPT-4o-mini or o3-mini
• Need structured reasoning + speed? → o3-mini

Bonus tip: You can combine models. For instance, you can use GPT-4o-mini for general queries and route complex reasoning tasks to o1.

 

8. Conclusion

When choosing between general-purpose and reasoning models, think about what you're optimizing for—speed, accuracy, or problem-solving depth. For real-time, broad-use applications, general-purpose models like GPT-4o and its mini/audio variants are ideal. For tasks requiring structured logic and deeper reasoning, models like o1 and o3-mini are the better fit.

As always, test multiple models to benchmark performance, cost, and latency for your specific use case. The best AI strategy often involves orchestrating a mix of both types to handle a diverse range of tasks efficiently.

Happy building!