Everything You Need to Know About Reasoning Models: o1, o3, o4-mini and Beyond

Think AI has hit a wall? The latest reasoning models will make you reconsider everything.

Contributors: Rafal Rutyna, Brady Leavitt, Julia Heseltine, Tierney Morgan, Liam Cavanagh, Riccardo Chiodaroli 

There are new models coming out every week. So why should you care about reasoning models?

Unlike previous AI offerings, reasoning models such as o1, o3, and o4-mini mark a fundamental shift in enterprise automation. For the first time, organizations can access AI with PhD-level intelligence—capable of automating business processes that require multi-step reasoning, expert-level analysis, and contextual decision making. Tasks that previously relied on human judgment—such as processing complex cases, analyzing fraud, or generating insights from data—can now be handled transparently, accurately, and at scale.

This is more than an incremental upgrade: reasoning models fundamentally change what’s possible. Not only can these models solve problems that conventional code or simple chatbots could not, but they are also being used to train each new generation of AI—creating a rapid, accelerating pace of improvement. The technology is advancing month over month, with each breakthrough quickly setting a new industry benchmark.

For business leaders, the urgency is clear. Organizations that do not begin leveraging reasoning models risk falling behind more agile competitors, unable to match the speed, quality, and auditability of AI-driven decision making.

This article outlines what sets reasoning models apart, where they deliver the greatest value, and what you need to know to take advantage of this emerging capability. If you are focused on maintaining a competitive edge, now is the time to try out the latest reasoning models through Azure OpenAI Service and experience first-hand power of reasoning models.

Table of Contents

• Video Overview
• What’s Different About Reasoning Models?
• The Three Signs of a Strong Reasoning Use Case
• Meet the o-Series Family
• Key Features of the o-Series Models
• Performance on Benchmarks
• Industry Applications of Reasoning Models
• Key Reasoning Patterns
• Vision and Multi-Modal Reasoning
• How to Move From Idea to Prototype
• Cost & Performance Considerations
• Responsible AI, Explainability & Auditability
• Guidance to Choosing Models
• The Pace of AI Advancement is Accelerating

Video Overview

If you'd prefer to watch a video, this 5 minute overview covers the key concepts of reasoning models. Otherwise keep reading for a deep dive into reasoning models.

What’s Different About Reasoning Models?

Quick vs. Deliberate Thinking

Reasoning models such as o4-mini behave fundamentally differently to other models such as GPT-4o. Let's consider two different types of people to better understand these differences:

• Dom is quick on the draw. Ask him a question, and you’ll get an answer almost instantly—perfect for straightforward, routine queries where speed matters most. Dom is like GPT-4o, and is your go-to for customer-facing chatbots or situations where latency is critical. But, like many of today’s LLMs, Dom's snap judgments can sometimes miss subtle details or underlying complexity.
• Gen takes a different approach. She spends 30 seconds thinking through a few different potential options, and carefully weighs all the information before offering a recommendation. Gen’s responses take longer, but they’re consistently more accurate. Gen is similar to o4-mini, and is perfect for situations where regulations are strict, the problem is very complex, or critical decisions need to be right the first time.

Most LLMs available today are like Dom: fast, intuitive, and surprisingly capable in many day-to-day scenarios. But when your business demands careful, defensible reasoning—where a wrong answer can mean regulatory risk or lost revenue—you need a Gen. The o-series of reasoning models on Azure OpenAI Service are built to be your “Gen”: deliberate, thorough, and dependable.

How Is This Possible?

The breakthrough performance of o-series reasoning models is due to their unique training using reinforcement learning, allowing them to reason through complex problems step-by-step and build logical "chains of thought" similar to how human experts reason through a problem. This approach involves rewarding detailed reasoning processes, resulting in outputs that are robust and reliable. Unlike traditional models that only predict the next token, o-series models engage in an internal dialogue to explore various approaches and refine their thoughts. The longer the model thinks, the better the result. You can even adjust the model's reasoning time for a balance between speed and depth.

An incredible finding is that the longer the model thinks, the better it does on a given task! https://openai.com/index/learning-to-reason-with-llms/

These models excel in fields like mathematics, physics, chemistry, and coding, as it is clear when the model has arrived at the correct answer, and whether the reasoning process used to arrive at that answer was right. Traditional language models may perform better on subjective tasks, such as creative writing or generating more empathetic responses to customers.

The Three Signs of a Strong Reasoning Use Case

So how do you know when to apply reasoning models for a given problem? These are the key signs:

1. Multiple datasets: The problem requires combining information from both structured sources (like tables and spreadsheets) and unstructured data (such as documents, emails, or images).
2. Multi-Step Reasoning: The solution isn’t a simple lookup. It involves calculations, scenario comparisons, planning, or multi-stage calculations.
3. Complex or Open-Ended Problem Space: There’s ambiguity, conflicting constraints, or more than one valid path forward. You need the model to “figure out the best way” rather than just follow a script.

Meet the o-Series Family

The o-series of models is divided into two tiers: full-size models and “mini” models:

• Full-Size Models (o1, o3):
  These heavyweights are engineered for the most demanding scenarios—where accuracy, depth of reasoning, and handling ambiguity are mission-critical. They’re ideal for legal analysis, financial workflows, and agentic planning, where the cost of a wrong answer is high and a slightly longer response time is acceptable.
• Mini Models (o1-mini, o3-mini, o4-mini):
  The “mini” models pack the distilled learnings and innovations of the full-size versions into a smaller, more efficient package. Minis are optimized for latency-sensitive and cost-constrained applications—think high-volume, lower-value tasks where it’s critical to respond quickly and keep costs down. You can set the reasoning effort to “low” for even faster responses, making these models a great fit for triage, customer support, or operational reviews.

The performance of the next generation mini model has tended to be similar to that of the full size model from the generation before. For example, o3-mini performs similarly to o1, and o4-mini performs similarly to o3.

Key Features:

• Reasoning effort parameter: Unique to reasoning models, you can dial up or down the model’s depth of reasoning (low, medium, high), to balance speed and thoroughness.
• Responses API with reasoning summary: Every answer is accompanied by a summary of the model's thought process.
• Multimodality: Reason over both text and images.
• Tool calling: The model is able to interact with APIs to take agentic actions or obtain additional data.
• Structured outputs: Generate structured JSON, making integration with downstream systems simple.
• Streaming: Supports streaming responses to reduce perceived latency.
• Cost efficiency: Each generation offers better performance while maintaining competitive pricing.

Performance on Benchmarks

The o-series of models achieve top-tier results on industry benchmarks across a wide variety of domains, including coding, mathematics and science. A small selection of these are shown below, contrasting the performance of non-reasoning models against reasoning models.

Source: https://openai.com/index/introducing-o3-and-o4-mini/

Industry Applications of Reasoning Models

Below are a variety of scenarios showcasing where reasoning models shine. Each example demonstrates not just what the model can do, but why reasoning matters as compared with non-reasoning models.

 

Key Reasoning Patterns

Drop-In Replacement for Classic RAG

Swap out your classic LLM in RAG pipelines with a reasoning model to instantly boost performance on complex queries. It is as simple as switching out the model deployment in Azure OpenAI (though make sure you have a process for evaluating performance!)

Agentic Workflows 

Build multi-agent systems where the reasoning model acts as the “planner” and orchestrates specialized agents (e.g., for HR, legal, supply chain), powered by lighter models like GPT-4o. Code here.

The Reasoning Pyramid 

Reasoning models work best as a precision tool at critical decision points—not as a replacement for all analytics. Think of your workflow as an inverted pyramid: start with rule-based analytics and conventional machine learning to filter down the volume; use lighter, faster language models for high-speed triage; and escalate only the most complex, high-stakes cases to advanced reasoning models (like o4-mini, o3, or o1) for expert, auditable handling. This approach ensures you maximize speed and efficiency while reserving deep, deliberate reasoning for where it matters most.

This video explains this in greater detail.

Vision and Multi-Modal Reasoning

A defining strength of the latest reasoning models is their robust multi-modal capability—they are equipped to interpret not just text, but also visual inputs such as images.

With vision capabilities, enterprises can now leverage GenAI to address challenges that were previously outside the reach of text-only models, including:

• Interpreting Complex Data Visualizations:
  The model can analyze dashboards, charts, and intricate reports, then answer nuanced business questions grounded in the visual context. This empowers decision makers to quickly derive insight from data without manual interpretation.
• Comprehending Specialized Visual Inputs:
  The ability to process scanned documents or specialized imagery opens up new use cases. Examples include medical image analysis (such as X-rays or MRIs), reading and understanding engineering schematics, analyzing agricultural surveys through aerial imagery, or reviewing detailed construction blueprints.
• Advanced Problem Solving:
  Reasoning models can tackle tasks that require multi-step reasoning over an image—such as solving puzzles, interpreting diagrams, or even troubleshooting issues by examining images of equipment or processes.

The greatest strength of multi-modal reasoning is its capacity to combine image inputs (e.g. an MRI) with text-based data sources, such as database extracts or unstructured documents (e.g. blood test results).

How to Move From Idea to Prototype

Getting started with building a reasoning model use case is surprisingly straightforward, as the reasoning model takes care of most of the heavy lifting. At the heart of every solution sits a carefully designed prompt. This GitHub repository provides a practical template to help you accelerate from concept to prototype in just a few hours.

Let’s walk through an example: suppose you want to quickly address customer enquiries about unexpected billing charges—an all-too-common “bill shock” scenario.

1. Define the Input Task:
   Clearly state what you want the AI to achieve.
   Example: “Explain the cause of an unexpected increase on a customer’s most recent bill.”
2. Provide Datasets:
   To start, you can simply copy-paste relevant data as plain text, such as recent invoices, service lists, or billing tables—similar to how a human expert would review them. Later, these placeholders can be replaced by live data sources or SQL queries.
   Example: Include a customer’s latest bill (PDF contents), their current services, and any usage data that might explain the charge.
3. Incorporate Rules and Policies (Optional):
   List any business logic or rules the model should follow.
   Example: “If the disputed amount is under $10 and the customer has not received a credit in the last 12 months, auto-approve a credit.”
4. Add a Glossary (Optional):
   Clarify organization-specific terminology to help the model understand your business language.
   Example: “SIO: Service in Operation – an active product or service.”
5. Extend with Examples, Schema, or Tool Calls (Optional):
   Strengthen performance with few-shot examples, structured output schemas for consistency and auditability, or integrate with external tools for automated actions.

With these components, teams can rapidly test and iterate on reasoning use cases, dramatically shortening time-to-value. This practical template forms a strong foundation for scaling to real-world, production deployments, and mirrors the approach used in real enterprise deployments.

Cost & Performance Considerations

Enterprises evaluating reasoning models often ask: how do their costs measure up against traditional LLMs? Interestingly, reasoning models have become quite cost competitive compared to their non-reasoning counterparts. For example, the o4-mini model is priced at roughly half the token cost of GPT-4o, offering significant savings on a per-token basis.

However, there is an important distinction: unlike standard models, reasoning models go through an intermediate “thinking” phase. This results in additional completion tokens being generated, which add to the overall cost. So what does this mean in practice?

To answer this, let’s examine two practical enterprise scenarios—use cases where the quality and reliability of answers are just as critical as cost:

• The first scenario looks at a classic Retrieval Augmented Generation (RAG) use case, where a user seeks detailed assistance with a complex question around a recent merger. In this case, GPT-4o was unable to produce a correct answer, whereas o3-mini succeeded.

• The second scenario focuses on assessing the optimal maintenance strategies to maximize uptime for a mining operation.

As illustrated in the table above, when the level of reasoning effort is configured to “low,” reasoning models can deliver results for only ~60% of the cost of non-reasoning models. When the effort level is set to “high” the cost between the two types of models is essentially equivalent. Notably, o4-mini delivers comparable performance to o3 for many applications, at a substantially lower price point.

[Note] The pricing basis for these calculations (as of 23/04/2025) was taken to be: GPT-4o is priced at $2.50 per million prompt tokens and $10 per million completion tokens. o4-mini is priced at $1.10 per million prompt tokens and $4.40 per million completion tokens. These are indicative costs provided for reference only—always consult the Azure OpenAI pricing page for the most up to date pricing.

Responsible AI, Explainability & Auditability

With the introduction of the o3 and o4-mini models, responsible AI is entering a new phase—delivering not just performance gains, but also a step-change in safety, explainability, and auditability. A known challenge with large language models is that they may at times back-rationalize explanations—producing plausible-sounding reasoning after already deciding on an answer.

o3 and o4-mini use a technique called deliberative alignment: the model is trained to explicitly reason about safety, compliance, and quality requirements before generating its answer. For every task, the model provides a high-level summary of its reasoning as part of the response, making it clear how conclusions were reached, as part of the Responses API. 

The Structured Outputs feature takes this further, enabling organizations to define the exact schema each answer should follow. For example, you can require the model to return source citations, step-by-step calculations, justification of its analytic process, and even flags that indicate if further human review is needed. This standardization is invaluable for audit, risk, and compliance teams.

Practical example

Let's explore an example to see what this looks like:

In this output:

• The response_to_input_task provides the step by step, detailed calculations required to get to the answer.
• The justification_of_analysis field explains the methodology used by the model.
• The citations field lists every source dataset used, along with specific reasons for referencing each, allowing anyone to independently trace and validate the analysis.
• The flag_for_human_review field indicates whether the model found anything uncertain or ambiguous, automatically triggering escalation if required.

All of this is generated reliably by the structured output feature, ensuring every answer from the model follows a schema suitable for audit and compliance teams.

Impact on regulated industries

In highly regulated environments such as credit risk assessment or claims adjudication, process transparency has historically depended on a combination of documentation and tribal knowledge held by experienced employees. Decision rationales often remain locked in email threads or are incompletely captured in case notes. In contrast to this, AI reasoning models persuasively document the entire decision pathway: every dataset accessed, every calculation performed, and every assumption made, providing a more exhaustive record than many human-driven processes.

This results in greater transparency, consistency, and audit-readiness—lowering the barrier for enterprise AI adoption, and shifting the conversation from “Can we trust AI?” to “How can AI improve the clarity and traceability of our business decisions?”

By bringing together deliberative alignment, reasoning summaries, and schema-driven outputs, the o-series models set a new standard for safe, explainable, and auditable AI.

Guidance to choosing models

With so many models available to us, it can be hard to know where to start. Here is a quick guide:

1. Recommended Default: o4-mini
   For most enterprise scenarios—particularly those involving complex, multi-step reasoning or challenging business logic—the o4-mini model with the reasoning effort parameter set to “high” is a strong starting point. It delivers exceptional performance, making it ideally suited for the most demanding tasks. When prototyping new solutions, start with the best performing models to deliver the best possible results. Once the solution is achieving the desired business outcomes, then seek to further optimize for cost and latency as needed.
2. Customer-Facing, Low Latency, or Highly Creative Use Cases: GPT-4.1
   If your application requires rapid responses, a conversational writing style, or excels in creative and emotionally intelligent outputs (such as customer support or content generation), GPT-4.1 is an excellent choice. Non-reasoning models like GPT-4.1 typically offer superior response speed and produce content with a tone and creativity that resonates with end-users.
3. Maximizing Cost Efficiency: GPT-4.1-mini
   When cost is the primary consideration and the task complexity is moderate to low, GPT-4.1-mini or GPT-4.1-nano models provide the best value. These models offer attractive pricing on a per-token basis while maintaining solid performance for a wide range of enterprise workloads.

The models themselves will change with time, however the trend in this recommendation will (likely) remain the same. For example, in the previous generation of models, the recommendation would have looked like: o3-mini ->GPT-4o -> GPT-4o-mini

The Pace of AI Advancement is Accelerating

In recent months, we have witnessed a dramatic acceleration in the capabilities of reasoning models, driven largely by innovations in test-time compute and current gen models being used to train new models. For enterprises seeking to harness the transformative potential of AI, keeping up with these advancements is essential—those who fail to prioritize use cases leveraging state-of-the-art reasoning models risk being outpaced by more agile competitors.

A striking example of this rapid progress is seen in the performance on the ARC-AGI benchmark. Historically, this benchmark posed a significant challenge: for over five years, even the most advanced AI systems struggled to come even close to human experts on these complex reasoning tasks. However, recent releases—such as o1 and o3—have demonstrated a dramatic leap in capability, achieving and quickly surpassing human-level performance in a matter of months.

While the precise details of these models’ training processes remain proprietary, the high-level methodology is increasingly understood within the research community. Typically, the process begins with a strong base language model generating thousands of step-by-step reasoning chains for each benchmark problem. Naturally, only a small fraction of these generated chains successfully arrive at the correct solution, demonstrating valid lines of reasoning. Through rigorous selection and filtering, these high-quality chains are curated and then used as training data for the next, more capable generation of models. While it is still early, these iterative cycles of generation, filtering, and retraining appear to be setting the stage for a genuine “self-improvement loop” in AI development, reflected in the rapid advancements since the release of o1.

A new era in AI reasoning

Reasoning models aren’t just another incremental model version—they represent a fundamental shift in how generative AI delivers value for enterprises. With o3 and o4-mini, AI becomes more than a source of answers: it becomes a trusted partner in automation, decision-making, and compliance. These models are designed to help you tackle complex business challenges with transparency and confidence.

Build your own reasoning prototype and explore what’s possible with the next generation of reasoning models in Azure OpenAI Service. Get started with o3 and o4-mini in Azure AI Foundry today, and see how reasoning models can drive better outcomes for your business.