Last update: March 2025

OpenAI's O1 and O3-mini are advanced "reasoning" models that differ from the basic GPT-4 (also known as GPT-4o) in how they process prompts and produce responses. These new AI models are specifically designed to think better and work very differently than GPT-4o when it comes to processing questions and solving complex problems. While GPT-4o excels in speed and everyday tasks, O1 and O3-mini are particularly strong in "reasoning" tasks that require multiple thinking steps.

In this blog post, we will compare these models with GPT-4o and examine how their improved reasoning capabilities can help companies enhance their AI applications. We will discuss the strengths of O1 and O3-mini, such as their larger memory and better accuracy, and provide practical tips for choosing the right model for your business needs.

Differences between O1/O3-mini and GPT-4o

Built-in reasoning vs. prompted reasoning

Where GPT-4o requires explicit instructions like "Let's think step by step", O1 models have this built-in already. This "chain-of-thought reasoning" means that O1 models reason step-by-step automatically without additional prompts. Reasoning is essentially the ability of AI models to logically deduce so that they can systematically analyze information, make connections, and draw well-founded conclusions. O1 models automatically dig deep into problems with this built-in reasoning ability, whereas GPT-4o does not do this by default.

💡 Tip: With O1/O3, you can present the problem directly without additional instructions for reasoning steps. This saves valuable prompt space that you can use for other important context.

Need for external information

GPT-4o has an impressively broad knowledge base and has access to tools such as browsing (internet), plugins (extensions), and vision (photos and images) in certain implementations. This makes the model a true jack of all trades that needs little external information. O1 models, on the other hand, act like specialized experts: exceptionally strong in reasoning tasks but with a narrower knowledge base outside their training focus. For example, O1-preview excelled in reasoning tasks but couldn’t answer questions about itself due to a limited knowledge context.

What does this mean for your business?

When using O1/O3-mini, you need to include essential background information or context in the prompt if the task goes beyond general knowledge. Do not assume that the model knows niche facts specific to your industry. Where GPT-4o may already be familiar with a legal precedent or obscure detail, O1 requires you to provide that text or data explicitly. However, this also gives you greater control over what information the model uses!

Practical example: At MSTR, we helped a financial institution that transitioned from GPT-4o to O1. While GPT-4o could quote financial legislation seamlessly, we had to incorporate relevant legal texts and regulations into the prompt for the same task with O1. The result was a much more focused analysis that used only the relevant provisions, without distraction from irrelevant case law.

Context window

The reasoning models come with a context window of up to 128k tokens for O1 and as much as 200k tokens for O3-mini (with up to 100k tokens output), significantly surpassing the context length of GPT-4o. These huge context windows are game changers for companies working with large datasets or documents. You can now input complete case files, technical specifications, or extensive datasets directly into O1/O3 for analysis without splitting them up.

💡 Tip: For effective prompt engineering with these large context windows:

• Structure your input with clear sections and headings (markdown text is industry standard)

• Use bullet points for key points

• Help the model navigate large amounts of information with clear references

Both GPT-4o and O1 can handle long prompts, but the increased capacity of O1/O3 means you can include more detailed context at once. This is useful for complex analyses where multiple information sources need to be evaluated simultaneously.

Reasoning capability

Depth of reasoning

O1 and O3-mini literally take more time to think before they answer. This methodical, multi-step reasoning leads to remarkably more accurate solutions for complex tasks. These models perform internal chain-of-thought reasoning and even check their own work. GPT-4o is also powerful, but provides answers more directly. Without explicit prompting, GPT-4o may not analyze as thoroughly, which can lead to errors in very complex cases that O1 would notice.

Visualization of the reasoning process:

GPT-4o process: Question → [Quick processing] → Answer

O1/O3 reasoning process: Question → [Internal step 1] → [Internal step 2] → [Self-verification] → [Internal step 3] → Answer

Complex vs. simple tasks: when to use which model?

O1 series models really shine in problems that require many thinking steps. For tasks with five or more reasoning steps, they perform significantly better (16%+ higher accuracy!) than GPT-4o.

💡 Tip: Note: this in-depth analysis also has a downside. With simple questions, O1 may "overthink".

Response characteristics and output optimization

Detail and comprehensiveness

Due to intensive reasoning processes, O1 and O3-mini often produce detailed, structured answers to complex questions. For example, they break down a mathematical solution into multiple steps or provide justification for each part of a strategy plan.

GPT-4o usually often gives a more concise answer or an outline summary unless explicitly asked to elaborate.

For your prompts, this means:

• With O1: Want conciseness? Ask for it explicitly, otherwise, it may get elaborate.

• With GPT-4o: Want a step-by-step explanation? Ask for it, otherwise, you may get a summary.

Example instruction for conciseness with O1:
"Analyze this building regulation and provide a summary of up to one paragraph on the main compliance requirements."

Accuracy and self-checking: how reliable is the answer?

O1 is noticeably better at noticing its own mistakes while generating answers, leading to improved factual accuracy. GPT-4o is generally accurate but can sometimes confidently present incorrect information if it lacks proper guidance. The architecture of O1 reduces this risk by verifying details during its "thinking process".

Speed and processing time

O1 models take more time for their analyses, which makes sense given their in-depth reasoning process. GPT-4o generally responds faster to typical questions, which is convenient for real-time interactions where immediate answers are desired. The newcomer O3-mini offers an interesting middle ground: a faster reasoning model with lower latency.

Response comparison in seconds (average times based on benchmarks):

How to get the most out of O1/O3 and GPT-4o

How do you communicate effectively with these powerful AI models? Effectively directing O1/O3-mini requires a different approach than with GPT-4o. Here are the smartest prompt engineering strategies to achieve the best results.

Powerful conciseness: less is more!

With traditional AI models, you often add a lot of context and examples. With O1 and O3, it works the opposite way. These reasoning models perform optimally with clear, direct prompts without unnecessary text. Avoid complex instructions or repetitions. The models are already performing intensive internal reasonings, exaggerated instructions can disrupt their thought process. Start with complex tasks using a zero-shot prompt (only the task description) and only add more if absolutely necessary.

Instead of this: "In this challenging puzzle, I want you to carefully go through each step to arrive at the right solution. Start with an analysis of the problem, then think logically about possible solutions, and reason step by step what the correct answer is. Let's approach it methodically..."

Simply do this: "Solve the following puzzle: [puzzle details]. Explain your reasoning."

Avoid unnecessary few-shot examples

A surprising insight: where GPT models often benefit from few-shot examples (demonstrations in your prompt), the opposite is true for O1/O3 models.

OpenAI's own guidelines align with this:

• Use zero-shot (no examples) as the default approach

• Add at most one example if absolutely necessary

• Keep any examples very relevant and simple

💡 Tip: Sometimes it’s more effective to describe the desired format than to provide an example.

System instructions (system prompts)

System/developer instructions are perfect for defining the role and output format. O1 and O3-mini respond excellently to clear role definitions:

• Define the role: "Act as a building inspector assessing a property" or "Work as a financial advisor analyzing real estate investments for a municipal project"

• Specify the output format: "Structure your inspection report with sections for construction, installations, and safety" or "Present the legal risks in a clear table with impact level and mitigation measures"

• Set clear boundaries: "Limit your analysis to the environmental impacts according to current environmental laws" or "Base your fiscal assessment solely on the provided annual figures without assumptions about future market developments"

Focus on what kind of output you want, not on how the model should think.

The unique capabilities of O3-mini

For O3-mini, OpenAI offers an additional powerful parameter: the "reasoning effort" (low, medium, high). At MSTR, we use this setting to optimize the balance between speed and depth:

• High: Ideal for complex analyses where maximum depth is crucial.

• Medium: Suitable for most business applications with a good balance between depth and speed.

• Low: Perfect for quick, concise answers where response time is prioritized.

Without direct access to this parameter, you can achieve similar effects by adjusting your prompt:

• For low effort: "Provide a quick answer without deep analysis"

• For high effort: "Take all necessary steps to arrive at a correct answer, even if the explanation is lengthy"

In our custom AI solutions, we carefully tune these parameters to meet the specific needs of our clients, ensuring that the output strikes the right balance between depth and conciseness.

When to choose which AI model?

The choice between these advanced AI models depends on your specific needs:

Choose O1/O3 when you:

• Need to perform complex reasoning tasks without extensive prompt engineering

• Are working with very large documents or datasets (up to 200k tokens!)

• Want full control over what information the model uses

• Need in-depth analysis of specific business data

• Prioritize maximum accuracy in complex reasoning tasks over speed

Choose GPT-4o when you:

• Want to perform a broader range of general tasks

• Can benefit from the larger general knowledge base

• Have access to the visual capabilities and plugins

• Need to process less specific business data

• Find faster response times more important than maximum reasoning capacity

  
  

*Cost considerations are based on prices at the time of publication and may change.