🔹 OpenAI o1 – Deep Reasoning for the Most Complex Problems

OpenAI o1 is the first model from the OpenAI “o-series” (OpenAI reasoning models) designed to focus on deep reasoning and solving difficult problems.

Unlike GPT-4o, which is a versatile multitalent, o1 is like a logic and strategy expert – it specializes in complex tasks in areas such as research, strategy planning, coding, mathematics, and science. Internally nicknamed “Strawberry” during development, o1 models bring a more thoughtful and systematic approach compared to classic GPT models.

In ChatGPT Plus, o1 is available to users who want the AI to “think twice” before answering: it uses the technique of simulated reasoning, where the model pauses during generation to internally evaluate its solution and improve it before presenting it to you.

The result are answers that can resemble detailed human deliberation – with clearly indicated steps of conclusion when needed.

Technical Characteristics: OpenAI o1 has fewer “parameters” than GPT-4o, but it compensates with special training focused on analytical thinking.

It is a “frontier model” – part of a pioneering class of AI systems that push the boundaries of context understanding and independent conclusion-making.

In practice, this means that o1 often achieves impressive results in tasks requiring multi-step reasoning (e.g., solving mathematical problems step-by-step, or developing a complex argument in multiple points).

Sometimes it may seem that o1 “talks to itself” while searching for a solution – that is part of its simulated internal logic.

One particular advantage of the o1 model is that it can be more reliable in generating correct program code or solving puzzles, because it is trained to precisely follow logical sequences.

Also, for visual tasks, it is interesting that o1 supports image uploading and can analyze them (OpenAI states that o1 is even the preferred choice for visual reasoning).

Limitations and Feature Availability: In the ChatGPT environment, o1 models have limited access to advanced tools and features.

Specifically, it has no “memory” outside the current conversation (Memory), does not use Custom instructions (personalized instructions), cannot open files other than images, nor use internet search or additional GPT tools and voice.

So, o1 works in a conversation vacuum – it relies exclusively on what you tell it and its internal knowledge.

This sometimes means slightly more concise or technical answers, as it does not easily integrate external styles (which you would enter through Custom instructions).

Keep in mind that o1 is primarily designed to solve a problem, even at the cost of a slightly longer answer generation time.

For everyday, simpler queries, a faster model exists (o3-mini, which we will discuss below).

In the Plus offer, o1 is available as an option when you need that “extra dose” of intelligence in specific domains. (Note: Pro plan users also have access to the o1-pro variant, which is even more powerful, but essentially the o1 will be sufficient for most Plus users.)

Concrete Business Applications: OpenAI o1 finds its niche in situations requiring analysis, precision, and long-term planning: 

• • Complicated Business Problems and Strategy: Say you run a company that needs to optimize its logistics network. You can ask o1: “Analyze these three delivery options for our main markets, consider costs, transport time, and risks, and propose the best strategy.” o1 will methodically go through every aspect (perhaps even numbering the steps in the answer) and give you a logically supported conclusion. For in-depth strategic planning or brainstorming complex decisions (mergers, entering new markets, R&D focus) – o1 provides an analytical partner.

• • Advanced Mathematics and Science: If your business involves complex calculations or scientific concepts (e.g., you are an engineering or pharmaceutical firm), o1 can help resolve these problems. An engineer can request proof of a mathematical theorem or the solution to a complex equation – o1 will patiently execute the steps and often arrive at the correct solution. Similarly, in the domain of finance, it can derive and explain complicated financial models or formulas.

• • Programming and Code Review: For technical teams, o1 is excellent at fixing difficult bugs or optimizing algorithms. Its deeper reasoning helps, for example, to find out why a certain dataset crashes a program or how to improve code execution time. A startup with a limited dev team can use o1 as a virtual architect, to check the logic of a complex software module.

  • Simulation of Expert Advice: o1 can also be used as a “virtual consultant” for domains requiring expert thinking. E.g., an HR team can ask: “How to resolve a complex conflict between two managers? Analyze the situation step-by-step.” o1 will thoroughly consider the psychological and organizational factors and propose thoughtful steps, similar to an experienced HR consultant methodically approaching the problem.

🔹 OpenAI o3-mini – Speed and Logic in a Small Package

While o1 is the “heavy artillery” for the most demanding tasks, OpenAI o3-mini represents a more agile, faster iteration in the same line of reasoning models. o3-mini is the latest addition to the o-series models and is designed to solve coding and other advanced reasoning tasks extremely well, but much faster than o1.

You can view it as a “small powerful engine” that combines the best of both worlds: analysis power close to o1 and efficiency almost at the level of GPT-4o. OpenAI recommends o3-mini as the default choice when you need the advantages of the o-series (depth and logic), but with better responsiveness.

Interestingly, internal evaluations show that o3-mini in many cases achieves results equal to or better than o1, while generating answers significantly faster.

It is also a cost-effective model (especially relevant for API users), which in the context of ChatGPT Plus means you can use it often without fear of hitting limits.

Key Characteristics: o3-mini is optimized for efficiency – it has a smaller “footprint” (parameters) than the full o1, but retains complex reasoning capabilities.

It particularly shines in programming tasks: writing and correcting code, explaining how to solve a certain algorithmic problem, and the like.

It is so good at coding that in some tests it surpasses significantly larger models (like GPT-3.5) in accuracy.

o3-mini can also do something that o1 could not: search the internet during operation. It has a built-in Search function, meaning that if you ask it about something current (e.g., “What are the latest stock market news?”), it can perform a web search and provide you with an answer supported by links to sources.

This makes it versatile for research tasks – a combination of speed and direct access to fresh information. However, keep in mind that it does not have visual skills: o3-mini does not understand images (for such tasks, o1 or GPT-4o are the choice).

In the ChatGPT Plus environment, o3-mini shares some limitations with o1 (no long-term memory or files), but due to integrated searching, it can compensate for the lack of memorized knowledge by checking external sources in real-time.

Advantages and Limitations: The biggest advantage of the o3-mini model is the balance of speed and intelligence.

In situations where GPT-4o might be “too much” (or busy), and GPT-3.5 (which we discuss later) too little, o3-mini jumps in as the golden mean for business users.

The answers are usually slightly more concise and direct than with GPT-4o, which is a plus when you want a quick fact-check or short advice.

However, it should be used with the awareness that it does not support tools like data analysis or file upload and has no personalized instructions – so, it does not “know” your specific business except what you tell it in the query.

When you assign it a search, you will get links to sources, which is great for transparency, but sometimes it also slows down the answer while it finds relevant information.

Also, for visual reasoning or audio, you need to switch to GPT-4o, as o3-mini has no capabilities there.

In the Plus offer, o3-mini has no specific limitations on usage other than the standard ones (you can use it as much as other models, it does not spend additional “tokens” or the like, only possibly limits search usage within reasonable bounds).

Practical Application Examples: o3-mini can be of enormous use to small and medium firms that need fast, informed answers in various sectors: 

• • Marketing Research: The marketing team can use o3-mini for quick trend analysis. For example, the query: “Research the latest digital marketing trends for a small e-commerce business and give me 5 tips”. The model will search the web, cite sources, and summarize the tips – all within a few seconds. This saves hours of manual Googling.

• • IT and Product Development: For coding, a startup programmer can ask: “How can I optimize this Python function to run faster?” o3-mini will understand the code (if you provide it in the text) and give concrete optimization suggestions. It can also be used for code review – a quick check of the code for bugs or security vulnerabilities.

• • Sales and Customer Support: The sales team can receive information about a potential client in real-time. Imagine you are preparing for a call with a client from a certain industry – you ask o3-mini “Find me the latest news about Company X and its industry”. You will receive a summary of the most important information + links to sources (e.g., news articles), which gives you an advantage in the conversation.

• • Finance and Accounting: For quick clarification of regulations or calculations, o3-mini can be the first advisor. E.g., an accountant can ask “What are the key changes in tax law for 2025?” – the model will search and list the most important points with references. Or it can quickly calculate something in text form (although it cannot directly create tables, it can describe the result).

  • Operational Support: o3-mini can serve as a free “analyst” for ad-hoc questions. Say a logistics manager needs a quick assessment: “What are the pros and cons of warehousing outsourcing?” The model will consolidate knowledge from various sources and give a well-founded answer. In situations where a decision needs to be made quickly, and you don’t have time for detailed research, o3-mini provides useful information instantly.

🔹 OpenAI o3-mini-high – Thorough Analysis with More Time

For particularly demanding situations, OpenAI has enabled a variant of the o3-mini model with a so-called “high” reasoning mode – designated as o3-mini-high.

Essentially, o3-mini-high is the same model as o3-mini, but configured to invest more computational power and time in each answer to achieve maximum accuracy and depth of reasoning.

You can think of it as the “turbo mode” for the AI brain, where it will not skimp on internal conclusion steps.

O3-mini-high uses the highest level of reasoning that the model supports, meaning that during answer generation, it will simulate more “thought” iterations and be more careful before responding.

When to use o3-mini-high: This option is ideal when the quality of the answer is more important than speed.

If you have a task where you absolutely want the model to deliberate from all angles – for example, a complex legal or financial question where error is not an option – then it is worth waiting a few seconds longer for the o3-mini-high answer.

Essentially, everything said for the application of o3-mini applies here, but with the addition that o3-mini-high will be more inclined to elaborate its answer in detail.

E.g., instead of just stating a recommendation, it is more likely to state the “why” behind that recommendation, because it “turned on” a higher level of thinking.

According to unofficial community tests, o3-mini-high achieves better results in evaluations of complex logical tasks and in more difficult programming challenges compared to the standard mode.

Advantages and Limitations: The advantage is obvious – better output for difficult problems.

O3-mini-high is especially useful if you notice that the standard o3-mini gives an insufficiently elaborated answer. Switching to “high” can then provide the additional content you need.

The limitation is the noticeably longer waiting time. For shorter and simpler queries, there is no need to use high mode – the faster standard mode will be quite sufficient with the same accuracy.

Another limitation is that higher reasoning also means a greater risk of the model “dragging out” the answer to a level you may not need. Sometimes it can sound too “academically” verbose. But you can always interrupt it or ask it to summarize.

Like o3-mini, the high mode also has no access to visual or special tools, but it does have web search if needed (and even there it will likely check the information more thoroughly before answering).

Practical Example: Imagine a medium-sized enterprise considering five different investment options for the next year. The question posed to the model might be: “Analyze five investment options (A, B, C, D, E) for our firm in detail, rank them from best to worst, and justify the ranking considering risk, ROI, and alignment with our strategy.” The standard o3-mini would give a good answer, but o3-mini-high will likely go even further: it will elaborate on each option separately, potentially create a textual table overview, and precisely justify the ranking. In this scenario, those extra few minutes can be worth gold because you get a truly simulated expert study instead of just advice.

Similarly, for a difficult software bug that multiple engineers failed to solve, o3-mini-high will try to understand the problem more deeply – it may test multiple hypotheses during deliberation and provide a more exhaustive step-by-step debug.

🔹GPT-4o mini – Maximum Speed for Everyday Tasks

If you need a quick answer for a routine task, there is GPT-4o mini – the “younger brother” of the powerful GPT-4o.

This model is the fastest ChatGPT model currently available and excels at everyday, simpler tasks.

GPT-4o mini uses the same core architecture as GPT-4o, but is optimized to consume fewer resources per query and deliver an answer in record time.

OpenAI even gives it to all users for free as a backup option: if a free user exceeds the usage limit of GPT-4o, ChatGPT automatically switches them to GPT-4o mini.

For Plus subscribers, GPT-4o mini is available in the model selection – you can consciously choose it when you need instant speed instead of supreme reasoning.

Characteristics: GPT-4o mini retains the “DNA” of the GPT-4 family – meaning it is still more intelligent than GPT-3.5 models in terms of context understanding and providing relevant answers.

However, it has certain functionality restrictions: unlike the full GPT-4o and GPT-4 (legacy), the mini version does not have access to advanced tools such as integrated data analysis or file uploading.

Simplified, this means GPT-4o mini is primarily for textual exchange – it cannot run Python code, generate charts from tables, or directly analyze large PDFs.

But it is very responsive and provides an answer almost instantly when you ask a question.

Advantages: The main advantage is saving time. When you are doing an operational task and need quick advice or generation, GPT-4o mini is ideal.

For example, you need five slogan proposals – the mini will deliver almost instantly, which is great for fast idea iterations.

For teams that ask the AI assistant dozens of short questions during the day, the mini allows this to flow smoothly without waiting.

Also, the mini model is available to everyone, so even if you work with colleagues who don’t have Plus, they will experience a similar quality (just with usage limits) – this facilitates collaboration because you know what the mini can do.

The quality of answers to common questions (e.g., term definitions, short explanations, grammatical corrections) will be very high, and you will hardly notice the difference compared to the larger GPT-4o.

Limitations: As expected, GPT-4o mini is not intended for complex tasks. If you assign it something that requires deep analysis or creativity, the result will likely be shallower than with the full GPT-4o. It does not have the “attention span” or capacity to hold much context, so longer conversations may lead to it losing the thread or starting to give generic answers.

OpenAI’s recommendation is: the mini is great for most standard queries, but as soon as a wider picture is needed – switch to GPT-4o. Also, if you try to use a tool like file upload while on the mini model, it will not work (those options are disabled while mini is active). The mini model is there to do the job “at the speed of light,” but not to bear all the weight.

Fortunately, switching between models is easy, so users often combine them: mini for quick iterations, GPT-4o for finalization.

Usage in SME Scenarios: Many small firms can use GPT-4o mini almost daily for quick tasks, for example: 

• • Office Obligations: Need to draft a short reply to a client’s email? The mini model can quickly draft a polite reply based on a few points you provide. Need a paragraph of text translated into English? The mini will do it instantly and quite accurately.

• • Brainstorming and Idea Documentation: During a team meeting, you can use GPT-4o mini in real-time as an “ideation” partner – e.g., assign: “List 5 innovative ideas for promoting our new product on social media”. It will immediately deliver a list; you choose and discuss further. Speed means the AI can actively participate in the meeting flow without delays.

• • HR and Administration: For routine tasks like compiling announcements for employees, internal manual policies, or summarizing received CVs, the mini model provides efficiency. HR can quickly get a summary of a candidate’s long CV instead of spending 15 minutes reading – ideal when there are many.

• • Reminders and Lists: Although Scheduled Tasks formally belong to GPT-4o, you can also use the mini model interactively for creating to-do lists, checklists, and quick reminders on the spot. It won’t remember it for tomorrow (that’s for the Tasks option), but for a meeting, you can say: “Create a 10-point checklist for what needs to be checked before launching the new version of the website.” – and you get a nice list in seconds.

  • Customer Support (Draft Replies): Customer service operators can use the mini model to quickly draft a reply to a customer for a common question, instead of constantly repeating themselves. Of course, the agent reads, checks, and sends it – but this boosts productivity.

🔹GPT-4 (Legacy Model) – Proven Creative Intelligence

GPT-4 (Legacy) is the original GPT-4 model that served as the most advanced brain of ChatGPT from March 2023 to mid-2024.

It is still available to Plus subscribers as a separate option – often designated as “GPT-4 Legacy” – indicating that it is the previous version of the GPT-4 model.

Don’t let the name fool you: GPT-4 legacy is still extremely powerful and useful, especially for tasks requiring creativity, sophisticated writing, and complex reasoning.

In many scenarios, the answers from this model will be almost identical in quality to GPT-4o, as they share the same core architecture prior to the enhancement.

Characteristics: GPT-4 (legacy) is known for its impressive ability to understand complex queries and generate comprehensive, coherent answers.

With a context of several thousand tokens, it can handle long conversations or texts. This model has been trained on a massive dataset and is especially good at creative tasks: writing stories, poems, slogans, but also formal documents like reports, contracts, or articles.

Plus users adored it for the way it combined creativity and analysis – it could invent an original metaphor one moment and solve an algorithmic problem the next.

It is important to note that GPT-4 legacy within ChatGPT Plus also supports some integrations: it can generate images via the DALL·E 3 model (so, if you request an image, it “calls” DALL·E and returns the result).

Also, when the ability to send images as input was introduced in mid-2023, it was GPT-4 that enabled it for Plus users. Thus, it has basic multimodality (primarily visual input).

Differences from GPT-4o: GPT-4 legacy is slightly slower and “heavier” – meaning it usually has a slightly longer response time and stricter usage limits (it was previously limited to, e.g., 50 messages per 3 hours for Plus users).

Now, with GPT-4o, these limits have been increased, while more conservative restrictions still apply to the legacy model.

The legacy model lacks some of the latest improvements: e.g., it is less adept with descriptions of complex images (GPT-4o was trained later with more such data), and voice communication is primarily integrated through GPT-4o.

However, some users notice that legacy GPT-4 sometimes provides more detailed or cautious answers, perhaps because it has been very well “tuned” over time through user feedback.

It is “battle-tested” and predictable in its behavior – which can be useful for projects where stability is desired (e.g., if you are accustomed to a certain answer format for your internal processes, the legacy model is less likely to change it).

Usage in Small and Medium Enterprises: GPT-4 (legacy) still performs the lion’s share of the work in many firms that adopted ChatGPT early:

• • Creative Industries and Marketing: Copywriters and content creators rely on GPT-4 for brainstorming campaigns, writing blog post drafts, and even video commercial scripts. The legacy model was “trained” through so many interactions during 2023 that it often provides very lyrical and imaginative answers, which is a boon for the marketing team. A small agency, for example, can use GPT-4 for every first draft of a text – from ads to newsletters – which people then minimally correct.

• • Legal and Financial Documents: Many consulting firms relied on GPT-4 legacy for generating contract templates, policies, and financial reports. Its ability to understand complex legal language and generate coherent text helped small firms draft documents faster (of course, with lawyer review afterwards). Similarly, in finance, it could extract trends from financial data described verbally and form meaningful conclusions.

• • Employee Training and Education: GPT-4 legacy was also used as a mentor for employees – e.g., a junior software engineer would ask GPT-4 to explain a specific code concept or suggest improvements, and would receive a detailed explanation almost like a senior programmer. In this sense, the legacy model was invaluable for SMBs that do not have large teams – the AI filled some knowledge gaps.

  • Product Development and Market Research: Entrepreneurs used GPT-4 to assess user feedback (e.g., they input a bunch of product reviews, and the AI summarizes the main complaints and praises). Also, for generating hypotheses and new ideas – the legacy model consistently provided quality suggestions. This helped small teams iterate on their products faster.

In short, GPT-4 legacy remains a mainstay for many tasks. Today it may not be the “star novelty,” but it is still there, part of the Plus subscription, as a safety net for those who want to compare answers or continue using a proven model while simultaneously testing GPT-4o and other options.