Just days after the launch of the GPT 4.1 family, OpenAI has released its o3 and o4-mini reasoning models, taking a leap towards AGI (Artificial General Intelligence). The o3 and o4-mini aren’t just AI models; they are AI systems that come with advanced intelligence, autonomy, tool calling function, and real-world software engineering skills. These new models don’t wait for you to do the work; they go ahead, use their tools, and autocomplete tasks themselves! So let’s dive in and explore the features, benchmark performances, and applications of the new o-series models – o3 and o4-mini.
o3 and o4-mini are OpenAI’s newest reasoning models, succeeding and replacing previous models in the o-series like o1 and o3-mini. Unlike standard LLMs that primarily focus on pattern recognition and text generation, these reasoning models employ a longer internal “chain of thought” process.
This allows them to break down complex problems, evaluate different steps, and arrive at more accurate and thoughtful solutions. Hence, they especially excel in domains like STEM, coding, and logical deduction. Furthermore, these models are the first in the o-series capable of agentically using and combining the full suite of tools available within ChatGPT.
o3 is OpenAI’s most advanced reasoning model to date, excelling in tasks that require deep analytical thinking across various domains. Built with 10 times the compute put into o1, this model introduces the ability to “think with images.” This allows it to process and reason about visual inputs directly within its cognitive processes, which is phenomenal.
o4-mini serves as a compact, efficient, and cost-effective counterpart to o3. While smaller in size, it delivers impressive performance, particularly in areas like math, coding, and visual tasks. Its optimized design ensures faster responses and higher throughput, making it suitable for applications where speed and efficiency are paramount.
Other Models: OpenAI has also released an o4-mini-high variant, which takes more time for potentially more reliable answers.
Future Releases: An even more powerful version, o3-pro, utilizing more compute resources, is planned for release to Pro subscribers in the near future.
Also Read: Llama 4 Models: Meta AI is Open Sourcing the Best
Here are some of the key features of these advanced and powerful reasoning models:
Both these ‘o-series’ models are specifically designed to think more deeply and perform complex, multi-step reasoning before generating a response.
When given a problem to solve, o3 first uses brute force to come up with a solution. The model then finds a smarter way to do the calculation and presents it in a neater format. It further goes on to recheck the answer and simplifies it to provide the user with a very simple and easily understandable response.
Now, although part of this thinking process is based on the compute and training, these models weren’t explicitly taught to simplify the answer or recheck it. This makes them self evolving and self learning models, which inch us closer towards AGI.
Moreover, o3 can autonomously decide when and how to use the various tools available within ChatGPT (web search, Python data analysis, DALL·E image generation, and vision) to solve complex, multi-faceted queries. It can chain multiple tool calls, search the web iteratively, analyze results, and synthesize information across modalities.
Also Read: Towards AGI: Technologies, Challenges, and the Path Ahead
Now let’s try out these promising new o-series models on some real-life applications. We’ll test out all the three models on tasks they are told to be best at. This includes:
Let’s get started.
Prompt: “Create a python simulation of 2 balls – one yellow and the other blue – bouncing off the walls of a pentagon that is spinning in clockwise direction inside a thick hexagonal frame. The balls must change their colour to green every time they bump into each other and return to their original colours when they bump again. They must move with increasing velocities.”
Output:
Review:
o3 generated a fully functional, error-free code along with its explanation in less than a minute, and what a great output! I’ve tried similar prompts on various other models, and this is surely one of the best simulations generated in the first attempt. Be it the shapes, the direction and speed of the movement, or the change of colours – it was all spot on! The only thing that went wrong was that the balls kept moving outside the frames, which I feel is a minor glitch.
Prompt: “Which two numbers, from amongst the given options, should be interchanged to make the given equation correct?
14 + 39 – (√256 ÷ 3) + (5 × 4) – 6 = 58″
Output:
Review:
o4-mini took just about 10 seconds to answer this question. It showed the thought process and the analysis before generating the final answer, which made it credible. While being accurate, it was fast as well. Also, the thought process mentioned my name which made the model look more intuitive.
Prompt: “What are the accent colours written on the soft board?”
Input Image:
Output:
Review:
o4-mini-high analyzed the image and read the handwritten text in about a minute. It first gauged the size of the image and zoomed in to the part where the sticky notes are posted. It then cropped the image, sharpened the blurry part, and then tried to read the text. This is brilliant and no other model is capable of doing this, as of now.
Although o4-mini-high could read “ACCENT COLOURS” written on the notes, it could only see 3 out of the 4 colours mentioned, and even ended up reading them wrong. However, interestingly, in it’s thought process the model did mention it couldn’t read the text clearly due to the small font size.
*Out of curiosity, I asked o4-mini-high “what brand is the monitor and the helmet?” and it promptly identified them correctly.
Both models are accessible through OpenAI’s ChatGPT platform and API services:
ChatGPT Access:
API Access: Developers can integrate o3 and o4-mini into their applications via OpenAI’s Chat Completions API and Responses API, enabling customized AI solutions across various platforms.
Both o3 and o4-mini models have demonstrated exceptional capabilities across a range of standard benchmark tests.
Want to better understand what these benchmarks mean? Read our comprehensive guide on LLM benchmarks.
The enhanced reasoning, tool use, and visual capabilities of o3 and o4-mini unlock a wide range of potential applications, including:
OpenAI’s o3 and o4-mini models represent a significant advancement in AI capabilities, particularly in reasoning and multimodal understanding. By integrating deep reasoning with versatile, agentic tool use and the novel ability to “think with images,” these models set a new standard for AI intelligence and utility. Their impressive performance across a variety of benchmarks underscores their potential to tackle complex, real-world tasks in fields ranging from software engineering to scientific research.
While o3 offers peak performance for the most demanding tasks, o4-mini provides a compelling blend of capability, speed, and cost-efficiency. Both models, however, share the same agentic and autonomous capabilities that showcase how advanced AI has become. As AI continues to evolve, such innovative models will pave the way for more sophisticated and versatile applications, bringing us closer to achieving AGI.
A. o3 is OpenAI’s most advanced reasoning model designed for deep analytical tasks. Meanwhile, o4-mini is a lighter, faster variant of o3 optimized for speed & efficiency, especially in math, coding, and visual tasks.
A. o3 uses 10x more compute than o1 and introduces advanced reasoning abilities, including the ability to “think with images.” It can analyze visuals, use tools agentically, and solve complex, multi-step problems far more accurately than o1.
A. o4-mini is faster, smarter, and significantly more capable than o3-mini. It excels in math, coding, and visual reasoning and also supports tool use. Moreover, its benchmark scores outperform not only o3-mini but also several competing models.
A. Yes, both models support multimodal reasoning. They can interpret complex visuals like charts, blurry images, and whiteboard sketches, and use that input as part of their problem-solving process.
A. You can use them via the ChatGPT app or web platform with a Plus, Pro, or Team subscription. They’re also available through the OpenAI API for developers and businesses.
A. Applications of o3 and o4-mini range from business strategy and data analysis to education and scientific research. At an enterprise level, they can help in organizational chart analysis for team insights, and image-based product discovery.