o3 and o4-mini: OpenAI’s Most Advanced Reasoning Models

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.

What are 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

Key Features of o3 and o4-mini

Here are some of the key features of these advanced and powerful reasoning models:

• Agentic Behavior: They exhibit proactive problem-solving abilities, autonomously determining the best approach to complex tasks and executing multi-step solutions efficiently.​
• Advanced Tool Integration: The models seamlessly utilize tools such as web browsing, code execution, and image generation to enhance their responses and tackle complex queries effectively.​
• Multimodal Reasoning: They can process and integrate visual information directly into their reasoning chain, which enables them to interpret and analyze images alongside textual data.​
• Advanced Visual Reasoning (“Thinking with Images”): The models can interpret complex visual inputs like diagrams, whiteboard sketches, or even blurry/low-quality photos. They can even manipulate these images (zoom, crop, rotate, enhance) as part of their reasoning process to extract relevant information.

Do o3 and o4-mini Reflect AGI?

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

Hands-on Testing of o3, o4-mini, and o4-mini-high

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:

1. Coding using o3
2. Mathematical reasoning using o4-mini
3. Visual reasoning using o4-mini-high

Let’s get started.

Task 1: Coding Using o3

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.

Task 2: Mathematical Reasoning Using o4-mini

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.

Task 3: Visual Reasoning Using o4-mini-high

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.

Availability of o3 and o4-mini

Both models are accessible through OpenAI’s ChatGPT platform and API services:​

ChatGPT Access:

• Users subscribed to ChatGPT Plus, Pro, and Team plans can utilize o3, o4-mini, and o4-mini-high models directly on the chat interface.
• Enterprise and Education users will gain access within a week.
• Free-tier users can experience o4-mini by selecting the ‘Think’ option before submitting their queries.​

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.​

o3 and o4-mini: Benchmark Performance

Both o3 and o4-mini models have demonstrated exceptional capabilities across a range of standard benchmark tests.

• SWE-Lancer: The high variants of both these models perform exceptionally well in this coding benchmark, putting their ancestors to shame.
• SWE-Bench Verified (Software Engineering): o3 achieved a score of 69.1%, while o4-mini closely followed with 68.1%. Both models significantly outperformed previous models like o3-mini (49.3%) and competitors such as Claude 3.7 Sonnet (63.7%).​
• Aider Polyglot (Code Editing): Both these models prove to be the best from OpenAI when it comes to this code editing benchmark, setting new records.

• AIME 2025 (Mathematics): o4-mini set a new benchmark here by scoring 99.5% when equipped with a Python interpreter, while o3 is right behind, scoring 98.4%.
• Codeforces (Competitive Programming): o4-mini achieved an Elo rating of 2719, reflecting its advanced problem-solving skills in competitive programming scenarios. Meanwhile, o3 scores 2706, still performing exponentially better than the other models.
• GPQA Diamond (PhD-Level Science): o3, without any tools, demonstrated advanced scientific reasoning by achieving an accuracy of 87.7% on this benchmark. o4-mini follows right behind with 81.4%.

• MMMU (Massive Multimodal Multitask Understanding): o3 excelled in this benchmark, showcasing its ability to handle diverse and complex tasks involving both textual and visual data.

• Humanity’s Last Exam: On this benchmark assessing expert-level reasoning across various domains, o3 achieved an accuracy of 26.6% outperforming all other OpenAI models. Meanwhile o4-mini significantly outperforms its predecessor, o3-mini.

Want to better understand what these benchmarks mean? Read our comprehensive guide on LLM benchmarks.

Applications of o3 and o4-mini

The enhanced reasoning, tool use, and visual capabilities of o3 and o4-mini unlock a wide range of potential applications, including:

• Complex Data Analysis & Reporting: Analyzing datasets by writing and executing Python code, fetching supplementary information from the web, and generating summaries or visualizations.
• Advanced Scientific Research: Assisting researchers by interpreting complex diagrams, analyzing experimental data, searching literature, and potentially suggesting new avenues of inquiry.
• Sophisticated Coding & Software Engineering: Debugging complex code, generating code based on visual mockups or diagrams, understanding repository structures, and performing multi-step software development tasks.
• Education & Tutoring: Explaining complex STEM concepts using step-by-step reasoning, interpreting textbook diagrams or handwritten notes, and providing interactive problem-solving assistance.
• Multimodal Content Creation & Understanding: Generating detailed descriptions or analyses of images, creating content that requires integrating text and visual elements, and answering questions based on visual evidence.
• Business Intelligence & Strategy: Analyzing market trends using real-time web data, developing forecasts, and creating strategic plans based on integrated information sources.
• Creative Problem Solving: Tackling open-ended challenges that require combining different types of information and reasoning steps.

Conclusion

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.​

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