OpenAI's o3 and its scaled-down counterpart, o3-mini, represent a significant leap in AI reasoning and problem-solving capabilities. These models are reshaping what general-purpose AI can achieve, outperforming specialized systems in areas like mathematics and coding. Here's everything you need to know.
Read also about DeepSeek R1:
TL;DR: OpenAI's o3 and o3-mini are general-purpose AI models that excel in complex tasks like mathematics. O3 models adapt their reasoning abilities based on the objective, and they dynamically adjust computational resources at test time based on task complexity, ensuring efficiency without compromising accuracy.
Notably,
- O3's wins gold medal at IOI 2024 in real conditions without domain based knowledge,
- It scored 96.7% performance on AIME 2024 (The AIME is one of the most challenging math contests available to high school students in the US).
- And it ranks currently at 175th position on Codeforces making it above 99.9% of human coders! OpenAI announces that its internal benchmarks puts its models at the 50th position today, and anticipate to have the best coder in the world by end of 2025!
- And that's not all, o3 crushes the ARC-AGI evaluation supposed to be the next frontier for AI and a benchmark for AGI… forcing the AI community to rethink its AGI evaluation.
But all of these comes at a cost…
What makes o3 so special compared to O1, DeepSeek-R1 and AlphaGeometry2? Let's dive into it! Here's everything you need to know!
What Are o3 and o3-Mini?
O3 model
OpenAI's o3 model is the latest general-purpose reasoning system, designed to excel at complex tasks that require deep logical thinking, such as mathematics, coding, and scientific problem-solving. It builds on its predecessor, o1, by relying on reinforcement learning — similar to DeepSeek, for instance — which enables substantial improvements in reasoning through techniques like simulated reasoning (SR) and chain-of-thought processing.
However, o3 distinguishes itself from o1 primarily through its innovative use of test-time compute.
While reinforcement learning remains essential for training intelligent agents, o3's true advantage lies in its ability to allocate additional computational resources during inference.
This extra compute allows the model to "think" for longer, exploring multiple solution paths and iteratively refining its answers, which leads to markedly improved performance on complex, multi-step tasks.
In effect, o3 outperforms o1 not merely through better training but by dynamically enhancing its reasoning process at test time, enabling more robust and precise outputs.
As we will see later in the section on the AGI benchmark, there are virtually no limits — the more resources allocated to a task, the better the performance!
O3-mini models
o3-mini, on the other hand, is a more lightweight version of o3. It offers three modes — low, medium, and high — that trade off computational resources for accuracy. Despite being cheaper and faster than o3, o3-mini still outperforms many previous-generation models, including the full-scale version of o1, across benchmarks like mathematics (AIME) and coding (CodeForces)
How Does O3 Differ from Other Models?
As already mentioned previsouly, o3 combines several training & inference techniques, some of which are not unique to o3 but combined efficiently lead to the best reasoning model available to date.
- Reinforcement Learning (RL): Similarly to deepseek r1, the training process for o3 heavily relies on RL to refine its reasoning capabilities. This approach allows the model to autonomously develop strategies for solving problems without manual intervention, as demonstrated by its gold medal performance at the International Olympiad in Informatics (IOI).
- Simulated Reasoning (SR): Unlike traditional large language models (LLMs) that rely on pattern recognition, o3 incorporates SR to refine its answers dynamically during inference. This allows it to analyze problems iteratively, generating multiple potential solutions and selecting the most accurate one.
- Inference-Time Scaling: This technique enables models to evaluate multiple options before converging to a solution. The number of options to be evaluated depend on teh time/compute available for the task. Both o3 and o3-mini adapt their computational effort based on task complexity. This flexibility enables them to perform well on both simple and highly challenging problems.
Why Is Winning a Gold Medal at IOI Important?
The International Olympiad in Informatics (IOI) is one of the most prestigious competitions in algorithmic problem-solving and programming. Winning a gold medal signifies that an AI model can match or exceed the skills of elite human competitors in tasks requiring advanced mathematical reasoning and algorithm design under strict time constraints.
Previous systems, such as AlphaGeometry2 (an amazing model that deserves its blog post) and o1-ioi, require domain specific knowlege via human-designed heuristics. o3, however, autonomously learns effective reasoning strategies through RL, eliminating the need for these pipelines!
This achievement underscores o3's adaptability and effectiveness in real-world scenarios where quick thinking is essential.
The Role of Test-Time Compute beating ARC-AGI benchmark
Test-Time compute, the Key to Solving Complex Tasks
Test-time compute (or inference-time compute) refers to the computational resources used by a model during inference (i.e., when solving problems). As mentionned previously, models like o3 leverage scalable test-time compute to dynamically allocate resources based on task difficulty. For example:
- In "high" mode, o3 (or o3-mini) uses more compute cycles to refine its answers for complex problems.
- This adaptability ensures that the model remains efficient while maintaining accuracy across diverse tasks
Efficient use of test-time compute is crucial for practical applications where both speed and accuracy are important.
Now when it's facing very complex tasks, such as the one specifically designed to elude AI models, aka ARC-AGI, o3's ability to allocate more compute to find a solution proves to be very (very!) efficient!
As you can see it in François Chollet's tweet, o3-high allocated up to ~$3k per task and achieved 88% accuracy on the benchmark.
Is Test-Time Compute the Real Differentiator?
Let's look at this recent AIME benchmark among the leading models. Setting aside prompting techniques and domain-based knowledge, we can observe the following:
- DeepSeek R1 performs better than o3-mini (low). In other words, o3-mini with minimal resources can't compete with DeepSeek R1 on these math questions.
- However, as you increase the allocated resources (to medium, then high), o3-mini rises to the top of the benchmark!
Test-Time inference does count!
Conclusion: Why Does This Matter?
O3 may be just an intermediary model, but it's already reshaping our expectations for what AI can achieve.
Its core innovations — reinforcement learning combined with dynamic test-time compute — aren't just improvements; they represent a fundamental shift in how we approach AI reasoning.
These ingredients, now accessible to everyone (assuming you have the needed compute resources), promise to underpin the next generation of models that will be even more efficient and capable.
The real takeaway is that we're witnessing a move away from rigid, domain-specific systems toward general-purpose reasoning engines that can tackle an ever-growing range of challenges.
With achievements like surpassing human-level performance on platforms like CodeForces and solving research-grade math problems, O3 sets a new benchmark for cost efficiency and scalability in AI. As the industry continues to evolve, we can expect these advancements to drive us closer to versatile AI systems that effectively assist with complex real-world tasks across multiple disciplines.