OpenAI has recently unveiled two new powerful language models, the O3 and O4 Mini, which are set to redefine the state-of-the-art in large language models (LLMs). These models boast exceptional capabilities across a wide range of tasks, from coding and math to scientific reasoning and visual analysis.

The O3, OpenAI's most powerful reasoning model to date, excels in areas such as coding, math, and science, setting new benchmarks on CodeForce, Swaybench, and MMU. While it comes with a higher price tag, the O3 delivers a 20% reduction in major errors and shines in programming, business, and creative tasks.

On the other hand, the O4 Mini is a compact and cost-efficient model that punches well above its weight. It dominates many benchmarks, outperforming even the O3 in certain areas. The O4 Mini is particularly well-suited for high-throughput use cases involving math, coding, and visual reasoning, with a more affordable pricing structure.

Both the O3 and O4 Mini have demonstrated significant gains across various benchmarks, showcasing their superior capabilities in coding, math, and reasoning tasks. The O3 leads on the Swaybench Rarified test, scoring 69.1%, while the O4 Mini tops the AIM 2024 and 2025 math benchmarks with scores of 93.4% and 92.7%, respectively.

These new models from OpenAI represent a major leap forward in the world of LLMs, offering unparalleled performance and versatility. As the AI landscape continues to evolve, the O3 and O4 Mini are poised to redefine the standards for language models, setting the stage for even more exciting advancements in the near future.

The O3 and O4 Mini models from OpenAI have demonstrated significant advancements in various benchmarks and capabilities.

The O3, OpenAI's most powerful reasoning model to date, excels in coding, math, science, and visual analysis. It has set new state-of-the-art scores on CodeForce, Swaybench, and MMU, outperforming previous language models. The O3 makes 20% fewer major errors and shines in programming, business, and creative tasks. However, the pricing for the O3 can be relatively expensive, with $10 per 1 million input tokens, $250 for 1 million cached input tokens, and $40 per 1 million output tokens.

On the other hand, the O4 Mini is a compact and cost-efficient model that punches above its weight. It dominates many benchmarks and outperforms the O3 Mini, making it an excellent choice for high-throughput use cases involving math, coding, and visual reasoning. The pricing for the O4 Mini is more affordable, with $1.10 per 1 million input tokens, $2.75 for 1 million cached input tokens, and $0.44 per 1 million output tokens.

In terms of benchmark scores, both the O3 and O4 Mini have shown significant improvements across the board, particularly in coding, math, and reasoning tasks. On the Swaybench Rarified test, the O3 scored 69.1%, and the O4 Mini scored 68.1%, outperforming the Gemini 2.5 Pro's 63.8%. While the O3 is slightly behind the GPT-3.7 in thinking, it still represents a major leap in performance.

In math, the O4 Mini tops the AIM 2024 and 2025 benchmarks with 93.4% and 92.7% respectively, surpassing both the O3 and Gemini. For reasoning, the O3 leads on MMU with 82.9% and on HLE with 20.3%, while Gemini edges slightly ahead on GBQA with 84%.

Overall, the O3 is a top-tier reasoning and coding model, while the O4 Mini offers unmatched performance for its size and price, making both models a clear leap over previous generations and current competitors.

The O3 and O4 Mini models from OpenAI offer impressive capabilities, but their pricing structures differ significantly. Let's take a closer look at the pricing details for each model:

O3 Pricing:

• Input Tokens: $10 per 1 million tokens
• Cached Input: $250 per 1 million tokens
• Output Tokens: $40 per 1 million tokens

The O3 is OpenAI's most powerful reasoning model to date, excelling in coding, math, science, and visual analysis. However, its pricing can be quite expensive, especially for the output tokens.

O4 Mini Pricing:

• Input Tokens: $1.10 per 1 million tokens
• Cached Input: $2.75 per 1 million tokens
• Output Tokens: $0.44 per 1 million tokens

In contrast, the O4 Mini is a more compact and cost-efficient model that still delivers impressive performance, outperforming the O3 on many benchmarks. Its pricing is significantly more affordable, making it a better choice for high-throughput use cases involving math, coding, and visual reasoning.

When considering the pricing, it's important to weigh the specific needs of your project. While the O3 may offer slightly better performance in some areas, the O4 Mini's lower costs can make it a more practical choice, especially for coding tasks where the performance difference may not justify the higher price tag.

When it comes to coding tasks, the choice between OpenAI's O3 and O4 Mini models depends on your specific needs and budget. While the O3 is a top-tier reasoning and coding model, the O4 Mini offers unmatched performance for its size and price.

The O3 excels in coding, math, science, and visual analysis, setting new state-of-the-art benchmarks. However, its higher pricing, with $10 per 1 million input tokens and $40 per 1 million output tokens, can make it less cost-effective for certain use cases.

On the other hand, the O4 Mini dominates many benchmarks and outperforms the O3 in several areas, including math, coding, and visual reasoning. Priced at $1.10 per 1 million input tokens and $4.40 per 1 million output tokens, the O4 Mini offers excellent performance at a more affordable cost.

For coding tasks, the O4 Mini may be the better choice, as it delivers similar performance to the O3 at a significantly lower price point. The O3's advanced reasoning capabilities may be better suited for tasks that require deeper analysis or complex problem-solving, where the additional cost can be justified.

Ultimately, the decision between the O3 and O4 Mini should be based on your specific requirements, budget, and the nature of the coding tasks you need to accomplish. Both models represent a significant leap in AI capabilities, and the choice will depend on your priorities and the trade-offs you're willing to make.

We first tested the models on a prompt to create the front-end for a modern note-taking app. The models were able to generate a functional sticky note app with features like exporting notes, clearing the board, and a dark mode. The second iteration looked even better with more animations and polish.

Next, we asked the models to create the game of life in Python. The code generated was fully functional, demonstrating strong algorithmic design and programming skills.

We then challenged the models to create a symmetrical SVG representation of a butterfly. The result was a beautifully designed SVG that showcased the models' spatial reasoning and geometry knowledge.

Moving on, we presented a math word problem about two trains meeting. The models were able to correctly solve the problem, showing strong math and problem-solving abilities.

For a creative coding prompt, we asked the models to generate a TV simulation with nine different channels. The result was an impressive interactive animation that performed well.

We also tested the models' scientific reasoning by having them read a climate modeling paper and explain why the hybrid model is better. The models were able to summarize the key points and advantages of the hybrid model.

Finally, we presented a detective case with conflicting statements, and the models were able to correctly identify the guilty party by logically deducing the truth based on the constraints.

Overall, these practical prompts demonstrated the models' exceptional capabilities in areas like front-end development, algorithmic design, spatial reasoning, math, creative coding, scientific reasoning, and logical deduction. The models consistently delivered high-quality, thoughtful, and contextual responses, showcasing their advanced reasoning and problem-solving skills.

The OpenAI's O3 and O4 Mini models represent a significant leap forward in AI capabilities. The O3 model excels in coding, math, science, and visual analysis, setting new state-of-the-art benchmarks. While it is more expensive, the model's ability to reason more deeply and use tools autonomously makes it a powerful tool for complex tasks.

The O4 Mini, on the other hand, offers exceptional performance for its size and price. Outperforming the O3 on many benchmarks, the O4 Mini is a cost-effective solution for high-throughput use cases involving math, coding, and visual reasoning. Its pricing structure, with lower input and output costs, makes it a more accessible option for many users.

Overall, these two models represent a significant advancement in AI capabilities, pushing the boundaries of what is possible with language models. While the O3 may be the more powerful option for certain tasks, the O4 Mini's impressive performance and cost-effectiveness make it a compelling choice for many applications. As the AI landscape continues to evolve, it will be exciting to see what the future holds, especially with the anticipated release of the O3 Pro and GPT-5.