OpenAI has recently released two state-of-the-art models, 03 and 04 Mini, that push the boundaries of reasoning and problem-solving capabilities.

03: The Powerful Reasoning Model

• 03 is the most powerful reasoning model, excelling in coding, math, science, and visual perception tasks.
• It has set new benchmarks on platforms like Codeforces and SBench, demonstrating exceptional coding abilities.

04 Mini: The Efficient Reasoning Model

• 04 Mini is a smaller, cost-effective model that still achieves remarkable performance in math, coding, and visual tasks.
• It is the best-performing model on the 2024 and 2025 AIMath benchmarks.

Thinking with Images: A Game-Changing Feature

• The models can now integrate images directly into their reasoning process, allowing them to analyze, manipulate, and draw insights from visual information.
• This capability enables the models to zoom, crop, and transform images to better understand the context and solve problems.

Limitations and Concerns

• While the models' vision capabilities are impressive, they still struggle with certain tasks, such as accurately identifying line intersections in complex drawings.
• The models' ability to accurately determine a user's location based on limited visual information has raised concerns about potential privacy and security implications.

Approaching AGI?

• Some experts have suggested that these models are approaching the level of Artificial General Intelligence (AGI), with one OpenAI researcher stating that they were "tempted to call this model AGI."
• However, others caution that while the models are highly capable, they still lack the ability to use tools with a low hallucination rate, which is a key requirement for true AGI.

Overall, OpenAI's 03 and 04 Mini models represent a significant leap forward in reasoning and problem-solving capabilities, blurring the lines between current AI systems and the potential for Artificial General Intelligence.

OpenAI has recently released two state-of-the-art models, GPT-3.5 and GPT-4 Mini, that push the boundaries of reasoning and vision capabilities.

GPT-3.5 is the most powerful reasoning model to date, excelling in coding, math, science, and visual perception. It has set new benchmarks on platforms like Codeforces and SBench, demonstrating exceptional coding abilities.

GPT-4 Mini, on the other hand, is a smaller model optimized for fast and cost-efficient reasoning. It achieves remarkable performance in math, coding, and visual tasks, outperforming other models on the AMath and MATH benchmarks.

One of the most significant advancements in these models is the "Thinking with Images" feature. This allows the models to integrate images directly into their reasoning process, going beyond simply viewing the image. They can zoom, crop, and analyze the image, using the visual information to enhance their understanding and problem-solving abilities.

This capability has been demonstrated in various examples, such as the model's ability to read and solve handwritten problems on a sticky note, or to accurately locate the source of an image based on limited information. These feats have led some to speculate that these models may be approaching a level of Artificial General Intelligence (AGI).

However, it's important to note that these models are not without their limitations. While they excel in many areas, they can still struggle with tasks that require more nuanced understanding, such as interpreting complex diagrams or identifying subtle visual cues. Additionally, the models' tendency to hallucinate or provide inaccurate information is an ongoing concern that requires careful monitoring and mitigation.

Despite these challenges, the advancements in reasoning and vision capabilities demonstrated by GPT-3.5 and GPT-4 Mini are truly remarkable. They represent a significant step forward in the field of artificial intelligence and have the potential to revolutionize how we approach a wide range of tasks, from coding and problem-solving to scientific research and visual analysis.

OpenAI's latest models, GPT-3.5 and GPT-4 Mini, have introduced a groundbreaking capability called "Thinking with Images." This feature allows the models to directly integrate images into their reasoning process, taking visual information beyond just passive observation.

With this new functionality, the models can now zoom, crop, and analyze images in detail, extracting relevant information and using it to inform their responses. This represents a significant leap in the models' ability to understand and reason about visual data, going far beyond simple image classification.

One example showcases the model's ability to accurately read and interpret small, handwritten text within an image, even when the image is blurry or low-quality. This demonstrates the model's capacity to actively engage with visual information, rather than just passively processing it.

Furthermore, the models have shown the ability to solve complex, visually-oriented problems, such as interpreting and solving handwritten diagrams. This suggests a level of visual understanding and reasoning that was previously considered the domain of human intelligence.

However, it's important to note that the models' visual capabilities are not without limitations. There have been instances where the models have struggled with tasks like accurately identifying line intersections in simple 2D plots. This highlights the need for continued refinement and improvement in the models' visual processing and reasoning abilities.

Overall, the integration of image-based reasoning represents a significant advancement in the capabilities of these language models. By combining visual understanding with their existing language processing and reasoning skills, they are taking a step closer to more holistic, human-like intelligence. As these models continue to evolve, it will be fascinating to see how their visual capabilities develop and the impact they have on various applications.

While the advancements in OpenAI's models, particularly the "thinking with images" capability, are remarkable, it's important to note that these systems are not without their limitations. The example provided, where the model struggled to accurately link the colors to the correct characters in a child's drawing, highlights an area where current vision-language models can still falter.

Research has shown that these models can have difficulties with tasks that require a deeper understanding of visual information, such as counting line intersections or interpreting complex scientific visualizations. A paper titled "LLMs are Blind" demonstrated several examples where state-of-the-art models struggled with seemingly simple visual reasoning tasks.

This suggests that while the models have made significant strides in their ability to reason with images, there are still areas where their performance falls short compared to human-level understanding. The ability to accurately process and interpret visual information, especially in more complex or ambiguous scenarios, remains an ongoing challenge for these systems.

As the technology continues to evolve, it's likely that the models will become increasingly adept at overcoming these limitations. However, it's important to recognize that even the most advanced AI systems today are not infallible and can still exhibit biases or errors when dealing with certain types of visual tasks. Continued research and development will be crucial in addressing these challenges and further enhancing the capabilities of these models.

The release of OpenAI's models, 03 and 04 Mini, has sparked a heated debate around the question of whether these systems are approaching Artificial General Intelligence (AGI). Prominent figures in the AI community have shared their perspectives on this matter.

Sam Altman, the CEO of OpenAI, has quoted a tweet that describes the capabilities of these models as being "at or near genius level." Another individual who was involved in the model training at OpenAI has stated that they were "tempted to call this model AGI." Tyler Cowen, an economist, has gone so far as to say, "I think it is AGI, honestly."

These statements suggest that the experts are grappling with the idea that OpenAI may have just unveiled a groundbreaking AGI model with the release of 03 and 04 Mini. The models' exceptional performance across a range of benchmarks, including coding, math, science, and visual perception, has led many to question whether they have indeed crossed the threshold into AGI territory.

John Hullman, a model trainer at OpenAI, has expressed a similar sentiment, stating that when 03 finished training and they were able to try it out, "for the first time, I was tempted to call a model AGI." He acknowledges that the model is not perfect, but believes it will outperform the vast majority of humans on a wide range of intelligent assessments.

However, not everyone is convinced that these models have achieved AGI. Noam Brown, who works on the reasoning models at OpenAI, has cautioned against such claims, stating that the models "still not great at writing mathematical proofs" and are "nowhere near close to getting International Mathematical Olympiad gold medals." He emphasizes that there is still a long way to go before these models can be considered to have truly solved mathematics.

The debate around the AGI question is complex and multifaceted. While the impressive capabilities of 03 and 04 Mini have undoubtedly pushed the boundaries of what was previously thought possible, experts remain divided on whether these models can be considered true AGI. The ongoing discussions and continued advancements in AI will likely shape the future of this debate.

OpenAI's latest models, 03 and 04 Mini, have achieved remarkable performance on the math benchmark, pushing the boundaries of what's possible in this domain. The Amy 2024 and 2025 math competition benchmarks are notoriously challenging, testing the AI's ability to solve complex mathematical problems.

The results are truly impressive, with 03 and 04 Mini achieving a near-perfect score of 99.5% on these benchmarks. This is a significant leap forward, as it demonstrates the models' exceptional mathematical reasoning capabilities.

However, it's important to note that while these models have excelled in the math benchmark, they are not yet close to solving mathematics entirely. As Noam Brown, a researcher at OpenAI, pointed out, the models still struggle with tasks like writing mathematical proofs and achieving gold medals in the International Mathematical Olympiad.

The implications of solving mathematics, even partially, are profound. Mathematics underpins many other fields, including biochemistry, robotics, spaceflight, cryptography, nuclear physics, and the blockchain. Mastering mathematics could lead to breakthroughs in these areas, potentially transforming various industries and scientific disciplines.

While the progress made by 03 and 04 Mini is undoubtedly impressive, it's crucial to maintain a balanced perspective. The path to truly solving mathematics is still long and complex, and there are many challenges that need to be overcome. Nonetheless, the achievements of these models on the math benchmark are a significant step forward, and they serve as a testament to the rapid advancements in AI capabilities.

When it comes to benchmarking the performance of OpenAI's latest models, 03 and 04 Mini, it's clear that they are pushing the boundaries of what's possible in AI reasoning and capabilities.

Firstly, let's look at the Artificial Intelligence Index, which incorporates seven different evaluations such as MMLU, Pro GPQA, Diamond, and Humanity's Last Exam. In this comprehensive benchmark, 04 Mini High manages to edge out Gemini 2.5 Pro, showcasing its impressive performance across a range of tasks.

Furthermore, on the Humanity's Last Exam, a 30,000-question benchmark covering mathematics, humanities, and natural sciences, 03 takes the top spot with a score of 19.20, surpassing even Gemini 2.5 Pro. This demonstrates the model's broad and deep understanding across a diverse range of subjects.

Turning to coding benchmarks, 03 High and 04 Mini High have surpassed Google's Gemini 2.5 Pro Experimental on the LiveBench, a real-world coding assessment. Additionally, on the SWE Lancer and SWB Bench Verified Software Engineering benchmarks, which simulate tasks on platforms like Upwork, 03 and 04 Mini have shown a significant jump in their ability to earn potential income, further highlighting their coding prowess.

It's worth noting that while 03 may be more expensive than some other models in terms of cost-effectiveness, OpenAI has made strides in improving the cost-performance ratio compared to previous iterations. This suggests that the incredible capabilities of 03 may be worth the investment for those seeking a highly capable reasoning agent.

Overall, the benchmarking results demonstrate that OpenAI's latest models are setting new standards in a wide range of domains, from coding and mathematics to general reasoning and problem-solving. As these models continue to evolve, it will be fascinating to see how they stack up against the competition and push the boundaries of what's possible in the world of artificial intelligence.

OpenAI's latest models, GPT-3.03 and GPT-3.04 Mini, have demonstrated remarkable capabilities in the realm of coding. These models have set new benchmarks on prestigious coding challenges, showcasing their prowess in real-world programming scenarios.

GPT-3.03 has been hailed as the most powerful reasoning model to date, pushing the boundaries in coding, math, science, and visual perception. It has set a new benchmark on the prestigious Codeforces and SBench challenges, which test coding abilities in real-world scenarios.

Complementing GPT-3.03, the GPT-3.04 Mini model is a smaller, more cost-efficient version that still achieves remarkable performance in math, coding, and visual tasks. It has emerged as the best-performing model on the AMY math benchmark for 2024 and 2025.

These models have not only excelled in traditional coding benchmarks but have also demonstrated their ability to tackle more complex, real-world coding challenges. The Charlie Labs AI evaluation, which tasks models with solving GitHub bug reports, optimizing database queries, and enforcing security policies, has seen GPT-3.03 set impressive benchmarks compared to other state-of-the-art models.

Furthermore, the SWE Lancer and SWE Bench verified benchmarks, which simulate the ability of AI systems to earn money on platforms like Upwork, have shown a significant jump in the earning potential of GPT-3.03 and GPT-3.04 Mini compared to previous models. While they may not yet be able to earn a full salary, the models' performance on these benchmarks highlights their potential to contribute to real-world coding tasks.

Overall, the coding capabilities of OpenAI's latest models are truly remarkable, pushing the boundaries of what is possible in the realm of artificial intelligence and programming.

OpenAI has taken significant steps to address the safety concerns surrounding their powerful language models, particularly the latest release of GPT-3.03 and GPT-3.04 Mini. The company has completely rebuilt its safety training data, adding new refusal prompts to address potential threats such as biological threats, malware generation, and jailbreaks.

One notable example is the work of a researcher named Ply, who has consistently managed to jailbreak these models without fail. This demonstrates that the current safety measures are not infallible, and there is still room for improvement in ensuring the robust security of these advanced language models.

Interestingly, the model card for GPT-3.03 reveals that the model tends to hallucinate, or generate content that is not grounded in reality, twice as much as its predecessor, GPT-3.01. This finding suggests that as the models become more capable in their reasoning abilities, they may also become more prone to producing unreliable or deceptive outputs.

OpenAI acknowledges this challenge, noting that outcome-based optimization, the approach used to train these models, can incentivize confident guessing, even if the model is unsure of the correct answer. This highlights the need for continued research and development in the area of safety and reliability for these advanced language models.

As these models continue to push the boundaries of what is possible in natural language processing, it is crucial that their safety and security remain a top priority. OpenAI's efforts to enhance the safety of their models are a step in the right direction, but there is still work to be done to ensure that these powerful tools are used responsibly and ethically.

OpenAI's release of models 03 and 04 Mini has pushed the boundaries of AI reasoning and capabilities. These models have demonstrated exceptional performance across a range of benchmarks, including coding, math, science, and visual perception.

The introduction of "thinking with images" is a game-changer, allowing the models to integrate visual information into their reasoning process. This enables them to analyze images, zoom in, crop, and even solve problems that require visual understanding.

While the models' capabilities are impressive, there are still limitations and challenges to address, particularly around safety and the tendency to hallucinate more as their reasoning abilities increase. Ongoing research and development will be crucial to refine these models and ensure their safe and responsible deployment.

Overall, the advancements showcased by 03 and 04 Mini are a significant step towards more agentic and capable AI systems, hinting at the potential for artificial general intelligence (AGI) in the future. However, the path to true AGI remains long and complex, requiring continued progress in areas such as reasoning, safety, and ethical considerations.