OpenAI's o3-Pro: Powerful but Slow, Worth It for Complex Tasks?

OpenAI recently unveiled o3-pro, a powerful new model designed to handle more complex and computationally intensive tasks. Available through a $200/month subscription for ChatGPT Pro users and in the API at a significantly higher per-use cost, o3-pro promises to deliver enhanced performance across various domains, including science, education, programming, data analysis, and writing. Key Features and Benefits o3-pro stands out for its ability to "think" more deeply and thoroughly before providing answers, often leveraging extensive computational resources to delve deeper into problems. According to OpenAI, expert evaluators consistently prefer o3-pro over the standard o3 model, noting improvements in clarity, comprehensiveness, and accuracy. Specifically, o3-pro excels at math, science, and coding tasks, as demonstrated in academic evaluations. One of the standout features of o3-pro is its access to a range of tools, including web searches, file analysis, visual reasoning, and Python scripting. These capabilities allow it to integrate external knowledge and data, enhancing its analytical power. Additionally, o3-pro can remember and personalize responses, making it more adaptive to user needs. Performance and User Feedback Despite its advanced features, o3-pro is notably slower than its predecessor, o3. Users report wait times ranging from 15 to 20 minutes or more, which can disrupt workflows and user engagement. For many, this delay is a significant drawback, leading them to opt for faster models like Opus or regular o3 for daily tasks. In practical tests, o3-pro showed mixed results. While it performed better in complex, domain-specific tasks, such as solving economics problems, it often struggled with simpler, more routine queries. This has led to frustration among users who encountered errors during long wait times, further reducing its usability. Several users noted that o3-pro’s reduced hallucination rate—a common issue with AI models—was a notable advantage. Tyler Cowen, for example, praised o3-pro for its lower tendency to generate false information, making it particularly useful for tasks requiring high accuracy and reliability. However, the improvement in hallucination rate was not universally confirmed, as some benchmarks showed no significant difference. Use Cases and Workflow Integration Given its slower response times, o3-pro is best suited for specific, high-value tasks where accuracy and thoroughness outweigh the need for speed. For instance, it excels in deep research projects, complex coding tasks, and writing detailed, nuanced content. Many users recommend queuing up questions and returning to them later, rather than trying to use o3-pro as a primary, everyday tool. The model's performance in creative writing and storytelling has also received positive feedback. Chris reported that o3-pro can craft medium to long-form stories with surprising twists, sophisticated character development, and emotional resonance, surpassing the capabilities of many human writers. However, for tasks like quick coding or broad overviews of multiple topics, other models like Opus and Gemini remain preferable due to their speed and efficiency. Even for complex tasks, some users found that running parallel queries using o3 and Opus could be more effective than relying solely on o3-pro. Industry and Expert Reactions industry experts and company insiders have shared mixed evaluations of o3-pro. Greg Brockman of OpenAI emphasized its superior performance, particularly in scientific and analytical tasks. Sam Altman noted that o3-pro is rolling out to all ChatGPT Pro users and API subscribers, praising its intelligence and potential. Miles Brundage critiqued OpenAI’s lack of a detailed system card for o3-pro, arguing that it is crucial to assess the model’s safety and preparedness. While OpenAI states that o3-pro uses the same underlying model as o3, the increased computational load and different performance characteristics suggest a need for a tailored evaluation. Cost and Accessibility The cost of using o3-pro in the API is a significant barrier for many users, as it is often an order of magnitude more expensive than the standard o3 model. This high cost means that it is primarily used for specific, high-stakes situations where the value gained from its improved performance justifies the expense. For everyday use, the price point makes it impractical for most. On the other hand, the recent 80% price cut for the standard o3 model has had a more significant impact on accessibility and adoption. Aaron Levie highlighted that this reduction opens up a broader range of use cases, allowing developers and businesses to build more ambitious AI agents that can leverage future cost reductions. Conclusion o3-pro represents a step forward in AI capabilities, offering superior analytical and creative performance. However, its slow response times and high cost limit its usefulness for everyday tasks. For users who require high accuracy and deep insights, such as researchers or economists, o3-pro can be a valuable tool, provided they can manage the workflow disruptions. The 80% price cut for the standard o3 model, meanwhile, has made it more accessible and economically viable for a wider audience, potentially shifting the focus away from o3-pro for many applications. Industry insiders generally agree that o3-pro is a powerful model, but its practical value depends heavily on the specific use case. The absence of a detailed system card and the need for careful workflow integration are areas that OpenAI should address to fully realize the model's potential. Despite these challenges, the advancements in o3-pro signal the ongoing progress in AI technology, with the promise of more powerful and efficient models like Gemini 2.5 Pro Deep Think on the horizon.