Can OpenAI’s New o1 Models Transform STEM with Superior Reasoning?

OpenAI has recently unveiled a new family of large language models (LLMs), dubbed “o1,” which aims to deliver superior performance and accuracy in science, technology, engineering, and math (STEM) fields. This launch came as a surprise, as many anticipated the release of either “Strawberry” or GPT-5 instead. The new models, o1-preview and o1-mini, are initially available to ChatGPT Plus users and developers through OpenAI’s paid API, enabling developers to integrate these models into existing third-party applications or create new ones on top of them.

Enhanced Reasoning Capabilities

A key feature of the o1 models is their enhanced “reasoning” capabilities. According to Michelle Pokrass, OpenAI’s API Tech Lead, these models employ a sophisticated reasoning process that involves trying different strategies, recognizing mistakes, and engaging in comprehensive thinking. In tests, o1 models have demonstrated performance on par with PhD students on some of the most challenging benchmarks, particularly excelling in reasoning-related problems.

Current Limitations

The o1 models are currently text-based, meaning they handle text inputs and outputs exclusively and lack the multimodal capabilities of GPT-4o, which can process images and files. They also do not yet support web browsing, restricting their knowledge to data available up to their training cutoff date of October 2023. Additionally, the o1 models are slower than their predecessors, with response times sometimes exceeding a minute.

Early Feedback and Practical Applications

Despite these limitations, early feedback from developers who participated in the alpha testing phase revealed that the o1 models excel in tasks such as coding and drafting legal documents, making them promising candidates for applications that require deep reasoning. However, for applications demanding image inputs, function calling, or faster response times, GPT-4o remains the preferred choice.

Pricing and Access

Pricing for the o1 models varies significantly. The main o1-preview model is the most expensive to date, costing $15 per 1 million input tokens and $60 per 1 million output tokens. Conversely, the o1-mini model is more affordable at $3 per 1 million input tokens and $12 per 1 million output tokens. The new models, capped at 20 requests per minute, are currently accessible to “Tier 5” users—those who have spent at least $1,000 through the API and made payments within the last 30 days. This pricing strategy and rate limit suggest a trial phase where OpenAI will likely adjust pricing based on usage feedback.

Notable Uses During Testing

Among the notable uses of the o1 models during testing include generating comprehensive action plans, white papers, and optimizing organizational workflows. These models have also shown promise in infrastructure design, risk assessment, coding simple programs, filling out requests-for-proposal (RFP) documents, and strategic engagement planning. For instance, some users have employed o1-preview to generate detailed white papers with citations from just a few prompts, balance a city’s power grid, and optimize staff schedules.

Future Opportunities and Challenges

While the o1 models present new opportunities, there are still areas where improvements are necessary. The slower response time and text-only capabilities are significant drawbacks for certain applications. However, the high performance in reasoning tasks makes them valuable for specific use cases, particularly in STEM-related fields.

How to Access the Models

Developers keen on experimenting with OpenAI’s latest offerings can access the o1-preview and o1-mini models through the public API, Microsoft Azure OpenAI Service, Azure AI Studio, and GitHub Models. OpenAI’s continuous development of both the o1 and GPT series ensures that there are numerous options for developers looking to build innovative applications.

In summary, OpenAI’s introduction of the o1 family marks a significant step in the evolution of reasoning-focused LLMs, particularly for STEM applications. While the models have some limitations in speed and input modalities, their advanced reasoning capabilities offer promising avenues for complex problem-solving tasks. As OpenAI continues to refine these models, developers can expect incremental improvements and adjustments in pricing and performance, heralding a new era of AI development.

Explore more

Is Embedded Finance the New Future of Brand-Integrated Banking?

Specialists like Adyen and Block provide the essential digital rails that allow non-bank brands to function as financial hubs for millions of global users every day. The classic architecture of personal finance is being completely dismantled as the barrier between commerce and banking dissolves into the background of the daily user experience. No longer confined to the sterile environments of

How Will Odoo 20 Transform Mexico’s Digital ERP Landscape?

The Mexican enterprise customer base for Odoo grew by 51 percent in 2024, signaling a massive shift toward consolidated business management software. This rapid expansion reflects a broader evolution in the local commercial environment, where organizations are increasingly abandoning the patchwork of disconnected applications that once defined their administrative workflows. By transitioning to a unified platform, these companies are effectively

Why Should You Replace Cloud Apps With Local Linux Tools?

Processing high-resolution images locally using a discrete GPU offers a more immediate and private result than waiting for remote machine-learning models to return processed data. This movement toward a local-first computing model represents a strategic reclamation of digital sovereignty, where the power of modern processors is finally being utilized to serve the individual rather than the data-harvesting algorithms of large

South African Payment Managers Take on Strategic Roles

The South African financial landscape has undergone a radical transformation where the role of the payment manager is no longer confined to the basement of operations. The historical focus on handling service escalations has been replaced by a need for technical fluency and deep understanding of the payment lifecycle. As 2026 progresses, these professionals are finding themselves at the center

How Poor Onboarding Processes Stifle Employee Potential

When companies prioritize excessive documentation over human connection and mentorship, they inadvertently create a culture of confusion and long-term inefficiency. This initial phase of employment is theoretically designed to integrate a professional into a new environment, but it frequently dissolves into a frantic scramble through digital portals and legal fine print. Instead of engaging with the nuances of their new