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

How Can We Build Trust Into Enterprise Document AI?

Forcing an artificial intelligence to retrieve actual documents before generating a response ensures that outputs remain accurate, verifiable, and grounded in the specific facts of an organization. In the current landscape of 2026, the corporate world has moved beyond the initial phase of novelty where simply having a chatbot was considered a significant achievement. Today, the focus has shifted toward

Ethereum Advances Technical Upgrades and Institutional Integration

The global financial landscape is witnessing a profound transformation as the Ethereum network transitions from an experimental sandbox into a high-performance infrastructure capable of supporting trillion-dollar institutional capital flows and decentralized innovation simultaneously. This current phase represents more than just a series of code updates; it is a fundamental reassessment of how a public ledger can serve as the backbone

Why Do Top Performers Quit Right After Their Biggest Wins?

Voluntary departures among top performers occur most frequently after significant achievements, yet seventy-five percent of these resignations are entirely preventable. When an organization hits a major milestone, such as a product launch or a record-breaking fiscal quarter, the atmosphere is usually celebratory. Executives hand out bonuses, teams enjoy dinners, and the general sentiment is one of extreme optimism. However, beneath

Is This Unofficial App the Copilot Linux Users Need?

The rapid proliferation of artificial intelligence across desktop environments has left many enthusiasts wondering when the most versatile operating systems will finally receive the specialized tools their counterparts enjoy. While Windows users have seen Microsoft’s Copilot woven into the very fabric of their daily interactions, those on Linux have largely been relegated to utilizing browser-based interfaces that often feel disconnected

How Should Retailers Choose the Right ERP System in 2026?

The landscape of enterprise resource planning has undergone a fundamental metamorphosis, moving away from the era of all-encompassing software suites toward a more modular, agile framework that prioritizes operational fluidity over rigid administrative control. In the current retail environment, the traditional method of selecting a vendor based on name recognition alone has become a liability, as modern commerce demands a