What to know about open reasoning models

What is a reasoning model?

Artificial intelligence (AI) is evolving quickly. One of the most important developments is the rise of reasoning models. Reasoning models are AI systems designed to perform multi-step logical analysis rather than simply providing quick answers. Unlike traditional AI models, a reasoning model shows its work, breaking down a problem and working through each step logically to explain how it reached its conclusion. This “chain of thought” approach makes the model’s output more transparent and easier to trust, and especially useful for high-stakes tasks such as financial risk analysis, fraud detection and legal review.

These models are designed for cognitive tasks. They can interpret complex data, weigh options and support decisions that require more than pattern recognition.

What does “open” mean and why does it matter?

Some reasoning models are "open". The term refers to how the model is built and shared; their core technology is transparent and accessible. With an open reasoning model, the underlying components such as model weights, code and sometimes the training data are made publicly available, opening up new possibilities for how AI can be used and controlled. This matters because it shifts the model from being a product you rent to a technology you can own and adapt.

For business leaders, open reasoning models have four strategic advantages:

1. Control over your data

Open models can run on your own servers. That means sensitive data stays within your environment. This is especially important for industries such as finance, healthcare and law, where privacy and compliance are critical.

2. Customization for your business

Your teams can fine-tune an open model using your company’s proprietary data. This creates a specialized AI that understands your internal processes, terminology and goals. A generic model cannot replicate that.

3. Freedom from vendor lock-in

With a closed model, you rely on a single provider for updates, pricing and access. Open models give you flexibility. You can switch between models, modify them or build your own roadmap - without losing access to the AI or its functionality.

4. New opportunities for innovation

Open models are supported by active communities. Developers contribute improvements, fix bugs and create new applications. Your business can benefit from this pace of innovation without being tied to a single vendor.

What does this mean for your AI strategy?

Choosing between open and closed models is not just a technical decision. It is a strategic one.

• Closed models are useful for quick adoption. They offer powerful capabilities out of the box but limit your control.
• Open models require more investment in internal teams. They give you the tools to build proprietary AI systems that reflect your business’s unique strengths.

Where open reasoning models make the biggest impact

These models are especially valuable in areas where data is sensitive, tasks are complex or differentiation matters. Because these models are open, companies can control how data is processed and ensure privacy, rather than sending sensitive information into a closed, third-party system.

• Regulated industries: Financial institutions and healthcare providers can use open models to analyze data securely, without sending it to external servers.
• Custom products: Law firms or biotech companies can build AI tools that reflect their own knowledge and processes.
• New business models: Software or consulting firms can embed open models into their offerings, creating new services and revenue streams.
• Strategic flexibility: Any organization looking to avoid vendor lock-in and maintain control over its AI roadmap can benefit from open models.

Additional considerations for strategic leaders: governance, risk and innovation in open reasoning models 

While open reasoning models offer flexibility and transparency, leaders must weigh critical considerations around governance and risk management to unlock sustainable innovation.

• Geopolitical and regulatory awareness: Running models locally can help meet emerging AI regulations and data sovereignty requirements. It also reduces exposure to cross-border compliance risks.
• Responsible deployment: Open models require thoughtful oversight. Their flexibility means they can be used in many ways, so internal safeguards and clear accountability are essential.
• Intellectual property strategy: Fine-tuning open models with proprietary data creates unique capabilities. This can become a source of competitive advantage and a defensible asset in your innovation portfolio.
• Ecosystem leverage: Open models benefit from community-driven development. Your teams can tap into shared knowledge, tools and benchmarks to accelerate progress without starting from scratch.

The bottom line

Open reasoning models are not just another AI tool. They represent a shift in how businesses can use, control and benefit from AI. For strategic leaders, the question is no longer just “Should we use AI?” It is “Should we build our AI capabilities on open foundations?” 

Making that choice could be the difference between adopting AI and shaping its future.

GenAI was used to develop an iteration of this article. In accordance with EY editorial guidelines, the end product was reviewed and edited by EY professionals before publication.