What is Reasoning Model?

What Is a Reasoning Model?

A Reasoning Model is an AI model that deliberately thinks through a problem in structured steps before providing an answer, rather than generating a response in a single pass. When you ask a traditional large language model a question, it produces output token by token in a continuous stream. A reasoning model, by contrast, engages in an internal chain of thought -- it plans, considers different angles, checks its own logic, and refines its answer before presenting a final response.

Think of the difference between a colleague who blurts out the first thing that comes to mind versus one who pauses, considers the question carefully, and then delivers a well-reasoned answer. Reasoning models are designed to be the latter.

How Reasoning Models Work

Reasoning models use a technique called chain-of-thought (CoT) reasoning at a deep, structural level. While earlier models could be prompted to "think step by step," reasoning models are specifically trained to do this internally:

• Problem decomposition: The model breaks a complex question into smaller, manageable sub-problems
• Self-verification: It checks intermediate conclusions for logical consistency before moving forward
• Backtracking: If an approach leads to a contradiction, the model can revise its reasoning path
• Multi-step planning: For tasks requiring several stages, the model maps out a plan before executing it

Notable reasoning models include OpenAI's o1 and o3 series, which introduced "thinking tokens" that represent the model's internal deliberation, and DeepSeek R1, which demonstrated that open-source models can achieve strong reasoning capabilities as well.

Why Reasoning Models Matter for Business

For business leaders, reasoning models address one of the biggest limitations of earlier AI tools: unreliable performance on complex, multi-step tasks. Traditional models frequently make errors on problems that require careful logic, such as financial analysis, strategic planning, legal interpretation, and technical troubleshooting.

Higher accuracy on complex decisions When your team uses AI for tasks like analyzing contract terms, evaluating investment scenarios, or comparing vendor proposals, a reasoning model is significantly less likely to produce errors that could lead to costly mistakes. The model's internal verification process catches logical inconsistencies that simpler models would miss.

Better support for strategic analysis CEOs and senior leaders increasingly want AI assistance with strategic questions that require weighing multiple factors. Reasoning models excel at scenarios like market entry analysis, competitive positioning, and risk assessment because they can hold multiple considerations in mind simultaneously.

Reduced hallucination rates By checking their own logic, reasoning models produce fewer confident-sounding but incorrect answers. For businesses in regulated industries across Southeast Asia -- such as financial services, healthcare, and legal sectors -- this reliability improvement is critical for building trust in AI-assisted workflows.

Key Examples and Use Cases

Financial analysis: A reasoning model can work through a multi-year revenue projection, identifying assumptions that need validation and flagging scenarios where the numbers do not add up, rather than simply generating a plausible-looking spreadsheet.

Legal document review: When reviewing contracts for a cross-border deal involving multiple ASEAN jurisdictions, a reasoning model can systematically evaluate clauses against different regulatory requirements, noting conflicts and ambiguities.

Technical troubleshooting: For technology companies, reasoning models can diagnose complex system issues by methodically working through potential causes, testing hypotheses against available evidence, and arriving at root causes more reliably.

Supply chain optimization: Businesses like Grab, Sea Group, and regional logistics providers can leverage reasoning models to evaluate complex routing and inventory decisions that involve dozens of interacting variables.

Audit and compliance: Accounting firms and internal audit teams across ASEAN can use reasoning models to trace complex transaction chains, identify discrepancies across multiple financial records, and evaluate whether business practices comply with regulatory requirements that span multiple jurisdictions. The model's ability to hold many rules and data points in mind simultaneously makes it far more reliable than standard models for these multi-factor assessments.

Strategic planning: When evaluating whether to enter a new market in Southeast Asia, a reasoning model can systematically assess factors like regulatory environment, competitive landscape, customer demographics, infrastructure availability, and partnership opportunities, weighing trade-offs and identifying risks that a faster but less thorough model might overlook.

Getting Started

1. Identify your high-stakes use cases: Reasoning models are most valuable for tasks where accuracy matters more than speed -- financial analysis, legal review, strategic planning, and technical problem-solving
2. Compare outputs: Run the same complex question through a standard model and a reasoning model to see the difference in quality and reliability for your specific needs
3. Budget for higher costs: Reasoning models use more compute per query because of their internal deliberation process, so they cost more per request -- reserve them for tasks where the accuracy improvement justifies the expense
4. Accept the speed trade-off: Reasoning models take longer to respond because they are genuinely working through the problem, but the improved accuracy is worth the wait for important decisions
5. Start with pilot projects: Deploy reasoning models for one or two critical business processes, measure the improvement in decision quality, and expand from there