What Are Reasoning Models and When to Use Them

How Reasoning Models Differ From Chat Models

A standard chat model like GPT-4.1-mini generates each word of its response one after another, choosing the most likely next word based on the conversation so far. This works well for most tasks, but it means the model cannot step back and reconsider its approach partway through a complex problem.

Reasoning models add an extra step before generating the visible response. They first produce a chain of thought, working through the problem step by step in an internal reasoning process. This means they can break complex problems into parts, check their work, consider alternative approaches, and arrive at more reliable answers. The trade-off is that this extra computation takes more time and costs more tokens.

Available Reasoning Models

GPT o3-mini

The primary reasoning model on the platform. GPT o3-mini is built specifically for tasks requiring logical thinking and multi-step problem solving. It is slower than chat models and costs more per request, but it achieves substantially higher accuracy on tasks involving math, data analysis, code debugging, and complex business logic.

When to Use a Reasoning Model

• Math and calculations: When you need exact arithmetic, percentages, financial calculations, or statistical analysis. Chat models sometimes make computation errors that reasoning models catch.
• Multi-step logic: When the answer requires working through several connected steps, such as analyzing a business scenario with multiple variables.
• Data analysis: When you need the AI to find patterns, compare metrics across time periods, or draw conclusions from complex datasets.
• Code debugging: When you need to trace through code logic to find a bug, especially in code with complex control flow or subtle edge cases.
• Decision trees: When the AI needs to evaluate multiple conditions and choose the correct path, such as determining which product to recommend based on several customer attributes.
• Quality-critical output: When getting the wrong answer would cause real problems, such as generating financial reports or compliance-related content.

When NOT to Use a Reasoning Model

• Simple conversations: Greeting customers, answering basic FAQ questions, or making small talk. A chat model handles these perfectly at a fraction of the cost.
• Content generation: Writing blog posts, emails, or marketing copy. Creativity and fluency are not improved by reasoning, and the slower response time hurts the user experience.
• Classification and routing: Sorting incoming messages into categories, detecting intent, or making simple yes/no decisions. These tasks are easy enough for cheap models.
• High-volume processing: When you are processing thousands of items and the per-item accuracy does not need to be perfect, the cost of reasoning models adds up quickly.

Using Reasoning Models in Workflows

The most cost-effective approach is to use reasoning models selectively within a larger workflow. For example, a workflow might use GPT-4.1-nano to classify incoming support tickets (cheap, fast), then route complex technical questions to a reasoning model for analysis (accurate, thorough), and use GPT-4.1-mini to draft the final response (natural writing). This way you only pay reasoning-model prices for the steps that actually need it.

You can configure which model to use at each step of a chain command workflow, mixing cheap models for simple steps with reasoning models for the hard parts.