What Is an AI Reasoning Model? How They Differ from Standard LLMs

Reasoning models — sometimes called "thinking models" — are AI models specifically trained to work through problems step by step before generating an answer. Unlike standard LLMs that predict the next token, reasoning models explicitly reason through intermediate steps, leading to better performance on complex tasks. They're particularly important for agent work that requires planning, multi-step decision-making, and complex tool use.

How reasoning models work

Standard LLMs generate responses by predicting the next token based on patterns in their training data. This works well for many tasks but struggles with complex reasoning that requires multiple steps.

Reasoning models add an explicit reasoning phase before generating the final response. When you ask a reasoning model a question, it:

1. Generates internal reasoning. The model works through the problem step by step, considering different approaches, checking its work, and refining its thinking. This reasoning is typically hidden from the user.
2. Produces the final answer. After reasoning, the model generates the response you see.

This is similar to the chain-of-thought prompting technique, but built into the model rather than requiring prompt engineering.

Reasoning models vs standard LLMs

Leading reasoning models in 2026

• OpenAI o3. OpenAI's reasoning model, excellent at complex reasoning and math. Available via ChatGPT Pro and API.
• Claude 4 Opus with extended thinking. Anthropic's reasoning capability, integrated into Claude. Excellent for agent workflows.
• Gemini 3 Ultra. Google's reasoning model, strong at multi-step problems.
• DeepSeek R2. Open-source reasoning model, competitive with proprietary options.

Why reasoning matters for agents

Reasoning models are particularly important for agent work because agents face complex, multi-step decisions:

• Tool selection. Deciding which tool to use for a given task requires reasoning about tool capabilities and task requirements.
• Workflow planning. Breaking a complex task into steps requires planning and reasoning.
• Error recovery. When something goes wrong, reasoning helps the agent diagnose and fix the problem.
• Multi-step tasks. Tasks that require chaining multiple tool calls benefit from reasoning about the overall approach.

Trade-offs of reasoning models

Reasoning models aren't always better. Their trade-offs:

• Slower. The reasoning phase takes time — seconds to minutes for complex queries. For simple tasks, this delay is unnecessary.
• More expensive. Reasoning uses more compute, making each query cost more.
• Less creative. Reasoning models tend to be more analytical and less creative, which is bad for tasks like creative writing.
• Overkill for simple tasks. Using a reasoning model to answer "what's the weather?" is wasteful.

When to use reasoning models

Use reasoning models for:

• Complex multi-step problems
• Mathematical or logical reasoning
• Agent workflows requiring planning
• Tasks where correctness matters more than speed

Use standard LLMs for:

• Simple questions and tasks
• Creative writing
• Conversational interaction
• High-volume tasks where cost matters

Many agent platforms let you choose between reasoning and standard models per task, letting you optimize for both quality and cost.