Chess game in progress with focus on a rook, illustrating the deliberative reasoning process in AI thinking models — Photo by Krists Luhaers on Unsplash

What Is a Reasoning Model?

Q: What is a reasoning model?

A reasoning model is a language model that spends additional compute time on internal deliberation before generating a final output. Instead of immediately producing a response, the model runs an extended thinking process — working through the problem step by step — which significantly improves accuracy on complex reasoning, planning, and analytical tasks.

Q: How do reasoning models differ from standard LLMs?

Standard LLMs generate output token by token in a single forward pass. Reasoning models run a multi-step internal deliberation process, sometimes visible as a 'thinking' or 'reasoning' trace, before producing their final output. This extended thinking requires more time and compute but produces substantially better results on tasks that require logical reasoning, complex analysis, or multi-step planning.

Q: When should I use a reasoning model in an agent?

Use reasoning models for tasks where accuracy matters more than latency or cost: complex planning decisions, nuanced policy evaluation, multi-step mathematical or logical analysis, and situations where the agent needs to work through ambiguous trade-offs. For simple, high-frequency tasks like classification or formatting, standard models are usually faster and cheaper.

Quick Definition#

A reasoning model is a large language model that has been trained or configured to spend additional computational time deliberating over a problem before generating its final output. Rather than immediately producing a response, the model runs an extended internal reasoning process — thinking through the problem from multiple angles, considering intermediate conclusions, and identifying potential errors before settling on an answer.

The result is substantially better accuracy on tasks requiring complex logical reasoning, multi-step analysis, and strategic planning. The tradeoff is higher latency and cost compared to standard models. For background on the prompting approach that preceded reasoning models, see Chain-of-Thought Reasoning. Browse the full AI Agents Glossary for more related terms.

Why Reasoning Models Matter for Agents#

AI agents frequently encounter tasks where the quality of a single decision determines whether the entire workflow succeeds or fails. A planning decision made at the start of a complex workflow propagates through every subsequent step. A policy evaluation error in a compliance check can have significant consequences. A reasoning error in tool argument construction can trigger failures that are difficult to debug.

Standard language models, even large ones, have known weaknesses on tasks requiring multi-hop reasoning, careful logical analysis, and tasks where the correct approach is not immediately obvious from the surface features of the input.

Reasoning models address this directly. For agent builders, they represent a practical tool for improving accuracy on the highest-consequence decision points in a workflow, even when they are not appropriate for every step. For the full landscape of model and platform options, see Best AI Agent Platforms in 2026.

Major Reasoning Models#

OpenAI o1 and o3#

OpenAI's o-series models were designed specifically for reasoning-intensive tasks. The o1 model was the first generation, showing significant improvements over GPT-4o on mathematical benchmarks, coding tasks, and complex analytical problems. The o3 model extended this capability further.

Key characteristics:

Extended internal chain-of-thought reasoning before producing output
Reasoning tokens are consumed internally, separate from the visible output tokens
Higher cost and latency than GPT-4o for equivalent tasks
Substantially better on multi-step reasoning and complex analysis

The reasoning process is partially exposed through a "reasoning" or "thinking" field in the API response, allowing developers to inspect the model's deliberation.

Anthropic Extended Thinking#

Anthropic's extended thinking capability allows Claude models to run extended internal deliberation before responding. When enabled, the model's thinking process is exposed as a separate block in the response, making the reasoning steps visible and auditable.

Key characteristics:

Configurable thinking token budget (developers can control how much compute the model spends on deliberation)
Full thinking trace is exposed and can be used for debugging
Significant improvements on planning, analysis, and complex instruction following
Cost is proportional to thinking token usage

Google Gemini Thinking#

Google has introduced thinking capabilities in Gemini models, enabling similar deliberative reasoning for complex tasks.

How Reasoning Models Work#

The core mechanism is an extended chain-of-thought process that runs internally before the final output is generated. The model:

Receives the input
Generates an internal reasoning sequence — a structured chain of thoughts working through the problem
Uses the reasoning to inform the final response
Outputs the final response (and optionally exposes the reasoning trace)

The key difference from prompting a standard model to "think step by step" is that reasoning models have been trained specifically to produce high-quality, reliable reasoning chains rather than sometimes-coherent chains that standard models produce when prompted.

When to Use Reasoning Models in Agent Workflows#

Not every step in an agent workflow benefits from reasoning model capabilities. The right strategy is selective deployment: use reasoning models for steps where accuracy is critical and standard models for steps where speed and cost matter more.

High-value use cases for reasoning models in agents:

Planning and task decomposition: Generating a high-quality execution plan for a complex multi-step task (see Agent Planning)
Policy and compliance evaluation: Determining whether a proposed action complies with complex or ambiguous rules
Ambiguous decision points: When the agent must choose between multiple plausible approaches and the wrong choice is costly
Complex analysis: Financial analysis, legal reasoning, scientific data interpretation
Debug and diagnosis: Identifying the root cause of multi-step workflow failures

Cases where standard models are sufficient:

High-frequency, simple classification tasks
Formatting and transformation tasks with clear rules
RAG retrieval (embedding-based, not model reasoning)
Simple data extraction with well-defined schemas

Cost and Latency Tradeoffs#

Reasoning models incur higher costs and latency than standard models. Key considerations:

Latency: Extended thinking adds seconds to minutes of processing time. For synchronous user-facing workflows, this may be unacceptable. For background agent workflows where quality matters more than speed, it is often worthwhile.

Cost: Reasoning token consumption adds significant cost per call. At scale, this means reasoning models should be used selectively at high-value decision points rather than as a blanket replacement for standard models.

Quality per dollar: For tasks where accuracy genuinely matters, reasoning models often provide better quality per dollar when accounting for the cost of errors in standard model outputs (rework, human review, downstream failures).

Practical Architecture Patterns#

Reasoning model for planning, standard model for execution#

Use a reasoning model to generate the agent's plan and make high-stakes decisions, then use a faster, cheaper standard model for routine tool calls and formatting steps.

Fallback to reasoning model on failure#

Run standard models by default and fall back to a reasoning model automatically when a step fails or when confidence in the output is below a threshold.

Reasoning model for evaluation#

Use a reasoning model as the evaluator in an LLM-as-judge evaluation setup, where its superior analytical capability improves evaluation quality.

Implementation Checklist#

Identify the two or three highest-consequence decision points in your agent workflow.
Test reasoning models on those specific steps against a baseline.
Measure accuracy improvement and cost increase to evaluate the tradeoff.
Implement model routing to use reasoning models selectively, not universally.
Monitor reasoning token consumption as part of cost tracking.
Expose reasoning traces for debugging when available.

Frequently Asked Questions#

What is a reasoning model?#

A reasoning model is a language model that runs an extended internal deliberation process before generating output. This produces substantially better accuracy on complex reasoning, planning, and analytical tasks, at the cost of higher latency and compute.

How do reasoning models differ from standard LLMs?#

Standard LLMs generate output in a single pass. Reasoning models run a multi-step internal thinking process before producing their final output. This extended deliberation improves performance on tasks requiring logical analysis, planning, and multi-step reasoning.

When should I use a reasoning model in an agent?#

Use reasoning models for high-consequence decision points where accuracy matters more than speed: complex planning, policy evaluation, ambiguous trade-offs. Use standard models for high-frequency routine steps.

Term Snapshot