Reasoning model
Akıl yürütme modeli
D2 · Generative AI, capabilities and limitations
A reasoning model is a language model trained or configured to work through intermediate steps before answering complex tasks.
Review status: 2026-12-05
Technical explanation
Training can encourage checking and revising a solution; inference settings can allocate effort before the final response.
Conceptual boundaries
The label describes model behavior and training or inference choices, not human consciousness or a guarantee of correct reasoning.
Provider-neutral example
Illustrative: a model checks candidate solutions to a scheduling problem before presenting one for review.
Limitations
A longer generated explanation does not establish that it faithfully reports how the answer was reached.
Related concepts
Atomic claims and evidence
1.1DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning
- Source
- DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning
- Source role
- Authoritative source
- Exact locator
- Abstract: reasoning abilities, reinforcement learning and verification
- Supported claim
- DeepSeek-R1 reports that reinforcement learning can encourage verification and other reasoning behaviors.
- Last verification
- Review due
- Scope limitation
- This is a result for the studied training approach, not a universal recipe.
2.1Google: Gemini thinking
- Source
- Google: Gemini thinking
- Source role
- Authoritative source
- Exact locator
- Thinking models; Best practices: thinking effort and task complexity
- Supported claim
- Gemini documents configurable thinking effort for its thinking models.
- Last verification
- Review due
- Scope limitation
- This describes a provider feature, not a universal interface for reasoning models.
3.1Anthropic: Reasoning models do not always say what they think
- Source
- Anthropic: Reasoning models do not always say what they think
- Source role
- Authoritative source
- Exact locator
- Results: models can use hints without acknowledging them in their chain of thought
- Supported claim
- Anthropic finds that generated reasoning can omit influences on an answer.
- Last verification
- Review due
- Scope limitation
- This is an experimental finding about the studied models, not every reasoning trace.