Reasoning Models Explained

Reasoning models represent a new paradigm in AI where the model "thinks" before answering, using internal reasoning tokens.

**What Are Reasoning Models?** Traditional LLMs generate responses token by token without explicit planning. Reasoning models (also called "thinking" models) first generate a chain of internal reasoning before producing their final answer.

**How They Work:** 1. The model receives your prompt 2. It generates "thinking" tokens (internal reasoning, not shown to user) 3. It produces the final answer based on its reasoning

**Key Reasoning Models:** - **OpenAI o1/o3**: Pioneered the reasoning approach - **DeepSeek R1**: Open-weight reasoning model that matched o1 - **Claude thinking**: Anthropic's reasoning mode for Claude models - **Gemini thinking**: Google's reasoning capability

**When to Use Reasoning Models:** - Complex math and logic problems - Multi-step coding tasks - Research and analysis requiring deep thought - Tasks where accuracy matters more than speed

**When NOT to Use Reasoning Models:** - Simple Q&A (overkill, wastes tokens) - Creative writing (reasoning doesn't help) - Real-time applications (reasoning adds latency) - Cost-sensitive applications (reasoning tokens cost money)

**Cost Impact:** Reasoning models use 3–10x more tokens than standard models, making them 3–10x more expensive for the same prompt.