OpenAI o-Series (Reasoning Models)

OpenAI's o-series models (o1, o3, o4-mini) are reasoning models that think step-by-step before answering. Unlike standard GPT models that generate responses token-by-token, o-series models use internal chain-of-thought reasoning to work through complex problems, making them significantly better at math, science, coding challenges, and multi-step logic.

Available Models

How Reasoning Models Differ

Standard GPT models generate responses immediately. Reasoning models:

1. Think first: Spend time reasoning internally before responding
2. Show reasoning: Can expose their chain-of-thought (with extended thinking)
3. Better at hard tasks: Significantly outperform GPT on math, coding competitions, and logic puzzles
4. Higher latency: Responses take longer due to the thinking phase
5. Variable cost: Reasoning tokens add to the total token usage

Getting Started

import { openai } from '@ai-sdk/openai';
import { generateText } from 'ai';

const { text } = await generateText({
  model: openai('o3'),
  prompt: 'Solve this step by step: If a train leaves at 3pm going 60mph...',
});

When to Use o-Series vs GPT

Use o-series when:

• The task requires multi-step reasoning or problem-solving
• Accuracy matters more than speed (math, science, legal analysis)
• You need the model to plan or strategize
• Standard GPT gives incorrect or shallow answers

Use GPT when:

• You need fast responses (chat, autocomplete)
• The task is straightforward (summarization, translation)
• Cost is a primary concern
• You need streaming token-by-token output

Best Practices

• Set higher maxDuration on API routes since reasoning takes longer
• Use o4-mini for cost-effective reasoning on routine problems
• Reserve o3 for the most challenging tasks where accuracy is critical
• Don't use reasoning models for simple tasks — they add cost and latency without benefit

Resources

• OpenAI Reasoning Models Guide
• o1 Research Blog Post
• AI SDK OpenAI Provider