GPT-5 Nano vs o1

1. Extremely fast performance

• Fastest model in the GPT-5 family.
• Great for real-time workflows, rapid responses, and high-throughput systems.

2. Most cost-efficient GPT-5 model

• Lowest input and output token costs.
• Suitable for large-scale or budget-sensitive applications.

3. Ideal for lightweight, well-scoped tasks

• Excels at summarization, classification, text extraction, and simple logic tasks.
• Best used when tasks are narrow and well-defined.

4. Multimodal input

• Accepts text + image as input.
• Outputs text only.

5. Broad tool support

• Supports Web Search, File Search, Image Generation (as a tool), Code Interpreter, and MCP.
• (Does not support Computer Use.)

1. Full-scale reasoning model

• Uses reinforcement learning to generate long internal chains of thought.
• Suitable for tasks requiring deep logic, multi-step planning, and rich analytical reasoning.

2. Strong performance across domains

• Excellent at math, science, coding, and structured analytical work.
• Handles multi-step workflows and complex problem-solving with high consistency.

3. High output capacity (100K tokens)

• Enables long, detailed explanations, large documents, and multi-part analyses.

4. Image-understanding capable

• Accepts text + image inputs for visual reasoning and mixed-modality tasks.
• Output is text only, optimized for clear explanations.

5. Advanced API compatibility

• Works with Chat Completions, Responses, Realtime, Assistants, and more.
• Supports streaming, function calling, and structured outputs.

6. Stable long-context performance

• 200K-token context window supports large files, multi-document analysis, and extended conversations.

7. Designed for correctness-oriented workloads

• Prioritizes rigorous reasoning over speed.
• Useful in auditing, verification, scientific thinking, policy analysis, and legal-style reasoning.

8. Powerful but expensive

• High token costs make it suitable for selective, mission-critical reasoning rather than high-volume usage.