ReasoningOpenAI

o3 Pro

o3 Pro is OpenAI's reasoning-focused model designed for complex problem-solving tasks, offering multimodal capabilities with a 200K token context window.

Context 200K
Tier Reasoning
Modalities text, image
Input from
$20.00 / 1M tokens
across 1 provider

API Pricing

ProviderInput / 1MOutput / 1MSpeedTTFTUpdated
$20.00$80.0024.1 t/s79.3s4/14/2026

Prices updated daily. Last check: 4/14/2026

Model Details

General

Creator
OpenAI
Family
o-series
Tier
Reasoning
Context Window
200K
Modalities
Text, Image

Capabilities

Tool Calling
No
Open Source
No

Strengths & Limitations

  • Specialized reasoning architecture optimized for complex problem-solving tasks
  • Multimodal support for both text and image inputs
  • 200K token context window for processing extensive documents
  • Part of OpenAI's focused o-series reasoning model family
  • Designed for scenarios prioritizing reasoning quality over speed
  • Suitable for research and analytical workflows requiring deep thinking
  • No tool calling or function execution capabilities
  • Significantly slower initial response time at 93 seconds to first token
  • Lower output speed at 21 tokens per second compared to general-purpose models
  • Proprietary model with no open-source weights available
  • Limited to reasoning tasks rather than general conversational use

Key Features

200K token context window
Text and image input processing
Specialized reasoning architecture
Extended deliberation before response generation
Multimodal document analysis
Research-oriented problem solving capabilities

About o3 Pro

o3 Pro is OpenAI's reasoning-tier model within the o-series family, positioned as a specialized tool for tasks requiring deep analytical thinking and problem-solving. Unlike OpenAI's general-purpose models, o3 Pro is specifically engineered for scenarios where reasoning quality takes precedence over response speed. The model supports both text and image inputs with a 200K token context window, enabling it to process extensive documents and visual content simultaneously. With an output speed of 21 tokens per second and a time to first token of approximately 93 seconds, o3 Pro demonstrates the deliberate trade-off between reasoning depth and response latency that characterizes reasoning-tier models. o3 Pro is typically deployed for applications requiring thorough analysis, complex problem decomposition, and multi-step reasoning workflows. Organizations use it for research tasks, technical analysis, and scenarios where the quality of reasoning justifies longer processing times compared to faster general-purpose alternatives.

Common Use Cases

o3 Pro is designed for applications where reasoning quality justifies extended processing times. Research institutions use it for literature analysis, hypothesis generation, and complex data interpretation. Technical teams deploy it for architectural decision-making, code review of complex systems, and troubleshooting intricate problems. The model excels in scenarios requiring multi-step analysis, such as financial modeling, strategic planning, and academic research where thoroughness outweighs speed. Its multimodal capabilities make it suitable for analyzing documents with charts, diagrams, and technical illustrations that require both visual and textual understanding.

Frequently Asked Questions

How much does o3 Pro cost per million tokens?

o3 Pro pricing varies by provider and usage patterns. Check the pricing table above for current rates across all providers offering o3 Pro access.

What is o3 Pro best used for?

o3 Pro excels at complex reasoning tasks, research analysis, technical problem-solving, and scenarios requiring deep analytical thinking. Its extended processing time makes it ideal for thorough document analysis, multi-step problem decomposition, and research workflows where reasoning quality is more important than response speed.

Why does o3 Pro take 93 seconds to generate the first token?

o3 Pro uses an extended reasoning process before generating responses, deliberately taking time to analyze problems thoroughly. This 93-second delay reflects the model's design philosophy of prioritizing reasoning depth over response speed, making it suitable for complex analytical tasks rather than real-time conversations.