On March 20, OpenAI unveiled o1-pro, a high-performance version of its reasoning model family—positioned as a premium tool for developers and researchers who need serious AI horsepower. Available via the OpenAI API to all paid-tier users, o1-pro significantly raises the bar in terms of capability, cost, and specialization.
o1-pro: Built for Deep Reasoning, Priced Accordingly
Unlike smaller models optimized for general tasks, o1-pro is purpose-built for heavy-duty reasoning, targeting domains like science, medicine, and advanced engineering—use cases where deep logic chains and structured outputs matter more than raw speed or surface-level fluency.
But that capability comes at a price:
- $150 per 1 million input tokens
- $600 per 1 million output tokens
That’s four times more expensive than the base o1 model ($15 input / $60 output) and even pricier than GPT-4.5 ($75 input / $150 output), which was previously OpenAI’s costliest offering.
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What do you get for that cash?For the price of o1-pro you get a 200,000 context window and 100,000 max output tokens.
OpenAI said o1-pro can interpret data from machine vision, with text and image input.However, it only produces text output. In the API it also supports:
- Function calling
- Structured Outputs (JSON schema enforcement)
- Responses API integration (for web-enabled agents)
- Batch API support (asynchronous, cost-effective job handling)
OpenAI has not publicly specified the exact knowledge cutoff date for o1-pro, but previous models had real-world knowledge of up to late 2023.
Aimed at Specialists, Not General Users
Given its steep pricing, OpenAI is clearly targeting institutional and professional users—think labs, enterprise R&D teams, or developers building high-stakes AI agents—rather than casual users or startups with lean budgets.
Meanwhile, for more common use cases like transcription or moderation, OpenAI recommends lighter (and cheaper) models better suited to those workloads.
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With o1-pro, OpenAI isn’t just pushing model size or output fluency—it’s doubling down on reasoning, structure, and task-specific intelligence. Whether that’s worth $600 per million tokens will depend entirely on how much those answers are worth to you.