o1-pro - An upgraded inference model from OpenAI

What is o1-pro?

o1-pro is an upgraded version of the officially released o1 series from OpenAI. It is currently OpenAI's most powerful inference model, with its core advantage being significantly enhanced computational power, enabling it to better handle complex problems and provide more consistent and high-quality responses. o1-pro is only available to select developers (Tier 1–5). It supports vision, function calls, and structured output, and is compatible with response and batch APIs. Its performance in programming and mathematical domains is only slightly better than the regular o1, with improved reliability.

Main functions of o1-pro

• Strong reasoning abilityThe o1-pro uses more computing resources, thinks more deeply, and provides more accurate and reliable responses, excelling in solving complex problems.
• Supports multimodal inputSupports text and image input; currently, output only supports text.
• Structured outputSupports structured output, ensuring that the output content conforms to a specific data format.
• function callIt supports function calls and can connect to external data sources.
• High context length and output limitsA context window with 200,000 tokens, with a maximum of 100,000 tokens generated per request.
• Compatible with multiple APIsIt is compatible with the Responses and Batch APIs, making it convenient for developers to use in different scenarios.

o1-pro's technical principles

• Reinforcement Learning (RL)o1-pro uses reinforcement learning to optimize its reasoning process. Through the Process Reward Model (PRM), the model receives immediate feedback when generating reasoning steps, gradually improving its reasoning strategy.
• Process Reward Model (PRM)PRM provides reward signals for each step of reasoning, not just based on the final result. This allows the model to better understand and optimize the reasoning process.
• Monte Carlo Tree Search (MCTS)o1-pro uses Monte Carlo Tree Search (MCTS) during inference to explore different inference paths. MCTS helps the model select the optimal path by simulating multiple possible inference steps. This method is similar to tree search in AlphaGo, enabling the model to find better solutions to complex problems.
• Self-consistency mechanismo1-pro employs a self-consistency mechanism during the inference phase, improving the accuracy and reliability of inference by generating multiple inference paths and conducting majority voting. This effectively reduces the accumulation of errors caused by a single inference path.
• Synthetic data generationTo train o1-pro, OpenAI developed a system called "Berry Training," which generates a large amount of synthetic data using Monte Carlo trees. The data is filtered through a functional validator and an optimized reward model to ensure the quality of the training data.
• Test-Time Computeo1-pro can utilize more computational resources during inference, improving the accuracy and depth of inference by increasing the computational load during testing. This allows the model to think more deeply when dealing with complex problems.

o1-pro project address

• Project official website:https://platform.openai.com/docs/models/o1-pro

O1-Pro pricing

• Enter priceThe fee is $150 per 1 million tokens (approximately 750,000 English words).
• Output PriceThe fee is $600 per million tokens.
• Batch API PricingInput price is $75 per million tokens, output price is $300 per million tokens.
• Other informationThe price of o1-pro is 10 times that of the regular o1 model and twice that of GPT-4.5 input. o1-pro is currently only available to select developers (Tier 1–5), who need to spend at least $5 in the API service to use it.

o1-pro performance test

• Mathematical reasoningWhen dealing with doctoral-level scientific problems, the accuracy rate of o1-pro improved to 79.3%; when solving American International Mathematics Examination (AIME) problems, the accuracy rate reached 85.8%.
• Programming skillsIn the International Olympiad in Informatics (IOI), o1-pro significantly outperformed the ordinary o1 model.
• Multimodal inputThe o1-pro supports both image and text input and can handle complex multimodal problems. For example, when dealing with thermal design problems in space data centers, the o1-pro can provide detailed solutions based on hand-drawn sketches and problem descriptions.
• Compared to the standard O1 modelo1-pro improved performance by 7.5% and 2 times in math and programming tasks, respectively.
• Compared to GPT-4.5The input and output prices of the o1-pro are 2 times and 10 times that of the GPT-4.5, respectively. In terms of performance, the o1-pro significantly outperforms the GPT-4.5 in multiple benchmark tests.

Application scenarios of o1-pro

• Interdisciplinary researchIt supports multimodal input and can handle complex tasks that combine images and text, such as analyzing thermal design problems in space data centers.
• Code generation and optimizationo1-pro can generate high-quality code based on flowcharts, supports multiple programming languages and frameworks, and is suitable for complex coding tasks.
• System architecture designIt provides in-depth code analysis and system architecture suggestions to help developers optimize software design.
• Visual reasoningIt can analyze and reason about images, such as performing complex calculations based on hand-drawn sketches.
• Academic writing assistanceIt can generate high-quality academic papers, debate speeches, poems, etc., and is suitable for academic writing and creative writing.