OpenAI: o3 Pro

OpenAI o3 Pro is a text model from OpenAI, released on 10 June 2025 as part of the o-series. The o-series models are trained with reinforcement learning to think before they answer and to perform complex reasoning. Within that family, o3-pro uses more compute to think harder and provide consistently stronger answers. It sits in the text category and is aimed at workloads where reasoning quality matters more than speed.

The model accepts text, files and images as input, and it returns text only. It has a context window of 200,000 tokens and can produce up to 100,000 output tokens in a single response. Its knowledge cutoff is 30 June 2024, so anything after that date is outside what it learned during training. Because images and files can be passed in alongside text, the model can reason over mixed material rather than plain prompts alone.

Those capabilities point to a fairly specific set of uses. Long documents, such as specifications, contracts or research papers, fit comfortably inside the 200,000 token window, and files can be supplied directly instead of being pasted as text. Image input makes it usable for tasks that combine a visual with a written question, for example interpreting a diagram, chart or screenshot alongside instructions. The reasoning training and the larger compute budget suit multi-step problems: analysing a codebase, working through a mathematical or logical argument, or producing a detailed plan that has to hold together across many constraints. Structured outputs and tool calling also make it a reasonable fit for agent-style workflows where the model must decide what to do next.

You call o3-pro through api14's OpenAI-compatible API using the model id "openai/o3-pro". The request parameters it supports are include_reasoning, max_tokens, reasoning, response_format, seed, structured_outputs, tool_choice and tools. In practice this means you can cap the response length with max_tokens, control how much reasoning effort is applied with reasoning and inspect that reasoning with include_reasoning, constrain the reply with response_format or structured_outputs, make runs more reproducible with seed, and give the model functions to call through tools and tool_choice. Because the interface follows the OpenAI format, existing clients and SDKs generally work with only the model id changed.

The main trade-offs are the output ceiling and the knowledge cutoff. Responses are capped at 100,000 tokens, so very large generations need to be split or streamed in stages, and questions about events after 30 June 2024 depend on information you supply in the prompt rather than on the model's training. The heavier reasoning also means this is not the model to reach for when latency or throughput is the priority; simpler or lighter models in the o-series or the general text lineup are usually a better fit for straightforward generation, classification or high-volume chat. Choose o3-pro when the task genuinely needs deep, multi-step reasoning over long or mixed inputs, and something faster when it does not.