o1 Preview

What was OpenAI o1 Preview?

OpenAI o1 Preview was an early reasoning model from OpenAI and the first public preview in the company’s o-series. OpenAI released it on September 12, 2024, describing it as a research preview trained with reinforcement learning to reason through difficult problems before producing a response.

Unlike a conventional fast-response language model, o1 Preview was intended to spend additional internal computation on a problem. In practical terms, that made it a candidate for tasks where reaching a correct, well-considered answer mattered more than minimizing response time or token cost. Its documented focus included mathematics, science, coding, and other tasks involving several connected steps.

It is important to separate the model’s historical capabilities from its current availability. OpenAI marked o1 Preview as deprecated on April 28, 2025, and shut down API access on July 28, 2025. The provider recommended o3 as a replacement. Consequently, o1 Preview is now mainly relevant as a historical model and as an example of the early development of OpenAI’s reasoning-model lineup.

Technical specifications and limits

The model had a 128,000-token context window. A context window is the amount of text the model can consider in a request and its surrounding conversation, including the prompt and other supplied material. This capacity made o1 Preview suitable for long technical prompts, substantial source material, and multi-step tasks, although a larger context did not by itself guarantee correct reasoning.

Its maximum output was 32,768 tokens. That limit was substantially larger than the amount needed for most ordinary answers, but it gave the model room to produce detailed solutions, code, or explanations when a task required it. The model’s documented knowledge cutoff was October 1, 2023, which is earlier than its September 2024 release date. It therefore should not be treated as having built-in knowledge of later events unless that information was supplied through the prompt or an external tool.

Reasoning, coding, and tool support

o1 Preview’s main purpose was extended reasoning. OpenAI positioned it for difficult mathematics, science, coding, and general multi-step problem solving rather than simple text generation. The supplied evaluation record gives it a reasoning score of 9 and a coding score of 7, but these are editorial or database evaluations, not provider-published benchmark results. They should be read as comparative indicators rather than formal OpenAI claims.

For coding work, the model could be useful when a task required understanding constraints, tracing several dependent steps, or reasoning about an implementation before proposing code. It was not necessarily the best choice for every programming request: a faster general-purpose model could be more appropriate for routine code transformations, simple explanations, or high-volume generation.

The model supported function calling, which allowed an application to define tools or external functions that the model could request. It also supported structured outputs, enabling responses to conform to an application-defined structure when that feature was used. These capabilities were useful for workflows that combined reasoning with software actions, data extraction, or validation, but they did not make o1 Preview a multimodal model or an autonomous system by themselves.

OpenAI’s documentation also listed streaming, prompt caching, and the Batch API as supported. Streaming allows a client to receive output progressively rather than waiting for the entire response. Prompt caching can reduce the price of repeated prompt prefixes, while batch processing is suited to asynchronous workloads that do not require an immediate response.

The original API behavior did not support system or developer messages. That limitation matters when adapting newer application designs to this historical model: an integration built around newer message-role conventions might require changes and should not assume feature parity with later OpenAI models.

Supported modalities and feature boundaries

o1 Preview was text-only. It accepted text input and returned text output. The supplied specifications do not list image, audio, or video input or output, and the model should not be selected for image understanding, speech processing, video analysis, image generation, audio generation, or other native multimodal tasks.

• Text input: Supported.
• Text output: Supported.
• Image input or output: Not supported.
• Audio input or output: Not supported.
• Video input or output: Not supported.
• Function calling: Supported.
• Structured outputs: Supported.
• Streaming: Supported.
• Prompt caching: Supported.
• Batch API: Supported.
• Fine-tuning: Not supported.

Pricing and cost trade-offs

At its documented API pricing, o1 Preview cost $15 per 1 million input tokens and $60 per 1 million output tokens. Cached input tokens cost $7.50 per 1 million tokens. The input and output rates were separate, so applications generating long responses could incur considerably more cost than applications that mainly send large prompts and receive short answers.

These prices describe the model’s historical API pricing and are not an indication that the retired model can currently be purchased. Prompt caching could lower the cost of repeated prefixes, such as a large reference document or common instruction block, but it would not remove the output-token charge. The supplied editorial cost score was 3, and its speed score was 4; both scores are subjective evaluations rather than official OpenAI specifications.

Main strengths and limitations

Strengths

• Complex problem solving: Its design emphasized difficult, multi-step reasoning rather than only rapid completion.
• Long inputs: The 128,000-token context window supported substantial prompts and technical material.
• Large response capacity: The 32,768-token output ceiling allowed detailed solutions and explanations.
• Application integration: Function calling, structured outputs, streaming, caching, and batch processing supported software workflows.
• Specialized reasoning role: It offered a clear alternative to smaller, faster models when the task justified additional computation.

Limitations

• Retirement: API access ended on July 28, 2025, so it is unsuitable for new production integrations.
• Cost: Its historical output price of $60 per 1 million tokens made extensive generation expensive compared with lower-cost options.
• Latency: Additional reasoning made it less suitable for applications that prioritize immediate responses.
• Text-only operation: It could not natively process or generate images, audio, or video.
• No fine-tuning: The supplied specifications list fine-tuning as unsupported.
• Knowledge cutoff: Its built-in knowledge ended on October 1, 2023.
• Older API behavior: The original model did not support system or developer messages.

When to choose this model

Historically, o1 Preview made sense when the central requirement was difficult text-based reasoning and the application could tolerate higher cost and slower responses. Examples included working through a demanding mathematics problem, analyzing a scientific question, reviewing a complicated coding design, or producing a carefully reasoned answer across a long technical prompt.

It was less appropriate for simple chat, high-volume classification, routine summarization, low-latency user interfaces, or workloads where every token had to be inexpensive. A smaller general-purpose model would typically be a better type of option for those requirements, while a multimodal model would be necessary for image, audio, or video tasks.

For current OpenAI deployments, o1 Preview should not be chosen because it is retired. OpenAI recommended o3 when announcing its deprecation and shutdown. That recommendation is the relevant positioning guidance supplied for this model, although the research here does not provide a detailed feature or price comparison between o1 Preview and o3.

Historical significance and final assessment

o1 Preview established an early public example of OpenAI’s approach to reasoning models: use reinforcement learning and additional internal computation to address problems that benefit from deliberate, multi-step analysis. Its combination of a large context window and application features made it more than a research demonstration, but its cost, latency, text-only design, and eventual retirement limited its practical lifespan.

The clearest assessment today is therefore historical rather than operational. o1 Preview was a capable early reasoning model for complex text and code tasks, but it is no longer a viable API choice. Readers evaluating it should focus on what distinguished its design—deliberation over speed—while treating the documented prices and capabilities as historical specifications rather than current availability.