What is OpenAI o1?
OpenAI o1 is a reasoning-focused model from OpenAI's o-series. It was trained with reinforcement learning and is intended to spend additional inference-time computation working through difficult problems. In practical terms, o1 is aimed at tasks where a carefully reasoned answer matters more than the lowest possible response time or token cost.
The model is suited to multi-step mathematics, science, coding analysis, research, technical interpretation, and other tasks where the answer depends on connecting several pieces of information. It can also analyze images, including visual material such as diagrams, screenshots, and scientific or technical documents.
OpenAI introduced the production API version as an improvement over o1-preview, adding or improving capabilities including function calling, developer messages, Structured Outputs, vision support, and lower latency relative to the preview model. The current canonical API alias points to the o1-2024-12-17 snapshot. However, OpenAI's current model catalog marks both o1 and that dated snapshot as deprecated.
Specifications and supported modalities
o1 accepts text and image inputs and generates text. It does not natively generate images, audio, or video. Audio and video input are also not supported according to the supplied model documentation.
| Specification | Documented value |
|---|---|
| Provider | OpenAI |
| Model family | o1 |
| Model type | Reasoning |
| Canonical snapshot | o1-2024-12-17 |
| Knowledge cutoff | October 1, 2023 |
| Context window | 200,000 tokens |
| Maximum output | 100,000 tokens |
| Input | Text and images |
| Output | Text |
| Current catalog status | Deprecated |
A token is a unit of text used for processing and billing; it may be a whole word, part of a word, punctuation, or another short text segment. The 200,000-token context window provides room for long prompts, source material, code, and conversation history, while the 100,000-token output limit is the maximum documented response size. These are capacity limits, not guarantees that every request will use or need the full amount.
Reasoning, coding, and vision capabilities
o1's defining purpose is complex reasoning. It is intended for problems that require several stages of analysis rather than a short factual response. Examples include evaluating a mathematical argument, working through a scientific question, comparing technical approaches, or tracing the behavior of a complicated program.
For coding, the model is most useful when the task involves understanding or analyzing code: identifying a difficult bug, reviewing an implementation, explaining interactions between components, designing an algorithm, or reasoning through edge cases. It can also work with screenshots and other images, so a developer can provide a visual representation of an interface, diagram, error, or technical document for analysis.
The available research gives o1 an editorial coding score of 8 out of 10 and a reasoning score of 9 out of 10. These scores are evaluations for this article's data model, not benchmarks or ratings published by OpenAI. The verified product capabilities are that o1 accepts image input, returns text, and is documented for reasoning-oriented tasks; the scores should therefore be treated as subjective guidance rather than formal performance measurements.
API capabilities and structured responses
OpenAI documents support for function calling, Structured Outputs, streaming, and batch processing.
- Function calling: The model can request that an application call a defined function or tool. The surrounding application remains responsible for executing that function and returning the result.
- Structured Outputs: Applications can request output that follows a specified structure, which is useful when responses must be parsed by software.
- Streaming: Responses can be delivered incrementally instead of waiting for the entire response to finish.
- Batch processing: Requests can be submitted for batch workloads where immediate, interactive responses are not the central requirement.
Structured Outputs are confirmed in the supplied documentation, but a separate legacy JSON-mode capability is not independently confirmed. These should not automatically be treated as the same feature. Similarly, the supplied research does not verify built-in web search support for o1, so current web information would require an external retrieval or search system if available in the surrounding application.
API pricing
OpenAI's documented API prices for o1 are:
| Usage type | Price per 1 million tokens |
|---|---|
| Input tokens | $15.00 |
| Cached input tokens | $7.50 |
| Output tokens | $60.00 |
These are API token prices, not ChatGPT subscription prices. Output tokens cost substantially more than ordinary input tokens, so applications that ask o1 to produce very long responses can incur significant usage costs. The editorial cost score in the supplied research is 3 out of 10; that is a comparative assessment, not an OpenAI-published rating.
For cost control, send only the context needed for the task, avoid requesting unnecessarily long answers, and consider batch processing where its operating requirements fit the workload. Cached input pricing may reduce the cost of repeated input material, but the supplied research does not specify the exact caching conditions or retention behavior.
Speed and cost trade-offs
o1 prioritizes difficult reasoning over minimum latency. The supplied editorial speed score is 4 out of 10, while its reasoning score is 9 out of 10 and its cost score is 3 out of 10. These are subjective evaluations, but they describe the central practical trade-off: o1 may be justified when solving the problem correctly is more important than producing the cheapest or fastest response.
That trade-off matters when choosing a model for a production application. A routine summarization, classification, simple extraction, or high-volume conversational workload may not need o1's reasoning orientation. A complex code review, mathematical derivation, scientific analysis, or multi-stage planning task is more likely to benefit from it. The research supplied for this page does not identify a current non-deprecated sibling model or provide comparative prices, so a precise model-by-model alternative cannot be stated here.
When to choose OpenAI o1
Choose o1 when the task has several interacting steps and the value of a careful answer outweighs latency and token cost. Strong use cases include:
- Complex mathematics and formal problem solving.
- Scientific reasoning and interpretation of technical material.
- Detailed code analysis, debugging, and algorithmic review.
- Analysis of diagrams, screenshots, and other technical images.
- Research workflows that require organizing and reasoning over a large supplied context.
- Tool-assisted or agentic workflows that benefit from function calling and structured responses.
Its large context window can be useful when a task involves extensive source material, a sizeable codebase excerpt, or a long conversation. The context limit does not give the model access to information that was not provided: its documented knowledge cutoff is October 1, 2023, and the supplied research does not verify native web search.
When another option may be more appropriate
Another model or workflow may be preferable when the priority is low cost, high throughput, or consistently fast responses. o1 is a poor fit for real-time audio, image generation, video generation, or video and audio understanding because those modalities are not supported in the supplied specifications. It is also not the natural choice for tasks requiring built-in, current web information unless an external retrieval system is added.
The model's deprecated status is an additional consideration. OpenAI still displays documentation and API specifications for o1, but the current catalog does not position it as a current, non-deprecated model. Before starting a new long-lived integration, verify the provider's current model recommendations, migration guidance, availability, and any applicable shutdown information. The supplied model page gives no explicit shutdown date.
Limitations and current status
o1's most important limitations are its dated knowledge cutoff, relatively high documented token prices, reasoning-oriented latency, and lack of native audio, video, or image output. It also should not be assumed to have a separately documented legacy JSON mode merely because Structured Outputs are supported.
OpenAI currently lists o1 as deprecated, and the dated o1-2024-12-17 snapshot is also listed as deprecated. No explicit shutdown date is provided in the supplied documentation. This means the model can still be relevant for understanding an existing integration or evaluating a documented capability, but teams should confirm current availability before making it the foundation of a new application.
Bottom line
OpenAI o1 is a specialized reasoning model for difficult text-and-image analysis rather than a general-purpose low-cost generation engine. Its 200,000-token context window, 100,000-token maximum output, function calling, Structured Outputs, streaming, and batch support make it technically capable for demanding workflows. Its higher price, slower profile, dated knowledge cutoff, unsupported audio and video modalities, and deprecated catalog status make careful model selection especially important.