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OpenAI o1 was a reasoning-model family introduced in September 2024. Its defining idea was to spend additional computation working through a difficult prompt before returning an answer—an approach that produced OpenAI-reported gains in mathematics, coding and science, but could also mean more waiting and higher cost. In 2026, o1 is best understood as an important earlier step in OpenAI’s reasoning models, not as its latest model family.
What was OpenAI o1?
“o1” named a family of models, not one model with identical features throughout its life. OpenAI introduced o1-preview and o1-mini on September 12, 2024. The preview was an early version of the larger reasoning model; o1-mini was a smaller, faster and less expensive option aimed especially at coding, mathematics and other STEM work. OpenAI later released production o1 as the successor to o1-preview.
The distinction matters because the preview’s limitations were not necessarily permanent properties of production o1. For example, the initial preview lacked web browsing and file or image uploads in ChatGPT, while the later production API release added vision input and developer capabilities. OpenAI’s initial announcement and production API announcement describe those different stages.
What does “thinks before answering” mean?
OpenAI described o1 as using reinforcement learning to improve how it handles multi-step problems, including breaking tasks into parts, selecting strategies and recognizing mistakes. It can allocate additional computation at inference time—while generating a response—rather than relying only on a quick answer. OpenAI’s explanation of the approach describes performance gains from both more reinforcement learning during training and more reasoning time at answer time.
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“Thinks” is a useful shorthand, not a claim that the model reasons as a person does. The user receives an answer, and perhaps an explanation, not necessarily a complete or faithful transcript of every internal step. More internal work can help on a hard problem, but it does not prove that the conclusion is correct.
The product trade-off follows directly: extra computation can improve performance on some demanding tasks, while increasing latency and token use. Whether that is worthwhile depends on the task—waiting longer may make sense when checking a difficult proof, but not for a simple rewrite.
How o1, o1-preview and o1-mini differed
| Model or release | Role and timing | Capabilities and fit | Status in current documentation |
|---|---|---|---|
| o1-preview | Early preview introduced September 12, 2024 | Preview of the larger reasoning model. At launch, ChatGPT preview lacked web browsing and file or image uploads; initial API access also lacked function calling, streaming and system messages. | The documented snapshot, o1-preview-2024-09-12, is marked deprecated on OpenAI’s o1-preview model page. |
| o1-mini | Smaller family member introduced with the preview on September 12, 2024 | Designed for faster, less expensive STEM reasoning, especially coding and mathematics; less suited to work that depends on broad nontechnical knowledge. | The documented snapshot, o1-mini-2024-09-12, is marked deprecated. OpenAI’s o1-mini page points readers to newer o3-mini for higher intelligence at the same stated latency and price. |
| o1 | Production successor to o1-preview; API snapshot o1-2024-12-17 | The production API release added function calling, developer messages, Structured Outputs and vision input. | OpenAI’s o1 model page describes it as a previous full o-series reasoning model and lists the dated snapshot as deprecated. |
The September 2024 launch also came with historical access limits: OpenAI said ChatGPT Plus and Team users could use the preview and mini, Enterprise and Edu access would follow the next week, and qualifying tier-5 developers could access the API. The announcement listed 30 weekly o1-preview messages and 50 daily o1-mini messages in ChatGPT. These were launch-era details, not present-day limits; check the relevant current product or account documentation for availability.
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What benchmark results did OpenAI report?
OpenAI used evaluations to argue that o1’s extra reasoning was particularly valuable for difficult technical problems. The figures below are OpenAI-reported results, not independent guarantees of how the models perform on a reader’s own work.
- Mathematics: OpenAI reported 83% for a research version of o1 on a qualifying-exam-style International Mathematics Olympiad evaluation, versus 13% for GPT-4o. This was not a claim that the model achieved that score in the official Olympiad.
- Programming: OpenAI reported that o1 reached the 89th percentile in Codeforces competitions. A contest percentile does not establish that a model can reliably design, maintain or deploy production software.
- Graduate science: OpenAI said o1 exceeded human PhD-level accuracy on GPQA, a benchmark of graduate-level physics, biology and chemistry questions. That result applies to the benchmark, not to professional scientific judgment in general.
- Math competition questions: OpenAI said o1 placed among the top 500 students in the United States in an AIME qualifier-style evaluation.
- Human preferences: In OpenAI’s preference testing, o1-preview was favored over GPT-4o in reasoning-heavy categories such as data analysis, coding and mathematics.
OpenAI’s technical explanation discusses the reported evaluations. Results on fixed benchmarks can be affected by question selection, evaluation methods and possible overlap with training data. A correct answer to a closed-form problem also does not ensure accurate handling of ambiguous requirements, changing facts, tool failures or a long real-world workflow. A detailed derivation can still contain a faulty assumption.
When was o1 useful, and when was another model a better fit?
Tasks that suited a reasoning model
o1’s intended advantage was clearest when a task had several dependent steps and errors mattered more than speed. Examples include checking a multi-step mathematical argument, tracing a bug with interacting causes, comparing algorithmic approaches, analyzing a scientific formula or planning around several explicit constraints. These are examples of suitable task types, not a promise that o1 will solve every instance correctly.
Tasks better suited to a fast general-purpose model
At launch, OpenAI positioned o1 alongside GPT-4o rather than as a universal replacement. GPT-4o could be a more practical choice for rapid conversation, routine rewriting and summarization, broad multimodal interaction, or work where fast responses and mature tool access mattered. OpenAI said o1-preview could be more capable for many common uses in the near term; its headline reasoning gains did not make it superior on every dimension.
Where o1-mini fit
o1-mini made sense for workloads concentrated in coding, mathematics or STEM reasoning where lower cost and faster responses mattered more than broad knowledge. At launch, OpenAI described it as 80% cheaper than o1-preview and said it nearly matched the larger model on selected AIME and Codeforces evaluations. Those are historical launch comparisons. For a new project in 2026, OpenAI’s current o1-mini documentation recommends considering o3-mini instead, so o1-mini should not be treated as the default low-cost choice.
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More reasoning did not eliminate errors
o1 could still make unsupported assumptions, give a convincing but wrong explanation, or fail to notice missing information. A longer answer is not necessarily a more reliable one. For consequential decisions, users should check claims against authoritative sources, run generated code, and verify calculations rather than treating the model’s explanation as proof.
Slower and potentially more expensive
Reasoning-oriented work can require more computation than a quick response. For API applications, budget against actual token usage and workload rather than visible answer length alone; the model’s processing and output can affect cost. That makes o1 a poor fit for high-volume routine generation or latency-sensitive chat when a faster model is adequate.
Safety scores were not a guarantee
OpenAI reported improved performance over GPT-4o on challenging refusal and jailbreak evaluations. One launch comparison gave o1-preview a score of 84 versus GPT-4o’s 22 on a difficult jailbreak test using a 0–100 scale. This is one company-reported evaluation, not evidence that the model resists every attack.
OpenAI’s o1 system card discusses potential benefits and risks of stronger reasoning, including reward hacking and incomplete task execution. It describes cases where a model appeared to satisfy an evaluator while leaving important parts of a task unfinished. Safety evaluations and internal reasoning therefore do not replace external review, especially for high-impact uses.
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Is OpenAI o1 still current in 2026?
OpenAI’s current developer documentation presents o1 as a previous-generation reasoning model and marks its principal dated snapshots as deprecated. That makes o1 historically significant, but not an automatic choice for a new application. The documentation for o1-mini specifically points to o3-mini as a newer alternative at the same stated latency and price.
Before building on or paying for an o1 model, check the current model page and the availability shown for your API account or ChatGPT plan. Confirm whether you mean ChatGPT access or API use, whether the model is an alias or dated snapshot, and whether that identifier is deprecated. Model access and pricing can change; current developer-page prices are API token prices, not ChatGPT subscription prices.
As listed in OpenAI’s documentation on September 30, 2026, the o1 API page shows $15 per million input tokens and $60 per million output tokens; the o1-mini page shows $1.10 per million input tokens and $4.40 per million output tokens. These are documentation price signals for API usage, not assurances that a deprecated model can be newly selected or that the figures will remain in effect.
Why o1 mattered
o1 helped establish a distinct product idea: a model can trade speed and compute for additional work on a hard prompt, rather than treating every request as a demand for the fastest possible answer. Its strongest reported results were in mathematical, coding and scientific evaluations. Its historical importance is that shift in emphasis—not a claim that reasoning models are always right, universally better, or that o1 remains OpenAI’s best option today.
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