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OpenAI o1-preview was an early reasoning model released on September 12, 2024. OpenAI designed it to spend more time working through difficult problems, especially in mathematics, coding, and science. Its launch benchmarks showed strong results on specific tests, but they do not establish that it is more capable than GPT-4o for every task—or that its answers are always correct. OpenAI later identified o1 as its successor; whether o1-preview is available to you now depends on your product and account.
What was OpenAI o1-preview?
OpenAI introduced o1-preview as an early model in a reasoning-focused series. The company said large-scale reinforcement learning trained the model to reason through problems and that giving it more time at response generation could improve performance on challenging tasks. In OpenAI’s September 12, 2024 launch post, it described the training approach this way: “Our large-scale reinforcement learning algorithm teaches the model how to think productively using its chain of thought in a highly data-efficient training process.” OpenAI’s launch announcement is the source for that description and the launch-era results below.
“Preview” matters: this was an early release, not a promise of a finished system or a description of every later o1 model. OpenAI initially made it available in ChatGPT and to trusted API users. The announcement presented it as useful for demanding, multi-step problems rather than as a universal replacement for other models.
What could o1-preview do?
Math and complex problem-solving
OpenAI highlighted mathematics as a strength. On the 2024 AIME, it reported that o1-preview averaged 11.1 out of 15, or 74%, using one sample per problem. GPT-4o averaged 1.8 out of 15, or 12%, under that launch evaluation. These are OpenAI-reported results on a particular competition benchmark, not a forecast of accuracy on every math question.
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OpenAI also reported 12.5 out of 15 (83%) on the same 2024 AIME using consensus among 64 samples, and 13.9 out of 15 (93%) after reranking 1,000 samples with a learned scoring function. Those multi-sample methods use substantially more computation than a single response, so they should not be read as ordinary one-shot performance.
Programming
OpenAI reported that o1-preview scored in the 89th percentile on Codeforces competitive-programming questions. That result suggests promise on challenging, well-specified coding problems; it does not guarantee that generated code will run, be secure, or fit an existing project. Code still needs review and testing.
Science and broad knowledge tests
OpenAI reported a 78.2% score on MMMU with vision perception enabled and said o1-preview improved over GPT-4o in 54 of 57 MMLU subcategories. It also described strong performance on GPQA, a difficult science benchmark. These tests assess defined question sets and evaluation conditions. OpenAI cautioned that a strong GPQA result does not mean the model is more capable than a PhD in all respects.
How was o1-preview different from GPT-4o?
The launch comparison focused on reasoning-heavy evaluations, not every use of a chatbot. OpenAI reported a large gap on the 2024 AIME under its one-sample setup, but that does not prove o1-preview was better for everyday conversation, speed, multimodal tasks, or every coding workflow. The fairest distinction is that o1-preview was positioned for difficult problems that benefit from deliberation, while the benchmark figures describe performance only on their named tests and conditions.
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Later model capabilities should not be retroactively attributed to the preview. In its December 17, 2024 developer announcement, OpenAI described features for the successor o1 API including function calling, structured outputs, developer messages, and vision. That announcement also reported that the o1-2024-12-17 snapshot used on average 60% fewer reasoning tokens than o1-preview for a given request. This is a dated, company-reported comparison, not a guarantee about every prompt or a specification of o1-preview’s feature set. OpenAI’s December 2024 o1 announcement distinguishes the successor from the preview.
What were the limitations and safety considerations?
Benchmark success is not the same as dependable judgment. A model may produce a plausible but incorrect answer, and a high score on a scientific test does not make it a qualified professional or a substitute for expert review where mistakes carry serious consequences.
OpenAI said it conducted safety testing and red-teaming before launch, and reported improved safe-completion results on selected challenging jailbreak evaluations. Those are company-reported evaluations, not an assurance that harmful or mistaken outputs cannot occur. The later o1 System Card discusses model-family risks including hallucinations, bias, harmful content, training-data regurgitation, and risks that can accompany more capable reasoning.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What replaced o1-preview, and can you still use it?
OpenAI described o1 as the successor to o1-preview in its December 2024 developer announcement. A December 12, 2024 release-note entry said o1 became available in ChatGPT Enterprise and Edu model selectors, replacing o1-preview in those workspaces. That dated change does not establish current availability for every ChatGPT plan, workspace, or API account.
To check access today, consult the model picker available to your ChatGPT account or workspace. For API access, check the model documentation and availability for the relevant API account; ChatGPT model access and API access are managed separately. OpenAI’s legacy model access guidance explains that available models can change and points Enterprise and Edu users to their workspace model settings and member model picker.
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