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OpenAI o1 was the company’s first model series publicly built and branded around extended reasoning—but it was not the first AI capable of reasoning-like tasks. Announced on September 12, 2024, o1 was designed to spend more computation working through difficult problems before answering. That made it a milestone in how OpenAI offered AI reasoning, not proof that the model thinks like a person or is better at every task.
What OpenAI launched as o1
OpenAI introduced o1-preview and o1-mini on September 12, 2024. The preview was the larger early-access model; o1-mini was a smaller, faster, less expensive option aimed especially at coding and STEM work. OpenAI later released a production o1 model and o1-pro.
The naming marked a product shift: rather than optimize every model primarily for quick, broad responses, OpenAI presented o1 as a family for problems that may benefit from more deliberate computation. The company’s o1 launch page described the models as spending more time thinking before responding.
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What “reasoning” means for o1
In this context, reasoning is best understood as performance on tasks that require several dependent steps: for example, working through a mathematical problem, debugging code, or satisfying multiple constraints in a plan. OpenAI says o1 uses reinforcement learning and extended internal reasoning to improve complex problem-solving. Its API documentation describes the model as producing a long internal chain of thought.
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- Reasoning performance means getting a task right when its solution depends on multiple steps.
- Test-time computation means the model can spend more processing on a response than a fast, ordinary exchange may require. It can improve the chance of solving a hard problem, but also adds delay and cost.
- Human-like thought is a different claim. Benchmark performance does not establish consciousness, subjective experience, or a human mental process.
- Reliability means whether an answer holds up when wording changes, the task is unfamiliar, or the result is checked independently. More internal work does not guarantee that it will.
Users should not assume that an explanation shown in a chat is a verbatim transcript of the model’s private reasoning. OpenAI’s o1 system card discusses chain-of-thought reasoning and summarized reasoning in ChatGPT; a displayed summary is not the same thing as access to the full internal process.
What the benchmark results show—and what they do not
OpenAI’s September 2024 announcement reported that o1-preview solved 83% of qualifying problems on an International Mathematical Olympiad exam, compared with 13% for GPT-4o. These are OpenAI-reported results on a particular evaluation, not independent proof of general intelligence or a guarantee of success on other mathematical problems.
| Evaluation | Reported result | What it can indicate | Important qualification |
|---|---|---|---|
| IMO qualifying problems | o1-preview: 83%; GPT-4o: 13% | Performance on the evaluated mathematics problems. | OpenAI-reported comparison; it does not show human-like reasoning or universal superiority. |
| Codeforces | OpenAI reported a rating around the 89th percentile. | Performance on competitive-programming tasks. | A contest-style result does not establish coding reliability in every real project. |
| GPQA | OpenAI reported performance approaching or exceeding expert-level results on some graduate-level science questions. | Performance on a selected science question benchmark. | “Expert-level” describes results on that evaluation, not professional competence across science. |
| US tax- and law-related evaluations | OpenAI reported improvements on selected tasks; a comparable score is not stated in the cited announcement. | Performance on particular professional-style questions. | These results do not substitute for qualified legal or tax advice. |
| MMMU | OpenAI reported 78.2% for a vision-enabled version. | Performance on a multimodal benchmark involving visual questions. | This is a reported benchmark result, not a measure of general visual understanding. |
Scores depend on the exact model version, dataset, prompt and evaluation setup. Results for o1-preview, production o1, o1-pro and later models should not be treated as interchangeable. Benchmarks can also be affected by overlap with training data, and strong scores on exam-style tasks do not rule out failures on ordinary or unfamiliar inputs. The claims above come from OpenAI’s announcement, rather than an independent validation of every reported result.
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1Fix the driver behind crashes, sound loss and screen glitches2Clear out junk files and repair common Windows errors3Scan for outdated or missing drivers - takes under a minuteWhy o1 differed from GPT-4o
OpenAI described o1 and GPT-4o as choices with different strengths, not as a simple old-versus-new ranking. GPT-4o was aimed at speed, breadth and multimodal interaction; o1 emphasized difficult, multi-step work and was often slower. OpenAI also cautioned that GPT-4o could be more capable for many common tasks.
| Choose based on the task | o1 | GPT-4o |
|---|---|---|
| Best fit | Hard mathematics, coding, science or other work with linked steps and checkable outcomes. | Routine questions, everyday assistance and tasks where quick, broad interaction matters. |
| Typical trade-off | More deliberate responses can mean more latency and higher API cost. | Generally faster and cheaper for routine API workloads, according to OpenAI’s launch comparison. |
| Product emphasis | Reasoning-focused; early versions had fewer integrations and features. | Broader feature integration and multimodal interaction. |
These are broad positioning differences, not a guarantee that one model wins every example in a category. For a summary, translation or rewrite, extra deliberation may add little. For a complex bug or constrained technical plan, the slower model may be worth trying—especially if the answer can be verified.
Where o1 can help
o1 is most useful when a task has multiple dependencies and a meaningful way to check the result. Potential applications include:
- Working through advanced mathematics or checking a derivation.
- Designing algorithms, solving competitive-programming problems or debugging complex code.
- Analyzing scientific questions and technical formulas.
- Developing a plan with interacting constraints.
- Breaking an unfamiliar technical problem into testable steps.
OpenAI used examples such as cell-sequencing research, quantum-optics formulas and multi-step software workflows in its o1-preview introduction. These illustrate intended uses, not a guarantee of professional-grade results.
Where o1 can fail
Reasoning-focused training does not remove common language-model risks. A longer answer can be wrong, and a polished explanation can make an unsupported conclusion sound convincing. OpenAI’s own announcement warned that the model could still make mistakes.
- Hallucinations and overconfidence: It can give a detailed but incorrect answer.
- Simple errors: Difficult-task strength does not prevent mistakes on basic arithmetic, ambiguity or underspecified instructions.
- Latency and cost: Additional computation takes time; API output is priced above many fast models.
- Prompt sensitivity: Small changes in wording or format can affect results.
- Planning limits: An independent evaluation identified bottlenecks involving memory management, spatial reasoning and solution optimality, even while finding strengths in constraint following and self-evaluation. See the planning-ability evaluation.
- No formal verification: Internal checking is not equivalent to a proof assistant, test suite, calculator or external source check.
- Early feature gaps: Early o1 versions lacked some capabilities and integrations available in GPT-4o; exact features depend on version and product surface.
For medical, legal, financial, safety or security decisions, and for production code or scientific conclusions, treat the response as a draft or analysis to verify—not as the final authority.
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Was o1 the first AI model that could reason?
No. Earlier AI systems, including previous GPT models, could perform tasks involving multi-step calculations, coding, analysis and planning. “Reasoning” also has no single threshold that universally determines whether a model can reason.
The defensible distinction is narrower: o1 was OpenAI’s first prominently branded and publicly released model series built around extended reasoning as a central capability. It was not the first AI to produce reasoning-like results, nor does its name establish human-level thought or artificial general intelligence.
Is o1 still relevant in 2026?
As of August 18, 2026, OpenAI’s API documentation calls o1 a “previous full o-series reasoning model.” That makes it historically important, but not an automatic first choice for a new project. OpenAI has released newer reasoning models since its launch; compare the task, current model catalog, cost and availability before building around o1.
The API documentation lists o1 with a 200,000-token context window, a maximum output of 100,000 tokens and a knowledge cutoff of October 1, 2023. Those are API model specifications, not promises about the ChatGPT interface. The same documentation lists o1-preview as a research-preview model with a 128,000-token context window and a maximum output of 32,768 tokens. Specifications and availability can change.
API pricing and choosing a model
OpenAI’s API pages list the following token prices. They are API charges, not ChatGPT subscription prices; API use is billed separately from ChatGPT Plus, according to OpenAI’s Plus help page.
| API model | Input price | Output price | Documentation status or qualification |
|---|---|---|---|
| o1 | $15 per million tokens | $60 per million tokens | Listed as a previous full o-series reasoning model. |
| o1-preview | $15 per million tokens | $60 per million tokens | Listed as a research-preview model. |
| o1-pro | $150 per million tokens | $600 per million tokens | Higher-compute variant; substantially more expensive. |
These are the prices listed in the respective o1, o1-preview and o1-pro API pages as of August 18, 2026. API rates and model availability may change; check the live pages before estimating a workload. ChatGPT plan access is a separate product question: check the current plan page and the model picker for your account rather than assuming a subscription includes original o1. ChatGPT access may vary by plan, region, account and product surface.
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Random freezes, missing sound and display glitches usually trace back to one bad driver. Find and replace yours safely.Free scan · under a minuteUse a reasoning model when
- The task has several dependent steps and mistakes are costly.
- Accuracy matters more than a rapid response.
- You can validate the answer with tests, calculations or authoritative source material.
- The task involves difficult code, mathematics, science or structured planning.
Use a faster general-purpose model when
- You need quick conversation, summarization, rewriting, translation or brainstorming.
- The work benefits more from voice, vision, tool use or broad multimodal features.
- The task is routine, or high-volume API cost matters more than extra deliberation.
If you are comparing providers, test current models on your own representative tasks and weigh quality alongside latency, price, context needs, tools, privacy terms, rate limits, regional availability and version stability. Alternatives include Claude, Gemini, the Gemini API, Microsoft Copilot and GitHub Copilot. They serve different products and workflows; no ranking or current price comparison is established here.
Why o1 mattered
o1’s significance is that OpenAI made additional computation on hard problems a central model feature and product choice. That helped establish reasoning-focused models as a major category. It did not make o1 universally superior: the practical choice remains a trade-off among task difficulty, correctness, response time, features and cost.
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