o1
o1 is a text generation model from OpenAI that uses internal chain-of-thought reasoning before producing responses.
OpenAIExtended reasoning for complex problem solving
o1 is a large language model developed by OpenAI and released in December 2024. Unlike standard chat models, o1 spends additional compute time reasoning through problems internally before generating a response, a process sometimes called "thinking" or chain-of-thought reasoning. This approach is designed to improve accuracy on tasks that benefit from multi-step reasoning, such as mathematics, science, and complex coding problems. The model supports a 200,000-token context window and can produce responses up to 100,000 tokens in length.
O1 is well-suited for tasks where deliberate, structured reasoning matters more than raw response speed. Developers can adjust the model's reasoning depth using the "Reasoning Effort" setting, which controls how much internal computation the model applies before answering. This makes it flexible for use cases ranging from quick analytical queries to deeply involved problem decomposition. It is available on MindStudio without requiring separate API key management.
What o1 supports
Chain-of-Thought Reasoning
The model reasons through problems internally before producing output, improving accuracy on multi-step tasks like math, logic, and science.
Adjustable Reasoning Effort
A configurable "Reasoning Effort" input lets users control how much internal computation the model applies, balancing depth versus latency.
Large Context Window
Supports up to 200,000 tokens of context, allowing it to process long documents, codebases, or extended conversation histories in a single request.
Long-Form Output
Can generate responses up to 100,000 tokens, making it suitable for producing detailed reports, extensive code, or lengthy structured documents.
Complex Code Generation
Applies its reasoning process to programming tasks, working through logic and edge cases before producing code solutions.
Mathematical Problem Solving
Designed to handle multi-step mathematical reasoning, including proofs, calculations, and quantitative analysis.
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Get Started FreeBenchmark scores
Scores represent accuracy — the percentage of questions answered correctly on each test.
| Benchmark | What it tests | Score |
|---|---|---|
| MMLU-Pro | Expert knowledge across 14 academic disciplines | 84.1% |
| GPQA Diamond | PhD-level science questions (biology, physics, chemistry) | 74.7% |
| MATH-500 | Undergraduate and competition-level math problems | 97.0% |
| AIME 2024 | American math olympiad problems | 72.3% |
| LiveCodeBench | Real-world coding tasks from recent competitions | 67.9% |
| HLE | Questions that challenge frontier models across many domains | 7.7% |
| SciCode | Scientific research coding and numerical methods | 35.8% |
Common questions about o1
What is the context window size for o1?
o1 supports a context window of 200,000 tokens, meaning it can process up to 200,000 tokens of combined input and conversation history in a single request.
How large can o1's responses be?
o1 can generate responses up to 100,000 tokens in length, which is suitable for producing extensive documents, detailed analyses, or large blocks of code.
What does the Reasoning Effort setting do?
The Reasoning Effort input allows you to control how much internal computation o1 applies before generating a response. Higher effort generally means more thorough reasoning, while lower effort trades depth for speed.
What is o1 best used for?
o1 is designed for tasks that benefit from deliberate, multi-step reasoning, such as complex mathematics, scientific analysis, advanced coding problems, and logic-heavy question answering.
What is o1's knowledge cutoff date?
OpenAI has not published a specific knowledge cutoff date in the provided metadata. Based on publicly available information, o1 was released in December 2024 and is expected to have a training data cutoff of early 2024, but you should verify this directly with OpenAI's documentation.
Is pricing information available for o1 on MindStudio?
Specific pricing details are not included in the available metadata. You can check MindStudio's pricing page or OpenAI's API pricing page for current cost information.
What people think about o1
Community discussions around o1 frequently reference its role in OpenAI's broader model lineage, with some researchers noting that o1 and o3 represented a significant capability milestone that informed later model naming decisions. Developers have also discussed the API availability of o1 Pro alongside GPT-4.5, with some questioning the positioning and pricing strategy.
A recurring concern in threads is how o1 fits into a rapidly evolving competitive landscape, with comparisons drawn to models from other organizations on reasoning benchmarks. Practical use cases mentioned include competitive programming, scientific problem-solving, and tasks requiring structured, multi-step outputs.
OpenAI's post-training lead leaves and joins Anthropic: he helped ship GPT-5, 5.1, 5.2, 5.3-Codex, o3 and o1 and will return to hands-on RL research at Anthropic
OpenAI released GPT-4.5 and O1 Pro via their API and it looks like a weird decision.
OpenAI Researcher: O1/O3 were undeniably GPT-5 level and it just took us time to have confidence to bump the name.
MASSIVE release from China Baidu - Ernie 4.5 VLMs & LLMs, Models beat DeepSeek v3, Qwen 235B and competitive to OpenAI O1 - Apache 2.0
Qwen 3 !!!
Parameters & options
Used to give the model guidance on how many reasoning tokens it should generate before creating a response to the prompt. Low will favor speed and economical token usage, and high will favor more complete reasoning at the cost of more tokens generated and slower responses. The default value is medium, which is a balance between speed and reasoning accuracy.
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