o1
o1 is a large language model developed by OpenAI, designed for complex reasoning tasks. Its defining characteristic is an extended internal chain-of-thought reasoning process performed before generating a response, enabling strong problem-solving performance in domains requiring multi-step derivation such as mathematics, science, programming, and logical analysis. o1 belongs to OpenAI's reasoning model series and is positioned distinctly from the GPT series, which is oriented toward general-purpose conversation.
Core Facts
Reasoning models, represented by OpenAI o1, DeepSeek-R1, and QwQ, have chain-of-thought expansion as their core mechanism, generating a large number of intermediate reasoning steps before producing a final answer.
O3 belongs to OpenAI's o-series reasoning models and introduces a Chain-of-Thought mechanism for multi-step internal reasoning.
OpenAI's o1 series models internalize Chain of Thought as a core mechanism of model training, using reinforcement learning to teach the model to automatically unfold detailed reasoning processes.
Timeline (last 90 days)
Models with reasoning as a core training objective, such as o1/o3, have also shown notable capability leaps in non-mathematical domains such as medical diagnosis and legal analysis.
The cost per million tokens for OpenAI's high-end reasoning models (such as the o1 and o3 series) can be 10 to 50 times that of lightweight models (such as GPT-4o mini).
OpenAI's letter-suffixed versions such as o1 and o3 sometimes represent an entirely new reasoning paradigm rather than a simple iteration.
Because Pro users can access inference-intensive models such as o1 without restriction, a single user's compute consumption may be several times to dozens of times that of an ordinary Plus user.
Many real-world business scenarios require private deployment (intranet) and cannot use closed-source models such as GPT-4, 4o, or o1, forcing teams to build their own large models.
Representative reasoning models include OpenAI's o1/o3 series and Google DeepMind's Gemini reasoning versions.
The GPT-4o, o1, and o3 series have continuously set records on specific benchmarks (such as MATH, HumanEval, and MMLU), yet many developers find it difficult to perceive improvements of the same magnitude in everyday use.
The reasoning chains of models such as o1 and Claude 3.7 often contain complex behavioral patterns including repeated attempts and self-correction.
System One models generally refer to lightweight models with relatively few parameters, shorter reasoning chains, and extremely low response latency, in contrast to slow-thinking reasoning models such as o1 and DeepSeek-R1.
Noam Brown is an OpenAI researcher and a core contributor to O1 and reasoning models, and is currently focused on multi-agent research.
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All Facts (20)
Reasoning models, represented by OpenAI o1, DeepSeek-R1, and QwQ, have chain-of-thought expansion as their core mechanism, generating a large number of intermediate reasoning steps before producing a final answer.
90%VerifiedO3 belongs to OpenAI's o-series reasoning models and introduces a Chain-of-Thought mechanism for multi-step internal reasoning.
90%VerifiedOpenAI's o1 series models internalize Chain of Thought as a core mechanism of model training, using reinforcement learning to teach the model to automatically unfold detailed reasoning processes.
90%VerifiedOpenAI's o1 series represents a new paradigm called "test-time compute"
80%VerifiedThe OpenAI o1 series models were released in September 2024, representing a paradigm shift in LLMs from fast, intuitive responses to deep reasoning.
80%VerifiedOpenAI的o1系列模型和DeepSeek-R1采用了类似Chain-of-Thought的显式推理机制
80%VerifiedIn early 2025, DeepSeek-R1 achieved reasoning capabilities comparable to OpenAI o1 at a training cost of approximately $6 million.
80%VerifiedOpenAI's o1/o3 series and Google's Gemini Thinking adopt a slow-thinking mechanism similar to extended thinking.
80%VerifiedGoogle's Gemini, Anthropic's Claude, and OpenAI's o-series models have all implemented different forms of extended thinking.
75%VerifiedThe reasoning paradigm is essentially the application of reinforcement learning to the post-training stage of language models, allowing the model to autonomously explore reasoning paths and receive reward feedback based on answer correctness, first publicly demonstrated in the o1 series of models.
75%VerifiedOpenAI has introduced a 'Thinking Budget' mechanism in its o-series reasoning models, which can dynamically control the length of the internal reasoning chain.
75%VerifiedThe number of internal reasoning tokens in OpenAI's o1/o3 series models is often several times to dozens of times that of the final output.
65%VerifiedOpenAI's naming system shifted during the GPT-4 era from decimal version numbers to letter suffixes (such as 4-turbo and 4o) to identify architectural optimizations.
65%VerifiedOpenAI's o1 model was released in September 2024; through reinforcement learning it was trained to perform internal reasoning before outputting a final answer, with intermediate reasoning steps existing as special tokens that are invisible to users.
65%VerifiedChain-of-Thought（思维链）提示技术通过强制模型先输出中间步骤来部分缓解自回归模型在逻辑推理中的缺陷
65%VerifiedIn the o1 series models, OpenAI internalized the chain-of-thought mechanism as a spontaneous model behavior and introduced the concept of Reasoning Tokens.
65%VerifiedReasoning models (such as the o1 series) can cost several times to more than ten times more than regular models because they need to generate a large number of intermediate thinking steps.
65%VerifiedOpenAI's o-series (o1, o3) are specifically reinforcement-trained for chain-of-thought reasoning capabilities, incorporating Monte Carlo Tree Search (MCTS) and process reward models.
65%VerifiedThe o1 model performs internal chain-of-thought reasoning before answering, far surpassing GPT-4o on tasks such as mathematics, programming, and scientific reasoning.
65%VerifiedGoogle's Gemini 2.0 Flash Thinking and models such as DeepSeek-R1 have followed the test-time compute scaling technical path of o1.
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