Chain-of-Thought Reasoning
Generates an internal chain of thought before responding, enabling systematic problem-solving across multi-step tasks. This reasoning process is produced automatically before each output.
OpenAI
o1
OpenAI o1 is a large language model developed by OpenAI and trained using reinforcement learning to perform complex, multi-step reasoning. Unlike standard language models that respond immediately, o1 generates an internal chain of thought before producing its final answer, allowing it to work through difficult problems more systematically. It supports a 200,000-token context window, tool use, and Structured Outputs via the API. The model is designed for tasks in coding, mathematics, and science where careful reasoning is more important than broad general knowledge. It has demonstrated notable benchmark results, including ranking in the 89th percentile on Codeforces competitive programming questions, placing among the top 500 students in the US on the AIME math qualifier, and exceeding human PhD-level accuracy on the GPQA benchmark covering physics, biology, and chemistry. It is well-suited for developers and researchers who need a model that can handle technically demanding problems within a large context.
Dec 17, 2024
200,000 context
100,000 tokens output
Chain-of-Thought Reasoning
Large Context Window
Structured Outputs
Tool Use
Math & Science Tasks
Code Generation
Model Overview
High-signal model metadata in a structured two-column overview table.
Provider
The entity that provides this model.
OpenAI
Model ID
The routed model identifier exposed by upstream providers.
openai/o1
Input Context Window
The number of tokens supported by the input context window.
200,000
tokens
Maximum Output Tokens
The number of tokens that can be generated by the model in a single request.
100,000 tokens
tokens
Open Source
Whether the model's code is available for public use.
No
Release Date
When the model was first released.
Dec 17, 2024
1 year ago
Knowledge Cut-off Date
When the model's knowledge was last updated.
2023
API Providers
The providers that offer this model. This is not an exhaustive list.
OpenAI
Modalities
Types of data this model can process.
Text
Image
Code
File
What is o1
A fuller summary of positioning, capabilities, and source-specific details for o1.
OpenAI o1 is a large language model developed by OpenAI and trained using reinforcement learning to perform complex, multi-step reasoning. Unlike standard language models that respond immediately, o1 generates an internal chain of thought before producing its final answer, allowing it to work through difficult problems more systematically. It supports a 200,000-token context window, tool use, and Structured Outputs via the API.
The model is designed for tasks in coding, mathematics, and science where careful reasoning is more important than broad general knowledge. It has demonstrated notable benchmark results, including ranking in the 89th percentile on Codeforces competitive programming questions, placing among the top 500 students in the US on the AIME math qualifier, and exceeding human PhD-level accuracy on the GPQA benchmark covering physics, biology, and chemistry. It is well-suited for developers and researchers who need a model that can handle technically demanding problems within a large context.
Capabilities
What o1 supports
Large Context Window
Supports up to 200,000 tokens of context, allowing long documents, codebases, or conversation histories to be processed in a single request.
Structured Outputs
Returns responses conforming to a specified JSON schema, making it straightforward to integrate model outputs into downstream applications.
Tool Use
Supports function calling and external tool integration, enabling the model to invoke developer-defined tools during a reasoning session.
Math & Science Tasks
Optimized for quantitative and scientific reasoning, with benchmark results including top-500 placement on the AIME qualifier and PhD-level accuracy on GPQA.
Code Generation
Handles complex programming tasks with documented performance at the 89th percentile on Codeforces competitive programming questions.
Pricing for o1
Primary API pricing shown in the same “quick compare” spirit as the reference page.
Input tokens
$15.00
Per million tokens
Output tokens
$60.00
Per million tokens
Price Comparison
Additional usage-cost dimensions synced into the project for this model.
Cache read
$7.50
maxTemperature
2
maxResponseSize
100,000 tokens
API Access & Providers
Places where this model is available, based on the synced detail-page metadata.
OpenAI
Provider Endpoints
Endpoint-level provider data currently available for this model.
OpenAI
Configuration & Parameters
The configurable options currently documented for this model.
Reasoning Effort
SelectUsed 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.
Supported Request Parameters
Parameters currently listed by OpenRouter or the local catalog for this model.
Model Performance
Benchmark scores synced from the current model source and normalized into the local catalog.
| Benchmark | Score |
|---|---|
|
AIME 2024
American math olympiad problems
|
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GPQA Diamond
PhD-level science questions (biology, physics, chemistry)
|
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HLE
Questions that challenge frontier models across many domains
|
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LiveCodeBench
Real-world coding tasks from recent competitions
|
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MATH-500
Undergraduate and competition-level math problems
|
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MMLU-Pro
Expert knowledge across 14 academic disciplines
|
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SciCode
Scientific research coding and numerical methods
|
Resources & Documentation
Official model cards, release notes, docs, and other references synced from the source page.
Community discussion
What people think about o1
o1 discussions are most active in r/singularity, r/ChatGPT, r/OpenAI. Top Reddit threads cluster around benchmark and model-comparison threads, coding workflow discussions.
The strongest match in this snapshot has 10759 upvotes and 138 comments.
What’s your go-to recipe for the OREA O1?
I’d also love to know your grinder settings (Fellow Ode Gen 1).
Model
[https://huggingface.co/HiDream-ai/HiDream-O1-Image-Dev](https://huggingface.co/HiDream-ai/HiDream-O1-Image-Dev)
[https://huggingface.co/HiDream-ai/HiDream-O1-Image](https://huggingface.co/HiDream-ai/HiDream-O1-Image)
HiDream-O1-Image for 50 steps
HiDream-O1-Image-Dev for 28 steps
HiDream-O1-Image is a natively unified image generative foundation model built on a Pixel-level Unified Transformer (UiT) without external VAEs or disjoint text encoders, which natively encodes raw pixels, text, and task-specific conditions in a single shared token space — supporting text-to-image, image editing, and subject-driven personalization at up to 2,048 × 2,048.
Key Features
* **Pixel-Level Unified Transformer** — One end-to-end model on raw pixels, no VAE, no disjoint text encoder.
* **One Model, Many Tasks** — Text-to-image, long-text rendering, instruction editing, subject-driven personalization, and storyboard generation in a single architecture.
* **Reasoning-Driven Prompt Agent** — Built-in "thinking" agent that resolves implicit knowledge, layout, and text rendering before generation.
* **Native High Resolution** — Direct synthesis up to 2,048 × 2,048 with sharp fine-grained detail.
* **Exceptional Efficiency and Versatility at 8B Scale** — With only 8B parameters, achieves performance parity with or even surpasses larger open-source DiTs and leading closed-source models.
My O1 visa was approved recently and it honestly feels like a big milestone after months of preparation. Putting the petition together took quite a bit of time because of the recommendation letters, supporting documents and organizing everything in a way that clearly showed my work in the field.
When the interview day finally came, it was actually calmer than I expected, the conversation was fairly brief and focused on the petition and the purpose of the visa. It thankfully got approved after a very stressful time just thoguht of sharing it here.
O1 visa petition approved online by USCIS then went to embassy for stamp and officer denied and gave us 214b letter saying cant appeal.
Now guys, I get it. You probably get asked this question **A THOUSAND TIMES** a day. But hear me out.
So I did it, I took the first step towards staying in the US. I got both my B.S. and my M.S. in the States (Florida). I was working CPT before my graduation for around 2 years, then became a Founding Data Scientist of a Startup alongside a UCLA and Stanford professor for OPT (at the time I was also an online course instructor for an AI course), and then got a job as an AI Data Scientist for a big corporation.
I do not, however, have any publications out there, I just have 1 conference talk.
Now, I get it, this is pretty bad considering O1, and that I should follow the H1B path. The thing is... H1B is a lottery, and even if I excel at my job I still might not get in. I talked with my old boss (Stanford & UCLA professor) and he wants me to join the research team at his startup to publish papers alongside them. He really cares for me, and wants to see me staying in the country.
Aside from that, my recommendations would probably be my next best strength. I can pull out 9/10 **REALLY GOOD ONES** by leaders in the industry I work in.
In any case, I have less than 2 years left on my OPT (started this late January). What can I do in... say the next year to build my case even more?
FAQ
Common questions about o1
What is the context window for o1?
The o1 model supports a context window of 200,000 tokens, allowing large volumes of text, code, or documents to be included in a single request.
What is the training data cutoff for o1?
Based on the available metadata, o1's training data has a cutoff of late 2023.
What types of tasks is o1 best suited for?
o1 is optimized for coding, mathematics, and science tasks that require complex, multi-step reasoning. It is particularly useful when the problem demands careful logical analysis rather than broad general knowledge.
Does o1 support tool use and structured outputs?
Yes. o1 supports both tool use (function calling) and Structured Outputs, which allows responses to conform to a developer-specified JSON schema.
How does o1 differ from standard OpenAI text generation models?
o1 is trained with reinforcement learning specifically to reason before responding. It produces an internal chain of thought prior to generating its final answer, which is distinct from models that respond without an explicit intermediate reasoning step.
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