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Draft:OpenAI o1-mini

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OpenAI o1-mini
Developer(s)OpenAI
Initial releaseSeptember 12, 2024; 9 months ago (2024-09-12)
Stable release
Preview
Written inPython, CUDA
PlatformCloud computing, API
TypeLarge language model
LicenseProprietary
WebsiteOpenAI.com

OpenAI o1-mini is a lightweight variant of the OpenAI o1 large language model (LLM) developed by OpenAI. Released in preview form on September 12, 2024, o1-mini was optimized for efficient coding, reasoning tasks, and edge deployment.[1] It is considered a condensed version of OpenAI's o1 architecture, tuned for performance in limited-resource environments and embedded systems.

Overview

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OpenAI o1-mini shares core architectural principles with its parent model, o1, but features reduced parameter count and memory usage. The model maintains high performance in multi-step reasoning and chain-of-thought prompting, particularly in programming-related tasks and logical analysis.[2]

Features

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  • Chain-of-thought reasoning: Supports step-by-step logical processing.
  • Code completion and generation: Performs well in code-heavy benchmarks.
  • Multimodal input handling: (text, code, partial visual support).
  • Low-latency inference: Designed for use on lower-resource hardware.

Deployment

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o1-mini is primarily available via:

Comparison with o1

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While o1 was designed as a general-purpose frontier model, o1-mini was developed with a more focused application in coding and lightweight inference. It sacrifices broad generalization for leaner runtime and specialized reasoning.

Reception

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Early technical reviewers noted that o1-mini offered GPT-4-level coding capabilities in a significantly smaller footprint. However, it also exhibited occasional hallucinations in open-ended reasoning and lacked the full multimodal capabilities of GPT-4o.

Licensing and availability

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Like other OpenAI models, o1-mini is not open source. It is accessible through proprietary platforms under usage restrictions, including rate limits and API gating.

See also

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References

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  1. ^ OpenAI. OpenAI o1 System Card. 2024.
  2. ^ Meinke et al. (2025). Frontier Models Are Capable of In-Context Scheming. arXiv:2504.10123.
  3. ^ Microsoft. Copilot Integration Notes. 2025.
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