All modelsOpenAI o4-mini
OpenAI o4-mini is a recent addition to OpenAI's 'o' series of models, designed to provide fast, cost-efficient, and capable reasoning for a wide range of tasks. It balances performance with efficiency, making advanced AI reasoning more accessible for various applications, including those requiring high throughput.
Intended Use
Fast technical tasks (data extraction, summarization of technical content)
Coding assistance (fixing issues, generating code)
Automating workflows (intelligent email triage, data categorization)
Processing and reasoning about multimodal inputs (text and images)
Applications where speed and cost-efficiency are key
Performance
OpenAI o4-mini is optimized for speed and affordability while still demonstrating strong reasoning capabilities. It features a substantial 200,000-token context window, allowing it to handle lengthy inputs and maintain context over extended interactions. The model supports up to 100,000 output tokens.
Despite being a smaller model, o4-mini shows competitive performance on several benchmarks, particularly in STEM and multimodal reasoning tasks. It has achieved scores such as 93.4% on AIME 2024 and 81.4% on GPQA (without tools). In coding, it demonstrates effective performance on benchmarks like SWE-Bench.
A key capability of o4-mini is its integration with OpenAI's suite of tools, including web Browse, Python code execution, and image analysis. The model is trained to intelligently decide when and how to use these tools to solve complex problems and provide detailed, well-structured responses, even incorporating visual information into its reasoning process. This makes it adept at tasks that require combining information from multiple sources or modalities.
Compared to larger models like OpenAI o3, o4-mini generally offers faster response times and significantly lower costs per token, making it a strong candidate for high-volume or latency-sensitive applications.
Limitations
Usage limits: Access to o4-mini on ChatGPT is subject to usage limits depending on the subscription tier (Free, Plus, Team, Enterprise/Edu).
Fine-tuning: As of the latest information, fine-tuning capabilities are not yet available for o4-mini.
Reasoning depth: While strong for its size, o4-mini may not offer the same depth of reasoning as larger, more powerful models like o3 for the most complex, multi-step problems.
Benchmark vs. real-world: As with any model, performance on specific benchmarks may not always perfectly reflect performance on all real-world, highly-nuanced tasks.
Citation
Information gathered from OpenAI's official announcements, documentation, and third-party analyses of model capabilities and benchmarks.