This is a simplified guide to an AI model called Gpt-5-Nano maintained by Openai. If you like these kinds of analysis, you should join AImodels.fyi or follow us on Twitter.
Model overview
gpt-5-nano represents OpenAI's most efficient implementation of their GPT-5 architecture, prioritizing speed and cost-effectiveness without sacrificing core capabilities. This model builds upon the foundation established by earlier iterations like gpt-4.1-nano and gpt-4o-mini, while delivering the enhanced reasoning and multimodal capabilities of the GPT-5 series. Unlike its larger counterpart gpt-5-mini, this nano variant optimizes for scenarios where rapid response times and operational efficiency take precedence over maximum model complexity. Developed by OpenAI, it serves as an accessible entry point to advanced language model capabilities.
Model inputs and outputs
The model accepts both text and image inputs through a flexible messaging system, allowing users to create rich multimodal conversations. Users can fine-tune the model's behavior through reasoning effort controls and verbosity settings, making it adaptable to different use cases ranging from quick responses to detailed analysis.
Inputs
- Messages: JSON-formatted conversation history with support for multiple roles and content types
- Image input: Array of image URLs for visual analysis and discussion
- System prompt: Instructions that define the assistant's behavior and personality
- Reasoning effort: Control parameter with minimal, low, medium, and high settings
- Verbosity: Output length control with low, medium, and high options
- Max completion tokens: Limit for response length, particularly important for high reasoning efforts
Outputs
- Text responses: Generated text content delivered as an array of strings for streaming output
Capabilities
The model demonstrates strong performa...
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