GPT-5 Nano

GPT-5 nano is a streamlined variant of OpenAI's GPT-5 model, designed to deliver advanced multimodal reasoning and contextual understanding with significantly reduced computational overhead. It serves as an efficient alternative for developers and enterprises prioritizing fast inference and cost-effectiveness while retaining key features of the full GPT-5 system.

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Technical Specifications

Context Window and Token Capacity

GPT-5 nano supports a large input context size of up to 400K tokens, matching GPT-5 full scale, enabling it to handle extensive documents and multimodal inputs such as text-to-text and image-to-text tasks efficiently.

Performance Benchmarks

• Speed & Latency: Optimized for low-latency inference with trade-offs favoring faster response times over the deepest reasoning layers of full GPT-5.
• Accuracy: Retains strong few-shot learning, multimodal understanding, and factual correctness, though with slightly less complexity handling than GPT-5 and GPT-5 mini.
• Multilingual support: Comprehensive, leveraging GPT-5’s expanded language capabilities.

Architecture Highlights

GPT-5 nano inherits the advanced transformer framework of GPT-5 with optimized attention and efficient utilization of sparsity and mixture-of-experts layers tuned for lightweight operation. It balances architectural scale to sustain high throughput and lower compute costs while focusing on core reasoning and multimodal processing capabilities.

API Pricing

• Input tokens: $0.0525 per million tokens
• Output tokens: $0.42 per million tokens
• Cached input tokens: $0.00525 per million tokens

Core Features & Capabilities

• Model Scale: Smaller parameter count than GPT-5 and mini, designed for speed and resource efficiency without substantial sacrifices in contextual understanding or multimodal tasks.
• Multimodality: Supports text-to-text and vision (image-to-text) input modalities through the API. Audio, video, and code input functionalities remain targeted for future expansions in the unified GPT-5 framework.
• Reasoning: Capable of stepwise logical reasoning and complex problem solving, though optimized for faster execution over the most compute-intensive scenarios.
• Fine-Tuning & Adaptability: Enables flexible customization for domain-specific tasks and enterprise needs.
• Bias & Safety: Implements advanced alignment, bias mitigation, and safety features consistent with GPT-5’s standards.

Code Sample

Use Cases & Applications

• Fast multimodal content understanding and generation in cost-sensitive environments.
• Scalable deployment for lightweight software engineering support, including code suggestions and debugging.
• Real-time large-scale document analysis with image context integration.
• Educational tools and research assistants requiring concise and accurate multi-step instruction processing.

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Comparison with Other Models

VS GPT-5 mini: GPT-5 nano focuses more on the fastest execution and lowest cost with basic multimodal support while GPT-5 mini balances speed and reasoning depth, supporting some expanded workflows with slightly higher pricing.

VS GPT-4o: GPT-5 nano significantly outperforms GPT-4o in reasoning accuracy, multimodal capabilities, and hallucination reduction, while maintaining much lower latency and cost compared to GPT-4o’s heavier but simpler model design.

VS OpenAI o3: GPT-5 nano provides more reliable fact-based answers and advanced reasoning than o3, with specialized alignment and safety mechanisms, delivering highly cost-efficient multimodal AI suitable for real-time applications.