1032K
0.000105
0.00042
Chat
Active
GPT-4.1 Nano
OpenAI's GPT-4.1 Nano: Blazing speed and lowest-ever pricing for classification, autocomplete, and data extraction with full million-token context window.
Try it now
Ultra-fast, cost-efficient AI with million-token context for lightweight applications.
Model Name: GPT-4.1 Nano
Developer/Creator: OpenAI
Release Date: April 14, 2025
Version: 1.0
Model Type: Text, Code, Vision (Multimodal)
GPT-4.1 Nano is OpenAI's fastest and most cost-efficient model in the GPT-4.1 family, designed for applications where speed and economy are paramount. While sacrificing some capabilities of its larger siblings, it maintains impressive performance for a wide range of practical use cases such as classification, autocomplete, and data extraction. GPT-4.1 Nano represents OpenAI's commitment to making advanced AI capabilities accessible to more developers and organizations with constrained resources and latency requirements.
GPT-4.1 Nano processes input contexts of up to 1,047,576 tokens (approximately 750,000 words), matching the full GPT-4.1 model's capacity. The model generates outputs of up to 32,768 tokens in a single response. All training data for GPT-4.1 Nano extends to May 31, 2024, which serves as its knowledge cutoff date.
Input tokens $0.105 per million tokens
Output tokens$0.42 per million tokens
Cost for 1,000 tokens $0.000105 (input) + $0.00042 (output) = $0.000525 total
Cost to process 1 page of text (~500 words / ~650 tokens) $0.00006825 (input) + $0.000273 (output) = $0.00034125 total
Despite its optimization for speed and cost, GPT-4.1 Nano maintains strong performance:
GPT-4.1 Nano delivers OpenAI's fastest response times with minimal latency for real-time applications. The model processes inputs and generates outputs at significantly higher speeds than other GPT models. It provides immediate responses for autocomplete suggestions and classification tasks. GPT-4.1 Nano optimizes for speed without excessive quality degradation on standard tasks. The system maintains high-performance characteristics even when handling million-token inputs.
GPT-4.1 Nano makes million-token context processing economically viable for large-scale applications. The system provides exceptional value for repetitive tasks and automated workflows with similar inputs.
GPT-4.1 Nano excels at text classification tasks for content moderation, sentiment analysis, and intent recognition. The model provides efficient autocomplete functionality for code, search, and text entry applications. It performs rapid data extraction from structured and semi-structured documents. GPT-4.1 Nano offers effective document categorization and metadata tagging capabilities. The system serves as an excellent workhorse for high-volume, straightforward AI tasks that prioritize speed over complexity.
GPT-4.1 Nano processes and maintains context across documents containing up to 1 million tokens. The model handles entire codebases or lengthy documents while maintaining essential information retrieval capabilities. It successfully performs "needle-in-a-haystack" retrieval tasks across the full context window. GPT-4.1 Nano maintains processing efficiency even with extremely large inputs. The system offers full long-context capabilities without the premium pricing of larger models.
GPT-4.1 Nano is available through AIML's API services for developers and organizations. OpenAI has not announced plans to integrate GPT-4.1 Nano directly into the ChatGPT interface. The system can be tested immediately through OpenAI's API Playground. GPT-4.1 Nano integrates seamlessly with existing workflows built for other OpenAI models.
API references - Documentation
GPT-4.1 Nano sacrifices some reasoning capabilities and complex task performance for speed and efficiency. The model demonstrates lower performance on sophisticated coding tasks compared to its larger siblings. It requires more specific and explicit prompts for optimal results, similar to other models in the GPT-4.1 family. GPT-4.1 Nano may struggle with nuanced instructions or multi-step reasoning tasks. The system prioritizes practical utility over cutting-edge capabilities for specialized domains.
GPT-4.1 Nano excels in high-volume classification tasks requiring rapid responses and cost efficiency. The model performs exceptionally well for autocomplete functionality in code editors and text interfaces. It provides cost-effective document processing and information extraction from large corpuses. GPT-4.1 Nano offers practical solutions for data tagging, categorization, and basic content generation. The system serves as an ideal backend for interactive applications requiring immediate responses with reasonable quality.
GPT-4.1 Nano achieves 80.1% on the MMLU benchmark despite being OpenAI's smallest and fastest model. It provides the full million-token context window at a fraction of the cost of other models with similar capabilities. The model maintains significantly lower latency than GPT-4.1 and GPT-4.1 Mini for time-sensitive applications. GPT-4.1 Nano costs 96% less than the full GPT-4.1 model while preserving essential functionality for many use cases. It represents the most economical entry point into OpenAI's advanced capabilities with the full context window.
GPT-4.1 Nano represents a significant milestone in making advanced AI capabilities accessible to more developers and organizations. With its unprecedented combination of speed, affordability, and practical performance, it opens up new possibilities for high-volume, latency-sensitive applications that previously couldn't justify the cost of more expensive models. While not designed for complex reasoning or sophisticated tasks, its balance of capability and efficiency makes it an ideal workhorse for a wide range of everyday AI applications.
.png){kind=small}
.png)
.webp)
