GPT-4.1 Nano, launched by OpenAI on April 14, 2025, is the company's fastest and most affordable model to date. Designed for low-latency tasks such as classification, autocomplete, and fast inference scenarios, it combines compact architecture with robust capabilities. Despite its size, it supports an impressive 1 million token context window and delivers strong benchmark results, achieving 80.1% on MMLU and 50.3% on GPQA. With a knowledge cutoff of June 2024, GPT-4.1 Nano offers exceptional value at just $0.10 per million input tokens and $0.40 per million output tokens, with a 75% discount applied to cached inputs, making it ideal for high-volume, cost-sensitive deployments.
Web Site AI Model Web Page | |
Provider The entity that provides this model. | |
Chat Input a message to start chatting | |
Release Date When the model was first released. | 1 year ago Apr 14, 2025 |
Modalities Types of data this model can process | text images |
API Providers The providers that offer this model. (This is not an exhaustive list.) | OpenAI API |
Knowledge Cut-off Date When the model's knowledge was last updated. | - |
Open Source Whether the model's code is available for public use. | No |
Pricing Input Cost for processing tokens in your prompts | $0.10 per million tokens |
Pricing Output Cost for tokens generated by the model | $0.40 per million tokens |
MMLU Massive Multitask Language Understanding - Tests knowledge across 57 subjects including mathematics, history, law, and more | 80.1% Source |
MMLU-Pro A more robust MMLU benchmark with harder, reasoning-focused questions, a larger choice set, and reduced prompt sensitivity | - |
MMMU Massive Multitask Multimodal Understanding - Tests understanding across text, images, audio, and video | 55.4% Source |
HellaSwag A challenging sentence completion benchmark | - |
HumanEval Evaluates code generation and problem-solving capabilities | - |
MATH Tests mathematical problem-solving abilities across various difficulty levels | - |
GPQA Tests PhD-level knowledge in chemistry, biology, and physics through multiple choice questions that require deep domain expertise | 50.3% Diamond Source |
IFEval Tests model's ability to accurately follow explicit formatting instructions, generate appropriate outputs, and maintain consistent instruction adherence across different tasks | 74.5% Source |
SimpleQA Assessing the accuracy of simple questions | - |
AIME 2024 | 29.4% Source |
AIME 2025 | - |
Aider Polyglot Multilingual programming benchmark. | - |
LiveCodeBench v5 Benchmark for real-time programming | - |
Global MMLU (Lite) A simplified version of the benchmark for assessing the universality of models at the global level. | 66.9% Source |
MathVista Evaluates the mathematical reasoning abilities of AI models within visual contexts | 56.2% Image Reasoning Source |
Mobile Application |
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