GPT-4.1 Nano
GPT-4.1 nano is the fastest, most cost-effective GPT-4.1 model.
OpenAI Fast, cost-efficient text generation at scale
GPT-4.1 Nano is a text generation model developed by OpenAI and released in April 2025. It is the smallest and most cost-efficient model in the GPT-4.1 family, designed for latency-sensitive and high-throughput applications. It supports a context window of over one million tokens (1,047,576 tokens), making it capable of processing very long documents or conversation histories in a single request. Its training data has a knowledge cutoff of May 31, 2024.
GPT-4.1 Nano is best suited for tasks where speed and cost efficiency are priorities, such as classification, summarization, autocomplete, and lightweight instruction-following. Because it sits at the smaller end of the GPT-4.1 family, it trades some capability headroom for significantly lower latency and cost per token. Developers building applications that require frequent, rapid model calls — such as real-time assistants, tagging pipelines, or high-volume data processing — are the primary target audience for this model.
What GPT-4.1 Nano supports
Long Context Window
Processes up to 1,047,576 tokens in a single request, enabling full-document analysis or extended multi-turn conversations without truncation.
Text Generation
Generates coherent natural language responses for tasks such as summarization, classification, autocomplete, and instruction-following.
Low-Latency Inference
Optimized for fast response times within the GPT-4.1 family, making it suitable for real-time or high-throughput production workloads.
Structured Output
Supports JSON mode and structured output formats, allowing developers to reliably extract machine-readable data from model responses.
Function Calling
Supports OpenAI's function calling interface, enabling the model to invoke developer-defined tools and return structured arguments.
Instruction Following
Trained to follow detailed system and user instructions, supporting use cases like content moderation, tagging pipelines, and templated generation.
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Get Started FreeBenchmark scores
Scores represent accuracy — the percentage of questions answered correctly on each test.
| Benchmark | What it tests | Score |
|---|---|---|
| MMLU-Pro | Expert knowledge across 14 academic disciplines | 65.7% |
| GPQA Diamond | PhD-level science questions (biology, physics, chemistry) | 51.2% |
| MATH-500 | Undergraduate and competition-level math problems | 84.8% |
| AIME 2024 | American math olympiad problems | 23.7% |
| LiveCodeBench | Real-world coding tasks from recent competitions | 32.6% |
| HLE | Questions that challenge frontier models across many domains | 3.9% |
| SciCode | Scientific research coding and numerical methods | 25.9% |
Common questions about GPT-4.1 Nano
What is the context window for GPT-4.1 Nano?
GPT-4.1 Nano supports a context window of 1,047,576 tokens, which allows it to process very long documents or extended conversation histories in a single request.
What is the knowledge cutoff date for GPT-4.1 Nano?
GPT-4.1 Nano has a training data cutoff of May 31, 2024, meaning it does not have knowledge of events that occurred after that date.
How does GPT-4.1 Nano differ from other GPT-4.1 models?
GPT-4.1 Nano is the smallest and most cost-efficient model in the GPT-4.1 family. It is optimized for speed and low cost per token, making it suitable for high-volume or latency-sensitive tasks compared to the larger GPT-4.1 and GPT-4.1 Mini variants.
What types of tasks is GPT-4.1 Nano best suited for?
GPT-4.1 Nano is well-suited for tasks that require fast, frequent model calls at low cost, such as text classification, summarization, autocomplete, tagging, and lightweight instruction-following pipelines.
Does GPT-4.1 Nano support function calling and structured outputs?
Yes, GPT-4.1 Nano supports OpenAI's function calling interface and structured output formats including JSON mode, consistent with other models in the GPT-4.1 family.
What people think about GPT-4.1 Nano
Community discussion around GPT-4.1 Nano on Reddit was largely anticipatory, emerging before the model's official release when artwork assets for the GPT-4.1 family were discovered on OpenAI's website. Users in the r/singularity thread expressed interest in the tiered model lineup, noting the inclusion of a nano-sized variant alongside GPT-4.1 and GPT-4.1 Mini.
Some commenters speculated about the intended use cases for a nano-scale model, with cost efficiency and speed being the most commonly cited reasons a developer might choose it. No significant concerns or criticisms were raised in the thread, as discussion was limited to pre-release speculation rather than hands-on evaluation.
Parameters & options
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