OpenAI model
GPT-4.1 Nano
The smallest and lowest-cost GPT-4.1 model for fast classification, extraction, routing, autocomplete, and high-volume API workloads.
- Context window
- 1.05M
- tokens
- Max output
- 32.8K
- tokens
- Input
- $0.10
- per 1M tokens
- Cached input
- $0.025
- per 1M tokens
- Output
- $0.40
- per 1M tokens
01 / Overview
What GPT-4.1 Nano Is
GPT-4.1 Nano is the smallest GPT-4.1 model, designed for workloads where low latency and very low per-token cost matter more than maximum model capability.
A Small Model for Work That Happens Constantly
Many production AI operations do not require a large general-purpose model. A request may only need to classify a message, extract a few fields, choose a route, normalize text, generate a short completion, or decide whether a more capable model should handle the next step.
GPT-4.1 Nano targets that layer of the stack. OpenAI introduced it as the fastest and cheapest GPT-4.1 model, while retaining a 1,047,576-token context window and the API features needed for structured application workflows.
The model does not use a configurable reasoning phase. That makes its operating profile straightforward: prompt quality, context size, output length, and task difficulty have a direct impact on whether Nano is the right fit.
- Lowest standard token pricing in the GPT-4.1 family.
- Designed for low-latency, high-frequency API calls.
- Keeps the family's 1M-token context window.
- Supports text and image input plus structured application features.
- Provider
- OpenAI
- Family
- GPT-4.1
- Tier
- Nano
- Knowledge cutoff
- Jun 1, 2024
- Input modalities
- Text, Image
- Output modality
- Text
- Model type
- Non-reasoning
02 / Use cases
Where GPT-4.1 Nano Can Be Most Efficient
Nano is most attractive when the task is narrow, repeatable, and inexpensive enough that running it at high volume becomes practical.
Put Cheap Intelligence Where You Would Otherwise Avoid an AI Call
At $0.10 per million standard input tokens and $0.40 per million output tokens, Nano can make model-backed processing economical in places where larger models would add too much cost.
A support system can classify incoming requests before escalation. A data pipeline can extract attributes from text. A product can generate autocomplete suggestions or short transformations. A multi-model workflow can use Nano as an inexpensive first-pass router and reserve stronger models for requests that actually need them.
The important constraint is task complexity. Nano is not simply a cheaper substitute for GPT-4.1 Mini or full GPT-4.1. Complex coding, difficult multi-step decisions, or fragile tool sequences should be benchmarked against stronger alternatives.
- 01
Classification
Assign categories, labels, intent, priority, or other compact decisions to incoming data.
- 02
Extraction
Pull names, attributes, entities, fields, or short structured records from text and image-aware inputs.
- 03
Routing
Choose a workflow, destination, tool, or stronger model based on a lightweight first-pass decision.
- 04
Autocomplete and short transformations
Generate concise continuations, rewrites, normalization, tagging, and other latency-sensitive microtasks.
03 / Pricing
GPT-4.1 Nano Pricing
GPT-4.1 Nano costs $0.10 per million standard input tokens, $0.025 per million cached input tokens, and $0.40 per million output tokens.
Tiny Unit Costs Matter Most at Large Volume
Nano's pricing is most meaningful when the same operation occurs many times. A difference of a fraction of a cent may be irrelevant for ten calls, but material across millions of classifications, enrichment jobs, routing steps, or background transformations.
Cached input is one quarter of the standard input price. That can improve economics further when many requests reuse the same system instructions, schema description, reference prefix, or other cacheable context.
Because output costs four times as much as standard input on a per-token basis, Nano is especially attractive when the result is intentionally small: a label, score, route, compact JSON object, or short completion.
- $0.10 per 1M standard input tokens.
- $0.025 per 1M cached input tokens.
- $0.40 per 1M output tokens.
- Particularly well matched to short-output workloads.
1M tokens · USD
- Input
- $0.10
- Cached input
- $0.025
- Output
- $0.40
Example: 10K input + 1K output
- Input cost
- $0.0010
- Output cost
- $0.0004
- Estimated total
- $0.0014
04 / Context
GPT-4.1 Nano Still Has a 1,047,576-Token Context Window
Despite being the smallest GPT-4.1 tier, Nano supports the same 1,047,576-token context window and 32,768-token maximum output listed for the larger GPT-4.1 variants.
Large Input Capacity Does Not Turn a Nano Model Into a Large Model
This distinction matters. Context capacity tells you how much information the API can accept; it does not guarantee that every task becomes equally reliable when the prompt grows.
Nano can be useful when a simple decision depends on a large source: classify a long document, extract a known set of fields, locate relevant information, or route an item based on extensive context. Those workloads use the large window without demanding the same depth of reasoning as complex synthesis.
For difficult long-context analysis, compare Nano with Mini or a newer model. The most efficient option is the smallest model that maintains the quality threshold your application requires.
- Context window: 1,047,576 tokens.
- Maximum output: 32,768 tokens.
- Useful when simple operations need access to large source material.
- Large context should be tested for retrieval quality, latency, and cost.
Context window
1,047,576
Max output
32,768
A large context limit defines capacity. Actual task quality still depends on model capability, prompt design, relevant information density, and output requirements.
05 / Routing & scale
GPT-4.1 Nano as the First Stage of a Model Router
One of Nano's most useful production roles is deciding which requests can stay cheap and which should be escalated to a stronger model.
Do Not Spend Large-Model Tokens on Every Request
A multi-model system can treat capability as an escalation path. Nano handles the easiest requests first. Requests that are ambiguous, high-risk, complex, or outside the smaller model's confidence envelope can then move to GPT-4.1 Mini, GPT-4.1, or another model selected by the application.
This architecture is useful only when routing itself is reliable. Simple classification and deterministic schema output are better candidates than complicated multi-tool plans. OpenAI's GPT-4.1 launch benchmarks show that Nano trails the larger family members substantially on difficult function-calling and coding evaluations, so complex agentic workflows should be tested rather than assumed to work because the API feature is supported.
The goal is not to force every task onto the cheapest model. It is to discover how much production traffic can safely remain on the inexpensive path.
- 01
Start with Nano
Send narrow, low-risk, high-volume requests to the lowest-cost candidate.
- 02
Validate the result
Use schema checks, confidence rules, business constraints, or eval-derived acceptance criteria.
- 03
Escalate difficult cases
Route ambiguous or complex requests to a more capable model instead of repeatedly retrying Nano.
- 04
Measure end-to-end economics
Track accepted-result cost and escalation rate rather than judging the architecture by Nano's token price alone.
06 / Capabilities
GPT-4.1 Nano API Capabilities
Nano supports modern OpenAI API features including image input, streaming, function calling, structured outputs, fine-tuning, and predicted outputs.
API Support and Task Difficulty Are Different Questions
Function calling means Nano can produce tool calls; it does not mean every complex tool workflow is an ideal Nano workload. The same distinction applies to structured outputs and long context: support enables an integration pattern, while evaluation determines whether the model is reliable enough for a specific task.
For narrow schemas, simple extraction, and straightforward routing, these capabilities can be highly practical. Image input also allows low-cost visual classification or extraction experiments using screenshots, diagrams, document pages, and other supported image content.
Fine-tuning provides another optimization path for supported specialized workloads, while predicted outputs can help in tasks where much of the expected output is already known.
- Supported
Text
Text input and text output.
- Supported
Image input
Analyze supported visual content together with text.
- Supported
Streaming
Receive generated output incrementally.
- Supported
Function calling
Generate application-defined function calls for supported workflows.
- Supported
Structured outputs
Constrain responses to a machine-readable structure.
- Supported
Fine-tuning
Customize supported behavior for specialized tasks.
- Supported
Predicted outputs
Optimize supported generation when much of the expected output is known.
07 / Evaluation
GPT-4.1 Nano Strengths and Limitations
Nano is strongest when the production problem rewards speed, low unit cost, and narrow task scope. It should not be evaluated as a miniature replacement for every larger model.
Strengths
Very low token cost
The $0.10 input and $0.40 output rates make high-frequency model calls economically practical.
Low-latency positioning
OpenAI designed Nano as the fastest GPT-4.1 tier for tasks where responsiveness matters.
1M-token context
Large inputs can be used even when the required operation itself is simple.
Production API features
Image input, structured outputs, function calling, streaming, fine-tuning, and predicted outputs support multiple integration patterns.
What to consider
Lower capability ceiling
Complex coding, difficult instruction following, and multi-step decision making are better candidates for stronger models.
Complex tool use needs careful evaluation
Feature support does not imply parity with larger GPT-4.1 models on difficult function-calling workflows.
No adjustable reasoning effort
Nano is a non-reasoning model and does not provide configurable reasoning levels.
Newer nano alternatives exist
OpenAI currently recommends starting with GPT-5 nano for more complex tasks, so migration value should be tested against your existing GPT-4.1 Nano baseline.
Test the smallest viable model
Find out where GPT-4.1 Nano is enough
Run your real classification, extraction, routing, and short-generation prompts, then compare quality, token usage, cost, and failure rate before choosing a larger model.
Start FreeConnect your own OpenAI API key and evaluate whether Nano can handle the inexpensive first stage of your production workflow.
Common Questions
What is GPT-4.1 Nano?
GPT-4.1 Nano is the smallest, fastest, and lowest-cost model in the GPT-4.1 family. It is designed for low-latency, high-volume tasks such as classification, extraction, routing, and autocomplete.
How much does GPT-4.1 Nano cost?
GPT-4.1 Nano costs $0.10 per 1M standard input tokens, $0.025 per 1M cached input tokens, and $0.40 per 1M output tokens.
What is the GPT-4.1 Nano context window?
GPT-4.1 Nano has a 1,047,576-token context window and supports up to 32,768 output tokens.
Is GPT-4.1 Nano a reasoning model?
No. GPT-4.1 Nano is a non-reasoning model with low latency and does not expose an adjustable reasoning-effort setting.
Does GPT-4.1 Nano support images?
Yes. GPT-4.1 Nano supports text and image input and produces text output. Audio and video are not supported as native input modalities.
Does GPT-4.1 Nano support function calling and structured outputs?
Yes. It supports function calling and structured outputs, but complex tool workflows should still be evaluated because API feature support does not guarantee the same reliability as a larger model.
What is GPT-4.1 Nano best used for?
It is best suited to high-volume narrow tasks such as classification, extraction, routing, tagging, short transformations, autocomplete, and inexpensive first-stage processing in multi-model systems.
Should I choose GPT-4.1 Nano or GPT-4.1 Mini?
Choose Nano when speed and unit cost dominate and your task is narrow enough to meet its quality threshold. Choose Mini when the workload needs more capability, stronger instruction following, or more reliable handling of difficult requests.
Is GPT-4.1 Nano still the best nano model to start with?
OpenAI currently recommends starting with GPT-5 nano for more complex tasks. Existing GPT-4.1 Nano applications should compare quality, latency, and total cost before deciding whether migration is worthwhile.
Model information
Last updated
Specifications, pricing, modalities, and supported features on this page are based on the official OpenAI documentation for GPT-4.1 Nano. OpenAI describes it as the fastest and most cost-efficient GPT-4.1 model and recommends GPT-5 nano as a starting point for more complex tasks.