GPT-4.1 nano
GPT-4.1 nano is the smallest and fastest model in the GPT-4.1 family, designed for high-volume, low-latency tasks like classification, autocomplete, and routing, delivering strong results on MMLU at the lowest price point in the GPT-4.1 lineup. Your use is subject to OpenAI's Terms & Privacy Policies.
View API reference- Input and output price
- Prices from: Input $0.10, Output $0.40, Per 1M tokens
- 24h uptime
- Loading AI Gateway uptime
import { streamText } from 'ai'
const result = streamText({ model: 'openai/gpt-4.1-nano', prompt: 'Why is the sky blue?'})Copy link to headingPlayground
Try out GPT-4.1 nano by OpenAI. Usage is billed to your team at API rates. Free users (those who haven't made a payment) get $5 of credits every 30 days.
GPT-4.1 nano
GPT-4.1 nano
Copy link to headingProviders
Route requests across multiple providers. Copy a provider slug to set your preference. Visit the docs for more info. Using a provider means you agree to their terms, listed under Legal.
Copy link to headingUptime24 hours
Direct request success rate on AI Gateway and per-provider. Visit the docs for more info.
Copy link to headingThroughput24 hours
P50 throughput on live AI Gateway traffic, in tokens per second (TPS). Visit the docs for more info.
Copy link to headingLatency24 hours
P50 time to first token (TTFT) on live AI Gateway traffic, in milliseconds. View the docs for more info.
Getting started
Call GPT-4.1 nano through AI Gateway with the AI SDK generateText and streamText functions, or through the OpenAI Chat Completions, OpenAI Responses, and Anthropic Messages APIs by changing the base URL. AI Gateway authenticates the request and routes it to an available provider.
Install the AI SDK (pnpm add ai dotenv), create an API key from the API Keys page, and set it as AI_GATEWAY_API_KEY in your environment. Full setup is covered in the text generation quickstart.
import { generateText } from 'ai';import 'dotenv/config';
async function main() { const result = await generateText({ model: 'openai/gpt-4.1-nano', prompt: 'Why is the sky blue?', });
console.log(result.text);}
main().catch(console.error);Top-level parameters
The same GPT-4.1 nano request in each API format AI Gateway supports.
import { generateText } from 'ai';import 'dotenv/config';
async function main() { const result = await generateText({ model: 'openai/gpt-4.1-nano', system: 'You are a concise technical assistant.', prompt: 'Summarize the tradeoffs between static generation and SSR.', maxOutputTokens: 1024, temperature: 0.5, });
console.log(result.text);}
main().catch(console.error);Standard parameters like prompt, messages, temperature, and tools work as documented in the AI SDK docs. These are the parameters with model-specific behavior.
| Parameter | Type | Required | Description |
|---|---|---|---|
model | string | Yes | Model ID in the form creator/model, e.g. openai/gpt-4.1-nano. AI Gateway routes the request to an available provider. |
maxOutputTokens | number | No | Hard cap on generated tokens. GPT-4.1 nano supports up to 32,768 output tokens. |
providerOptions | Record<string, JSONValue> | No | AI Gateway routing options under gateway, plus any provider-native options under the provider’s own namespace — see the table below. |
Input limits
| Input | Formats | Sources | Max count | Max size | Limits |
|---|---|---|---|---|---|
| Text | — | — | — | — | Prompt and response share the 1M-token context window |
| Image | — | URL, base64, Uint8Array | — | — | Sent as image parts in messages; counts as input tokens |
| — | URL, base64, Uint8Array | — | — | Sent as file parts in messages; counts as input tokens |
Provider options
Set AI Gateway routing options under providerOptions.gateway. For provider-specific options, pass them under the provider’s namespace as documented by the AI SDK.
Learn more in the AI SDK openai provider docs.
import { generateText } from 'ai';import 'dotenv/config';
async function main() { const result = await generateText({ model: 'openai/gpt-4.1-nano', prompt: 'Why is the sky blue?', providerOptions: { gateway: { only: ['azure', 'openai'], }, }, });
console.log(result.text);}
main().catch(console.error);These AI Gateway routing options apply to every model. Provider-specific options pass through under the provider’s own namespace (for example providerOptions.anthropic) exactly as documented by the AI SDK.
| Parameter | Type | Required | Description |
|---|---|---|---|
providerOptions.gateway.only | string[] | No | Restrict routing to these provider slugs. Requests fail over only within the listed providers. |
providerOptions.gateway.order | string[] | No | Preferred provider order. Listed providers are tried first; unlisted providers remain available as fallbacks. |
providerOptions.gateway.sort | 'cost' | 'ttft' | 'tps' | No | Rank candidate providers by price, time to first token, or tokens per second instead of the default routing order. |
providerOptions.gateway.zeroDataRetention | boolean | No | Route only to providers with a zero-data-retention policy for this model. |
Routing across providers
AI Gateway serves the same model through multiple providers and fails over automatically. order expresses a preference while keeping every provider eligible; only is a hard allowlist — if none of the listed providers are available the request fails instead of falling back.
Options under a provider's own namespace (for example providerOptions.anthropic) are forwarded to that provider with the request. Providers ignore option namespaces that don't apply to them, so it is safe to set provider options alongside gateway routing options.
Image input
Send images alongside text as message parts. Images count as input tokens.
import { generateText } from 'ai';import 'dotenv/config';
async function main() { const result = await generateText({ model: 'openai/gpt-4.1-nano', messages: [ { role: 'user', content: [ { type: 'text', text: 'Describe this image.' }, { type: 'image', image: 'https://example.com/photo.jpg' }, ], }, ], });
console.log(result.text);}
main().catch(console.error);PDF input
Attach PDFs as file parts. Their contents count as input tokens.
import { generateText } from 'ai';import 'dotenv/config';
async function main() { const result = await generateText({ model: 'openai/gpt-4.1-nano', messages: [ { role: 'user', content: [ { type: 'text', text: 'Summarize this document.' }, { type: 'file', mediaType: 'application/pdf', data: 'https://example.com/document.pdf', }, ], }, ], });
console.log(result.text);}
main().catch(console.error);Tool calling
Expose tools the model can call. Define each tool’s inputs with a Zod schema.
import { generateText, tool } from 'ai';import { z } from 'zod';import 'dotenv/config';
async function main() { const result = await generateText({ model: 'openai/gpt-4.1-nano', prompt: 'What is the weather in San Francisco?', tools: { getWeather: tool({ description: 'Get the current weather for a location', inputSchema: z.object({ location: z.string() }), execute: async ({ location }) => ({ location, temperatureC: 18 }), }), }, });
console.log(result.text);}
main().catch(console.error);Copy link to headingMore models by OpenAI
| Model |
|---|
| 1.1M | 3.5 s | 72 tps | $10/M+2 more | $50/M+2 more | Read$1/M Write$12.50/M | $10/K | +5 |  | 09/04/2026 | |||||
| 1.1M | 2.2 s | 146 tps | $0.20/M+2 more | $1.20/M+2 more | Read$0.02/M Write$0.25/M | $10/K | +5 |  | 07/09/2026 | |||||
| 1.1M | 5.9 s | 69 tps | $4/M+2 more | $20/M+2 more | Read$0.40/M Write$5/M | $10/K | +5 | | 07/09/2026 | |||||
| 1.1M | 1.9 s | 85 tps | $2/M+2 more | $12/M+2 more | Read$0.20/M Write$2.50/M | $10/K | +5 | | 07/09/2026 | |||||
| 400K | 0.9 s | 159 tps | $0.05/M | $0.40/M | Read$0.005/M | $14/K | +3 | | 08/07/2025 | |||||
| 400K | 3.2 s | 119 tps | $0.25/M+1 more | $2/M+1 more | Read$0.03/M | $14/K | +3 | | 08/07/2025 |
Copy link to headingAbout GPT-4.1 nano
GPT-4.1 nano was introduced on April 14, 2025 as the smallest and most latency-optimized model in the GPT-4.1 family. OpenAI designed it specifically for tasks where speed and cost efficiency take priority over frontier reasoning depth: classification, autocomplete, routing decisions, and other lightweight inference workloads that need to run at high volume.
Despite being the entry-level tier of the GPT-4.1 family, GPT-4.1 nano posts creditable benchmark scores for its size: 80.1% on MMLU (Massive Multitask Language Understanding) and 50.3% on GPQA (Graduate-Level Google-Proof Q&A). These numbers show that the GPT-4.1 training improvements carried down to the smallest variant. Like its larger siblings, it supports the full context window of 1.0M tokens, which is a notable capability for a model at its price point and enables it to handle tasks that involve reading long inputs even if the outputs remain short.
GPT-4.1 nano inherits the GPT-4.1 family's 75% prompt caching discount and the removal of surcharges for long-context usage. For applications that preload a large knowledge base or system prompt once and then issue many rapid short queries against it, these economics make nano an attractive option for the query stage of a retrieval-augmented pipeline.
Copy link to headingWhat To Consider When Choosing a Provider
- Configuration: For event-driven pipelines that fire many rapid inferences per user action (real-time intent classification, content routing), GPT-4.1 nano's speed and low cost make it practical to run inference inline without queuing.
- Zero Data Retention: Zero Data Retention is available for this model. It is offered on a per-provider and model basis. See the documentation for details.
- Authentication: AI Gateway authenticates requests using an API key or OIDC token. You do not need to manage provider credentials directly.
Copy link to headingWhen to Use GPT-4.1 nano
Best for
- Real-time classification: Sentiment analysis, intent detection, and topic labeling at high request volume
- Autocomplete features: Inline suggestion experiences requiring sub-second response times
- Routing and triage: Logic within multi-model pipelines that decides which downstream model handles a request
- Short-answer extraction: Pulling answers from long documents where the context window of 1.0M tokens and nano's low cost combine well
- Cost-sensitive batch jobs: Millions of inferences that need to run economically
Consider alternatives when
- Complex reasoning: GPT-4.1 mini or GPT-4.1 provide meaningfully higher capability for multi-step reasoning, code generation, or complex instruction following
- Edge-case quality: Larger models in the family handle nuanced or ambiguous inputs better
- Hard STEM problems: O1-mini or o1 are purpose-built for chain-of-thought reasoning on difficult STEM tasks
Copy link to headingConclusion
GPT-4.1 nano brings the GPT-4.1 family's architectural improvements, including the context window of 1.0M tokens and 75% caching discount, to the fastest and most affordable tier, making it the right choice for classification, routing, and high-throughput lightweight inference through AI Gateway.
Copy link to headingFrequently Asked Questions
What tasks is GPT-4.1 nano specifically designed for?
OpenAI designed it for classification, autocomplete, and routing where response speed and low cost outweigh the need for frontier reasoning.
Does GPT-4.1 nano really support a context window of 1.0M tokens?
Yes. All three GPT-4.1 family members share the context window of 1.0M tokens, which is unusual for a model at nano's price and speed tier.
What benchmark scores does GPT-4.1 nano achieve?
At launch, GPT-4.1 nano scored 80.1% on MMLU and 50.3% on GPQA, showing that the family's training improvements extended to the smallest variant.
How does GPT-4.1 nano's pricing compare to the rest of the GPT-4.1 family?
See the pricing section on this page for today's rates. AI Gateway exposes each provider's pricing for GPT-4.1 nano.
Is GPT-4.1 nano suitable as the query model in a RAG pipeline?
Yes. Pairing a large preloaded knowledge base or system prompt (benefiting from the 75% cache discount) with rapid, inexpensive nano queries is a practical pattern for retrieval-augmented generation at scale.
When should I use nano versus mini versus GPT-4.1?
Nano: classification, routing, autocomplete. Mini: GPT-4o-class quality with lower cost and latency. GPT-4.1: maximum coding and instruction-following accuracy.
What are typical latency characteristics?
This page shows live throughput and time-to-first-token metrics measured across real AI Gateway traffic.