GPT-5 nano
GPT-5 nano is the fastest and most affordable model in the GPT-5 family, designed for high-throughput, low-latency tasks like classification, routing, autocomplete, and lightweight inference at scale.
View API reference- Input and output price
- Prices from: Input $0.05, Output $0.40, Per 1M tokens
- 24h uptime
- Loading AI Gateway uptime
import { streamText } from 'ai'
const result = streamText({ model: 'openai/gpt-5-nano', prompt: 'Why is the sky blue?'})Copy link to headingPlayground
Try out GPT-5 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-5 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.
| Provider |
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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-5 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-5-nano', prompt: 'Why is the sky blue?', });
console.log(result.text);}
main().catch(console.error);Top-level parameters
The same GPT-5 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-5-nano', system: 'You are a concise technical assistant.', prompt: 'Summarize the tradeoffs between static generation and SSR.', maxOutputTokens: 1024, });
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-5-nano. AI Gateway routes the request to an available provider. |
maxOutputTokens | number | No | Hard cap on generated tokens. GPT-5 nano supports up to 128,000 output tokens. Reasoning tokens count toward this limit. |
reasoning | 'provider-default' | 'none' | 'minimal' | 'low' | 'medium' | 'high' | 'xhigh' | No | Provider-agnostic reasoning effort, available in AI SDK 7 or later. Maps to the provider’s native reasoning configuration; reasoning settings under providerOptions take precedence when both are set. See the Reasoning section below. |
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 400K-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-5-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.
Reasoning
AI Gateway bridges reasoning across every API format. The AI SDK exposes a provider-agnostic top-level reasoning level (none, minimal, low, medium, high, or xhigh); the Chat Completions and Responses formats take the same effort under reasoning.effort; and the Anthropic Messages format uses a native thinking token budget. Whichever you send, the gateway maps it to the target model’s native configuration, converting between effort levels and token budgets as needed. Reasoning-related settings under providerOptions take full precedence over the top-level reasoning value and are never merged. Reasoning tokens typically count toward your output-token usage, though how they’re reported and billed varies by provider.
Learn more in the AI Gateway reasoning guide.
import { generateText } from 'ai';import 'dotenv/config';
async function main() { const result = await generateText({ model: 'openai/gpt-5-nano', prompt: 'Explain the Monty Hall problem step by step.', reasoning: 'high', });
console.log(result.text);}
main().catch(console.error);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-5-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-5-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-5-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 headingAbout GPT-5 nano
GPT-5 nano was released on August 7, 2025 as the entry-level tier of the GPT-5 model family. It's optimized for the highest throughput and lowest latency in the family, targeting workloads where speed and cost matter more than reasoning depth.
Despite being the smallest GPT-5 variant, GPT-5 nano benefits from the family's architectural improvements. It handles classification, routing, extraction, and simple generation tasks with quality that reflects the generational leap from GPT-4.1 nano. The context window of 400K tokens is notable for a model at this tier, enabling it to process long inputs even when outputs remain short.
The model is designed to serve as a building block in larger systems: classifying incoming requests, routing them to appropriate handlers, extracting key fields from documents, and providing instant responses for simple queries, all at a cost that makes per-request inference viable for the highest-traffic applications.
Copy link to headingWhat To Consider When Choosing a Provider
- Configuration: GPT-5 nano prioritizes throughput and latency over reasoning depth. It's the right choice when you need fast answers to simple questions at minimal cost.
- Configuration: At its price point, GPT-5 nano is practical as a classifier, router, or preprocessor that runs on every request, deciding which downstream model or action to invoke.
- 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-5 nano
Best for
- Real-time classification: Sentiment analysis, intent detection, and topic labeling at high request volume
- Routing and triage: Deciding which model or workflow handles each incoming request
- Autocomplete and suggestions: Sub-second inline suggestions in editors and search interfaces
- Lightweight extraction: Pulling specific fields from structured or semi-structured text
- Cost-sensitive batch processing: Millions of simple inferences at minimal aggregate cost
Consider alternatives when
- Complex reasoning needed: GPT-5 mini or GPT-5 for tasks requiring multi-step analysis
- Code generation: Codex mini or GPT-5 codex for coding-specific tasks
- Deep deliberation: O3 or o4-mini for problems that benefit from chain-of-thought reasoning
- Rich multimodal analysis: Full GPT-5 for complex vision and document understanding tasks
Copy link to headingConclusion
GPT-5 nano brings GPT-5 family improvements to the fastest and most affordable tier, making it the right choice for classification, routing, and high-throughput lightweight tasks through AI Gateway.
Copy link to headingFrequently Asked Questions
What tasks is GPT-5 nano designed for?
Classification, routing, autocomplete, lightweight extraction, and any high-volume workload where speed and cost outweigh the need for deep reasoning.
How does GPT-5 nano compare to GPT-4.1 nano?
GPT-5 nano is the next generation, inheriting GPT-5 family improvements in quality and instruction following while maintaining the speed and cost profile expected of a nano-tier model.
What context window does GPT-5 nano support?
400K tokens, which is substantial for a model at this price and speed tier.
Can GPT-5 nano handle long documents?
It can read long inputs within its window of 400K tokens, but it's optimized for short outputs. For detailed analysis of long documents, consider GPT-5 mini or GPT-5.
How does AI Gateway handle authentication for GPT-5 nano?
AI Gateway accepts a single API key or OIDC token for all requests. You don't embed OpenAI credentials in your application; AI Gateway routes and authenticates on your behalf.
What are typical latency characteristics?
This page shows live throughput and time-to-first-token metrics measured across real AI Gateway traffic.