OpenAI

GPT-5 nano

Public pricingLarge memoryTool use

GPT-5 nano is OpenAI's large-memory model with tool use. This page shows current pricing, an interactive cost calculator, and a side-by-side with similar models.

Input

$0.05/1M tokens

Output

$0.40/1M tokens

Cached

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Batch

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Interactive

Calculate your GPT-5 nano bill.

Adjust the workload below and watch the monthly cost update in real time.

What would GPT-5 nano cost you?

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1,00010,00050,000250,0001M10M

Technical specifications

GPT-5 nano at a glance.

Memory

400,000

tokens

Max reply

16,384

tokens

Memory tier

Large

an entire book or large codebase

Tokenizer

tiktoken-o200k

Released

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Training cutoff

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Availability

Public pricing

Status

active

What it can do

Capabilities & limits.

  • Understands images
  • Deep step-by-step thinking
  • Uses tools / calls functions
  • Strict JSON output
  • Streams replies
  • Fine-tunable on your data

When to pick GPT-5 nano

  • Agentic workflows that call tools or APIs.
  • Long documents, full codebases, or extensive chat histories.
  • High-volume workloads where unit cost matters.

When to look elsewhere

  • Your workload involves images — pick a vision-capable model instead.

Alternatives

Similar models to compare.

FAQ

GPT-5 nano — the questions we see most.

Pricing, capabilities, alternatives — generated from the same data that powers the calculator above.

Get instant answers from our AI agent

At a typical workload of 50,000 conversations a month with 1,500-token prompts and 800-token replies, GPT-5 nano costs roughly $20 per month. Input is $0.05 /1M tokens and output is $0.40 /1M tokens.
GPT-5 nano has a 400,000-token context window (large memory — an entire book or large codebase). That means you can fit about 75,000 words of input and history in a single call.
Beyond text generation, GPT-5 nano supports calling functions / tools, strict JSON output. It streams replies by default.
Models in a similar class include GPT-4.1 nano, GPT-4o mini, GPT-4.1 mini. The "Similar models" section below this FAQ links into each.

Still unsure?

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