GPT-5 nano
Ultra light-touch assistant for simple interactions.
You always get the exact model you pick — we never silently route you to another.
About GPT-5 nano
When the goal is to process millions of tokens at the lowest possible cost, GPT-5 nano delivers. At $0.05 per million input tokens — five times cheaper than GPT-5 mini — it is purpose-built for high-volume, latency-tolerant workloads: classifying support tickets, extracting structured fields from documents, routing messages, and running bulk summarization across 400K-token contexts without truncation. Developers running these pipelines appreciate the throughput — 163.8 tokens per second, above average for this tier — and the 80% prompt-caching discount that makes repeated-context jobs even cheaper. The tradeoff is real: time-to-first-token sits around 84 seconds, far above the median of roughly one second, so anything interactive is a poor fit. The model also tends toward verbosity, generating far more tokens than similarly-sized peers. If your workload is batch-oriented and the unit cost per operation matters more than conversational snappiness, nano earns its place. For tasks requiring nuanced reasoning or real-time responses, step up to GPT-5 mini.
Best for
- High-volume document classification and content labeling at scale
- Multi-document summarization using its full 400K-token context window
- Structured data extraction from invoices, contracts, and unstructured text
- Automated content routing and support-ticket triage
- Bulk translation and text transformation pipelines where cost per token is the primary constraint
Specifications
| Provider | OpenAI |
|---|---|
| Released | 2025-08 |
| Intelligence | Low |
| Speed | Fast |
| Context window | 400,000 tokens |
| Max output | 128,000 tokens |
| Knowledge cutoff | May 30, 2024 |
| Input price | $0.05 / 1M tokens |
| Output price | $0.40 / 1M tokens |
| Request cost | 1 base request |
| Plan tier | Base |
| Model ID | gpt-5-nano |
Frequently asked questions
Input is $0.05 per million tokens and output is $0.40 per million tokens. Cached input hits receive an 80% discount, dropping to $0.01 per million tokens — a significant saving for workflows that reuse long context repeatedly.
GPT-5 nano supports up to 400,000 input tokens and can generate up to 128,000 output tokens, making it well suited for processing full documents or long conversation histories in a single call.
No. Its time-to-first-token averages around 84 seconds, which is far above the median of about one second for comparable models. It is better suited to batch and asynchronous workloads than interactive, low-latency use cases.
GPT-5 nano is roughly 5x cheaper on blended pricing and faster in raw token throughput, but trails GPT-5 mini by around 10 percentage points on structured tasks and is meaningfully weaker at multi-step reasoning. Choose nano for bulk pipelines where cost dominates; choose mini when accuracy or reasoning depth matters more.
Training data ends May 30, 2024. Events and developments from mid-2024 onward are outside its knowledge, which is worth considering for tasks that depend on current information.
No. Unlike GPT-5 mini, nano is only accessible via the OpenAI API. It is not available in the ChatGPT consumer interface.