How Many Tokens?

← All models

GPT-5 Nano: token counter & pricing

OpenAI · exact (uses official tokenizer) · pricing as of 2026-04-27.

Provider
OpenAI
API model ID
gpt-5-nano
Context window
400,000 tokens
Input price
$0.05 per 1M tokens
Output price
$0.40 per 1M tokens
Tokenizer accuracy
exact (uses official tokenizer)
Pricing as of
2026-04-27

Open the counter to count tokens for GPT-5 Nano in real time.

What is GPT-5 Nano?

GPT-5 Nano is the smallest, cheapest, fastest member of OpenAI's GPT-5 family. Designed for high-volume cost-sensitive workloads where you want GPT-5's tokenizer and ecosystem at near-throwaway prices.

At $0.05 per 1M input tokens, GPT-5 Nano is the cheapest exact-tokenizer model on this counter — cheaper than Gemini 2.5 Flash-Lite ($0.10), Gemini 3.1 Flash-Lite Preview ($0.25), GPT-4.1 Nano ($0.10), and any open-source model.

How tokens are counted here

GPT-5 Nano uses OpenAI's o200k_base tokenizer. Counts computed in your browser via js-tiktoken. Exact.

When to use GPT-5 Nano

When not to use it:

Pricing notes

$0.05 input / $0.40 output per 1M tokens. Cached input is $0.005/M — effectively free.

For comparison, a 1,000-token prompt with a 200-token reply on GPT-5 Nano:

The same workload on GPT-5.5 costs $4,300. Per-million-call savings: $4,170.

Common questions

Is GPT-5 Nano good enough for production?

For the workloads it's designed for — yes. Run a labeled eval set; if it hits your accuracy bar, the savings are massive.

What's the difference between GPT-5 Nano and GPT-5.4 Nano?

Same tokenizer and ballpark capability tier. GPT-5 Nano ($0.05/$0.40) is cheaper. GPT-5.4 Nano ($0.20/$1.25) is incrementally better on hard reasoning. The gap is narrow; default to GPT-5 Nano unless you measure the upgrade winning.

How does GPT-5 Nano compare to Gemini 2.5 Flash-Lite?

Both target the same workloads. GPT-5 Nano: $0.05/$0.40. Gemini 2.5 Flash-Lite: $0.10/$0.40. Nano is half the input price and tied on output. Gemini Flash-Lite has a 1M context window and multimodal input — both advantages over Nano's text-only 400K context. Choose by what your workload actually needs.

Compare GPT-5 Nano to other models