GPT-4.1 nano API Pricing

OpenAI has deprecated this model. The price is kept here for reference; check the provider's documentation before building on it.

OpenAI · released Apr 14, 2025 · 1.05M context · API model ID gpt-4.1-nano

GPT-4.1 nano by OpenAI API pricing: $0.10 per 1M input tokens and $0.40 per 1M output tokens (provider list price via models.dev, Oct 10, 2026).

Prices

GPT-4.1 nano prices, USD per 1M tokens
PriceUSD per 1M tokens
Input$0.10
Output$0.40
Cache read$0.025
Cache writeNot published

OpenAI's list price for its own API, as compiled by models.dev and checked Oct 10, 2026, 09:00 UTC. Prices on cloud platforms and resellers can differ. Provider documentation

Estimated monthly cost

$90.00

per month

per day
$3.00
per request
$0.0003

Cost = input tokens × input price + output tokens × output price, 30 days a month. Cached input uses the cache read price. Cache write fees are not included.

Compare this workload across models

Monthly cost by workload

WorkloadPer month
Chatbot1,000 in / 500 out tokens, 10,000 requests a day$90.00/mo
RAG8,000 in / 500 out tokens, 5,000 requests a day$150.00/mo
Coding agent50,000 in / 2,000 out tokens, 1,000 requests a day, 80% cached input$84.00/mo

Context and capabilities

Context
1,047,576 tokens
Max output
32,768 tokens
Input
text, image
Output
text
Knowledge cutoff
April 2024
Open weights
No
Tool calling
Yes
Reasoning
No
Structured outputs
Yes

Capabilities and limits as listed by models.dev.

Similarly priced models

Price history

No price changes since we started tracking on Oct 8, 2026.

Questions

How much does GPT-4.1 nano cost per month?
For a chatbot handling 10,000 requests a day with 1,000 input and 500 output tokens each, GPT-4.1 nano costs about $90.00 per month at OpenAI's list prices ($0.10 per 1M input tokens, $0.40 per 1M output tokens).
Does GPT-4.1 nano support prompt caching?
Yes. Cached input costs $0.025 per 1M tokens, compared with $0.10 for regular input.
How long is GPT-4.1 nano's context window?
GPT-4.1 nano accepts up to 1,047,576 tokens (1.05M) of context and returns up to 32,768 tokens per response.