Context Window
128,000
Tokens
Model Cost Profile
OpenAI: GPT Audio Mini
Developer: openai
Pricing updated Mar 11, 2026
Input rank: #212Output rank: #234
OpenAI's GPT Audio Mini is designed for applications requiring extensive audio processing capabilities, making it ideal for transcription services, voice assistants, and interactive audio experiences. With a context window of 128,000 tokens, teams can manage large audio datasets efficiently, ensuring coherent and contextually relevant outputs. The pricing structure, at $0.60 per million input tokens and $2.40 per million output tokens, allows teams to budget effectively based on their audio processing needs.
📋 Structured Output
Input Price / 1M
$0.6000
Prompt tokens
Output Price / 1M
$2.40
Completion tokens
Intelligence (MMLU)
Benchmark Pending
Massive Multitask Language Understanding
Side-by-Side Pricing Table
| Usage Type | Price / 1M Tokens |
|---|---|
| Input (Prompt) | $0.6000 |
| Output (Completion) | $2.40 |
Price History
OpenAI: GPT Audio Mini Pricing Trend
Input / 1M tokens0.0%Output / 1M tokens0.0%
Mar 7 — Mar 11
Current Input / 1M
$0.6000
Current Output / 1M
$2.40
Cost Calculator
Estimate monthly spend for OpenAI: GPT Audio Mini based on your workload.
01.0B
0500M
Estimated Monthly Cost
$44
25M input + 12M output tokens
Cheaper Alternatives to Compare
Quick links for cost-down decisions before production rollout.
FAQ
Common pricing and benchmark questions for OpenAI: GPT Audio Mini.
How much does OpenAI: GPT Audio Mini cost per 1M input tokens?
OpenAI: GPT Audio Mini input pricing is $0.6000 per 1M tokens based on the latest synced provider data.
How much does OpenAI: GPT Audio Mini cost per 1M output tokens?
OpenAI: GPT Audio Mini output pricing is $2.40 per 1M tokens based on the latest synced provider data.
What context window does OpenAI: GPT Audio Mini support?
OpenAI: GPT Audio Mini supports a context window of 128,000 tokens.
How can I compare OpenAI: GPT Audio Mini with cheaper alternatives?
Use the comparison links on this page to open direct model-vs-model pricing and benchmark pages, then evaluate monthly spend projections for your workload.