OpenAI Realtime API: Understanding the Costs

The OpenAI Realtime API is a tool for developers who want to create apps with real-time audio features. It lets apps handle voice conversations smoothly and quickly.

This makes it great for building things like voice assistants or phone systems that respond instantly.

The API works with both audio and text, but its main focus is on fast speech-to-speech interactions.

How the Pricing Works

The cost of the Realtime API depends on audio tokens.

Tokens are small units of data that measure how much audio is processed. There are two types of charges:

• Input Audio: This is what the user says. It costs $100 for every 1 million tokens.
• Output Audio: This is what the system says back. It costs $200 for every 1 million tokens.

To make it simpler, we can think about costs per minute. Based on normal audio settings:

• Input audio uses about 600 tokens per minute. That’s roughly $0.06 per minute ($100 ÷ 1,000,000 × 600).
• Output audio uses about 1,200 tokens per minute. That’s roughly $0.24 per minute ($200 ÷ 1,000,000 × 1,200).

These numbers are estimates. The real cost can change depending on things like audio quality or how the sound is processed. So, your bill might not match these figures exactly.

Calculating Costs with an Example

Let’s break it down with a real example. Imagine a 5-minute conversation using the API.

Input Audio Calculation:

• Tokens: 5 minutes × 600 tokens per minute = 3,000 tokens
• Cost: 3,000 ÷ 1,000,000 × $100 = $0.30

Output Audio Calculation:

• Tokens: 5 minutes × 1,200 tokens per minute = 6,000 tokens
• Cost: 6,000 ÷ 1,000,000 × $200 = $1.20

Total Cost:

$0.30 (input) + $1.20 (output) = $1.50

So, a 5-minute chat costs about $1.50. But this assumes the system talks back for the full 5 minutes. In real life, responses might be shorter or longer, which changes the token count and cost. For instance, if the system only replies for 2 minutes, the output cost drops to $0.48 (2 × 1,200 ÷ 1,000,000 × $200), making the total $0.78.

What Users Are Saying

Some people using the API have noticed their costs are higher than expected.

For example, one user said they spent $6 for just 75 seconds of use. Let’s check that against our estimates:

• Input: 75 seconds = 1.25 minutes × 600 tokens = 750 tokens. Cost = $0.075.
• Output: If the same length, 1.25 minutes × 1,200 tokens = 1,500 tokens. Cost = $0.30.
• Total: $0.075 + $0.30 = $0.375.

That’s way less than $6. So why the difference? Here are some possibilities:

• Text and Audio Together: If the app uses both audio and text, text charges might add up separately.
• Extra Fees: There could be hidden costs, like a fee to keep the connection open, not shown in the token rates.
• Mistakes: Billing or usage tracking might go wrong, charging more than it should.

This shows why it’s smart to keep an eye on your usage. The way tokens are counted isn’t fully explained, so costs can be hard to guess sometimes.

Other Things to Know

The API isn’t just for audio—it handles text too. If your app sends or receives text messages, those have their own prices.

For example, using a model like GPT-4o, text input costs $10 per 1 million tokens, and text output costs $30 per 1 million tokens. If your app mixes audio and text, you’ll pay for both. A 1,000-token text reply, for instance, costs $0.03, on top of any audio charges.

What about discounts?

OpenAI has special plans for big users with lower rates, but there’s no clear info on this for the Realtime API. If you plan to use it a lot, you might ask OpenAI about cheaper options.

Tips to Keep Costs Down

Here’s how to manage your spending with the API:

• Watch Output Audio: It costs four times more per minute than input ($0.24 vs. $0.06). Short replies save money.
• Check Usage: Track how many tokens you’re using to spot surprises early.
• Test Small: Start with short interactions to see how costs add up before going big.

Wrapping Up

The OpenAI Realtime API is a strong choice for apps needing fast audio conversations.

Its pricing is based on tokens: $100 per million for input audio ($0.06 per minute) and $200 per million for output audio ($0.24 per minute).

But those are just starting points—real costs can shift based on how you use it. Some users have seen bills much higher than they planned, possibly from text charges, extra fees, or errors.

To use it well, pay attention to your usage, especially output audio, since it’s pricier.

Know that text adds separate costs if your app uses it. And if you’re a heavy user, look into special pricing.

With careful planning, you can keep your budget in check while building with this powerful tool.