If you're building anything with conversational AI, you've probably noticed things are moving fast. OpenAI, in particular, seems to be launching new developer tools all the time. When you’re putting together a conversational app, one of the first big decisions you have to make is choosing the right API. It’s a choice that shapes your app's speed, the user's experience, and, of course, your budget.
For a good while, the Chat Completions API was the default choice for just about everyone. But now, there’s a new option built specifically for high-speed, voice-first chats: the Realtime API. So, which one should you actually use?
This guide will walk you through the differences between the Realtime API vs Chat Completions API. We’ll get into their architecture, speed, cost, and the best situations to use each one. By the end, you’ll have a much clearer idea of which is right for your project, especially if you’re working on customer support tools.
What is the OpenAI Chat Completions API?
You can think of the OpenAI Chat Completions API as the reliable engine for text-based AI. It’s the industry-standard tool that developers have used for years to power everything from chatbots to writing assistants with models like GPT-4. The best part about it is its straightforward and dependable nature.
The process is simple: you send a structured list of messages using a standard HTTP request. Each message gets a role ("system", "user", or "assistant") to give the model some context. The API takes your request, thinks for a moment, and sends back a complete text response. Because every one of these calls is its own separate transaction, the API is "stateless."
This request-and-response model makes it super flexible for a ton of different tasks. But when you try to bring voice into the mix, it starts to feel a bit clunky. To build a voice assistant with this API, you have to string together a few different services: a speech-to-text model (like Whisper) to figure out what the user said, the Chat Completions API to generate a reply, and then a text-to-speech model to turn that reply into audio. This chain of events adds a noticeable delay, making the conversation feel less natural.
What is the OpenAI Realtime API?
The OpenAI Realtime API is OpenAI’s solution to that lag. It’s a specialized tool built from the ground up for creating incredibly fast, speech-to-speech conversations that feel a lot more like talking to a real person.
Instead of the simple request-response model, the Realtime API uses a persistent WebSocket connection. This opens up a two-way street where audio can stream back and forth without interruption. This design is the secret to its speedy performance, allowing for the kind of natural back-and-forth that’s just not possible with the older API.
It manages the entire voice pipeline, speech recognition, thinking, and speech generation, all in one go. One of its coolest features is how it handles interruptions. A user can jump in and talk over the AI, just like in a normal conversation, and the API can adjust right away. That’s a huge improvement over the stiff, turn-by-turn interactions of a chained API setup.
Key differences: Realtime API vs Chat Completions API
Even though both APIs let you use OpenAI’s powerful models, they’re fundamentally different tools for different jobs. Let's dig into where they part ways.
Architecture and communication protocol
The biggest difference is how they talk to each other.
The Chat Completions API works on standard HTTP requests. Every call is a fresh, independent transaction. It’s a simple, time-tested method that pretty much every developer knows. Think of it like sending a letter and waiting for a reply, it works, but it’s not instant.
The Realtime API, on the other hand, uses WebSockets to create a steady, two-way connection. This is a bit more involved to set up, but it's what you need for the constant data streaming that real-time interaction requires. It’s more like having an open phone line where both people can talk and listen at the same time.
Latency and user experience
This architectural choice has a massive effect on speed and what the user actually experiences.
With the Chat Completions API, the delay is just naturally higher. You have the lag from the HTTP request itself, plus the time it takes for each step in the voice chain (transcription, processing, speech synthesis). This makes it a poor fit for fluid, natural voice conversations. That little pause before a response can make an interaction feel robotic and awkward.
The Realtime API is built for speed, with response times often clocking in under a few hundred milliseconds. This allows for smooth, human-like voice chats where the conversation can flow. Users can interrupt, and the AI can respond almost immediately, which makes for a much more engaging experience.
Modalities and core function
At their heart, the two APIs are made for different kinds of data.
The Chat Completions API is text-in, text-out. Its entire setup is geared toward processing and generating words on a screen. You can add audio capabilities to it, but it’s more of a workaround than its main purpose.
The Realtime API is natively speech-to-speech. It’s designed to understand and generate audio directly. This lets it hold on to subtleties like tone and inflection that often get lost when you convert speech to text and back again.
Here’s a quick table to sum up the main differences:
| Feature | Chat Completions API | Realtime API |
|---|---|---|
| Primary Use Case | Text-based chat, content generation | Real-time voice agents, live transcription |
| Communication | HTTP (Request-Response) | WebSockets (Persistent Streaming) |
| Latency | Higher | Very Low ( |
The simpler alternative for support teams
Let's be real: building directly on these APIs, especially the more complex Realtime API, is a big project. It takes a lot of engineering time, continuous maintenance, and a deep understanding of how to manage streaming infrastructure. For most support teams, that's just not practical.
This is where a platform like eesel AI can be a huge help. It gives you all the power of these advanced models without the engineering headache. eesel AI is designed specifically for support teams, not just developers. You can set up a powerful AI agent that handles tickets, pulls answers from your knowledge sources like Notion or Confluence, and even takes custom actions, all from a simple dashboard. You can be up and running in minutes, not months.
The eesel AI platform allows teams to connect various knowledge sources to train their AI agent, simplifying the backend complexity of using either the Realtime API vs Chat Completions API.Pricing comparison
Cost is always a big piece of the puzzle, and the pricing for these two APIs is pretty different.
The pricing for the Realtime API is split between text and audio:
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Text input tokens: $5 per 1 million tokens
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Text output tokens: $20 per 1 million tokens
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Audio input: $100 per 1 million tokens (which is about $0.06 per minute)
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Audio output: $200 per 1 million tokens (about $0.24 per minute)
For the Chat Completions API, pricing depends on the model you use. For a popular and powerful model like GPT-4o, the cost is:
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Input: $5 per 1 million tokens
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Output: $15 per 1 million tokens
The main thing to notice here is that processing audio through the Realtime API costs quite a bit more than standard text processing. When you add that cost to the development complexity, building a voice agent from scratch becomes a serious investment.
This is a big contrast to the straightforward pricing of a platform like eesel AI. We offer clear monthly or annual plans based on how much you use, with no hidden fees per resolution. That means you won't get a surprise bill after a busy month, giving you the kind of cost predictability you need to grow your support.
A look at the eesel AI pricing page, which offers a clear alternative to the complex token-based costs of the Realtime API vs Chat Completions API.Choosing between Realtime API vs Chat Completions API
The choice between the Realtime API and the Chat Completions API really comes down to what you’re willing to trade. The Chat Completions API is the versatile, dependable, and budget-friendly option for anything text-related. The Realtime API is the high-performance specialist, built specifically for natural, low-latency voice chats.
Your decision should be guided by what you're trying to accomplish. If your app's success depends on real-time voice, the Realtime API is where you should be aiming. For almost everything else, the Chat Completions API is the more sensible and efficient place to start. But for many, there's an even better way.
Build powerful AI agents without the complexity
If you're looking for the power of real-time conversational AI without the massive engineering lift, eesel AI is the bridge. We offer a powerful and easy-to-use platform that lets you deploy advanced AI for your support team.
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Go live in minutes, not months: Integrate with help desks like Zendesk or Freshdesk with just a single click.
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Total control: Tweak your AI's personality, what it knows, and what it can do without writing any code.
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Test with confidence: Use our simulation mode to see exactly how your AI will handle past tickets before you ever let it talk to customers.
Ready to automate your frontline support without the engineering overhead? Start your free eesel AI trial today.
Frequently asked questions
Your decision should hinge on the primary modality. If your project's success relies on fluid, human-like voice conversations with minimal latency, the Realtime API is the clear choice. For text-based interactions, content generation, or backend processing where real-time voice isn't critical, the Chat Completions API is more suitable.
While they serve different primary functions, you could use both in a sophisticated application. For instance, the Realtime API could handle the live voice interaction, while the Chat Completions API could power asynchronous tasks like summarizing the conversation or generating follow-up emails in the background.
If your application needs full, natural speech-to-speech voice interactions, the Realtime API will be more cost-effective despite its higher per-token audio cost, as it’s designed to handle the entire voice pipeline efficiently. Trying to chain multiple services with the Chat Completions API for voice can lead to significantly higher overall costs and a much worse user experience due to added complexity and latency.
Transitioning from a text-based Chat Completions API setup to a full voice experience with the Realtime API can be quite complex. The Realtime API requires a different architectural approach (WebSockets for streaming) and managing the integrated voice pipeline, which is a significant engineering effort compared to simple HTTP requests.
Implementing the Realtime API requires setting up and managing persistent WebSocket connections for continuous audio streaming, which is more involved than the stateless HTTP requests of the Chat Completions API. You'll need to handle real-time audio input/output, connection stability, and potentially client-side buffering to ensure a smooth conversational flow.
Both APIs can handle complex conversational logic, as they leverage powerful underlying language models. The Chat Completions API might be simpler to manage for very deep, text-centric multi-turn dialogues where real-time speech is not required. However, the Realtime API excels in complex, fluid voice dialogue, managing context implicitly within the continuous stream.
