ElevenLabs Agents vs OpenAI Realtime API: Conversational Agents Showdown

We have significantly expanded our conversational agents offering through major releases this year and rebranded it as ElevenLabs Agents. Meanwhile, OpenAI released major updates to the gpt-realtime model and its Realtime API capabilities.  

This guide compares the latest version of the two products to help you evaluate the right fit for your conversational agent development needs.

Overview

Conversational agents are systems where people can speak naturally, have the agents understand what they mean, and hear a spoken response back in real time. Both products allow developers to build conversational agents, but they take different architectural approaches.

OpenAI's Realtime API employs an integrated speech-to-speech model that streamlines processing by reducing intermediate steps. ElevenLabs Agents, on the other hand, uses a modular architecture that chains together separate Speech to Text, LLM, and Text to Speech components.

While OpenAI offers strengths in emotional understanding and dynamic voice adjustment, ElevenLabs Agents stand out with several key advantages over the Realtime API:

• Consistently reliable agent performance at lower cost for production-ready use case
• More advanced reasoning and function-calling capabilities
• A superior voice experience, featuring natural turn-taking and a diverse range of voices
• A complete developer platform, including built-in support for multi-agent workflows, testing tools, analytics, and more telephony integrations

Comparison Breakdown

Reliable Agent Performance 

Benchmark

Independent evaluation show advantages for ElevenLabs Agents across reasoning, instruction following, and function calling:

• Function Calling: 80% accuracy on ComplexFuncBen vs OpenAI’s 66.5% (1).
• Instruction Following: over 50% accuracy on Multichallenge vs OpenAI’s 30.5% (2).
• Reasoning:  over 90% accuracy on Big Bench Audio vs OpenAI’s 82% (3).

Higher benchmark performance translates directly into reduced error handling, smoother end-user experiences, and lower operational overhead. With ElevenLabs Agents, you can design systems which will respond more accurately and consistently.

Output Consistency

With OpenAI’s Realtime API, developers have limited control over the system’s output. Transcripts often fail to accurately capture the original audio input. Language handling is also less predictable: the API may switch between languages mid-conversation without user intent, leading to confusing interactions.

ElevenLabs Agents, by contrast, deliver greater output reliability. Its modular architecture allows us to leverage a highly specialized Speech to Text model, with the transcription output flowing directly into the language model without any intermediary processing.

This streamlined pipeline enables ElevenLabs to produce transcripts that more faithfully represent the original audio. In addition, developers can specify exactly which languages an agent is able to understand and speak, ensuring conversations remain consistent and aligned with user expectations. 

Flexibility

OpenAI Realtime API is limited to gpt-realtime models, which may concern organizations seeking to avoid vendor lock-in or requiring specific model characteristics.

ElevenLabs Agents provides flexibility by supporting multiple LLM providers, including open-source alternatives, GPT models, Claude, Gemini, and custom-trained models. This enables you to leverage the latest SOTA LLM models or use your own models when privacy is a priority. 

Natural Voice Experience

Turn Taking

Imagine talking to someone who constantly interrupts mid-sentence or leaves awkward silences when they should respond. This is why turn-taking represents one of conversational AI's greatest challenges: knowing when to respond.

OpenAI's Realtime API relies on simple voice activity detection (VAD) that frequently responds before users complete their thoughts. The system also often lacks contextual awareness, treating natural conversational signals like "hmm," "okay," as interruptions rather than normal speech patterns. This leads to frustrating exchanges where the agent jumps in prematurely or creates unnatural conversation flow.

ElevenLabs has developed a proprietary turn-taking model that analyzes both text and audio simultaneously. By incorporating prosodic cues - tone, rhythm, and vocal emphasis - alongside linguistic content, our system genuinely understands the difference between a mid-sentence pause and an actual conversation endpoint.  We also apply domain-specific optimization, recognizing that turn-taking patterns vary dramatically across contexts. For example, ElevenLabs agents adapt to the context of different use cases such as customer support calls, web interactions, and questions with numerical answers. 

Voice Options

While OpenAI Realtime API provides only 10 preset voices, ElevenLabs Agents offers the largest voice library in the market with more than 5,000 voices across languages and regional accents. In addition, developers can also create entirely custom voices with cloning, design, or remixing features. This means that you can easily design a voice for your brand or choose a high-quality voice for your use case. 

Latency

OpenAI prioritizes low latency as essential for natural conversational experiences. While absolute latency matters, its consistency is equally important for end user experience. OpenAI Realtime API delivers superior absolute latency but depends exclusively on OpenAI models, creating vulnerability to service disruptions that can cause unexpected latency spikes.

Due to a diverse ecosystem of LLM providers, ElevenLabs Agents shows a wider range of latency performance. Our self-hosted models deliver latency comparable to OpenAI's best performance, while third-party providers may introduce additional delays depending on the model selected.

What sets us apart is our cascading fallback architecture - when a primary model experiences issues, the system automatically switches to backup LLMs. This approach ensures more consistent performance even when individual providers face outages or slowdowns.

Complete Developer Platform

Complex Workflow

OpenAI RealTime API operates only in single-agent mode, which limits its applicability for complex customer business scenarios.

ElevenLabs Agents enables multi-agent architectures where specialized agents handle distinct functions (billing, support, sales) and seamlessly transfer conversations to other agents or humans. The no-code workflow builder can help create these processes without coding knowledge. The support for multi-agent setup allows agents naturally adapt to organizational growth instead of requiring developers to work around platform limitations.

Testing Tools

OpenAI's Realtime API uses end-to-end speech processing, making testing complex since both inputs and outputs are audio-based. Creating and evaluating audio test cases is technically challenging.

ElevenLabs takes a different approach, allowing text-based testing of individual components. Our Agents platform is built for test-driven development—you can define behavioral expectations, generate test scenarios from real conversations, and automatically validate changes before production deployment. This testing framework is available through both UI and API.

Analytics

Our Agents platform also includes integrated analytics featuring granular performance metrics and evaluation standards, plus automated call recording and transcript archiving for thorough data collection supporting both analysis and regulatory compliance.

In contrast, OpenAI's Realtime API lacks these enterprise-grade capabilities, leaving developers to build their own analytics systems, and handle data storage management independently.

Telephony Integration

OpenAI Realtime API recently introduced SIP trunking support. ElevenLabs Agents provides broader telephony capabilities, including native integrations with Twilio and Genesys alongside SIP trunking. 

Additionally, ElevenLabs offers comprehensive outbound calling features like voicemail detection, IVR navigation, and batch calling. This can unlock outbound use cases such as lead qualification, customer follow-ups, appointment notifications, debt collection, etc.

Pricing

ElevenLabs Agents has a business-tier rate of $0.096 per minute at the higher end, with substantial volume and enterprise discounts available. LLM costs are additional and vary by model selection.

OpenAI Realtime API uses token-based pricing: $32 per 1M audio input tokens ($0.5 for cached input) and $64 per 1M audio output tokens. Converting to per-minute estimates, basic usage would start around $0.1 per minute but frequently exceeds $0.2 per minute when incorporating typical production system prompts.

For simple prototypes, OpenAI may offer lower costs. However, ElevenLabs Agents becomes significantly more cost-effective for production deployments requiring high volume usage and comprehensive system prompts.

Summary Table

The Key Takeaway

OpenAI's Realtime API focuses on good latency and dynamic voice adaptation, making it ideal for making prototypes and applications such as personal companions. 

ElevenLabs Agents emphasizes reliable agent performance, natural conversational experiences and an end-to-end developer platform with competitive price at large scale. Developers who value reliability, extensive customization options, and enterprise-ready infrastructure will find our Agents offer a broader foundation for developing sophisticated voice AI applications.

Reference

1. https://github.com/zai-org/ComplexFuncBench Note: for ElevenLabs Agents, accuracy can be reached by leveraging GPT-4o's industry-leading function calling capabilities.
2. https://scale.com/leaderboard/multichallenge  Note:  for ElevenLabs Agents, accuracy can be reached by using Geminis 2.5 Flash & Claude models.
3. https://artificialanalysis.ai/models/speech-to-speech Note: for ElevenLabs Agents, accuracy can be reached by using the architecture of Whisper speech recognition, GPT-4o reasoning, and TTS-1 synthesis.