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GPT Audio
- Text-to-Speech
GPT Audio is an OpenAI model that can understand and generate natural-sounding speech in real time. It is notable for combining strong language understanding with fast, conversational audio input and output.
About the model
What is GPT Audio?
GPT Audio is an OpenAI model designed for real-time speech understanding and generation. It is mainly used to power voice-based assistants, enabling spoken conversations that include tasks like answering questions, controlling applications, and assisting with productivity. It is also used for interactive experiences such as hands-free interfaces, accessibility tools, and multimodal applications where speech is combined with text or other inputs. GPT Audio is part of OpenAI’s GPT family of generative models, extending them from text and images into low-latency voice interaction.
Model capabilities
5 Core Capabilities
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Voice Conversation
Engages in natural, low-latency spoken dialogue, handling interruptions and back-and-forth conversation while reasoning about user intent.
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Audio Transcription
Converts spoken language in audio into accurate text transcripts, supporting multiple speakers and diverse recording conditions.
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Text-to-Speech
Generates natural-sounding speech from text input, enabling interactive voice experiences and read-aloud functionality.
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Spoken-Language Translation
Listens to speech in one language and outputs translated text or speech in another, preserving meaning and conversational flow.
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Audio Understanding
Interprets audio content beyond transcription, using it as context for reasoning, answering questions, or following spoken instructions.
Use cases
6 Most Valuable Use Cases
- Customer Support Voicebots
- Hands-Free Voice Interfaces
- Real-Time Voice Translation
- Interactive Language Tutoring
- Voice-Driven Accessibility Tools
- Meeting Transcription Assistance
Transparent pricing
Cost Comparison
LLM API offers the lowest audio prices and latency for GPT Audio–class models.
| Provider | Region | Latency | Throughput | Uptime | Input ($/1M) | Output ($/1M) | Context |
|---|---|---|---|---|---|---|---|
| LLM API BEST | Global | ~150ms | ~120 req/s | 99.99% | ~$0.10/hr | ~$0.10/hr | ~10 hr audio |
| OpenAI | Global | ~400ms | ~40 req/s | 99.9% | ~$0.36/hr | ~$0.36/hr | ~4 hr audio |
| Azure OpenAI | US East | ~450ms | ~35 req/s | 99.9% | ~$0.40/hr | ~$0.40/hr | ~4 hr audio |
| Google Cloud (Speech/Audio Gen) | US Central | ~500ms | ~30 req/s | 99.9% | ~$0.50/hr | ~$0.50/hr | ~3 hr audio |
| Amazon Web Services (Bedrock Audio) | US West | ~550ms | ~25 req/s | 99.9% | ~$0.55/hr | ~$0.55/hr | ~3 hr audio |
Performance benchmarks
Technical Specifications
| Metric | GPT Audio (OpenAI) | Whisper v3 (OpenAI) | Google Speech-to-Text v2 |
|---|---|---|---|
| Avg Latency | ~180ms | ~250ms | ~300ms |
| Languages Supported | ~50+ | ~50+ | ~70+ |
| Price per Minute | $0.015 | $0.010 | $0.016 |
| Max Duration per Request | 60 min | 60 min | 60 min |
| Accuracy (WER, English clean) | ~5.0% | ~6.0% | ~7.5% |
| Accuracy (WER, noisy) | ~9.5% | ~11.0% | ~12.5% |
| Uptime SLA | 99.9% | 99.9% | 99.5% |
30-day usage via LLM API
- 620M
- Audio minutes processed
- 42M
- API requests served
- 11.5M
- Unique developers & creators
- 99.95%
- Avg API uptime
Architecture & Integration
Why Build on LLM.API?
One unified API. Every major model. Built-in reliability, cost control, and observability.
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Unified AI Routing
Automatically route each request to the optimal model across providers based on latency, cost, or quality. One API, zero vendor lock-in, instant flexibility.
One endpoint, any model -
Cost-Aware Orchestration
Control spend with smart routing, tiered models, and granular usage limits. Optimize every token without rewriting application logic or duplicating integration work.
More performance, less spend -
Resilient Fallback Logic
Define automatic fallbacks when a provider throttles, fails, or degrades. Keep your production workloads online without custom retry code per vendor.
Stay up when models fail -
End-to-End Observability
Trace every request across providers with logs, metrics, and structured events. Debug latency, errors, and drift from a single, provider-agnostic dashboard.
See every token’s path -
Task-Native Abstractions
Call models by task—chat, tools, embeddings, rerank—through a consistent API. Swap providers or upgrade models without touching downstream application code.
Code to tasks, not vendors -
High-Throughput Batch
Run massive batch jobs across providers with automatic chunking, retries, and aggregation. Process millions of inputs efficiently without hand-rolled job infrastructure.
Ship batch at scale
Decision guide
When to Use — When NOT to Use
Use it if...
- You need speech-to-text transcription for meetings, calls, or voice notes with good accuracy.
- You need text-to-speech generation to produce natural-sounding spoken responses from text output.
- Your use case involves building voice-enabled assistants that understand and respond to spoken queries.
- Your use case involves converting podcasts, lectures, or webinars into readable, searchable transcripts.
- You need interactive voice experiences where users talk instead of typing, like IVR replacements.
- Your use case involves accessibility features, such as reading on-screen content aloud to users.
- You need to prototype audio-centric applications quickly using a single provider for speech and language.
Avoid if...
- You need complex document reasoning or large-context analysis where audio is not involved at all.
- Your workload requires ultra-low-latency, on-device audio processing without relying on cloud services.
- You need specialized audio editing, music generation, or sound design beyond speech-focused capabilities.
- Your workload requires heavy numerical computation or code execution rather than language or audio understanding.
- You need long-term archival storage or streaming infrastructure, not primarily transcription or voice generation.
- Your workload requires strict offline processing due to regulatory prohibitions on sending audio to cloud.
- You need extremely fine-grained control over phonemes and prosody like professional TTS engineering tools.
FAQ
Frequently Asked Questions
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What is GPT Audio?
GPT Audio is an OpenAI model on LLM.API that adds low-latency, bidirectional audio input and output to the GPT language capabilities.
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What modalities does GPT Audio support?
GPT Audio supports text input, audio input, and audio or text output, enabling real-time voice assistants and conversational interfaces.
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How is GPT Audio accessed via LLM.API?
You call the unified LLM.API endpoint with the GPT Audio model name, sending text or audio input and receiving streaming audio or text responses.
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What is GPT Audio best suited for?
GPT Audio is best for real-time voice agents, interactive assistants, and applications needing natural, low-latency spoken conversations.
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What is the context window of GPT Audio?
GPT Audio inherits the underlying GPT model’s context window, typically up to 128K tokens depending on the configured base model.
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How fast is GPT Audio in terms of latency?
GPT Audio is optimized for sub-second token-level streaming, allowing responses to start playing almost immediately after user speech.
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How is GPT Audio priced on LLM.API?
GPT Audio is billed per input and output token, with audio tokens counted similarly to text tokens according to LLM.API’s OpenAI pricing schedule.
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How does GPT Audio compare to text-only GPT models?
Compared to text-only GPT models, GPT Audio adds speech recognition and speech synthesis, enabling end-to-end voice experiences without separate ASR or TTS services.
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Can GPT Audio handle long or continuous audio streams?
GPT Audio can handle interactive conversational streams, but very long uninterrupted audio may require chunking and session management in your application.
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What are the main limitations of GPT Audio?
GPT Audio may struggle with heavy background noise, highly technical jargon, or strict real-time requirements below typical network round-trip latencies.