A transcription model hears “account number 8-4-7-2” but returns “account number eighty-four seventy-two.” A single error can break a downstream automation workflow. Developers building voice applications need transcription models that can handle real-world audio conditions, natural speech patterns, and business-critical details, including codes, dates, addresses, account numbers, mixed-language conversations, specialized terminology, and quiet or low-volume speech.

GPT-transcribe and GPT-live-transcribe do just that and are available in Microsoft Foundry today. Two updates to the audio model family designed to improve automatic speech recognition across asynchronous transcription and live streaming scenarios.

Built for More Accurate Transcription in Real-World Audio

 GPT-transcribe is the highest accuracy ASR model from Open AI, designed for asynchronous speech-to-text transcription of completed audio files and batch workloads. It accepts audio input and returns text output, making it a strong fit for workflows that process recorded, uploaded, or submitted audio, including meeting recordings, voicemails, and media files.

GPT-live-transcribe is designed for low-latency streaming transcription through the Realtime API. It supports real-time audio input and text output, helping developers build live experiences where speech needs to be transcribed continuously as audio arrives. This model also introduces “tunable latency” where developers can adjust the latency/accuracy trade-off for streaming. It is a strong fit for live captions, voice assistants, contact center workflows, accessibility experiences, field service applications, real-time intake, and monitoring systems.

Together, these models give developers transcription options in Microsoft Foundry for stored audio and live voice interactions. Their text output can support downstream workflows such as search, summarization, routing, analytics, automation, and quality review.

What’s New in Both Models

The features of the new transcription models focus on improving transcription quality in real-world audio environments where speech can be brief, noisy, accented, quiet, domain-specific, or mixed across languages. Key capabilities include:

• Background noise: Helps isolate speech in noisy environments so transcription quality can remain more reliable when audio conditions are not controlled.


• Short utterances: Improves recognition of brief commands, confirmations, interruptions, and clipped speech that can be difficult to capture accurately.


• Alphanumeric perception: Strengthens transcription of IDs, codes, phone numbers, dates, addresses, account numbers, and mixed letter-number sequences.


• Domain terminology understanding: Improves recognition of specialized vocabulary used in product, workflow, industry, and business-process contexts.


• Codemix: Improves understanding when speakers switch between languages within a conversation or utterance.


• Context awareness: Uses topic hints and past conversation context to improve transcription accuracy and help maintain consistency.


• Accent robustness: Improves handling of regional accents, non-native accents, dialects, and varied speaking styles.


• Whispering: Improves recognition of quiet or low-volume speech, including whispered commands and private dictation.


• Live captioning and accessibility experiences: Generate real-time captions for meetings, events, media experiences, and assistive applications.


• Contact center and voice workflows: Capture spoken details as conversations happen, supporting routing, quality review, summarization, and downstream automation.


• Monitoring, analytics, and compliance workflows: Provide text visibility into ongoing spoken input so teams can analyze, review, and act on conversation data.

Also Available: GPT-realtime-2.1 and GPT-realtime-mini-2.1

gpt-realtime-2.1 and gpt-realtime-mini-2.1 are also available in Microsoft Foundry for developers building speech-to-speech applications. Unlike GPT-transcribe and GPT-live-transcribe, which return text, these models accept audio and generate audio for low-latency conversational experiences over the Realtime API. gpt-realtime-2.1 focuses on interaction quality and robustness, while gpt-realtime-mini-2.1 provides a smaller, faster, and more cost-efficient option for high-volume deployments.

Together with GPT-transcribe and GPT-live-transcribe, these realtime audio updates give developers more flexibility to build voice applications that need both accurate transcription and responsive spoken interaction, whether the experience is centered on capturing speech as text, responding with audio, or combining both patterns in a single workflow.

Use Cases by Model

GPT-transcribe

Use GPT-transcribe when the application needs accurate text transcripts from recorded, uploaded, or submitted audio. It is a strong fit for meeting and call transcription, media transcription, customer support intake, voicemail and message processing, quality review, compliance workflows, and domain-specific transcription where short utterances, structured alphanumeric details, specialized terminology, accents, background noise, code-mixed speech, or quiet audio can affect downstream accuracy.

GPT-live-transcribe

Use GPT-live-transcribe when the application needs live streaming transcription with low latency. It is designed for real-time captions, accessibility experiences, contact center transcription, voice-enabled workflows, live monitoring, operational dashboards, and agent-assist scenarios where spoken input needs to become text continuously as the interaction unfolds.

Pricing

The following pricing example shows Global Standard rates by model and modality. Rates for GPT-realtime-2.1 and GPT-realtime-mini-2.1 are listed per 1 million tokens. GPT-transcribe and GPT-live-transcribe are listed per audio hour.

Getting Started

Choose GPT-transcribe when your application processes complete audio files asynchronously, or GPT-live-transcribe when it needs text continuously as speech arrives. Try the models in Microsoft Foundry, then use the resources below to explore the Realtime API, follow the audio quickstart, compare available models, and review Azure OpenAI in Foundry Models documentation.

For asynchronous transcription, submit a complete audio file to GPT-transcribe and process the returned transcript after the request completes. This pattern works well for recordings, voicemails, and uploaded media. For streaming transcription, open a Realtime API session with GPT-live-transcribe, send audio as it is captured, and handle incremental transcript events. This pattern supports live captioning and agent-assist experiences that need text during an active interaction. Refer to the linked quickstart and Realtime API documentation for current SDK setup, authentication, request schemas, and supported audio formats.

Explore Microsoft Learn documentation to learn more:

• Use GPT Realtime API for speech and audio with Azure OpenAI in Foundry Models


• GPT Realtime audio quickstart


• Azure OpenAI in Foundry Models overview