Elon Musk’s AI company xAI has launched two standalone audio APIs — a Speech-to-Text (STT) API and a Text-to-Speech (TTS) API — both built on the same infrastructure that powers Grok Voice on mobile apps, Tesla vehicles, and Starlink customer support. The release moves xAI squarely into the competitive speech API market currently occupied by ElevenLabs, Deepgram, and AssemblyAI.
Speech-to-Text is the technology that converts spoken audio into written text. For developers building meeting transcription tools, voice agents, call center analytics, or accessibility features, an STT API is a core building block. Rather than developing this from scratch, developers call an endpoint, send audio, and receive a structured transcript in return.
The Grok STT API is now generally available, offering transcription across 25 languages with both batch and streaming modes. The batch mode is designed for processing pre-recorded audio files, while streaming enables real-time transcription as audio is captured. Pricing is kept straightforward: Speech-to-Text is $0.10 per hour for batch and $0.20 per hour for streaming.
The API includes word-level timestamps, speaker diarization, and multichannel support, along with intelligent Inverse Text Normalization that correctly handles numbers, dates, currencies, and more. It also accepts 12 audio formats — nine container formats (WAV, MP3, OGG, Opus, FLAC, AAC, MP4, M4A, MKV) and three raw formats (PCM, µ-law, A-law), with a maximum file size of 500 MB per request.
Speaker diarization is the process of separating audio by individual speakers — answering the question ‘who said what.’ This is critical for multi-speaker recordings like meetings, interviews, or customer calls. Word-level timestamps assign precise start and end times to each word in the transcript, enabling use cases like subtitle generation, searchable recordings, and legal documentation. Inverse Text Normalization converts spoken forms like ‘one hundred sixty-seven thousand nine hundred eighty-three dollars and fifteen cents’ into readable structured output: “$167,983.15.”.
xAI research team is making strong claims on accuracy. On phone call entity recognition — names, account numbers, dates — Grok STT claims a 5.0% error rate versus ElevenLabs at 12.0%, Deepgram at 13.5%, and AssemblyAI at 21.3%. That is a substantial margin if it holds in production. For video and podcast transcription, Grok and ElevenLabs tied at a 2.4% error rate, with Deepgram and AssemblyAI trailing at 3.0% and 3.2% respectively. xAI team also reports a 6.9% word error rate on general audio benchmarks.
Text-to-Speech converts written text into spoken audio. Developers use TTS APIs to power voice assistants, read-aloud features, podcast generation, IVR (interactive voice response) systems, and accessibility tools.
The Grok TTS API delivers fast, natural speech synthesis with detailed control via speech tags, and is priced at $4.20 per 1 million characters. The API accepts up to 15,000 characters per REST request; for longer content, a WebSocket streaming endpoint is available that has no text length limit and begins returning audio before the full input is processed. The API supports 20 languages and five distinct voices: Ara, Eve, Leo, Rex, and Sal — with Eve set as the default.
Beyond voice selection, developers can inject inline and wrapping speech tags to control delivery. These include inline tags like [laugh], [sigh], and [breath], and wrapping tags like <whisper>text</whisper> and <emphasis>text</emphasis>, letting developers create engaging, lifelike delivery without complex markup. This expressiveness addresses one of the core limitations of traditional TTS systems, which often produce technically correct but emotionally flat output.
[laugh], [sigh], and <whisper> giving developers fine-grained control over vocal delivery — priced at $4.20 per 1 million characters.Check out the Technical details here. Also, feel free to follow us on Twitter and don’t forget to join our 130k+ ML SubReddit and Subscribe to our Newsletter. Wait! are you on telegram? now you can join us on telegram as well.
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Michal Sutter is a data science professional with a Master of Science in Data Science from the University of Padova. With a solid foundation in statistical analysis, machine learning, and data engineering, Michal excels at transforming complex datasets into actionable insights.
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