OpenAI TTS/Whisper

OpenAI charges $0.006 per minute for standard Whisper transcription and a rock-bottom $0.003 per minute for the new gpt-4o-mini-transcribe model. That’s $0.18 per hour of audio—cheaper than almost any managed competitor including Deepgram’s Nova-2. For a startup processing 5,000 hours of customer support calls monthly, you’re spending just $900 on the Mini model versus roughly $1,300 on Deepgram or significantly more on Google Cloud.

The audio stack consists of two distinct halves: transcription (Whisper) and synthesis (TTS). Whisper is the industry’s workhorse. It handles accents, background noise, and technical jargon with a resilience that older models like Google Speech-to-Text struggle to match. The introduction of gpt-4o-transcribe-diarize finally solves the platform's biggest headache—native speaker identification—without requiring third-party libraries like Pyannote, though it locks you into the slightly pricier $0.006/min tier.

On the flip side, the TTS API is a different beast. It offers six non-clonable voices (Alloy, Echo, etc.) that sound startlingly human. Unlike the robotic artifacts of AWS Polly, OpenAI’s voices breathe, pause, and intone naturally. However, this naturalness comes at the cost of control. You cannot adjust pitch, speed, or emotion via SSML tags. You get what the model gives you. It’s like hiring a brilliant voice actor who refuses to take direction—excellent performance, but you can’t make them sound angrier or faster on command.

For most developers, this trade-off is acceptable because of the price. At $15 per 1 million characters, OpenAI TTS is roughly 10-20x cheaper than ElevenLabs, which charges ~$330 for the same volume on their Scale plan. If you need a generic "good" voice for reading articles or basic assistants, OpenAI is the default choice. If you need to clone a specific celebrity or direct a character to whisper fearfully, you must pay the premium for ElevenLabs.

Skip this tool if you are building a low-latency real-time voice bot using the standard REST API; the latency (300ms+) is too high for natural turn-taking. For that, you’d need the new (and expensive) Realtime API or a dedicated specialized provider like Cartesia. But for batch transcription and general-purpose text-to-speech, OpenAI has effectively commoditized the middle of the market.

Pricing

There is no free tier specifically for the Audio API; you burn your standard OpenAI credits. The 'Mini' transcription model ($0.003/min) is the best value in the industry, undercutting Deepgram's pay-as-you-go rates. The real cost cliff hides in the Realtime API (WebRTC), which charges ~100x more per minute than the standard batch API due to token-based audio processing. For TTS, the $15/1M char price is a steal compared to ElevenLabs ($100+ for equivalent volume), but the HD model doubles the price to $30/1M chars with diminishing returns on quality for standard devices.

Technical Verdict

The developer experience is seamless—standard REST endpoints that accept binary uploads and return clean JSON. Native diarization (via gpt-4o-transcribe-diarize) eliminates the complex post-processing pipelines previously required. However, the lack of SSML support for TTS is a major limitation for precise audio generation. Latency on the standard TTS endpoint averages 200-400ms, which is acceptable for content reading but sluggish for conversational IVR without streaming.

Quick Start

from openai import OpenAI
client = OpenAI()
 
# Transcribe audio
audio_file = open("speech.mp3", "rb")
transcript = client.audio.transcriptions.create(
  model="whisper-1", 
  file=audio_file
)
print(transcript.text)

Watch Out

• The standard TTS API has no SSML support; you cannot force pauses or change pronunciation.
• Diarization is only available on specific model versions (gpt-4o-transcribe-diarize), not the default whisper-1.
• The Realtime API (WebRTC) is priced by tokens, making it significantly more expensive than the standard audio endpoints.