GPT-4o Mini Transcribe API

openai/gpt-4o-mini-transcribe
GPT-4o Mini Transcribe excels in delivering fast, cost-efficient, and highly accurate audio transcriptions, especially in noisy and accented speech conditions.
Output
$5 / 1M tokens
Released
Dec 30, 2025

Overview

GPT-4o Mini-Transcribe API Overview

GPT-4o Mini Transcribe is a speech-to-text model from OpenAI designed to deliver highly accurate and efficient audio transcription. It represents a lighter, faster version of the full GPT-4o-Transcribe model, optimized for lower latency and resource consumption while maintaining excellent transcription quality. This model is ideal for developers seeking quick, reliable speech recognition in diverse and challenging acoustic environments.

Technical Specifications

  • Model Type: Speech-to-text transcription model
  • Architecture Basis: Built on GPT-4o-mini architecture, pretrained on specialized audio-centric datasets
  • Token Context Window: Supports long audio inputs with up to 16,000 tokens context window
  • Maximum Output Tokens: Up to 2,000 tokens per transcription output
  • Training Data: Diverse, high-quality audio datasets including various accents, noise conditions, and speech speeds
  • Training Techniques: Supervised fine-tuning and reinforcement learning to minimize word error rate and hallucinations

Performance Benchmarks

  • Word Error Rate (WER): Significantly improved compared to earlier Whisper models and similar baselines
  • Reliability: Performs robustly in noisy environments, with diverse accents, and varying speech speeds
  • Language Recognition: Enhanced accuracy and language understanding capabilities across multiple languages

Key Features

  • Efficiency: Lightweight model with fast inference times for quick transcription turnaround
  • Robustness: Handles challenging audio with background noise, different accents, and speech variations
  • Scalability: Can transcribe lengthy audio inputs without losing context due to the large token window
  • Streaming Capability: Supports continuous audio streaming and transcription in real time
  • Customizable Integration: Fits smoothly into voice agents, call centers, transcription services, and meeting applications

GPT-4o Mini Transcribe API Pricing

  • $0.63 per 1M input tokens

Code Sample

Comparison with Other Models

vs GPT-4o Transcribe: Mini Transcribe is better for low-latency applications, whereas the full Transcribe model suits accuracy-critical environments like legal or medical transcription.

vs OpenAI Whisper-Large: GPT-4o Mini Transcribe outperforms Whisper-Large in word error rate (WER) and streaming latency, thanks to reinforcement learning and specialized audio training. Whisper is more general-purpose but tends to be slower and less precise on noisy or accented speech.

vs Eleven Labs Scribe: While both models excel in streaming transcription, Eleven Labs Scribe reportedly matches or slightly exceeds GPT-4o-Mini-Transcribe in accuracy benchmarks in some third-party tests. GPT-4o-Mini speeds and integration with OpenAI’s ecosystem remain strong advantages.

Uptime and performance

  • End-to-end latency (24h median): 5.0s

Measured on the AI/ML API gateway across live traffic. Figures are medians unless stated. Last updated 2026-10-12.

Use cases

  • Customer service call transcription and analytics
  • Meeting and conference note-taking
  • Voice assistant and voice agent transcription
  • Legal and medical dictation transcription services

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How to use GPT-4o Mini Transcribe API

Install any OpenAI-compatible SDK, point it at api.aimlapi.com/v1, and set the model to openai/gpt-4o-mini-transcribe.
import requests, time

headers = {"Authorization": "Bearer " + AIMLAPI_KEY}
job = requests.post(
    "https://api.aimlapi.com/v1/stt/create",
    headers=headers,
    json={
      "model": "openai/gpt-4o-mini-transcribe",
      "url": "https://example.com/audio.mp3"
    },
).json()
gid = job["generation_id"]

while True:
    res = requests.get(f"https://api.aimlapi.com/v1/stt/{gid}", headers=headers).json()
    if res.get("status") in ("completed", "error", "failed"):
        break
    time.sleep(3)
print(res)
const headers = {
  Authorization: `Bearer ${process.env.AIMLAPI_KEY}`,
  "Content-Type": "application/json",
};
const job = await (await fetch("https://api.aimlapi.com/v1/stt/create", {
  method: "POST",
  headers,
  body: JSON.stringify({
    "model": "openai/gpt-4o-mini-transcribe",
    "url": "https://example.com/audio.mp3"
  }),
})).json();

let res;
do {
  await new Promise((r) => setTimeout(r, 3000));
  res = await (await fetch(`https://api.aimlapi.com/v1/stt/${job.generation_id}`, { headers })).json();
} while (!["completed", "error", "failed"].includes(res.status));
console.log(res);
# submit the job — the response contains "generation_id"
curl -X POST https://api.aimlapi.com/v1/stt/create \
  -H "Authorization: Bearer $AIMLAPI_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"openai/gpt-4o-mini-transcribe","url":"https://example.com/audio.mp3"}'

# then poll for the result until it is ready
curl "https://api.aimlapi.com/v1/stt/{generation_id}" -H "Authorization: Bearer $AIMLAPI_KEY"

OpenAI-compatible — swap the base URL and it works with your existing SDK.

GPT-4o Mini Transcribe API Pricing

TypePrice
Output
$5 / 1M tokens

GPT-4o Mini Transcribe vs other models

ModelInputOutputContextBest for
$5 / 1M tokens
Transcription
$100 / 1M characters
$130 / 1M
Speech synthesis
$100 / 1M characters
$0.13 / 1M tokens
Speech synthesis
$60 / 1M characters
$0.078 / 1M tokens
Speech synthesis
$10 / 1M characters
$0.013 / 1M tokens
Speech synthesis

Frequently asked questions

GPT-4o Mini Transcribe takes audio as input and returns text.

GPT-4o Mini Transcribe became available on December 30, 2025.

GPT-4o Mini Transcribe is priced at $6.5 / 1M tokens.

GPT-4o Mini Transcribe is billed per generation — a fixed charge per output rather than by prompt length.

GPT-4o Mini Transcribe was built by OpenAI.

Send a request to v1 with openai/gpt-4o-mini-transcribe as the model id.

Yes. GPT-4o Mini Transcribe is served through AI/ML API, so the same key and endpoint format used for other models applies.

Yes, OpenAI describes it as having improved accuracy compared to Whisper.

It has a 16,000 token context window and a maximum output of 2,000 tokens.

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