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GPT-4o mini Transcribe

OpenAI · GPT-4o Audio

Lower-cost OpenAI speech-to-text tier for high-volume transcription pipelines.

Part of GPT family · Other versions: GPT-5.3-Codex , GPT-5.3 , GPT-5.4-Pro , GPT-5.4 , GPT-5.2-Codex , GPT-5.2-Pro , GPT-5.2 , GPT-5-Codex , GPT-5 nano , GPT-5 mini , GPT-5 , GPT-4.1 , GPT-4o mini TTS , GPT-4o Transcribe , GPT-4o mini , GPT-4o
Type
audio
Context
N/A
Max Output
N/A
Status
current
API Access
Yes
License
proprietary
speech-to-text audio cost-efficient transcription api
Released March 2025 · Updated March 6, 2026

Overview

Freshness note: Model capabilities, limits, and pricing can change quickly. This profile is a point-in-time snapshot last verified on February 15, 2026.

GPT-4o mini Transcribe is OpenAI’s efficiency-focused STT tier for teams running high-volume audio-to-text workloads. It is designed for production pipelines where cost control is a primary constraint and perfect transcription quality is not required on every clip.

Capabilities

The model handles common transcription and audio normalization tasks with practical quality for many operational use cases. It is well suited to routing pipelines where premium quality tiers are reserved for difficult clips and the majority of traffic needs cheaper transcription.

Technical Details

As a speech model, token context/output fields are represented as 0 in this repository and treated as N/A for token-based UI. Evaluate this model using transcription quality metrics and latency under target audio conditions.

Pricing & Access

OpenAI’s current pricing docs list GPT-4o mini Transcribe at $3.00 per 1M audio input tokens. It is exposed through OpenAI audio model APIs, but teams should still verify endpoint support and throughput limits before forecast modeling.

Best Use Cases

Strong fit for large-scale meeting ingestion, support call transcription, media indexing, and telemetry-heavy voice analytics pipelines.

Comparisons

Compared with GPT-4o Transcribe, mini offers lower cost with quality tradeoffs on difficult audio. Compared with ElevenLabs audio stack options, decision depends on end-to-end voice platform requirements and cost targets.