- Speech-to-Text
Voxtral Mini Transcribe is a speech-to-text model from Mistral focused on lightweight, efficient audio transcription. It is designed to provide accurate transcriptions while being small and…
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GPT-4o Mini Transcribe
- Speech-to-Text
GPT-4o Mini Transcribe is an OpenAI model specialized for converting spoken language in audio into accurate text. It is optimized for lightweight, fast transcription while maintaining good recognition quality across common speech scenarios.
Providers Comparison
Speed
Output tokens per second · Higher is better
L
LLM API
S
SiliconFlow (FP8)
G
GMI
C
CoreWeave
N
Novita
D
DeepSeek
P
Parasail (FP8)
D
DeepInfra (FP4)
End-to-End Response Time
Seconds · Lower is better
L
LLM API
S
SiliconFlow (FP8)
G
GMI
C
CoreWeave
N
Novita
D
DeepSeek
P
Parasail (FP8)
D
DeepInfra (FP4)
Price
USD per 1M tokens (blended) · Lower is better
L
LLM API
S
SiliconFlow (FP8)
G
GMI
C
CoreWeave
N
Novita
D
DeepSeek
P
Parasail (FP8)
D
DeepInfra (FP4)
About the model
What is GPT-4o Mini Transcribe?
GPT-4o Mini Transcribe is an OpenAI speech-to-text model focused on efficient transcription of spoken audio into written text. It is mainly used for transcribing meetings, calls, lectures, and voice notes into searchable, editable text. It is also used to power voice interfaces, captioning, and assistive tools that need near real-time recognition on constrained compute. It belongs to the GPT-4o model family, representing a smaller, transcription-oriented variant derived from OpenAI’s multimodal GPT-4o capabilities.
Model capabilities
5 Core Capabilities
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Speech Transcription
Converts spoken audio into accurate written text, supporting various speakers, accents, and recording conditions for reliable transcripts.
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Conversation Support
Enables interactive chat experiences around transcribed content, answering questions and clarifying details extracted from speech or audio recordings.
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Audio Monitoring
Supports applications that continuously process audio streams, providing up-to-date transcriptions for live or recorded monitoring workflows.
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Language Translation
Can be integrated into pipelines that translate transcribed speech content between languages for subtitles, localization, or accessibility services.
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Transcription Metadata
Provides structured text outputs that can be paired with timestamps or speakers, enabling downstream processing and search across transcriptions.
Use cases
6 Most Valuable Use Cases
- Meeting Audio Transcription
- Customer Call Transcripts
- Lecture and Webinar Notes
- Podcast Content Transcription
- Voice Message Logging
- Speech-to-Text Preprocessing
Transparent pricing
Cost Comparison
LLM API offers the lowest per‑minute transcription cost and best overall SLAs for GPT-4o Mini–class speech models.
| Provider | Region | Latency | Throughput | Uptime | Input ($/1M) | Output ($/1M) | Context |
|---|---|---|---|---|---|---|---|
| LLM API BEST | Global | ~150ms | ~120 min/s | 99.99% | $0.004/min | $0.004/min | ~4 hr audio |
| OpenAI | Global | ~250ms | ~60 min/s | 99.9% | ~$0.006/min | ~$0.006/min | ~2 hr audio |
| Azure OpenAI | US East / EU West | ~280ms | ~45 min/s | 99.9% | ~$0.007/min | ~$0.007/min | ~90 min audio |
| Google Cloud (Gemini Transcribe-equivalent) | Global | ~320ms | ~40 min/s | 99.9% | ~$0.009/min | ~$0.009/min | ~60 min audio |
| Amazon Bedrock (Whisper-equivalent) | US East | ~350ms | ~35 min/s | 99.9% | ~$0.010/min | ~$0.010/min | ~60 min audio |
Performance benchmarks
Technical Specifications
| Metric | GPT-4o Mini Transcribe (OpenAI) | Whisper v3 Large (OpenAI) | Amazon Transcribe Standard |
|---|---|---|---|
| Avg Latency | ~350ms | ~600ms | ~800ms |
| Languages Supported | ~100+ | ~100+ | ~80+ |
| Price per Minute | $0.015 | $0.010 | $0.024 |
| Max Duration | 6 hours | 12 hours | 4 hours |
| Accuracy (WER) | ~7% | ~6% | ~10% |
| Uptime | 99.9% | 99.9% | 99.9% |
30-day usage via LLM API
- 620M
- Audio minutes transcribed
- 42M
- API requests served
- 7.8M
- Unique apps & workflows using this model
- 99.9%
- Avg uptime over last 30 days
Architecture & Integration
Why Build on LLM.API?
One unified API. Every major model. Built-in reliability, cost control, and observability.
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Adaptive Model Routing
Route each request to the optimal model across providers based on latency, cost, and quality—without changing your integration or redeploying your app.
One endpoint, every model. -
Cost-Aware Orchestration
Automatically steer traffic to the most cost-effective models for each workload, with caps and policies that keep your AI bill predictable at scale.
Max performance, minimal spend. -
Resilient Fallback Flows
Define multi-provider fallback chains so requests seamlessly fail over when a model or region degrades—no downtime, no manual incident playbooks.
Stay online, even when LLMs fail. -
Deep LLM Observability
Get end-to-end traces, latency and error metrics, and per-model cost insights so you can debug prompts, tune routing, and ship confidently in production.
See every token, everywhere. -
Task-Level Abstractions
Describe intent as tasks—chat, classify, extract, generate—and let LLM.API pick the right models, parameters, and tools for each use case.
Think tasks, not models. -
High-Throughput Batch Jobs
Run massive batch inference across providers with automatic sharding, concurrency control, and retries, turning hours of manual scripting into a single API call.
Batch at cloud scale.
Decision guide
When to Use — When NOT to Use
Use it if...
- You need fast, low-cost automatic speech-to-text transcription for audio or video files.
- Your use case involves turning meeting recordings into searchable, time-stamped text transcripts.
- You need to quickly transcribe user-uploaded voice notes for downstream text-based processing.
- Your use case involves captioning podcasts, webinars, or lectures for accessibility and SEO.
- You need a lightweight transcription model to preprocess audio before passing text to larger LLMs.
- Your use case involves batch-processing large volumes of short audio clips efficiently.
Avoid if...
- You need rich summarization, Q&A, or reasoning over transcripts directly from the same model.
- Your workload requires multilingual translation, not just transcription, of spoken content.
- You need high-accuracy understanding of complex domain-specific jargon beyond basic transcription.
- Your workload requires real-time interactive dialogue management rather than one-way audio transcription.
- You need advanced content moderation, sentiment analysis, or classification directly on audio inputs.
- Your workload requires multimodal image or video understanding beyond extracting spoken words.
FAQ
Frequently Asked Questions
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What is GPT-4o Mini Transcribe?
GPT-4o Mini Transcribe is an OpenAI model optimized for fast, low-cost automatic speech recognition and transcription via the LLM.API gateway.
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What is GPT-4o Mini Transcribe best suited for?
It is best for real-time or batch audio-to-text transcription, meeting notes, call logs, captions, and developer pipelines needing inexpensive speech recognition.
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How is GPT-4o Mini Transcribe priced on LLM.API?
Pricing is usage-based per audio duration; check your LLM.API dashboard or pricing page for current per-minute or per-second rates.
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What context window does GPT-4o Mini Transcribe support?
The effective context corresponds to the transcribed text length supported by the underlying GPT-4o Mini architecture through LLM.API.
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How fast is GPT-4o Mini Transcribe in terms of latency?
It is optimized for low latency, typically suitable for near real-time streaming and interactive transcription use cases.
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Which modalities does GPT-4o Mini Transcribe support?
It accepts audio input and produces text output, focusing specifically on speech-to-text rather than general multimodal reasoning.
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How do I access GPT-4o Mini Transcribe through LLM.API?
Call the LLM.API endpoint with the provider set to OpenAI and the model name 'gpt-4o-mini-transcribe', including your audio payload and configuration.
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How does GPT-4o Mini Transcribe compare to general GPT-4o Mini models?
It is specialized and more cost-efficient for transcription, but not intended for broad text or multimodal reasoning tasks.
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What languages does GPT-4o Mini Transcribe support?
It supports English and many other major languages, but accuracy may vary by language and audio quality.
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What are the main limitations of GPT-4o Mini Transcribe?
It can struggle with heavy background noise, overlapping speakers, domain-specific jargon, and does not perform complex reasoning over the transcript.
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