gpt-4o-mini-transcribe
Version: 2025-12-15
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Key capabilities
About this model
The gpt-4o-mini-transcribe model is a highly efficient speech-to-text solution designed to deliver accurate audio transcriptions while optimizing for speed and resource consumption.Key model capabilities
This model offers significant improvements in word error rate and language recognition, making it particularly effective in scenarios involving accents, noisy environments, and varying speech speeds. gpt-4o-mini-transcribe is ideal for applications that require quick and reliable transcription services.Use cases
See Responsible AI for additional considerations for responsible use.Key use cases
- Enhanced Customer Service: gpt-4o-mini-transcribe can be integrated into customer support systems to transcribe customer calls in real-time. This allows for more dynamic and comprehensive interactions, enabling support agents to quickly understand and resolve customer issues.
- Meeting Transcription: The model is highly effective for transcribing meeting notes, capturing detailed discussions and decisions made during meetings. This can be particularly useful for creating accurate records of meetings, ensuring that all participants have access to the information discussed.
Out of scope use cases
Our models are not specifically designed or evaluated for all downstream purposes. Developers should consider common limitations of language models as they select use cases, and evaluate and mitigate for accuracy, safety, and fairness before using within a specific downstream use case, particularly for high-risk scenarios. Developers should be aware of and adhere to applicable laws or regulations (including privacy, trade compliance laws, etc.) that are relevant to their use case.Pricing
Pricing is based on a number of factors, including deployment type and tokens used. See pricing details here.Technical specs
gpt-4o-mini-transcribe has been pretrained on specialized audio-centric datasets, which include diverse and high-quality audio samples, ensuring a deep understanding of speech nuances. This model supports a substantial context window of 16,000 tokens, allowing it to process longer audio inputs effectively. With a maximum output of 2,000 tokens, gpt-4o-mini-transcribe can generate detailed and comprehensive transcriptions. The training process incorporates rigorous enhancement techniques, including supervised fine-tuning and reinforcement learning, to optimize performance and accuracy.Training cut-off date
The provider has not supplied this information.Training time
The provider has not supplied this information.Input formats
The provider has not supplied this information.Output formats
With a maximum output of 2,000 tokens, gpt-4o-mini-transcribe can generate detailed and comprehensive transcriptions.Supported languages
The provider has not supplied this information.Sample JSON response
The provider has not supplied this information.Model architecture
The provider has not supplied this information.Long context
This model supports a substantial context window of 16,000 tokens, allowing it to process longer audio inputs effectively.Optimizing model performance
The provider has not supplied this information.Additional assets
The provider has not supplied this information.Training disclosure
Training, testing and validation
gpt-4o-mini-transcribe has been pretrained on specialized audio-centric datasets, which include diverse and high-quality audio samples, ensuring a deep understanding of speech nuances.Distribution
Distribution channels
This model is provided through the Azure OpenAI Service.More information
The following documents are applicable:Responsible AI considerations
Safety techniques
The provider has not supplied this information.Safety evaluations
The provider has not supplied this information.Known limitations
Our models are not specifically designed or evaluated for all downstream purposes. Developers should consider common limitations of language models as they select use cases, and evaluate and mitigate for accuracy, safety, and fairness before using within a specific downstream use case, particularly for high-risk scenarios. Developers should be aware of and adhere to applicable laws or regulations (including privacy, trade compliance laws, etc.) that are relevant to their use case.Acceptable use
Acceptable use policy
The provider has not supplied this information.evaluation.md
Model Specifications
Context Length16000
LicenseCustom
Training DataMay 2024
Last UpdatedDecember 2025
Input TypeText,Audio
Output TypeText
ProviderOpenAI
Languages57 Languages