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whisper-large-v3 Cost Calculator - OpenRouter

Calculate the cost of using whisper-large-v3 from OpenRouter for your AI applications

whisper-large-v3 Cost Calculator

Mode: Chat

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Cost Breakdown

Input Cost$0.00011100
Total Cost$0.00011100

Pricing Details

Input: $0.0000001110 per token
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About whisper-large-v3

whisper-large-v3 is a powerful chat AI model offered by OpenRouter. This comprehensive guide provides detailed pricing information, technical specifications, and capabilities to help you understand the costs and features of using whisper-large-v3 in your applications.

Pricing Information

Input Cost$0.11 per 1M tokens

Note: Use the interactive calculator above to estimate costs for your specific usage patterns.

Model Capabilities

✓Audio Input - Accept audio data as input
✓Response Schema - Structured output formatting
When should you use whisper-large-v3?

whisper-large-v3 is best suited for the following scenarios:

  • Speech-to-text (STT) transcription for meetings and calls
  • Captioning and subtitle generation
  • Audio analytics pipelines (keywording, searchable archives)
When should you avoid whisper-large-v3?
  • General-purpose text generation or conversational AI
  • Image understanding or vision tasks
  • Use cases where you only need embeddings or reranking
How does whisper-large-v3 compare to similar models?

This is a specialized speech-to-text model. When comparing similar options, prioritize transcription accuracy, language support, diarization/format needs (if available), and whether pricing is tied to seconds of audio, tokens, or both.

Understanding whisper-large-v3 pricing
  • whisper-large-v3 is a speech-to-text (transcription) model provided by OpenRouter.
  • Input tokens are priced at $0.11 per 1M tokens.
  • Supports audio input for processing audio data.
  • OpenRouter offers whisper-large-v3 for speech-to-text (transcription) workloads, meeting transcription, call analytics, and captioning workflows.

How to Use This Calculator

Step 1: Enter the number of input tokens you expect to use. Input tokens include your prompt, system messages, and any context you provide to the model.

Step 2: Specify the number of output tokens you anticipate. Output tokens are the text generated by the model in response to your input.

Step 3: Review the cost breakdown to see the total estimated cost for your usage. The calculator automatically updates as you adjust the token counts.