Large Language Models (LLMs) like ChatGPT are often described as token-based systems. Most people know that text usage is measured in tokens.
But what happens when we use audio or video instead of text?
This blog post explains how LLM usage is measured across different input and output types—text, audio, images, and video—in a clear and practical way.
1. Tokens: The Foundation of LLM Usage
Before discussing audio and video, let’s briefly revisit tokens.
What is a token?
A token is a piece of text, not always a full word
Example:
"Hello world"→ ~2 tokens"Unbelievable"→ may be split into multiple tokens
Token-based usage includes:
Input tokens (what you send to the model)
Output tokens (what the model generates)
This token system works perfectly for text-only models.
2. Why Audio and Video Are Measured Differently
Audio and video are not text, but LLMs still need to understand them.
So the system follows a two-step process:
Convert audio/video into intermediate representations
Convert those representations into text or embeddings
Process them internally using tokens
Because of the extra compute required, usage is measured differently.
3. How Audio Input Usage Is Measured (Speech → Text)
Step-by-step processing
When you provide audio input:
The model measures the duration of the audio
Speech is transcribed into text
The text is processed by the LLM
How usage is calculated
Primary unit:
✅ Seconds or minutes of audioSecondary unit:
✅ Tokens generated internally from transcription
Example
If you upload:
3 minutes of spoken audio
Then:
You are billed for 3 minutes of audio processing
Plus the tokens used to generate the response
📌 Important:
You are not charged per sound file size or per byte—time duration matters most.
4. How Audio Output Usage Is Measured (Text → Speech)
When the model speaks back to you, usage is based on:
Length of generated speech
Usually measured in:
Seconds or minutes
Or estimated from characters produced
Longer spoken responses = more usage
Even though speech starts as text internally, the final audio generation cost is calculated separately.
5. How Image Input Usage Is Measured
Images are measured based on visual complexity, not tokens alone.
Key factors:
Number of images
Image resolution (width × height)
Level of visual detail
High-resolution images require more compute, so they cost more than small images.
6. How Video Input Usage Is Measured
Video is the most compute-intensive input type.
How models process video
The video is split into:
Sampled frames (images)
Audio track (if present)
The model does not analyze every frame
Frames are sampled (e.g., 1 frame per second)
Usage measurement includes:
Video duration
Number of frames analyzed
Audio duration (if speech exists)
Example
For a 60-second video:
~60–120 frames may be processed
60 seconds of audio transcription
Each part contributes to total usage.
7. Everything Eventually Becomes Tokens (Internally)
Even though usage is measured differently:
Audio → transcribed text → tokens
Video → frames + text → tokens
Images → visual embeddings → tokens
But billing and limits use modality-specific units because they better represent real compute cost.
8. Summary Table: How Usage Is Measured
| Input / Output Type | Usage Measurement |
|---|---|
| Text input/output | Tokens |
| Audio input | Seconds or minutes |
| Audio output | Speech duration |
| Image input | Image count + resolution |
| Video input | Frames sampled + audio duration |
9. Simple Mental Model
Text = Tokens
Audio = Time
Video = Frames + Time
This model helps when designing AI apps or estimating costs.
10. Why This Matters for Developers and Businesses
Understanding usage measurement helps you:
Optimize costs
Choose between text vs audio interfaces
Design efficient AI workflows
Avoid unexpected billing surprises
For example:
Text chat is cheaper than audio
Short audio commands are better than long conversations
Summarizing video before sending it saves cost
11. Official References & Further Reading
Here are authoritative references :
OpenAI – Tokenization explained
https://help.openai.com/en/articles/4936856-what-are-tokens-and-how-to-count-themOpenAI – Speech-to-Text & Audio models
https://platform.openai.com/docs/guides/speech-to-textOpenAI – Vision and multimodal models
https://platform.openai.com/docs/guides/visionOpenAI – Pricing and usage concepts
https://platform.openai.com/docs/pricingGeneral overview of multimodal AI
https://arxiv.org/abs/2209.03430
Final Thoughts
LLMs may feel like “text-only” systems, but modern AI is fully multimodal.
Understanding how audio and video usage is measured is essential for anyone building or using AI-powered products.
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