Quick definition
Tokenization is the process of splitting text into tokens.
- Category: Models
- Focus: model capability and fit
- Used in: Choosing a model that fits latency and cost constraints.
What it means
Different models use different tokenizers, affecting length and cost. In models workflows, tokenization often shapes model capability and fit.
How it works
Model architecture and scale determine capability. Context length, parameter count, and modality support vary across models.
Why it matters
Model architecture affects capability, context length, and speed.
Common use cases
- Choosing a model that fits latency and cost constraints.
- Selecting longer context for document-heavy workflows.
- Using specialized models for code, vision, or speech.
Example
Emoji and code can tokenize differently than plain words.
Pitfalls and tips
Bigger is not always better. Match the model to the task and evaluate in production.
In BoltAI
In BoltAI, this shows up in model selection and configuration.
