Model Token
A model token is a unit from one specific tokenizer. The tokenizer turns text or bytes into number IDs for a model. A token is not always one word or character. Its split and count depend on the tokenizer version.
Another example
A support app has an 8,192-token request budget, including 512 for output and a 76-token safety buffer. Its tokenizer counts 7,604 input tokens, so low-priority history is cut while policy and evidence stay. Some providers set separate input and output limits, and word-count guesses are not an acceptance test.
Main parts
- 01source text or bytes
- 02tokenizer vocabulary and version
- 03token IDs and offsets
Use it when
Use this term with the exact production tokenizer when planning context, cost, delay, trimming, chunk size, or model use.
Do not use it when
Do not treat tokens as fixed words. Do not compare counts from different tokenizers as equal. Leave room for output and tool results.
Name used in code
tokenizer(version) → IDs + offsets Before you ship
Check the model and prompt versions, source data, versioned evaluation set, and measures. Verify links to evidence, tool permissions, privacy, common failures, decline-to-answer and fallback behavior, human review, monitoring, cost, speed, and rollback.
Outcome: Use or evaluate Model Token to make AI behavior measurable and tied to evidence. User context: A support app has an 8,192-token request budget, including 512 for output and a 76-token safety buffer. Its tokenizer counts 7,604 input tokens, so low-priority history is cut while policy and evidence stay. Some providers set separate input and output limits, and word-count guesses are not an acceptance test. AI method or concept: Model Token. Why it fits: Use this term with the exact production tokenizer when planning context, cost, delay, trimming, chunk size, or model use. Do not use it when: Do not treat tokens as fixed words. Do not compare counts from different tokenizers as equal. Leave room for output and tool results. AI requirements: Define the input and source evidence. Set model and tool permissions. Use versioned evaluation data and measures. Define failure, decline-to-answer, privacy, speed, and cost limits. Operational safeguards: Keep a clear trace and hide sensitive log data. Show users a safe fallback. Mark steps that need human review. Define how to roll back the model or prompt. Acceptance criteria: Record baseline and target measures on a versioned evaluation set. Test edge cases and hostile inputs. Verify fallback, monitoring, permissions, and rollback. Evidence and limits (evidence boundary): No single official standard controls the whole term. Its meaning may change across teams, platforms, or frameworks. Unknowns to confirm: Target task, model and version, evaluation owner, source data, risk limit, tool permissions, and production fallback.
Check this request
Review the current use of Model Token. Definition: A model token is a unit from one specific tokenizer. The tokenizer turns text or bytes into number IDs for a model. A token is not always one word or character. Its split and count depend on the tokenizer version. Release checks: Check the model and prompt versions, source data, versioned evaluation set, and measures. Verify links to evidence, tool permissions, privacy, common failures, decline-to-answer and fallback behavior, human review, monitoring, cost, speed, and rollback. Before changing code, report the evidence you found, gaps, severity, and the smallest safe fix.