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Token Budget

Benched.ai Editorial Team

A token budget is the maximum number of input plus output tokens that an application allocates to a single model request, constrained by context window limits and cost targets.

  Budget Planning Factors

FactorTypical RangeImpact
Model context window8 k – 200 k tokensHard upper bound
Prompt overhead (system + meta)100–500 tokensFixed cost
Retrieval inserts0–8 k tokensScales with documents
Expected response length50–2 k tokensVaries by task

  Budget Allocation Process

  1. Determine business cost ceiling (USD / request).
  2. Translate to max total tokens using provider pricing.
  3. Subtract constant prompt overhead.
  4. Allocate remaining budget between context expansion (e.g., retrieved docs) and expected output.

  Design Trade-offs

  • Larger budgets improve answer completeness but raise latency and price.
  • Tight budgets may truncate retrieved evidence, reducing factuality.
  • Dynamic budgets per request optimize spend but complicate monitoring.

  Current Trends (2025)

  • Automatic context compression selects most relevant sentences to fit within budget.
  • Providers expose pricing simulators that calculate cost before invocation.
  • Clients track rolling average tokens / request to stay within monthly budget1.

  Implementation Tips

  1. Log input and output token counts separately.
  2. Warn users in UI when they approach budget limit.
  3. Back-off retrieval depth when context window <10 % free.

  References

  1. OpenAI Pricing Guide 2025.