Prompt Token Budget Calculator

Calculator Inputs

Example Data Table

Example figures show how overhead and history can squeeze output space.

Formula Used

HistoryTokens   = HistoryTurns × AvgTokensPerTurn
MessageOverhead = (HistoryTurns + 2) × OverheadPerMessage

PromptTotal =
  ReservedSystem + ReservedTools +
  HistoryTokens + FewShotTokens +
  UserPromptTokens + MessageOverhead +
  (SchemaOverhead if enabled) +
  (RetrievalOverhead if enabled)

OutputAvailableRaw  = max(0, ContextWindow − PromptTotal)
OutputAvailableSafe = floor(OutputAvailableRaw × (1 − SafetyMargin%))

RecommendedMaxOutput = min(OutputTargetTokens, OutputAvailableSafe)

EstimatedCost =
  (PromptTotal/1000 × PromptPricePer1K) +
  (RecommendedMaxOutput/1000 × CompletionPricePer1K)

Token counts are approximations because tokenization depends on text patterns, languages, and model rules.

How to Use This Calculator

1. Pick a context window matching your chosen model.
2. Enter reserved tokens for system rules and tools.
3. Estimate conversation history size using turns and average tokens.
4. Add few-shot examples and your current prompt size.
5. Set an output target, then apply a safety margin.
6. Enable schema or retrieval overhead when using strict formats or RAG.
7. Review the recommended output cap to avoid truncation.
8. Use the export buttons to store budgets for your team.

Practical Notes for Token Budgeting

Context windows and real capacity

A model’s context window is a hard ceiling for everything in a request: system rules, tool schemas, conversation history, retrieved passages, and your new prompt. This calculator treats the window as a single shared budget, then reports the remaining output space after overhead and a safety margin. In production, tokenization differences across languages and punctuation can shift counts, so budgeting should be conservative.

Overhead drivers you can measure

Teams often underestimate structural overhead. Message wrappers, role labels, separators, JSON formatting, and citations add tokens even when user text is short. A simple way to calibrate is to log a few representative requests, compare actual prompt tokens to your visible text length, and set the overhead-per-message field to match your average. This makes forecasts more stable across workflows.

Conversation history scaling

History grows linearly with the number of turns you retain, but the impact on output can feel exponential when you are near the limit. If your utilization rises above 85%, small increases in history or retrieval can force truncation. Summarizing older turns, keeping only decision-relevant messages, or compressing templates typically frees thousands of tokens without losing intent.

Few-shot and schema tradeoffs

Few-shot examples improve consistency but consume predictable budget. For repetitive tasks, one strong example plus a clear rubric can outperform multiple long demonstrations. Similarly, strict schemas help reliability but add schema tokens and response verbosity. Use the schema toggle to see how much budget strict formatting costs, then adjust output targets accordingly.

Cost planning and risk flags

The cost estimate combines prompt and completion pricing so you can compare design options. When the status is “Tight” or “High utilization,” reduce history, shorten retrieved text, or lower the output target. A stable token plan lowers retries, improves latency predictability, and keeps multi-step tool pipelines within budget.

For planning, run two passes: one with today’s typical values, and one with worst‑case spikes in history and retrieval size. If the worst case turns “Tight,” set your production cap to the recommended max output and add a fallback summarization step. This approach prevents sudden failures during peak usage and keeps downstream parsing, logging, and evaluation runs consistent overall.

FAQs

1) Are these token numbers exact?

They are estimates. Tokenization varies by model and text patterns. Use the safety margin and calibrate overhead from real logs to keep results reliable.

2) What safety margin should I use?

Use 5–15% for stable English prompts, and 10–20% for mixed languages, heavy punctuation, or strict JSON output. Increase it when you see occasional truncation.

3) Why does enabling retrieval reduce output space so much?

Retrieved passages can be long, and citations or tool outputs add structure. Tightening retrieval, chunking shorter, or summarizing documents before insertion often saves the most tokens.

4) How do I pick an output target?

Start from the longest acceptable answer for your UI. Then cap it using the recommended max output so responses remain complete and don’t overflow the context window.

5) What if I frequently exceed the budget?

Reduce history turns, compress few-shot examples, and lower schema verbosity. If quality drops, add a short summary step rather than carrying full transcripts.

6) Can I use this for batch or agent workflows?

Yes. Treat each agent step as its own prompt budget. Reserve extra tool and schema overhead for function calls, and keep a larger safety margin for multi-step pipelines.