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AI Code Review Token Budget Planner

This planner estimates the token volume and context-window demand created by an ai code review workflow. It separates reusable prompt instructions, retrieved context, user or source content, and expected model output so teams can see both per-task and period-wide token requirements. It is useful when sizing repository guidance, patch or diff content, retrieved code context, and generated review comments.

The results can support model selection, context trimming, batching decisions, and cost forecasting. Because tokenization varies by model and content type, the planner uses the token counts you enter rather than converting characters or words automatically.

Calculator inputs

tokens
tokens
tokens
tokens
tasks
tokens
Result
—
Calculated result
Input tokens per task—
Total tokens per task—
Tokens per period—
Context window used—

1. Enter fixed instructions

Add the system prompt, policies, formatting rules, and other instructions included in every task.

2. Add dynamic context

Enter retrieved records, catalog details, code context, or other material supplied to the model.

3. Enter the task payload

Provide the expected token count for the user message, transcript, patch, diff, or source text.

4. Estimate the response

Enter a realistic output-token allowance rather than the model maximum unless every response can reach that limit.

5. Set workload and model limit

Add the number of tasks for the planning period and the model context window.

6. Review the budget

Compare tokens per task, total period usage, and context-window utilization.

Input tokens = System tokens + Retrieved context tokens + Source tokens
Total tokens per task = Input tokens + Output tokens
Period tokens = Total tokens per task × Tasks per period
Context use (%) = Total tokens per task ÷ Context window × 100

The model assumes one complete request-response cycle per task. Cached-token discounts, retries, tool calls, and multi-turn carryover are not automatically included.

What the result means

The main result is the combined token allowance for one task.

Use measured workload data and current provider or hardware benchmarks when available; production results may vary.

Given: 1,200 system tokens, 6,500 context tokens, 4,200 source tokens, 900 output tokens, 1,800 tasks, and a 32,000-token context window.

Calculation: Input = 1,200 + 6,500 + 4,200 = 11,900 tokens. Total per task = 11,900 + 900 = 12,800 tokens. Period total = 12,800 × 1,800 = 23,040,000 tokens. Window use = 12,800 ÷ 32,000 × 100 = 40.0%.

Result: The workflow needs about 12,800 tokens per task and 23,040,000 tokens for the period, using 40.0% of the selected context window per task.

Should I use average or maximum token counts?

Use averages for expected cost and capacity planning, then run a second scenario with high-percentile or maximum values to test context-window risk.

Does the output allowance count against the context window?

For most chat-style models, input and generated output share the model’s overall context limit. The calculator therefore compares their combined total with the context window.

How should I handle prompt caching?

Enter the full token volume here to understand workload size. Apply any provider-specific cached-input discount separately when estimating cost because eligibility and pricing can vary.

What if one task makes several model calls?

Treat each call as a separate task or multiply the per-task result by the average number of calls. Include any repeated context in each call where it is actually transmitted.

Why can actual token counts differ?

Tokenizers split text differently by model, language, code, punctuation, and structured data. Use counts from the intended model’s tokenizer when precision matters.