Estimate how many model tokens an AI-assisted code review workflow may consume before committing to a review cadence or model context window. Enter the code size, non-code prompt overhead, expected output, number of passes, and daily review count. The calculator separates tokens per review from daily demand and shows how much of the selected context window remains available. Results are planning estimates because tokenization varies by model, language, comments, and formatting.
Input tokens = lines of code × tokens per line + prompt overhead
Total per review = (input tokens + output tokens) × review passes
Daily tokens = total per review × reviews per day
Context utilization compares one pass, not all sequential passes, with the selected context window.
The main result is the estimated combined input and output token volume for one complete review. Daily demand supports quota and cost planning, while context utilization indicates whether a single pass is likely to fit.
Token counts are estimates. Tokenization and billed cached tokens depend on the selected model and API terms.
For 500 lines at 8 tokens per line, 1,500 overhead tokens, 1,200 output tokens, and 2 passes: input is 5,500 tokens and total demand is (5,500 + 1,200) × 2 = 13,400 tokens per review. At 20 reviews, daily demand is 268,000 tokens.
Tokenize several representative files, divide their token total by their line count, and use the resulting average.
Yes. The calculator compares prompt, code, and expected output for one pass with the context window.
Each pass sends another input and produces another output, so each pass contributes to total token demand.
Keep them in volume planning, then apply the provider’s cached-token price separately when estimating cost.
Yes. Tokens per review still calculate, while daily demand becomes zero.
| Variable | Unit | Role |
|---|---|---|
| Code size | lines | Scales code input |
| Tokens per line | tokens/line | Converts code to tokens |
| Overhead | tokens | Instructions and extra context |
| Output | tokens/pass | Expected review response |