Paste text and pick a tokenizer family; Model Ruler returns a low/high token estimate plus byte measurements so you can size a prompt before any provider pricing is applied. Tokenizers differ by model — treat this as a planning estimate and confirm exact counts with your provider.
Estimates prompt and output-token budget before provider pricing is applied. This is table-stakes input hygiene for all downstream Model Ruler cost calculators.
Formula: estimated_input_tokens = tokenizer_family_estimate(text_bytes); total_planning_tokens = estimated_input_tokens + expected_output_tokens
Audit coverage: Context growth
What does this calculator compute?
It estimates the input-token budget for a piece of text under a chosen tokenizer family, plus an optional output reserve, as a low/high range.
What should I check before committing to a workload shape?
Token counts depend on the specific model's tokenizer, and output-token reserve is easy to underestimate. Verify the tokenizer for your target model and budget for expected output length — a prompt that fits one model's context can exceed another's once the response is included.
Is this a quote or a benchmark?
Neither. It is a computed estimate. Real-world cost varies by rate-limit shape, caching, token-count precision, and provider-specific discounts not reflected in the public pricing table.
This is a tokenizer-sizing tool; results depend on the tokenizer behavior of the target model and the output-token reserve you enter, not on provider price rows. Confirm tokenizer behavior for the model you plan to use, since tokenization can differ across providers and model versions. View methodology.