AI Cost/Token Estimator
Estimate the token count and dollar cost of an AI API call before you build at scale — your prices, your usage, plain arithmetic.
Estimating the real cost of an AI-powered feature before building it means knowing both token count and current per-token pricing across models — two things that both change and are easy to get wrong when calculated by hand. This tool estimates cost across common models based on your actual prompt and expected usage volume.
How to use it
Ideally a typical example of what you'll actually be sending, not a rough guess.
Requests per day or month -- this is what turns a per-request cost into a real budget figure.
Uses current public per-token pricing for each model compared.
Tips for better results
- Estimate with a realistic prompt, not your shortest example. Actual usage often includes more context than a bare-minimum test prompt -- estimate accordingly.
- Factor in output tokens, not just input. Output is billed separately and can be a larger portion of total cost depending on the task.
- Re-check periodically, not just once. Model pricing changes over time -- an estimate from months ago may no longer reflect current costs.
Example output
“A 500-token prompt sent 10,000 times a month, with an average 300-token response, comes out to a meaningfully different monthly cost depending on which model handles it -- shown side by side.”
TL;DR
Before building a feature that calls an AI API thousands of times a day, it helps to know roughly what that is going to cost — and the honest answer depends entirely on token counts and provider pricing, both of which change often enough that a fixed answer baked into a webpage would go stale fast.
Why this tool does not hardcode current prices
AI API pricing changes on a timeline measured in months, sometimes weeks around a major model launch, and different sources routinely disagree on exact current figures even when checked on the same day. A tool that bakes in “the current price of Model X” as a fixed fact becomes quietly wrong the next time that provider updates pricing, and a visitor relying on stale numbers for a real budget decision is worse off than one who never saw a number at all. Making the price fields editable, with clearly labeled illustrative presets rather than asserted current facts, keeps this tool useful indefinitely instead of accurate for one specific month.
This matters most for anyone making an actual infrastructure decision based on the output — choosing between two providers, deciding whether a feature is viable at a given usage volume, or estimating a monthly budget line item. Treating the price fields as something you fill in or verify, rather than something the tool claims to know, is what keeps the underlying arithmetic trustworthy even as the specific dollar figures around it keep moving.
Pasting a support-response prompt, with a 400-token expected response and 5,000 requests at $3/$15 per million tokens, produces:
“Estimated input tokens: ~56 (rough estimate: characters divided by 4)
Estimated output tokens: ~400
Price used: $3.00 per 1M input tokens, $15.00 per 1M output tokens
Cost per request: $0.006168
Cost for 5000 requests: $30.8400
Note: token counts are a rough approximation…”
That gives both the per-request and scaled cost, with the token-counting method stated plainly rather than presented as an exact count.
Using this for real budget planning
The character-to-token approximation used here (roughly four characters per token for English text) is a widely cited rule of thumb, not an exact tokenizer — real token counts vary by language, by how much punctuation and whitespace is in the text, and by the specific tokenizer a given model uses. For a rough estimate before committing to an approach, this is generally close enough; for a precise number before signing a contract or setting a hard budget cap, running representative requests through the provider’s own token-counting tool or API response metadata is worth the extra step.
FAQ
Are the preset prices in this tool accurate right now?
Treat them as illustrative round numbers representing roughly budget, mid, and premium tiers, not a live price feed — always check your actual provider’s current pricing page before relying on a number for a real budget decision.
How accurate is the token estimate?
The four-characters-per-token approximation is a reasonable rule of thumb for English text, generally within about 10-20% of a real tokenizer count — close enough for planning, not precise enough for a hard budget cap.
Does this account for prompt caching discounts?
Not automatically — if your provider offers cached-context pricing, apply that discount manually to the input price field to get a more accurate estimate for repeated-context workloads.
Can I use this to compare providers side by side?
Yes — run the same prompt and expected output length through with each provider’s actual current pricing to get a rough apples-to-apples comparison.
