AI Token Cost Calculator
Estimate your AI API costs before you spend. Compare GPT, Claude, and Gemini pricing in seconds.
$2.5/1M input tokens · $10/1M output tokens · Context: 128,000 tokens
Used to project your monthly cost (assumes 30 days).
Input Cost
$0.0025
Output Cost
$0.0050
Total Cost / Request
$0.0075
Projected Monthly Cost
Based on your requests/day input
$22.50
Compare All Models
Cost for the same 1,000 input / 500 output tokens, cheapest first.
| Model | Provider | Input Cost | Output Cost | Total Cost | Context Window |
|---|---|---|---|---|---|
| Gemini 1.5 FlashCheapest | $0.0001 | $0.0002 | $0.0002 | 1,000,000 | |
| GPT-4o mini | OpenAI | $0.0002 | $0.0003 | $0.0005 | 128,000 |
| Claude Haiku | Anthropic | $0.0008 | $0.0020 | $0.0028 | 200,000 |
| Gemini 1.5 Pro | $0.0013 | $0.0025 | $0.0038 | 1,000,000 | |
| GPT-4o | OpenAI | $0.0025 | $0.0050 | $0.0075 | 128,000 |
| Claude Sonnet | Anthropic | $0.0030 | $0.0075 | $0.0105 | 200,000 |
| Claude Opus | Anthropic | $0.0150 | $0.0375 | $0.0525 | 200,000 |
AI Token Cost Estimator
Before you have real users, you don't have real usage data — so any cost number you put in a pitch deck, budget sheet, or roadmap is really an estimate built on assumptions. Growfiy's AI Token Cost Estimator helps you build that estimate properly: from sample prompts to a defensible range, with a worst-case number you can actually plan around instead of getting surprised by later.
Built for the pre-launch stage — before a live dashboard exists to tell you what you're really spending.
How to Build a Pre-Launch Cost Estimate
- Write your actual production prompt template — system instructions, injected context, and all — not a simplified test version.
- Run 5-10 realistic sample inputs through the calculator to get an average cost per request.
- Estimate projected daily requests from comparable products, a waitlist size, or a conservative early-adoption assumption.
- Multiply average cost per request by projected daily volume, then by 30 for a monthly figure.
- Build a worst-case version alongside it — longer prompts, higher usage, more retries — and budget against that number, not the average.
Why Your Estimate Should Be a Range, Not a Number
Real-world prompts rarely match test samples exactly. Users phrase things longer than expected, follow-up messages add to session length, and edge cases trigger longer model responses than your average test case did. A single-number estimate tends to look right on a spreadsheet and wrong on an invoice.
A more reliable approach: calculate a best-case number from your test samples, then a worst-case number assuming 30-50% longer prompts and responses plus higher-than-expected volume. Plan your budget around the worst-case figure, and treat anything better as margin rather than the baseline expectation.
Frequently Asked Questions
How do I estimate AI costs before my product has any real users?
Write out 5-10 realistic sample prompts and expected responses for your core use case, run them through the calculator to get a per-request cost, then multiply by your projected request volume from comparable products or your own conservative launch assumptions.
What's a reasonable margin of error for a pre-launch cost estimate?
Treat a pre-launch estimate as a range, not a fixed number — real prompts often run 20-50% longer than test samples once users start phrasing things unpredictably, so it's safer to plan around your worst-case number than your average-case number.
How do I build a worst-case scenario into my estimate?
Take your average per-request cost and model it against your highest plausible usage assumption — longer average prompts, higher-than-expected daily active users, and more retries or follow-up messages per session than your baseline assumes.
When should I stop estimating and start measuring real usage?
As soon as you have any live traffic — even a small beta group — pull actual token usage from your provider's dashboard or API response `usage` field and replace your estimate with real numbers. Estimates are for the pre-launch gap only; actuals are always more reliable once available.
Does prompt engineering change my cost estimate significantly?
Yes — a verbose system prompt or unnecessarily long few-shot examples can add a meaningful fixed cost to every single request. It's worth estimating with your actual production prompt template, not a simplified test version, since that fixed overhead repeats on every call.
How do I estimate costs for a feature I haven't built yet?
Draft the exact prompt structure you plan to use — including system instructions and any injected context — as if it were final, then estimate cost per call and multiply by your target usage. Estimating on a placeholder prompt tends to understate real cost once the full context is added later.