AI Token Cost Calculator
Estimate your GPT, Claude, and Gemini API costs before you spend a single dollar — paste your prompt, pick a model, and see the real price.
$5/1M input tokens · $30/1M output tokens · Context: 1,048,576 tokens
Used to project your monthly cost (assumes 30 days).
Input Cost
$0.0050
Output Cost
$0.0150
Total Cost / Request
$0.0200
Projected Monthly Cost
Based on your requests/day input
$60.00
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 2.5 Flash-Lite (legacy)Cheapest | $0.0001 | $0.0002 | $0.0003 | 1,000,000 | |
| GPT-4o mini | OpenAI | $0.0002 | $0.0003 | $0.0005 | 128,000 |
| GPT-5.6 Luna | OpenAI | $0.0002 | $0.0006 | $0.0008 | 1,048,576 |
| Gemini 3.5 Flash-Lite | $0.0003 | $0.0013 | $0.0016 | 1,048,576 | |
| Claude Haiku (legacy) | Anthropic | $0.0008 | $0.0020 | $0.0028 | 200,000 |
| Claude Haiku 4.5 | Anthropic | $0.0010 | $0.0025 | $0.0035 | 200,000 |
| GPT-5.2 | OpenAI | $0.0009 | $0.0035 | $0.0044 | 400,000 |
| Gemini 3.6 Flash | $0.0015 | $0.0038 | $0.0052 | 1,048,576 | |
| Claude Sonnet 5 | Anthropic | $0.0020 | $0.0050 | $0.0070 | 1,000,000 |
| GPT-4o | OpenAI | $0.0025 | $0.0050 | $0.0075 | 128,000 |
| GPT-5.6 Terra | OpenAI | $0.0020 | $0.0060 | $0.0080 | 1,048,576 |
| Gemini 3.1 Pro | $0.0020 | $0.0060 | $0.0080 | 1,048,576 | |
| Claude Sonnet (legacy 4.6) | Anthropic | $0.0030 | $0.0075 | $0.0105 | 200,000 |
| Claude Opus 5 | Anthropic | $0.0050 | $0.0125 | $0.0175 | 1,000,000 |
| GPT-5.6 Sol | OpenAI | $0.0050 | $0.0150 | $0.0200 | 1,048,576 |
| Claude Fable 5 | Anthropic | $0.0100 | $0.0250 | $0.0350 | 1,000,000 |
| Claude Opus (legacy) | Anthropic | $0.0150 | $0.0375 | $0.0525 | 200,000 |
AI APIs are billed by token, not by request or by word, which makes real costs easy to underestimate — especially once output length, system prompts, and daily request volume are factored in. This AI token cost calculator estimates what a given prompt will actually cost across the major providers, so you can compare GPT, Claude, and Gemini pricing side by side and catch an expensive model choice before it shows up on your bill.
How the estimate is calculated
We count tokens the way providers bill them
Your text is split using each model's own tokenizer, not a word-count guess. That's the same unit OpenAI, Anthropic, and Google use to generate your invoice, so the number you see here is the number that shows up on your bill.
Input and output are priced separately
Every major provider charges more for what the model generates than for what you send it — often 3 to 5 times more. We apply each model's current published input and output rate to your exact token counts, not a blended average.
You get a real monthly number
Add your expected requests per day and the calculator projects a monthly spend, so you can catch an expensive model choice before it's baked into your product — not after the first invoice.
What actually moves your AI API bill
The headline per-token rate is only half the story. These two factors explain most of the gap between what people expect to pay and what they actually get billed.
Output length is usually the bigger lever
A short prompt with a long, detailed response often costs more than a long prompt with a one-line answer. If your bill is higher than expected, check output length before you blame input size.
Cached and repeated context costs less
If your app resends the same system prompt or reference document on every call, prompt caching can cut that portion of the input cost by 80–90% on providers that support it. This calculator estimates standard rates; check your provider's caching pricing separately.
AI API pricing changes frequently as providers release new models and adjust rates. We update this page as new pricing is announced, but for production budgeting, always cross-check against the provider's official pricing page linked from the model selector above.
Frequently Asked Questions
How accurate is this AI token calculator?
Token counts come from each provider's own tokenizer logic, so they closely match what you'd see on your actual API bill. Pricing reflects each provider's publicly listed rates at the time of writing — always confirm against the provider's official pricing page before a large-scale deployment, since rates change and providers sometimes offer volume, cached-input, or batch discounts this calculator doesn't model.
Why do GPT, Claude, and Gemini charge different prices for input vs. output tokens?
Generating text costs more compute than reading it, so every major provider prices output tokens higher than input tokens — commonly 3 to 5 times more. A short prompt that triggers a long, detailed answer can end up costing more than the input token count alone would suggest.
What's a token, in plain terms?
A token is a chunk of text — often a word or part of a word — that a model processes as one billing unit. English text averages roughly 4 characters per token, but this varies by language and content type, which is why an exact tokenizer count is more reliable than a rough word-count estimate.
Which AI model is cheapest for high-volume use?
It depends on your workload, but as a rule of thumb: lighter, faster models built for high-volume tasks (classification, extraction, routing, simple chat) are priced far below flagship reasoning models. Route the bulk of your traffic to a cheaper model and reserve the expensive flagship for tasks that genuinely need it — this single change is usually the biggest lever on your bill.
Does this calculator send my text anywhere?
No. Tokenization and cost calculation run entirely in your browser — your text isn't uploaded, logged, or stored.
Why do costs here look different from what I'm seeing in my provider dashboard?
Differences usually come from cached-input pricing, batch API discounts, system prompts or tool-call overhead not included in your test input, or a rate change on the provider's end since this page was last updated. Use this calculator for quick estimates and planning, and your provider's billing dashboard for exact spend.
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.