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 |
Token Usage Calculator
Once your product is live, "how many tokens am I using" stops being a single number and becomes a pattern — which users, which features, and which days are actually driving your usage. Growfiy's Token Usage Calculator helps you check individual requests against expected token counts, so you can spot when a feature or user is consuming more than it should before it shows up as an unexplained spike on your bill.
Built for teams already in production who need to understand where usage is going, not just what a single prompt costs.
What to Track in Your Token Usage
- Per-user usage: tag each API call with a user ID so you can identify whether a small number of heavy users are driving a disproportionate share of total usage.
- Per-feature usage: if your product calls the model from multiple features, track them separately — one expensive feature can quietly dominate your total spend.
- Usage over time: daily or weekly totals make it easier to spot a sudden spike against a normal baseline than a single cumulative number ever will.
- Request count vs token volume: a rise in token usage without a matching rise in request count often points to longer prompts or responses, not more traffic.
Spotting Usage Spikes and Managing Rate Limits
A usage spike usually traces back to one of a few common causes: a retry loop firing on a failing request, unbounded chat history being resent on every message, a bug triggering duplicate calls, or a handful of unusually heavy users. Comparing daily usage against a rolling average makes these stand out quickly.
Rate limits typically apply per minute on both request count and token volume separately, so a burst of large requests can hit a token-based limit even while staying well under the requests-per-minute cap. Tracking both together gives a clearer picture of which limit you're actually approaching.
Frequently Asked Questions
How do I break down token usage by user or feature?
Log the token count from each API response's `usage` field alongside a user ID or feature tag at the point of the call, then aggregate those logs by user or feature over time — most providers return prompt and completion token counts directly in the response.
What usually causes an unexpected spike in token usage?
Common causes include a retry loop firing repeatedly on a failing request, a chat feature accumulating unbounded conversation history, a bug sending duplicate calls, or a small group of heavy users running far more requests than the typical user.
How do rate limits affect my usage, and how do I plan around them?
Most providers cap requests-per-minute and tokens-per-minute separately, so a usage spike can hit the token limit even with relatively few requests if each one is large. Tracking both request count and token volume together helps you see which limit you're actually closer to.
Should I set per-user usage limits in my product?
For consumer-facing AI features, a soft cap (daily or monthly token allowance per user) is common practice to prevent a small number of heavy users from disproportionately driving your total cost, especially on a free or flat-fee tier.
How often should I review my token usage data?
Weekly is reasonable for a growing product — frequent enough to catch a runaway bug or unusual spike early, without over-monitoring a metric that shouldn't move dramatically day to day under normal usage.
Can I estimate future usage from current usage trends?
Yes — if you have a few weeks of stable usage data, a simple trend line based on user growth rate is more reliable than any pre-launch estimate. Once you have real usage numbers, they should always take priority over projected figures.