How to use the AI API Cost Calculator

Pick the model. Enter your average input tokens per call (1000 ≈ 750 words of context) and average output tokens (500 ≈ 375 words). Enter your expected monthly call volume. The cost per call, per month, and per year update in real time.

Why this tool matters

AI API costs scale unpredictably. A feature that costs $20/month at 1K calls can cost $20,000/month at 1M calls — the difference between profitable and ruinous. Modeling cost before building (or before scaling) prevents surprise bills and informs which model to choose. Cheap-but-capable models like GPT-4o-mini and Gemini 1.5 Flash are often 95% as good for 20× less money.

Common use cases

• Forecasting monthly costs before launching an AI-powered feature
• Comparing model costs to choose the right one for your use case
• Building business cases for AI product investments
• Budgeting AI marketing automation programs
• Estimating ROI of switching between models
• Planning safety margins for unexpected usage spikes

Token estimation rules of thumb

1 token ≈ 0.75 English words ≈ 4 characters. A typical email is ~200 tokens. A blog post draft is ~2,000 tokens. A 10-page document is ~5,000 tokens. Multilingual content uses more tokens (CJK and code-heavy content can be 2-3× higher per character).

Frequently Asked Questions

Are these prices current?
Prices are accurate as of mid-2026 list pricing. Check the model providers\u2019 pricing pages before committing to budgets — pricing changes frequently and volume discounts apply at scale.

What about caching and batch discounts?
OpenAI offers 50% off via batch API, Anthropic offers prompt-caching discounts up to 90% on repeated context. For high-volume systems, factor those in — our calculator uses standard rates.

Why is the output more expensive than input?
Output tokens require generation (compute-intensive). Input tokens are just read (cheaper). The output/input ratio varies by model — usually 3-5× more expensive on output.

Should I use the cheapest model?
Test before committing. GPT-4o-mini and Gemini 1.5 Flash handle 80% of marketing tasks indistinguishably from their flagship siblings at 5-20% the cost. Reserve flagship models for tasks requiring deep reasoning.