LLM API Cost Estimator
Estimate and compare API costs for GPT-4o, Claude, Gemini, Mistral, and more. Enter your expected token counts and daily call volume to get a monthly cost projection and a side-by-side comparison table.
System prompt + user message
Model response / completion
API requests per day
| Model | Input ($/1M) | Output ($/1M) | Per Call | Monthly |
|---|
Pricing based on publicly published rates (May 2026). Actual billing may differ. Does not include batch, cached, or discounted pricing tiers.
Disclaimer: Free tool provided “as is” by MonitorGiant. No warranty or liability for any data loss, security issues, or infrastructure problems arising from use of this tool. Results are for informational purposes only. · A Free Tool by MonitorGiant
What is LLM API Cost Estimator?
LLM API pricing is billed per million tokens — separately for input (prompt) and output (completion) tokens. Most providers charge 4–10× more for output tokens than input tokens, because generation is more compute-intensive than prefill. This means a high-output use case (long documents, detailed answers) costs disproportionately more than a low-output one (classification, yes/no answers). Monthly cost scales linearly with call volume, making it critical to estimate costs before committing to a model in production.
How to use this tool
- 1 Enter the average input tokens (system prompt + user message) and output tokens (model response) per API call. Use the Token Counter to measure a representative call if you are unsure.
- 2 Set your expected daily call volume. The tool projects a 30-day month and calculates total tokens and costs for every model in the table.
- 3 Read the table sorted cheapest-first — the green dot marks best value. Input and output pricing are shown separately so you can see where costs concentrate.
- 4 Use the preset buttons (Low-Traffic Chatbot, RAG Pipeline, Agent, Long-Doc Summariser) to pre-fill realistic token and volume numbers for common workloads.
When would you use this?
- Engineering managers seeking budget approval for a new AI feature who need a credible monthly cost estimate.
- Start-ups comparing GPT-4o vs. Claude vs. Gemini to find the right cost/quality trade-off for their use case.
- Developers switching from prototyping to production who need to plan pricing for their own product.
- Teams benchmarking a common workload — RAG pipeline, agent, chatbot — without manually working through token math.
Related tools
How works
- 1
Enter your token counts
Set the average input tokens (system prompt + user message) and output tokens (model response) per API call. Use the Token Counter tool to measure a representative call if you are not sure.
- 2
Set your daily call volume
Enter how many API calls you expect per day. The tool projects a 30-day month and calculates total tokens and costs accordingly.
- 3
Compare the table
All models are sorted cheapest-first. The green dot marks the best value. Hover rows to highlight them. Input and output pricing are shown separately.
- 4
Use presets for common workloads
The preset buttons pre-fill token and volume numbers for typical use cases — low-traffic chatbot, RAG pipeline, high-volume agent, and long-document summariser.
All calculations run in your browser. No data is sent to any server. Pricing figures are hardcoded from published list rates and may not reflect current promotions or tier discounts.
Comments & Feedback
Found a bug? Have a suggestion? We'd love to hear from you.
Related Tools
From the makers of this tool
Need deeper observability?
MonitorGiant tracks real-time AI performance, infrastructure health, and system reliability — far beyond what free utilities can show.