AI Cost Calculator: How to Estimate LLM API Costs

Before you ship that AI feature, you need to know what it's going to cost. This guide shows you exactly how to calculate LLM API costs for OpenAI, Anthropic, and Google Gemini.

Understanding AI costs upfront helps you make better decisions about model selection, prompt design, and pricing your own products. Let's break down exactly how to estimate your AI bill.

The AI Cost Formula

Every LLM API charges based on tokens. The formula is simple:

Cost = (Input Tokens × Input Price) + (Output Tokens × Output Price)

Example with GPT-4o:
- Input: 1,000 tokens × $2.50/1M = $0.0025
- Output: 500 tokens × $10.00/1M = $0.005
- Total: $0.0075 per request

Current Pricing Reference (2026)

Here are the current prices for popular models:

Cost Calculator: Common Scenarios

Scenario 1: Customer Support Chatbot

Average conversation: 10 messages, 100 tokens per user message, 200 tokens per AI response.

At 10,000 conversations/month:

• GPT-4o: $750/month
• GPT-4o-mini: $50/month

Scenario 2: Document Summarizer

Average document: 5,000 tokens input, 500 tokens output.

At 5,000 documents/month:

• GPT-4o: $87.50/month
• GPT-4o-mini: $5/month

Scenario 3: Code Assistant

Average request: 2,000 tokens context + prompt, 500 tokens generated code.

Estimating Monthly Costs

Use this framework to estimate your monthly AI bill:

Monthly Cost = Daily Requests × Avg Cost Per Request × 30

Step 1: Estimate daily requests per feature
Step 2: Calculate average tokens per request (input + output)
Step 3: Apply pricing formula
Step 4: Multiply by 30 for monthly estimate
Step 5: Add 20% buffer for variance

Quick Estimation Table

Cost Reduction Strategies

1. Model Tiering

Use expensive models only when necessary. Route simple tasks to cheaper models:

// Route based on task complexity
function selectModel(task) {
  if (task.requiresReasoning) return 'gpt-4o';
  if (task.isSimpleQA) return 'gpt-4o-mini';
  if (task.isClassification) return 'gpt-4o-mini';
  return 'gpt-4o-mini'; // Default to cheaper
}

2. Prompt Optimization

Every token counts. Reduce prompt length without sacrificing quality:

• Remove redundant instructions
• Use concise system prompts
• Limit context to what's necessary

3. Response Length Limits

Set max_tokens to prevent unnecessarily long responses. If you need a yes/no answer, don't let the model write paragraphs.

From Estimate to Actual: Tracking Real Costs

Estimates are just the starting point. In production, actual costs often differ:

• Users send longer prompts than expected
• Some features get more usage than projected
• Edge cases and retries add overhead

That's why tracking actual costs is essential once you're in production.