AI API Pricing Calculator
Calculate costs for using different AI model APIs
Calculate AI API Costs
Model Selection
OpenAI
Input Details
≈ 750 words
≈ 7,500 words
AI Models Pricing
All prices are per 1 million tokens. Some models offer cached input pricing for cost optimization.
| Provider | Model | Input Price | Output Price | Cached Input |
|---|---|---|---|---|
| OpenAI | GPT-4.5 | $75.000 | $150.000 | $37.500 |
| o1 | $15.000 | $60.000 | $7.500 | |
| o3-mini | $1.100 | $4.400 | $0.550 | |
| GPT-4o | $2.500 | $10.000 | $1.250 | |
| GPT-4o mini | $0.150 | $0.600 | $0.075 | |
| Anthropic | Claude 3.5 Sonnet | $3.000 | $15.000 | — |
| Claude 3.5 Haiku | $0.800 | $4.000 | — | |
| Claude 3 Opus | $15.000 | $75.000 | — | |
| DeepSeek | deepseek-chat | $0.140 | $0.280 | — |
| deepseek-reasoner | $0.550 | $2.190 | — |
Complete Guide to AI API Pricing
Quick Reference
Token Basics
- 1 token ≈ 4 characters in English
- 100 tokens ≈ 75 words
- 1 page of text ≈ 750 words ≈ 1000 tokens
Cost Structure
- Input tokens are cheaper
- Output tokens cost more
- Prices per million tokens
Understanding AI API Costs
AI language models process text in chunks called tokens. When using these APIs, you're charged based on the number of tokens processed, with separate pricing for input (your prompts) and output (the AI's responses). Understanding how tokens work is crucial for cost optimization.
Token Counting Guide
What Counts as a Token?
- Common English words: 1-2 tokens
- Long or uncommon words: 2-3+ tokens
- Numbers: ~1 token per 2-3 digits
- Spaces and punctuation count
- Special characters may use more tokens
Token Examples
- "Hello" = 1 token
- "artificial intelligence" = 2 tokens
- "123456" = 2 tokens
- "https://" = 3 tokens
- Emojis: 1-3 tokens each
Cost Optimization Strategies
1. Input Optimization
- Use Clear, Concise Prompts: Shorter prompts mean fewer input tokens. Be specific but brief.
- Leverage Input Caching: Some models offer discounted rates for cached inputs, perfect for repeated queries.
- Batch Similar Requests: Combine related queries when possible to reduce overhead.
2. Output Management
- Set Token Limits: Always specify maximum output tokens to prevent unexpected costs.
- Choose the Right Model: Use cheaper models for drafts and more expensive ones for final versions.
- Implement Retry Strategies: Handle API failures gracefully to avoid wasting tokens.
💡 Pro Tips for Cost Efficiency
- Monitor token usage patterns to identify optimization opportunities
- Use model-specific features like caching when available
- Consider breaking long inputs into smaller chunks
- Test with smaller outputs before scaling up
- Keep track of costs across different models to optimize spending
Model Selection Guide
Different models offer varying price-performance ratios. Here's how to choose:
Budget-Friendly
- DeepSeek models
- Mini variants
- Best for testing
Balanced
- Claude 3.5 Sonnet
- GPT-4o mini
- Good performance/cost
Premium
- Claude 3 Opus
- GPT-4.5 and o1
- Best quality
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