TokenCalculator.com

LLM Price Comparison

Compare Pricing Across Different LLM Providers

Use this calculator to compare costs between different AI model providers for your specific use case.

For providers that offer reduced pricing for cached inputs (e.g., OpenAI)

Note: The pricing estimates are based on current published rates from each provider. Always refer to the provider's official pricing page for the most up-to-date information.

Pricing Comparison Results

Model Provider Context Length Input Cost (per 1K) Output Cost (per 1K) Cost per Request Monthly Cost

Price Comparison Chart

Cost Optimization Tips

Smart Strategies to Reduce LLM Costs

  • Use Cached Prompts: For providers like OpenAI that offer cached prompt pricing, reuse the same input prompts to take advantage of reduced rates.
  • Choose the Right Model Tier: Don't use a high-end model when a smaller, more affordable model can achieve similar results.
  • Optimize Prompt Length: Shorter, more precise prompts can reduce input token costs significantly.
  • Implement Streaming: For user-facing applications, streaming responses can improve user experience while potentially using fewer tokens.
  • Use Fine-tuned Models: In some cases, fine-tuning a model for your specific task can lead to shorter prompts and outputs, reducing costs.
  • Consider Local Models: For high-volume applications, running open-source models locally might be more cost-effective despite higher initial setup costs.

Cost Patterns by Use Case

  • Content Generation: Usually output-heavy; choose models with lower output token prices.
  • Data Analysis: Usually input-heavy; choose models with lower input token prices.
  • Customer Support: Balance of input/output; benefit from Cached Prompt pricing.
  • Coding Assistance: Usually high on both input and output; consider code-specific models.
  • Knowledge Q&A: Often input-heavy; consider RAG approaches to reduce context size.

Pro Tip: For high-volume applications, consider a hybrid approach: use inexpensive models for initial processing and reserve premium models only for complex cases that require their advanced capabilities.

Frequently Asked Questions