Compare LLM Models
Select up to 4 models and compare their costs side-by-side. Find the most cost-effective solution for your specific use case.
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Comparison Tips
Get the most out of your model comparison with these helpful tips.
Realistic Ratios
Use realistic input/output token ratios based on your actual use case for accurate comparisons.
Scale Testing
Test with different volume levels - costs can vary significantly at scale.
Performance vs Cost
Consider quality and speed alongside cost - the cheapest option isn't always the best value.
Context Limits
Check context window limits - some tasks require models with larger context windows.
Understanding the Numbers
All costs are calculated based on official provider pricing and are subject to change.
Input Token Costs
Charged for tokens you send to the model (prompts, context, examples). Generally lower cost per token than output.
Output Token Costs
Charged for tokens the model generates in response. Typically higher cost per token due to computational requirements.
Pro Tip: Optimize your prompts to reduce unnecessary input tokens while maintaining quality output. This can significantly reduce costs, especially for high-volume applications.