LLM Price Check Howto
LLM Price Check is a comprehensive tool for instantly comparing and calculating prices of Large Language Model APIs from leading providers.
View MoreHow to Use LLM Price Check
Visit the LLM Price Check website: Go to https://llmpricecheck.com/ in your web browser to access the LLM Price Check tool.
View the price comparison table: On the homepage, you'll see a table comparing prices and features of various LLM APIs from different providers.
Use the search function: Use the search bar at the top of the table to find specific models or providers you're interested in.
Sort the table: Click on column headers to sort the table by different criteria like model name, provider, quality score, context length, or pricing.
Access the pricing calculator: Click on the 'Pricing Calculator' link in the navigation menu to use the more detailed cost estimation tool.
Enter usage details: In the calculator, input your expected usage in terms of input tokens, output tokens, and number of API requests.
Calculate costs: Click the 'Calculate Cost' button to get estimated pricing for your specific usage across different LLM APIs.
Compare options: Review the calculated costs and other factors like quality scores and context lengths to find the best LLM API for your needs and budget.
Try out models: Use the 'Chat' links provided for each model to test them out on platforms like OpenRouter or provider-specific playgrounds.
LLM Price Check FAQs
LLM Price Check is a tool that allows users to compare and calculate the latest prices for Large Language Model (LLM) APIs from leading providers such as OpenAI, Anthropic, Google, and others. It aims to help users optimize their AI budget by providing up-to-date pricing information in a streamlined format.
LLM Price Check Monthly Traffic Trends
The LLM Price Check achieved 40,070 visits with a 39.7% growth in traffic. This moderate growth can be attributed to the LLM Knowledge Graph Builder's first release of 2025 on February 6, which included several new features such as Community Summaries, Parallel Retrievers, and Expanded Model Support. These enhancements likely increased user engagement and attracted more users to the platform.
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