LLM Pricing Calculator

LLM API Pricing Comparison

Compare the cost of a single prompt across the top AI models.

~750 words = 1k tokens.
Length of AI response.

How This Comparison Tool Works

The LLM Pricing Comparison tool aggregates current "Rate Card" data from major AI providers (OpenAI, Anthropic, Google) to calculate the exact cost of a single API call. Because every model uses different pricing for Input (Prompts) and Output (Generations), a model that looks cheap on the surface might be more expensive for long-form writing.

How to Use the Calculator

  • Input Tokens: This is the size of your prompt. A standard double-spaced page is ~500-700 tokens.
  • Output Tokens: The predicted length of the AI's response.
  • The Comparison Table: Look for the "Total" column to see which model provides the best value for your specific use case.
Case Study: Support Ticket Automation

Imagine processing 1,000 customer tickets daily, each with a 2,000-token prompt and a 200-token response.

- GPT-4o: ~$10.00/day
- Claude 3.5 Sonnet: ~$7.50/day
- GPT-4o-mini: ~$0.15/day

By switching "non-critical" tasks to Mini models, businesses can save over 95% on their annual AI spend without sacrificing accuracy.

Pro Tip: Implement Token Pruning. By stripping irrelevant metadata and whitespace from your prompts, you can often reduce input costs by 15-20% without changing your model choice.

AI Pricing Intelligence FAQ

Why is there a separate cost for Input vs. Output?

Large Language Models process input tokens in one go (prefill phase) which is highly efficient for GPUs. Generating output tokens is "auto-regressive," meaning it predicts one word at a time based on the previous ones, which consumes much more active GPU memory over time.

What are "Batch" API calls?

Providers like OpenAI and Anthropic offer 50% discounts if you submit your prompts in a "Batch" for processing within 24 hours. This is ideal for tasks that aren't real-time, like data scraping or periodic reports.

Is it cheaper to host my own model (Open Source)?

Only at extreme scale. For most small to mid-sized applications, managed APIs (Serverless) are far cheaper than paying for 24/7 dedicated GPUs on AWS or Azure.