AI API Pricing Guide: Every Provider Compared

Example 1: Chatbot Application (10,000 conversations/month)

Assuming each conversation averages 2,000 input tokens and 1,000 output tokens:

The takeaway: there is a 175x cost difference between the most expensive and cheapest options for the same workload. Choosing the right model matters enormously.

Example 2: Document Summarization (1,000 documents/month)

Assuming each document is 10,000 input tokens and the summary is 500 output tokens:

Example 3: Code Generation (500 requests/day)

Assuming 1,500 input tokens (prompt + context) and 2,000 output tokens (generated code) per request:

1. Use the smallest model that works

This is the single most impactful optimization. For many tasks, GPT-4o-mini or Gemini Flash produces results that are nearly as good as frontier models at a fraction of the cost. Test your use case with cheaper models first and only upgrade if quality is genuinely insufficient. A model that is 10x cheaper and 95% as good is almost always the right choice.

2. Implement caching

If users ask similar questions, cache the responses. Both Anthropic and OpenAI offer prompt caching features that can reduce costs by up to 90% for repeated prefixes. Even simple application-level caching (storing responses for identical inputs) can save significant money.

3. Optimize your prompts

Shorter prompts cost less. Remove unnecessary instructions, examples, and context. Use system prompts efficiently. If you are including few-shot examples, test whether you really need all of them. Often 1-2 examples work nearly as well as 5-6.

4. Set max token limits

Always set a max_tokens parameter to prevent unexpectedly long (and expensive) responses. For a summarization task, you probably do not need more than 500 output tokens. For code generation, 2,000 is usually plenty.

5. Use model routing

Route different requests to different models based on complexity. Simple questions go to a cheap model; complex ones go to a frontier model. You can implement this with a classifier (which itself can be a cheap model) or with simple heuristics based on input length or keywords.

6. Batch your requests

Both OpenAI and Anthropic offer batch APIs with 50% discounts. If your use case does not require real-time responses (e.g., processing a backlog of documents), batching can cut your costs in half.

7. Consider open source models

For high-volume applications, self-hosting an open source model like Llama 4 or Mistral can be dramatically cheaper than API calls. The upfront infrastructure cost is higher, but per-request costs approach zero. See our open source LLM guide for details.