Calculate exact costs for GPT-4o, GPT-4o-mini, GPT-4-turbo, and GPT-3.5 based on your real usage.
| Model | Input $/1M tokens | Output $/1M tokens | Best for |
|---|---|---|---|
| GPT-4o | $2.50 | $10.00 | High-quality reasoning, vision |
| GPT-4o-mini | $0.15 | $0.60 | High-volume, cost-sensitive |
| GPT-4-turbo | $10.00 | $30.00 | Legacy high-quality tasks |
| GPT-3.5-turbo | $0.50 | $1.50 | Simple tasks, fast responses |
| o1-preview | $15.00 | $60.00 | Complex multi-step reasoning |
| o1-mini | $3.00 | $12.00 | Reasoning at moderate cost |
GPT-4o-mini is 16× cheaper than GPT-4o on input and 17× on output. For most production use cases — classification, summarization, structured extraction, basic Q&A — GPT-4o-mini matches or exceeds GPT-4o quality at a fraction of the cost. Use GPT-4o for complex reasoning, coding, multimodal (vision) tasks.
Prompt caching: OpenAI caches repeated prompt prefixes, reducing input costs by up to 50% for requests sharing a system prompt. Batching: The Batch API reduces costs by 50% with a 24-hour turnaround. Context management: Truncate conversation history — every message in history is billed as input tokens.
OpenAI uses the tiktoken tokenizer. Roughly 1 token = 4 characters or 0.75 words in English. You can count tokens exactly using our token counter tool or the tiktoken Python library: tiktoken.encoding_for_model("gpt-4o").
OpenAI doesn't offer a permanent free tier. New accounts receive a small credit ($5-18 depending on region) that expires after a few months. After that, all usage is billed per token. Unlike the ChatGPT web product, the API requires a paid plan.
Yes. The system prompt is sent with every API call and billed as input tokens each time. A 1,000-token system prompt on 10,000 daily requests = 10M extra input tokens per day. Keeping system prompts concise or using prompt caching significantly reduces costs.
As of 2026, it's crucial to optimize your token usage to reduce costs on the OpenAI API. One effective strategy is to use the 'max_tokens' parameter wisely—set it to the minimum required for your application to prevent unnecessary expenses. Additionally, consider implementing a pre-processing step to clean and shorten inputs before sending them to the API. This can significantly lower your token count while maintaining output quality, ultimately leading to substantial savings over time.