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An AI token is roughly 4 characters or 0.75 words, depending on the model. AI models like GPT-4o and Claude process text as tokens rather than words, which is what determines API pricing. The word "hamburger" counts as about 2 tokens because the tokenizer splits long words. The word "cat" is typically 1 token. Most languages other than English have lower token efficiency, meaning the same sentence in French or Spanish costs more tokens than its English equivalent. Use the AI token counter above to see exact token counts for any text.

Multiply your expected token count by the model's per-token rate. Input and output tokens are priced separately, and output tokens typically cost more. Use the AI token counter above to count tokens for your prompt template, then multiply by your expected call volume. Add 10 to 20% as a buffer for response length variation. For production deployments, also factor in prompt caching credits if your prompts contain repeated system-level content, which can reduce input costs by up to 90% on Claude and similar models.

Input tokens are the tokens you send to the model, including your system prompt, conversation history, and user message. Output tokens are the tokens the model generates in its response. Output tokens almost always cost more per token than input tokens because generating text requires more computation than reading it. For a typical GPT-4o request, output tokens cost four times the input rate. Reducing output length by requesting shorter responses or structured JSON instead of prose is often the most effective way to reduce API costs.

The main OpenAI API models in 2026 are GPT-4o, o1, o3, and GPT-4o mini. GPT-4o is the general-purpose flagship model with vision capabilities and a 128K context window. The o1 and o3 models are reasoning models that think longer before responding and perform better on complex logic and math tasks. GPT-4o mini is the low-cost option for simpler classification and extraction tasks. Pricing varies significantly: GPT-4o mini costs about 15 times less per token than GPT-4o. Use the OpenAI cost calculator above to compare monthly costs.

A prompt library is a collection of pre-tested, optimized prompts for specific tasks. Instead of writing a new prompt from scratch every time, you start from one that is known to produce good results for your use case and customize the task-specific variables. Good prompt libraries organize prompts by category such as writing, coding, analysis, and research. Browse the AI Prompt Examples Library above to find prompts for over 100 tasks. Copy any prompt and modify the variables for your specific situation.

The most effective strategies are model selection, prompt caching, and output length control. Use smaller models (GPT-4o mini instead of GPT-4o, or Claude Haiku instead of Claude Sonnet) for tasks that do not require advanced reasoning. Enable prompt caching for system prompts that repeat across calls. Limit output length explicitly in your instructions. Use the batch API for non-realtime workloads to get a 50% discount on most providers. The API cost calculators above let you compare monthly costs across all models and configurations before committing to a deployment plan.