LLM Token Cost Calculator

Frequently Asked Questions

How many tokens is my text?

For English, tokens ≈ word count × 1.3, or characters ÷ 4. Code tokenizes differently. Use the provider's tokenizer playground for precise counts on your actual prompts.

Why is output more expensive than input?

Generating output requires running the model autoregressively - one token at a time - which is far more compute-intensive than reading the input in a single parallel forward pass.

Does streaming affect cost?

No. You pay for the same total token count whether you use streaming or wait for the complete response. Streaming affects latency, not cost.

How accurate is the projection?

Only as accurate as your average token counts. Real traffic has significant variance. Sample actual production calls to measure representative averages before committing a budget.

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Important Disclaimer: Estimates for informational purposes only.

This calculator provides estimates for informational purposes only. Results are based on assumptions and may not reflect actual outcomes. Consult qualified professionals in relevant fields before making important decisions based on these results.

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How It Works

Large language model APIs charge by the token - a unit roughly equal to three-quarters of an English word, or about four characters. Providers charge two different rates: a lower price for input tokens (your prompt, system message, retrieved context, and conversation history) and a higher price for output tokens (the model's generated response). Prices are typically quoted per 1,000 or per 1,000,000 tokens.

Enter the average prompt and completion token counts per request, the input and output prices (select the appropriate per-1K or per-1M basis), and optionally the number of requests per day. The calculator returns the cost breakdown per request and projects daily, monthly (30-day), and annual spend.

Use Cases

Token cost estimation is essential for LLM-powered product development:

Budgeting a new AI feature before launch by projecting monthly spend at expected traffic levels

Comparing two models - for example, Claude 3.5 Sonnet vs. Claude 3 Haiku - to quantify the cost difference for a given quality level

Identifying whether a long system prompt is worth the recurring per-request cost across millions of calls

Modeling the impact of prompt caching on total spend for applications with repeated system prompts

Justifying infrastructure decisions by showing stakeholders the cost per user or per conversation

Tips

Output tokens typically cost 3-10x more per token than input tokens; a long generated response can dominate the total cost even with a short prompt.

Conversation history is billed as input tokens on every turn - as a chat grows longer, input costs grow with it. Consider summarizing or trimming history for long-running conversations.

Prompt caching (available on Anthropic and OpenAI) stores repeated system prompts at a fraction of the standard input rate and can cut costs dramatically for applications that reuse long system prompts.

Routing simple queries to a cheaper model and complex queries to a powerful model can halve overall costs with minimal quality impact.

The per-1K vs. per-1M price basis matters by a factor of 1,000 - always confirm which basis a provider uses before entering prices here.

FAQ

How many tokens is my text?

For English text, a rough estimate is tokens ≈ word count × 1.3, or characters ÷ 4. Code and non-English languages tokenize differently - Python code tends to tokenize more efficiently than English prose. Use the model provider's tokenizer playground for precise counts on your actual prompts.

Why is output more expensive than input?

Generating output tokens requires running the model autoregressively - producing one token at a time - which is far more compute-intensive than reading the input in a single forward pass. Input tokens can be processed in parallel; output tokens cannot.

Does streaming affect cost?

No. You pay for the same total token count whether you use streaming (receiving tokens incrementally) or wait for the complete response. Streaming affects perceived latency and user experience, not cost.

How accurate is the projection?

Only as accurate as your average token counts. Real production traffic has significant variance - a few unusually long prompts or responses can skew the mean considerably. Sample actual production calls to measure representative averages before committing a budget.

LLM Token Cost Calculator

Frequently Asked Questions

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