AI Token Cost Calculator — API Usage

Calculate how much your AI API requests cost. Enter input/output token counts and select your model for instant pricing.

Tokens in your prompt/request

Estimated tokens in AI response

Input/Output pricing per million tokens

Input Cost = (Input Tokens ÷ 1,000,000) × Input Price; Output Cost = (Output Tokens ÷ 1,000,000) × Output Price; Total = Input + Output; 1 token ≈ 0.75 words; Monthly = Total × 30 (daily usage)
Example: 1,000 input + 500 output tokens with GPT-4o; Input: $0.0025, Output: $0.0050; Total per request: $0.0075; Monthly (daily use): $0.23; Yearly: $2.74; Equivalent: 750 input words + 375 output words

What is a token in AI/API pricing?

In AI language models, a token is a unit of text that the model processes. Roughly: 1 token ≈ 4 characters of English text, 1 token ≈ 0.75 words, 1,000 tokens ≈ 750 words (a page of text). Tokens include: Words (each word = 1+ tokens), Punctuation (periods, commas = separate tokens), Spaces (counted as part of tokens), Special characters. Token count differs from word count. "Don't" = 2 tokens ("Do" + "n't"), "Hello!" = 2 tokens ("Hello" + "!"). Most AI APIs (OpenAI, Anthropic, Google) charge per token for both input (prompt) and output (response).

How much do AI API tokens cost?

Current AI API pricing (2025, per 1 million tokens): GPT-4o: Input $2.50/M tokens, Output $10/M tokens, GPT-4o-mini: Input $0.15/M tokens, Output $0.60/M tokens, Claude 3.5 Sonnet: Input $3/M tokens, Output $15/M tokens, Claude 3 Haiku: Input $0.25/M tokens, Output $1.25/M tokens, Gemini 1.5 Pro: Input $1.25/M tokens, Output $5/M tokens. Example costs: 1,000-word blog post (~1,333 tokens input + 1,333 output): GPT-4o: $0.017, Claude Sonnet: $0.024, 100 customer support chats/day (~50,000 tokens/day): GPT-4o-mini: $1.13/month, GPT-4o: $4.50/month.

How can I reduce my AI API costs?

Effective cost reduction strategies: Use cheaper models for simple tasks: GPT-4o-mini or Claude Haiku for basic tasks, save GPT-4/Claude Sonnet for complex reasoning, Optimize prompts: Shorter, focused prompts = fewer input tokens, Cache responses: Don't re-process identical prompts, use response caching, Batch requests: Process multiple items in one API call, Use streaming: Stop generation early if response is already good enough, Monitor usage: Set budgets and alerts, track token usage per feature, Right-size the model: Don't use GPT-4 for tasks GPT-3.5 handles fine, Compress input: Summarize long documents before sending to API, Typical savings: 50-80% cost reduction with these strategies.

How many tokens do I need for common AI tasks?

Token estimates for common tasks: Chat message: 20-50 tokens per message, Email generation: 200-500 tokens (input + output), Blog post (1,000 words): ~1,333 output tokens, Code generation (100 lines): ~500-800 tokens, Document summarization: Input = document length, output = 200-500 tokens, Translation: Roughly 1:1 token ratio (input ≈ output), Customer support chat: 100-300 tokens per conversation, Image analysis (GPT-4 Vision): Image = 85+ tokens, text varies, Data extraction (100 rows): 1,000-3,000 tokens depending on format. Budget planning: 100 users/day × 500 tokens = 50,000 tokens/day = 1.5M tokens/month ≈ $2-$15/month depending on model.