🤖 AI Token Calculator

Estimate token counts and API costs for GPT-4o, Claude, DeepSeek, Gemini, Mistral, and more. Supports multilingual text, batch API discounts, and context window sizes. Plan your AI budget before you build.

📊 Token & Cost Estimator
Non-English text uses more tokens per character — select your language for a more accurate estimate.
Batch API processes requests asynchronously (within 24 h). Only applies to OpenAI and Anthropic models.
📈 Cost Estimate
Estimated Monthly Cost
Input Tokens (prompt)
Output Tokens (est.)
Cost Per Call
Daily Cost
Annual Cost
Cost per 1K calls

📊 Model Price Comparison (same volume)

Input vs Output Cost
Model Comparison
⚠️ Prices updated June 2026. Verify current rates on each provider's pricing page. All providers bill in USD regardless of your location. API availability varies by country — some models may require VPN or regional alternatives in restricted markets. Token counts are approximations (~4 chars/token for English; Japanese/Chinese ~1–2 chars/token; Arabic/Korean ~2–3 chars/token).
🤖

Select a model and enter your usage details

Guide

About the AI Token Calculator

This AI token calculator covers how AI providers like OpenAI, Anthropic, Google, DeepSeek, and Mistral bill API usage by the token, not by the request — so the same feature can cost wildly different amounts depending on the model you pick. Acting as an all-in-one API token estimator and LLM cost calculator, it estimates your token usage and projected AI API cost for 2026 across 15 models, including GPT-4o token cost, o1 token cost, Claude token calculator figures for Claude Sonnet, DeepSeek V3 pricing, DeepSeek R1 cost, Mistral Large pricing, and more — so you can budget an AI feature before you build it, and compare OpenAI pricing against Anthropic pricing side by side at the same volume.

How It Works

Select a model, paste a sample prompt (or use the default), and set your expected output length and daily call volume. The calculator estimates input tokens from your prompt text using an approximate characters-per-token ratio, adjusted by a language multiplier for a multilingual token estimate, since non-English text — especially Japanese, Chinese, Korean, and Arabic — tends to use more tokens per character than English. It then multiplies input and output tokens by each model's per-million-token pricing — whether that's GPT-4o token cost, o1 token cost, DeepSeek V3 pricing, DeepSeek R1 cost, or Mistral Large pricing — optionally applying the 50% batch API discount that OpenAI and Anthropic offer for asynchronous requests, and rolls the result up into per-call, daily, monthly, and annual cost estimates.

Why It Matters

Output tokens typically cost several times more than input tokens, and context window size, model choice, and caching strategy can change your bill by an order of magnitude. Estimating your AI API cost for 2026 up front helps you choose the right model for a task — a cheap, fast model for simple classification versus a frontier model for complex reasoning — before committing to an architecture at scale, whether you're comparing OpenAI pricing, Anthropic pricing, or a DeepSeek token cost alternative.

Tips for Accurate Results

  • Always verify token counts with the provider's official tokenizer (like OpenAI's tiktoken) before finalizing a budget — this AI token calculator's character-based estimates are approximate.
  • If your application reuses a long system prompt or document across many calls, factor in prompt caching, which can cut input costs by roughly 90% and meaningfully change your LLM cost calculator projection.
  • Re-check pricing periodically — AI providers update per-token rates frequently as new model versions ship, so GPT-4o token cost, Claude token calculator figures, and DeepSeek V3 pricing can shift without notice.
  • When budgeting for non-English use cases, rely on the multilingual token estimate rather than an English-only character count, since languages like Chinese and Japanese can use noticeably more tokens per character.
  • Compare context window size alongside price — a model with a larger context window may let you send fewer, larger batched requests and reduce overhead versus many small calls.
About

Understanding AI Token Pricing

🔤

What is a Token?

Tokens are chunks of text — roughly 3–4 characters or 0.75 words in English. "Hello world!" ≈ 3 tokens. Non-English text uses more tokens per character: Japanese/Chinese ~1–2 chars/token, Arabic/Korean ~2–3 chars/token, Hindi ~1.5× English. Always use the provider's tokenizer API for accurate billing estimates.

💰

Input vs Output Pricing

Most providers charge separately for input tokens (your prompt + context) and output tokens (the model's response). Output tokens typically cost 3-5× more than input tokens. Minimizing output length (e.g., using structured JSON, bullet points) reduces costs significantly.

Cost Optimization Tips

Use smaller models for simple tasks (GPT-4o mini, Claude Haiku). Cache repeated system prompts where supported. Use streaming to detect early completion. Compress context with summarization. Monitor actual token usage with provider dashboards.

FAQ

Frequently Asked Questions

Common questions about AI Token calculations

How do I count tokens accurately?
Use the official tokenizer for each model. OpenAI provides tiktoken (pip install tiktoken). Anthropic's Claude uses similar tokenization — roughly 1 token per 3-4 English characters. For production systems, always use the provider's token counting API before billing rather than estimating.
What is context window size?
Context window is the maximum tokens a model can process in one request (input + output combined). GPT-4o: 128K tokens, Claude Sonnet 4.6: 1M tokens, Gemini 1.5 Pro: 2M tokens, DeepSeek V3: 128K tokens. Larger contexts enable longer documents and conversations but increase costs. Only include relevant context to control costs.
How can I reduce my AI API costs?
Key strategies: (1) Use cheaper models for simple tasks — GPT-4o mini is 20× cheaper than GPT-4o; (2) Implement prompt caching (Anthropic's cache tokens are 90% cheaper); (3) Batch non-urgent requests; (4) Fine-tune a smaller model for your specific use case; (5) Compress system prompts; (6) Use streaming to detect natural stopping points.
What is prompt caching and how does it save money?
Prompt caching stores repeated prefixes (system prompts, documents) so they're not re-processed each call. Anthropic charges cached tokens at 10% of regular input price. If your system prompt is 2K tokens and you make 10K calls/day, caching saves ~90% of those input costs. OpenAI also offers automatic prompt caching for qualifying requests.
Which AI models does this calculator support?
15 models across five providers: OpenAI (GPT-4o, GPT-4.1, GPT-4o mini, o1, o3-mini), Anthropic (Claude Fable 5, Claude Opus 4.8, Claude Sonnet 4.6, Claude Haiku 4.5), Google (Gemini 1.5 Pro, Gemini 1.5 Flash), DeepSeek (V3, R1), Mistral Large, and self-hosted Llama 3 70B.
Does the batch API discount apply to every model?
No — Batch Mode gives a 50% discount for asynchronous processing (results within 24 hours), but it only applies to OpenAI and Anthropic models in this calculator. Toggling it on for Gemini, DeepSeek, Mistral, or Llama 3 has no effect on the price shown.
How does the calculator estimate tokens from my prompt text?
It approximates tokens using roughly 4 characters per token for English text, then multiplies by the selected language multiplier. This character-based method is a fast estimate — for exact billing figures, always run your text through the model provider's official tokenizer, such as OpenAI's tiktoken.
Why does language affect token count?
Tokenizers are trained mostly on English text, so non-Latin scripts and character-dense languages need more tokens to represent the same content. This calculator applies multipliers of 1× for English/Spanish/French/German, 1.5× for Hindi/Arabic, 2× for Korean/Russian, and 2.5× for Japanese/Chinese to approximate the difference.
How is the cost estimated for a self-hosted model like Llama 3 70B?
Llama 3 70B has no per-token API price since it's typically self-hosted, so the calculator shows "Infrastructure cost varies" instead of a dollar figure. Running a 70B-parameter model generally requires a GPU instance (A100 or H100 class) costing roughly $2–8 per hour, and that infrastructure cost doesn't scale directly with token volume the way API pricing does.
What's the difference between this calculator and the API Cost Calculator?
This tool estimates the cost of calling AI/LLM APIs based on token usage and per-model pricing. The API Cost Calculator estimates general REST API infrastructure costs (servers, requests, bandwidth) and isn't specific to AI token pricing. Use this calculator for AI model spend and the API Cost Calculator for broader backend infrastructure budgeting.
How accurate are the price estimates in this calculator?
Prices are current as of June 2026 and are approximations based on published per-token rates and a character-based token estimate. AI providers update pricing frequently, so always verify current rates on the provider's official pricing page before finalizing a budget, and use the provider's tokenizer for exact token counts.
Why do output tokens cost more than input tokens?
Generating new tokens requires the model to run a forward pass for every output token, while input tokens can often be processed in parallel and, for supported providers, cached. Most providers price output tokens 3–5× higher than input tokens, so minimizing response length (e.g., using structured JSON or bullet points) can meaningfully cut costs.

Related Calculators

Explore other tech tools