Updated May 2026 · 70+ Models

AI Model Pricing Calculator

Compare real-time token costs across OpenAI, Anthropic, AWS, Azure, GCP & Mistral.
Type your prompt — see costs instantly.

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AI Token Calculator i

Enter your prompt and expected output — costs update live as you type

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What are Tokens?
When you run inference with AI models, you're charged for the number of tokens in your input and output. Tokens are text pieces that models process - different providers count tokens differently. Use this calculator to estimate token counts and refine your prompts to optimize costs and stay within quotas.
Input Tokens: Hover models for accurate counts
Output Tokens: Hover models for accurate counts

How We Calculate Tokens ▼

85-95% accuracy with provider-specific tokenization

OpenAI

OpenAI / Azure

tiktoken library

GPT-4 encoding (cl100k_base)

95% Accurate
Anthropic

Anthropic

~3.8 chars/token

Claude-optimized approximation

90% Accurate
AWS

AWS Bedrock

~3.6 chars/token

Mixed model families

85% Accurate
Google Cloud

Google Cloud

~4.2 chars/token

Gemini/PaLM optimized

90% Accurate
Mistral

Mistral AI

~3.8 chars/token

SentencePiece tokenization

88% Accurate
💡 Why This Matters: Different providers tokenize the same text differently, affecting actual costs. Hover over model cards to see provider-specific token counts for your text.

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FAQ

AI Model Token/Pricing Calculator

What is a token and how does it affect AI pricing? +

A token is a chunk of text that AI models process — roughly 3-4 characters or ¾ of a word. You're charged per token for both input (your prompt) and output (the AI response). For example, "Hello world" is about 2 tokens. Understanding tokens helps you estimate costs before running expensive API calls. Different providers tokenize text differently, which is why the same prompt can cost different amounts across OpenAI, Anthropic, and Google.

How do I calculate the cost of an AI API call? +

Cost = (Input Tokens × Input Price) + (Output Tokens × Output Price). For example, with GPT-4o at $0.0025/1K input and $0.01/1K output: a 500-token prompt with a 1000-token response costs (500/1000 × $0.0025) + (1000/1000 × $0.01) = $0.01125. Use our calculator above — just paste your prompt and expected output length to see real-time costs across all 70+ models.

Which AI model is cheapest for my use case? +

It depends on your needs. For simple tasks (classification, extraction): GPT-4o mini ($0.00015/1K) or Claude 3.5 Haiku ($0.0008/1K) are extremely cheap. For complex reasoning: GPT-5.3 or Claude 4.6 offer best quality but cost more. For code: Codestral 2501 or GPT-4o are cost-effective. For high-volume production: Amazon Nova Micro ($0.000035/1K) is the cheapest option available. Use the provider filter tabs above to compare within your budget.

What's the difference between GPT-5.3, Claude 4.6, and Gemini 2.5 Pro? +

GPT-5.3 (OpenAI) — 256K context, strongest at general reasoning and instruction following, $0.015/$0.06 per 1K tokens. Claude 4.6 (Anthropic) — 500K context (largest), excels at long document analysis and coding, $0.012/$0.06. Gemini 2.5 Pro (Google) — 1M context, best for multimodal tasks (text+image+video), cheapest at $0.00125/$0.01. Choose based on: context length needed, multimodal requirements, and budget.

Why do different providers charge different prices for the same model? +

When you access Claude on AWS Bedrock vs directly from Anthropic vs on Google Vertex AI, prices are usually identical — providers pass through the model maker's pricing. However, differences arise from: (1) commitment discounts (AWS Savings Plans), (2) regional pricing variations, (3) provisioned throughput vs on-demand, and (4) additional platform features like guardrails, caching, or fine-tuning. Always compare the "per 1K token" rate using our calculator for apples-to-apples comparison.

How accurate is this token calculator? +

Our calculator uses provider-specific tokenization: OpenAI/Azure models use the tiktoken library (cl100k_base encoding) for ~95% accuracy. For Anthropic, AWS, GCP, and Mistral, we use calibrated character-to-token ratios (3.6-4.2 chars/token) giving 85-90% accuracy. For exact counts, hover over any model card to see provider-specific token counts. The actual billed amount may vary slightly due to system tokens and formatting overhead.

What is context length and why does it matter for pricing? +

Context length is the maximum number of tokens a model can process in a single request (input + output combined). Claude 4.6 supports 500K tokens (~375K words), Gemini 2.5 Pro supports 1M tokens. Longer context = you can send entire documents, codebases, or conversation histories. But you pay for every token in the context window, so sending a 100K-token document costs 100× more than a 1K-token prompt. Optimize by only including relevant context.

How can I reduce my AI API costs? +

Key strategies: (1) Use smaller models for simple tasks — GPT-4o mini or Nova Micro instead of GPT-5.3. (2) Prompt engineering — shorter, clearer prompts = fewer input tokens. (3) Set max_tokens to limit output length. (4) Use caching (Anthropic prompt caching saves 90% on repeated prefixes). (5) Batch API calls for 50% discount (OpenAI Batch API). (6) Fine-tune a smaller model for your specific task. (7) Use embeddings + RAG instead of stuffing everything in context.