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Token & Cost Calculator

Understand how AI models count tokens, estimate your API costs, and optimize your budget—all in one interactive tool.

What you'll learn and do:

✓Visualize how text splits into tokens with interactive examples
✓Calculate real costs for GPT, Claude, and Gemini models
✓Compare pricing across models to find the best value
✓Learn optimization strategies to reduce your API bill

1. What are tokens?

Tokens are the basic units that AI models use to process text. When you send a message to ChatGPT, Claude, or any other AI, your text isn't read character-by-character or word-by-word. Instead, it's broken into tokens—chunks that might be whole words, parts of words, or even single characters.

Why does this matter? Because AI APIs charge you per token. Understanding how text splits into tokens helps you:

Estimate costs before building features
Optimize prompts to use fewer tokens
Avoid surprise bills when scaling up
Choose the right model for your budget

Quick rules of thumb

1 token ≈ 4 characters (for English text)
1 token ≈ 0.75 words (on average)
100 words ≈ 133 tokens
1,000 characters ≈ 250 tokens

Examples

"Hello world"

2 tokens (common words are usually 1 token each)

"ChatGPT"

2 tokens (uncommon words often split: "Chat" + "GPT")

"🌍" (Earth emoji)

2-4 tokens (emojis are expensive!)

"function hello() {}"

~6 tokens (code has lots of symbols/punctuation)

Interactive Token Demo

Type or select an example below to see how text splits into tokens. This is an approximation—real tokenization varies by model.

Type your text:

Characters

Tokens (approx.)

Tokens (4-char rule)

Visual breakdown:

Helloworld!

Note: This is a simplified visualization. Real tokenizers (like GPT's BPE or Claude's tokenizer) work differently and may split text in unexpected ways. Use the "4 characters ≈ 1 token" rule for quick estimates, or use official tokenizer tools for accuracy.

Important: Different models use different tokenizers. GPT models use BPE (Byte-Pair Encoding), while Claude and Gemini use their own systems. The exact token count for the same text can vary slightly between models. For precise counts, use official tools:

• OpenAI Tokenizer (for GPT models)
• Claude token counting (via API)
• Use your model's API to get exact counts before sending requests

2. How AI API pricing works

AI APIs charge separately for input tokens (your prompt) and output tokens (the AI's response). Output tokens are typically 2-3x more expensive because generating text requires more computation than reading it.

The formula

Total Cost = (Input Tokens × Input Price) + (Output Tokens × Output Price)

Example: GPT-4 Turbo

• Input: 500 tokens (your prompt)
• Output: 200 tokens (AI's response)
• Input price: $0.01 per 1,000 tokens
• Output price: $0.03 per 1,000 tokens

Calculation:

(500 × $0.01 / 1000) + (200 × $0.03 / 1000) = $0.011 per request

Current pricing (as of 2025-01-20)

Model	Provider	Input (per 1K)	Output (per 1K)	Best for
GPT-4o	OpenAI	$0.002500	$0.0100	Most advanced multimodal flagship model
GPT-4o mini	OpenAI	$0.000150	$0.000600	Affordable small model for fast
GPT-4 Turbo	OpenAI	$0.0100	$0.0300	Previous generation flagship
GPT-4	OpenAI	$0.0300	$0.0600	Original GPT-4 model with highest quality reasoning
GPT-3.5 Turbo	OpenAI	$0.000500	$0.001500	Fast and affordable
o1	OpenAI	$0.0150	$0.0600	Reasoning model designed for complex problem-solving
o1-mini	OpenAI	$0.003000	$0.0120	Faster
Claude 4.1 Opus	Anthropic	$0.0150	$0.0750	Exceptional model for specialized reasoning tasks
Claude 4.5 Sonnet	Anthropic	$0.003000	$0.0150	Smartest model for complex agents and coding. 1M token context available in beta
Claude 4.5 Haiku	Anthropic	$0.001000	$0.005000	Fastest model with near-frontier intelligence
Claude 3 Opus (Legacy)	Anthropic	$0.0150	$0.0750	Previous generation flagship model
Claude 3.5 Sonnet (Legacy)	Anthropic	$0.003000	$0.0150	Previous generation balanced model
Claude 3 Haiku (Legacy)	Anthropic	$0.000250	$0.001250	Previous generation fastest model
Gemini 2.0 Flash (Experimental)	Google	$0.000000	$0.000000	Experimental next-generation model. Free during preview. Official pricing not yet available
Gemini 1.5 Pro	Google	$0.001250	$0.005000	Massive 2M token context window
Gemini 1.5 Flash	Google	$0.000075	$0.000300	Ultra-fast and affordable with 1M token context
Gemini 1.5 Flash-8B	Google	$0.000037	$0.000150	Smallest
Command A	Cohere	$0.002500	$0.0100	Most performant model
Command R+ (Legacy)	Cohere	$0.002500	$0.0100	Legacy model from Aug 2024
Command R (Legacy)	Cohere	$0.000150	$0.000600	Legacy balanced model from Aug 2024
Command R7B	Cohere	$0.000037	$0.000150	Fast and extremely cost-effective for simple tasks
DeepSeek Reasoner	DeepSeek	$0.000550	$0.002190	Reasoning model with prompt caching. Cache-miss: $0.55/1M input
DeepSeek Chat	DeepSeek	$0.000270	$0.001100	Chat model with prompt caching. Cache-miss: $0.27/1M input
Sonar	Perplexity	$0.001000	$0.001000	Lightweight
Sonar Pro	Perplexity	$0.003000	$0.0150	Advanced search with 200K context length and grounding
Sonar Reasoning	Perplexity	$0.001000	$0.005000	Fast
Sonar Reasoning Pro	Perplexity	$0.002000	$0.008000	Precise reasoning powered by DeepSeek-R1
Sonar Deep Research	Perplexity	$0.002000	$0.008000	Expert research model. Additional costs: $0.002/1k citation tokens
Mistral Large 2	Mistral	$0.002000	$0.006000	Top-tier model for high-complexity tasks with 128K context
Mistral Small 3.2	Mistral	$0.000200	$0.000600	Cost-efficient model for low-latency tasks with 128K context
Codestral	Mistral	$0.000200	$0.000600	Specialized coding model with 256K context window
Llama 3.1 8B Instant	Groq	$0.000050	$0.000080	Llama 3.1 8B on Groq's LPU. Extremely fast and affordable
Llama 3.3 70B Versatile	Groq	$0.000590	$0.000790	Llama 3.3 70B on Groq's high-speed infrastructure
Llama 4 Scout	Groq	$0.000110	$0.000340	Llama 4 Scout (17Bx16E) multimodal model on Groq
Qwen3 32B	Groq	$0.000290	$0.000590	Qwen3 32B model served on Groq's infrastructure
Llama 3.1 8B Instruct Turbo	Together AI	$0.000180	$0.000180	Llama 3.1 8B with 128K context on Together AI
Llama 3.1 70B Instruct Turbo	Together AI	$0.000880	$0.000880	Llama 3.1 70B with 128K context on Together AI
Llama 3.1 405B Instruct Turbo	Together AI	$0.003500	$0.003500	Frontier open-source Llama 3.1 405B with 128K context
Mixtral 8x7B Instruct	Together AI	$0.000600	$0.000600	Mixtral 8x7B Instruct v0.1 on Together AI
Llama 3 70B	Replicate	$0.000650	$0.002750	Llama 3 70B on Replicate with 8K context
Claude 3.5 Sonnet	Replicate	$0.003000	$0.0150	Claude 3.5 Sonnet served via Replicate infrastructure

Pricing changes frequently! The prices above were last updated on 2025-01-20. Always check official provider websites for current rates before making budget decisions.

3. Calculate your costs

Use this calculator to estimate your API costs based on your expected usage. You can compare models, use custom pricing for enterprise deals, and see how different usage patterns affect your bill.

Cost Calculator

Select a model and enter your expected usage to calculate costs. Toggle custom pricing for enterprise or private models.

Select AI Model

Most advanced multimodal flagship model, faster and cheaper than GPT-4 Turbo

View official pricing →

Use custom pricing (for enterprise/private models)

How do you want to specify input?

Type text (auto-count tokens)Enter token count manually

Input text (estimated tokens: 0)

Using 4 characters ≈ 1 token approximation

Expected output tokens per request

Number of requests per month

Estimated Costs

Per Request

$0.005000

Input: $0.000000 + Output: $0.005000

Per Month (1,000 requests)

$5.00

Based on 0 input + 500 output tokens per request

Note: Prices shown are approximate and may change. Last updated: 2025-01-20. Always verify current pricing on provider websites before budgeting.

4. How to reduce costs

Once you understand token costs, here are proven strategies to reduce your API bill:

1. Choose the right model for each task

Don't use GPT-4 for everything. Use smaller, cheaper models for simple tasks:

• Simple classification: Use GPT-3.5 Turbo or Claude Haiku (90% cheaper)
• Data extraction: Use Gemini Flash or GPT-3.5 Turbo
• Complex reasoning: Use GPT-4 or Claude Opus only when needed

Potential savings: 80-95% on routine tasks

2. Optimize your prompts

Shorter prompts = lower costs. Every token in your prompt costs money on every request.

• Remove unnecessary examples (or use just 1-2 instead of 5)
• Cut verbose instructions ("Please analyze" → "Analyze")
• Use abbreviations where clear (e.g., "Q:" instead of "Question:")
• Avoid repeating context—send it once, not in every message

Potential savings: 20-50% on input costs

3. Limit output length

Output tokens are 2-3x more expensive than input. Control response length:

• Use max_tokens parameter to cap output
• Ask for concise responses ("Answer in 50 words or less")
• Request bullet points instead of paragraphs
• For code, ask for snippets, not full implementations

Potential savings: 30-60% on output costs

4. Use caching (where available)

Some providers offer caching discounts for repeated content:

• Anthropic Claude: Prompt caching can save 90% on cached input tokens
• Structure prompts to reuse system messages and context
• Cache large knowledge bases, style guides, or examples

Potential savings: Up to 90% on repeated content

5. Batch requests when possible

Process multiple items in one request instead of many small ones:

• Classify 10 emails in one request vs. 10 separate requests
• Extract data from multiple records at once
• Use JSON output for structured batch processing

Potential savings: 40-70% by reducing prompt repetition

Real example: Chatbot optimization

❌ Before optimization

• Model: GPT-4o ($0.0025/$0.01)
• Prompt: 800 tokens (verbose)
• Response: 300 tokens (unlimited)
• Requests: 10,000/month

Monthly cost:

$50

✓ After optimization

• Model: GPT-3.5 Turbo ($0.0005/$0.0015)
• Prompt: 200 tokens (optimized)
• Response: 150 tokens (capped)
• Requests: 10,000/month

Monthly cost:

$3.25

Savings: $46.75/month (93.5% reduction)

Learn more

Cost & Latency Guide

Deep dive into optimizing AI performance and budget, including advanced cost optimization strategies.

Read guide →

Prompting 101

Learn how to write better prompts that are both more effective and more cost-efficient.

Read guide →

Key terms

Token API LLM Context Window