tech calculator

AI Tokens & Cost Calculator

Estimate tokens, simulate prompt caching savings, compare API costs across leading LLM models, and calculate multi-turn chat context growth.

Bookmark this AI tokens calculator

Keep this tool handy to size prompts, audit chat context retention overheads, and optimize pricing before deploying LLM features.

Saved runs

Cheapest

$92.50

LLaMA 3.1 8B/mo

Active Model

$3,450.00

GPT-5.4/mo

Context Used

16.0K

of 128.0K

Models

compared live

Interactive Tokenizer Visualizer

Type or paste your prompt context here. Tokenizers segment text into sub-words like BPE or SentencePiece to help LLM engines compute context lengths. For example, the word "calculator" is segmented into calc, ula, and tor.

Characters: 222

Words: 35

Estimated Tokens: 46

Multiplier: 1.3 tokens/word

Cost Simulator

System instructions

Document context / RAG

User message tokens

AI response tokens

Sessions per month

Prompt Caching50%

Simulate chat growth

Turns per session

Batch API discount (50%)

Model Pricing Comparison

Best: LLaMA 3.1 8B

Model	Provider	Input / 1M	Output / 1M	Monthly Cost
LLaMA 3.1 8B	Meta	$0.10	$0.10	$92.50
GPT-5.4 Nano	OpenAI	$0.20	$1.25	$281.00
LLaMA 3.3 70B	Meta	$0.59	$0.79	$565.75
GPT-5.4 Mini	OpenAI	$0.75	$4.50	$1,035.00
Claude 4.5 Haiku	Anthropic	$1.00	$5.00	$1,280.00
Gemini 3.5 Flash	Google	$1.50	$9.00	$2,070.00
Gemini 3.1 Pro	Google	$2.00	$12.00	$2,760.00
GPT-5.4	OpenAI	$2.50	$15.00	$3,450.00
Claude 4.6 Sonnet	Anthropic	$3.00	$15.00	$3,840.00
Claude 4.6 Opus	Anthropic	$5.00	$25.00	$6,400.00
GPT-5.5	OpenAI	$5.00	$30.00	$6,900.00

Cost Breakdown: GPT-5.4

128,000 token limit

Input Cost

$0.19

77,500 tokens

Cached Savings

-$0.09

5,000 cached

Output Cost

$0.15

10,000 tokens

Total single session cost$0.35

Total single session tokens92,500

Total monthly cost (10,000 runs)$3,450.00

Model Advisor

Recommendation

GPT-5.4$3,450.00/mo

OpenAI · 128,000 context · Caching supported

Code Exporter

# Model: GPT-5.4 (OpenAI)
# Estimated Input Tokens (Single Run): 77,500
# Estimated Output Tokens (Single Run): 10,000
# Estimated cost per single API call: $0.35
# Estimated monthly cost (10,000 sessions): $3,450.00
from openai import OpenAI

client = OpenAI(
    api_key="your-api-key",
    base_url="https://api.openai.com/v1"
)

completion = client.chat.completions.create(
    model="gpt-5.4",
    messages=[
        {"role": "system", "content": "1000"},
        {"role": "user", "content": "Your user prompt content here."}
    ],
    max_tokens=1000
)
print(completion.choices[0].message.content)

Formula

How cost is calculated

Session Cost = (Input Tokens × Input Price) + (Cached Input Tokens × Cached Price) + (Output Tokens × Output Price)

Calculations convert raw costs per million tokens to unit rates. Caching discount reduces input rates for static context.

Step by step

How the calculation works

1Word-to-token ratios are estimated based on statistical averages for different languages. For instance, code and languages with non-Latin scripts split into more tokens per word.
2Under the Chat Context growth setting, calculations simulate multi-turn sessions where previous messages are quadratically concatenated as prompt inputs in each successive turn.
3Caching optimization models prompt context caching features provided by leading proprietary model APIs to reduce repeated static prompt costs by 90%.

Example

Prompt caching support guide

Google Gemini (1.5 and 3.5 series) supports context caching for inputs over 32k tokens. Anthropic Claude (3 and 4 series) supports caching for contexts over 1,024 tokens. OpenAI supports automatic prompt caching for requests exceeding 1,024 tokens.

Related guides

Learn more about this calculation

Guide

Understanding AI Tokens and Cost: How API Pricing Works for GPT-5, Claude 4, and Gemini 3

A developer's guide to understanding Large Language Model (LLM) tokens, API cost calculations, prompt caching optimizations, and chatbot context accumulation mathematics.

Guide

How to Use DotheCalculation Calculators: A Complete Walkthrough

Learn how to use calculators on DotheCalculation, enter inputs, read results, explore formulas, and navigate between related tools for comprehensive planning.

Guide

Understanding Calculator Formulas: How Our Calculations Work

Understand the formulas behind each calculator, how inputs map to results, the assumptions used, and how to verify calculations for your specific scenario.

Model

Provider

Monthly Cost

Relative

LLaMA 3.1 8B

Understanding AI tokens and context pricing

Large Language Models process text in units called tokens rather than raw characters or words. A token is typically a sub-word chunk of characters (e.g. "calcul" and "ator" for calculator). In English, 1 token is roughly 4 characters or 0.75 words.

API providers bill developers per million input (prompt) tokens and output (completion) tokens. Using this calculator, you can estimate these token counts and instantly compare pricing across the absolute latest LLM models from OpenAI, Anthropic, Google, and Meta.

The impact of chat history context growth

When building conversational AI systems like chatbots, the context grows with each interaction. To maintain conversation memory, developers must resend all previous user inputs and model outputs along with the latest user query as the prompt.

This causes prompt tokens to accumulate quadratically over the session. For instance, a 10-turn conversation can cost over five times as much as ten single-turn queries! Our simulator models this context growth to show you the true cost of multi-turn chat applications.

Maximizing savings with prompt caching

To optimize developer costs, frontier models like Claude 4.6 and Gemini 3.5 support prompt caching. This allows developers to cache static portions of the prompt—such as long system instructions, PDF reference documentation, or RAG inputs—for a fraction of standard input prices.

Google Gemini 3.5 offers a 90% discount on cache hits, and Anthropic Claude 4.6 provides 90% savings for cached context. By configuring caching percentages in our simulator, you can analyze exactly how much prompt caching will reduce your monthly bill.

Related concepts

Tokenization

The preprocessing step where input text is split into numeric tokens mapped to the model's vocabulary.

Context Window

The maximum limit of input and output tokens a model can process in a single API request.

Prompt Caching

A performance and cost optimization that caches repeated prompt segments to reduce latency and billing.

FAQs

What is a sub-word token?

Tokens are the atomic units processed by LLMs. Instead of whole words, tokenizers break text into sub-words (such as word segments, punctuation, and common suffixes) using algorithms like Byte Pair Encoding (BPE) or SentencePiece. This allows the model to handle rare words and typos efficiently.

How does prompt caching work?

When you send a request with prompt caching enabled, the API provider stores the compiled system prompt or document context in its fast-access server memory. Subsequent requests that reuse this exact same context read it from the cache, which is much faster and up to 90% cheaper than standard processing.

Why does LLaMA have different pricing providers?

LLaMA models are open-weights, meaning Meta does not host a centralized billing API. Developers can choose to host LLaMA on their own servers or use third-party serverless API providers (like Together AI, Groq, or OpenRouter), who establish their own competitive token rates.

How the calculation works

1Word-to-token ratios are estimated based on statistical averages for different languages. For instance, code and languages with non-Latin scripts split into more tokens per word.

2Under the Chat Context growth setting, calculations simulate multi-turn sessions where previous messages are quadratically concatenated as prompt inputs in each successive turn.

3Caching optimization models prompt context caching features provided by leading proprietary model APIs to reduce repeated static prompt costs by 90%.

Monthly Cost Comparison

Compare the projected monthly cost across all models based on your simulator configuration.

How to understand and use this calculator

Understanding AI tokens and context pricing

The impact of chat history context growth

Maximizing savings with prompt caching

Related concepts

Tokenization

The preprocessing step where input text is split into numeric tokens mapped to the model's vocabulary.

Context Window

The maximum limit of input and output tokens a model can process in a single API request.

Prompt Caching

A performance and cost optimization that caches repeated prompt segments to reduce latency and billing.

FAQs

What is a sub-word token?

How does prompt caching work?

Why does LLaMA have different pricing providers?

AI Tokens & Cost Calculator

Bookmark this AI tokens calculator

Cost Simulator

Model Pricing Comparison

Cost Breakdown: GPT-5.4

Multi-Turn Chat Simulation (10 turns)

Model Advisor

Code Exporter

How cost is calculated

How the calculation works

Prompt caching support guide

Learn more about this calculation

Understanding AI Tokens and Cost: How API Pricing Works for GPT-5, Claude 4, and Gemini 3

How to Use DotheCalculation Calculators: A Complete Walkthrough

Understanding Calculator Formulas: How Our Calculations Work

AI Tokens & Cost Calculator

Bookmark this AI tokens calculator

Cost Simulator

Model Pricing Comparison

Cost Breakdown: GPT-5.4

Multi-Turn Chat Simulation (10 turns)

Model Advisor

Code Exporter

Key output metrics

Monthly Cost Comparison

GPT-5.4 Monthly Cost Breakdown

Chat Session Token Growth

How to understand and use this calculator

Understanding AI tokens and context pricing

The impact of chat history context growth

Maximizing savings with prompt caching

Related concepts

FAQs

How cost is calculated

How the calculation works

Prompt caching support guide

Learn more about this calculation

Understanding AI Tokens and Cost: How API Pricing Works for GPT-5, Claude 4, and Gemini 3

How to Use DotheCalculation Calculators: A Complete Walkthrough

Understanding Calculator Formulas: How Our Calculations Work

Key output metrics

Monthly Cost Comparison

GPT-5.4 Monthly Cost Breakdown

Chat Session Token Growth

How to understand and use this calculator

Understanding AI tokens and context pricing

The impact of chat history context growth

Maximizing savings with prompt caching

Related concepts

FAQs