AI & LLM

AI Token & Cost Calculator

Estimate token usage, character counts, and compare real-time API pricing across GPT-4o, Claude 3.5 Sonnet, Gemini 1.5, DeepSeek, and Llama 3.

Sample Prompts:
1,308 chars
Estimated Tokens
301
~1.89 tokens / word
Word Count
159
~46s reading time
Total Characters
1,308
1,129 excl. spaces
Lowest Call Cost
$0.00017
via Gemini 1.5 Flash

API Cost & Monthly Volume Estimator

Simulate total monthly API expenditures based on prompt size, output response length, and user traffic.

Expected Completion / Output Tokens:500 tokens
Short (100)Medium (500)Long Form (2,000)Max (4,000)
API Calls per Month:10,000 requests
1k (Hobby)10k (Startup)50k (Production)200k (Scale)
Filter by Provider:
Pricing based on 1M Tokens (Current 2026 API Tariffs)
Model & ProviderCost / 1M Tokens (In / Out)Cost per Single CallMonthly Cost (10,000 reqs)
Gemini 1.5 FlashUltra Fast
Google • 1000k context (0.03% used)
Input: $0.07
Output: $0.30
$0.00017
$1.73
/ month
DeepSeek V3Lowest Cost
DeepSeek • 64k context (0.47% used)
Input: $0.14
Output: $0.28
$0.00018
$1.82
/ month
GPT-4o miniBudget
OpenAI • 128k context (0.24% used)
Input: $0.15
Output: $0.60
$0.00035
$3.45
/ month
Llama 3.3 70B (Groq/Together)
Meta • 128k context (0.24% used)
Input: $0.59
Output: $0.79
$0.00057
$5.73
/ month
DeepSeek R1Open Reasoning
DeepSeek • 64k context (0.47% used)
Input: $0.55
Output: $2.19
$0.00126
$12.61
/ month
Claude 3.5 Haiku
Anthropic • 200k context (0.15% used)
Input: $0.80
Output: $4.00
$0.00224
$22.41
/ month
o1-mini
OpenAI • 128k context (0.24% used)
Input: $1.10
Output: $4.40
$0.00253
$25.31
/ month
Gemini 1.5 Pro2M Context
Google • 2000k context (0.02% used)
Input: $1.25
Output: $5.00
$0.00288
$28.76
/ month
GPT-4oFrontier
OpenAI • 128k context (0.24% used)
Input: $2.50
Output: $10.00
$0.00575
$57.52
/ month
Claude 3.5 SonnetCoding Benchmark
Anthropic • 200k context (0.15% used)
Input: $3.00
Output: $15.00
$0.00840
$84.03
/ month
o1 (Reasoning)
OpenAI • 200k context (0.15% used)
Input: $15.00
Output: $60.00
$0.03451
$345.15
/ month
Claude 3 Opus
Anthropic • 200k context (0.15% used)
Input: $15.00
Output: $75.00
$0.04201
$420.15
/ month
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How LLM Tokens and API Pricing Work Under the Hood

Large Language Models (LLMs) like OpenAI's GPT-4o, Anthropic's Claude 3.5 Sonnet, and Google's Gemini 1.5 do not read text character-by-character or word-by-word. Instead, they digest text using Byte-Pair Encoding (BPE) subword tokenization algorithms (such as cl100k_base and o200k_base).

English Text
~0.75 Words / Token
~3.8 - 4.0 characters per token
Source Code & JSON
~0.4 - 0.6 Words / Token
Indentation & syntax split frequently
Non-Latin Scripts
~0.2 - 0.4 Words / Token
Multi-byte UTF-8 character splitting

4 Strategies to Cut Your AI API Bills by 60% or More

  1. Model Routing & Cascading: Never route simple classification, summary, or JSON extraction tasks to expensive frontier models like GPT-4o ($2.50/1M) or Claude 3.5 Sonnet ($3.00/1M). Use ultra-fast budget models like Gemini 1.5 Flash ($0.075/1M) or GPT-4o mini ($0.15/1M), reserving frontier models exclusively for complex coding and multi-step reasoning.
  2. Leverage Prompt Caching: If your system instruction or RAG context is over 1,024 tokens and stays consistent across user requests, structure your API calls to utilize prompt caching to slash input costs by up to 90%.
  3. Strict max_tokens Caps: Because output tokens cost 3x to 5x more than input tokens, always set precise max_tokens limits on completions to prevent runaway model loops.
  4. Self-Host Open Weights for Heavy Workloads: If your application handles millions of queries monthly, running models like DeepSeek V3 or Llama 3.3 70B on dedicated cloud GPUs (e.g., DigitalOcean GPU Droplets) often beats metered proprietary APIs at high volume.

How to Use

  1. 1

    Enter Prompt or Context

    Type or paste your prompt, system instructions, or code snippet into the textarea, or pick a sample preset.

  2. 2

    Review Real-Time Token Stats

    Inspect estimated tokens, word count, character density, and context window consumption instantly.

  3. 3

    Simulate Monthly Spend

    Adjust completion tokens and monthly request volume sliders to compare costs across all major AI providers.

Frequently Asked Questions

How many words is 1,000 tokens?+

In standard English prose, 1,000 tokens equals approximately 750 words (a ratio of ~0.75 words per token or ~4 characters per token). For code, JSON schemas, or non-English languages, the ratio is higher—1,000 tokens may represent only 400 to 500 words because brackets, whitespace, and non-Latin characters are split into more subword tokens.

Why do AI models charge more for Output (Completion) tokens than Input (Prompt) tokens?+

Input tokens are processed in parallel via transformer matrix multiplications, allowing high GPU throughput. Output tokens must be generated auto-regressively one token at a time—each new token depends on the previous ones—requiring significantly more GPU compute, memory bandwidth, and inference time.

What is Prompt Caching and how does it save money?+

Prompt Caching (supported by Anthropic, OpenAI, and DeepSeek) allows developers to cache repetitive context—like system instructions, large PDF documents, or RAG reference code. Subsequent API calls referencing the cached prompt receive discounts of 50% to 90% on input costs.

Is my prompt text or proprietary code sent to any server?+

No. The entire token estimation and cost simulation runs 100% client-side in your web browser. No text, prompts, or API keys are ever transmitted over the network.