OpenAI & Claude Token Counter: GPT-5.5/5.6 & Claude 5 Cost

Free Multi-Model Token Calculator with Cost Estimation

Count tokens for OpenAI, Anthropic Claude, and open-source models like Llama 3.1, Qwen 2.5, Gemma 2, and DeepSeek. See exact token counts, estimated API costs, and compare how different models tokenize the same text. All tokenization runs in your browser — your text is never sent to any server.

How to Calculate Tokens and Estimate API Costs

  1. Select Target Model — Choose from OpenAI models (GPT-5.6, GPT-5.5, GPT-4o, o1), Claude models (Claude Fable 5, Claude Sonnet 5, Claude 3.5 Sonnet), or open-source weights (Llama 3.1, Qwen 2.5, DeepSeek).
  2. Enter Prompt Text — Type or paste your context block directly into the input workspace.
  3. Inspect Byte-Pair splits — The tool highlights character groupings in real-time, showing exactly where token splits occur.
  4. Review cost estimation telemetry — Monitor input and output pricing estimations based on real-world API provider metrics.
  5. Compare model footprints — Switch between models to see how different tokenizers impact overall token usage and budget.

SimplyUtils vs Other Token Counters: Feature Comparison

Looking for a robust alternative to the standard OpenAI Tokenizer, Tiktokenizer, or token-counter.dev? See how SimplyUtils delivers comprehensive multi-model analysis in a single tool:

Feature
SimplyUtils
OpenAI Tokenizer
Tiktokenizer
token-counter.dev
Completely Free
No Account Required
OpenAI Models
18
3
5
4
Claude Models
7
Open-Source (Llama/Qwen)
Custom Hugging Face ID
API Cost Estimation
Word & Character Counts
Client-Side Processing

Standard Tokenization Encoding Families

LLM providers utilize specialized compression algorithms to serialize text characters into integers. Review the dominant tokenization architectures below:

Encoding Name
Vocabulary Size
Primary Models
Efficiency Characteristics
o200k_base
200,000 tokens
GPT-5.6, GPT-5.5, GPT-5, GPT-4o, o1, o3, o4-mini
Highly optimized for non-English languages, reducing cost by up to 50% compared to legacy tokenizers.
Claude BPE
Approx. 150,000 tokens
Claude Fable 5, Sonnet 5, Claude 3.5 Sonnet, Claude 3 Opus
Anthropic's proprietary tokenizer vocabulary, reverse-engineered for client-side token count estimations.
cl100k_base
100,000 tokens
GPT-4, GPT-3.5-Turbo, embedding models
Standard high-performance encoding for classic GPT integrations.
Llama 3 BPE
128,000 tokens
Llama 3.1, Llama 3.2, Llama 3
Meta's optimized Byte-Pair Encoding designed for extensive multi-lingual pre-training scopes.
Qwen / DeepSeek BPE
151,936 tokens
Qwen 2.5, DeepSeek V3, DeepSeek R1
Advanced tokenizer vocabulary optimized for math, code representations, and multi-lingual compression.

Target Personas & Concrete Use Cases

1. AI Application Developers & Integrators

Verify prompt lengths against strict LLM context limits (such as o1 or DeepSeek limits) beforehand. Bypassing context errors during batch agent executions prevents sudden system runtime failures.

2. Prompt Engineers & AI Content Designers

Optimize complex context structures or few-shot examples. Seeing exactly how target models partition system templates enables you to write highly compressed, token-efficient structures.

3. Financial Managers & Operations Leads

Calculate projected monthly operating costs. Knowing the exact token parameters of your datasets lets you build accurate ROI models and choose cost-effective API options.

4. NLP Researchers & Data Engineers

Compare tokenization characteristics across proprietary and open-source models side-by-side. Loading custom Hugging Face parameters helps audit custom tokenizer performance before hardware training.

Frequently Asked Questions

What is a token and how does it relate to words or characters?

Tokens are chunks of text that language models process. A token can be a single word, part of a word, or even punctuation. For standard English, 1 token is roughly equivalent to 4 characters or 0.75 words.

Why do different AI models return different token counts for the identical prompt?

Each model uses a unique tokenizer vocabulary. GPT-4o uses the optimized o200k_base vocabulary containing 200,000 parameters, whereas older models like GPT-4 rely on cl100k_base (100,000 parameters). Larger vocabularies compress text more efficiently, resulting in lower token counts.

Does this token counter send my inputs to OpenAI or external hosts?

No. All calculations are performed entirely locally inside your browser using standard JavaScript runtimes. No text, data, or API key parameters are ever sent to external networks, maintaining 100% privacy.

What is the difference between cl100k_base and o200k_base encodings?

o200k_base is a newer, larger encoding standard that compresses non-English scripts far better than cl100k_base. This yields lower costs and faster inference on newer models like GPT-4o and o1.

How can I calculate token metrics for local custom Hugging Face model architectures?

Simply input your target Hugging Face Model ID in the options bar. The tool will safely retrieve the repository's configuration parameters locally to calculate exact character groupings.

All tokenizer calculation is compiled client-side in browser volatile memory. Zero inputs or cost calculation details are tracked or sent to external servers.