AI Token & Cost Calculator
See how the same text tokenizes differently across every major model family. Compare token counts, explore the split, and estimate what a prompt will cost across OpenAI, Anthropic, and Google.
Your text
Cross-model comparison
The same text produces a different number of tokens depending on the model. More tokens means higher cost and more context-window usage.
| Model family | Maker | Tokens | vs GPT-6 | Accuracy |
|---|---|---|---|---|
| GPT-6 / GPT-5.6Selected | OpenAI | 37 | same | Exact |
| GPT-4 / GPT-3.5 | OpenAI | 37 | same | Exact |
| Llama 3 | Meta | - | Not loaded | |
| Qwen 2.5 | Alibaba | - | Not loaded | |
| Gemma 2 | - | Not loaded | ||
| Mistral | Mistral | - | Not loaded | |
| DeepSeek V3 | DeepSeek | - | Not loaded | |
| Claude | Anthropic | ~43 | +16.2% | Estimated |
| Gemini | ~39 | +5.4% | Estimated |
Click a row to select that model for the token split below. Open-model counts use each maker's real tokenizer, loaded on demand. Claude and Gemini are estimated, because neither publishes a public tokenizer; an exact count needs the provider's API. Why tokenizers differ.
Token split: GPT-6 / GPT-5.6
Hover over a token to see its ID and position. Each colored block is one token for GPT-6 / GPT-5.6.
Cost estimator
| Model | Input $/1M | Output $/1M | This prompt | 1,000 requests |
|---|---|---|---|---|
| GPT-6 Astra | $10.00 | $50.00 | $0.0254 | $25.37 |
| GPT-6.1 Sol | $2.00 | $10.00 | $0.005074 | $5.07 |
| GPT-6 Luna | $0.10 | $0.50 | $0.000254 | $0.2537 |
| GPT-5.6 Sol | $4.00 | $20.00 | $0.0101 | $10.15 |
| GPT-5.6 Cyber | $12.50 | $75.00 | $0.0380 | $37.96 |
| GPT-5.3 Codex | $1.75 | $14.00 | $0.007065 | $7.06 |
| Model | Input $/1M | Output $/1M | This prompt | 1,000 requests |
|---|---|---|---|---|
| Claude Fable 5.1~ | $10.00 | $50.00 | $0.0254 | $25.43 |
| Claude Opus 5.5~ | $4.00 | $20.00 | $0.0102 | $10.17 |
| Claude Sonnet 5.5~ | $2.00 | $10.00 | $0.005086 | $5.09 |
| Claude Haiku 5.5~ | $0.10 | $0.50 | $0.000254 | $0.2543 |
| Model | Input $/1M | Output $/1M | This prompt | 1,000 requests |
|---|---|---|---|---|
| Gemini 3.8 Flash~ | $0.75 | $3.75 | $0.001904 | $1.90 |
| Gemini 3.1 Pro Preview~ | $2.00 | $12.00 | $0.006078 | $6.08 |
| Gemini 3.5 Flash-Lite~ | $0.30 | $2.50 | $0.001262 | $1.26 |
| Gemini 2.5 Pro~ | $1.25 | $10.00 | $0.005049 | $5.05 |
Cost = (input tokens × input rate) + (output tokens × output rate). Anthropic and Google token counts are estimated. Prices verified October 2026 from official sources. Check OpenAI, Anthropic, and Google for current rates.
Want to understand what tokens are and why they drive both speed and cost? Read How to Choose a Local LLM.
Why Token Counts Differ Across AI Models
Every large language model uses a tokenizer to break text into smaller pieces called tokens before processing it. The same sentence can produce wildly different token counts depending on which model you use. OpenAI's GPT-6 uses the o200k_base tokenizer with a 200,000-token vocabulary, while GPT-4 and GPT-3.5 use cl100k_base with 100,000 tokens. Meta's Llama 3, Alibaba's Qwen 2.5, Google's Gemma 2, Mistral, and DeepSeek V3 each ship their own tokenizer with different vocabulary sizes and splitting strategies.
This matters because API pricing is per token. A model that tokenizes your text into fewer tokens will cost less per request. Our AI token calculator lets you paste your actual text and compare the token count across all major model families side by side, so you can make informed decisions about which model to use for your workload.
How This AI Token & Cost Calculator Works
This tool runs real tokenizers directly in your browser using WebAssembly. For OpenAI models (GPT-6, GPT-5.6, GPT-4, GPT-3.5), it uses the official gpt-tokenizer library with the exact o200k_base and cl100k_base encodings. For open-source models (Llama 3, Qwen 2.5, Gemma 2, Mistral, DeepSeek V3), it loads the actual Hugging Face tokenizer files and runs them client-side through the Transformers.js library.
Claude and Gemini token counts are estimated based on known ratios relative to GPT tokenizers, since Anthropic and Google do not publish their tokenizers publicly. No text is ever sent to a server. Everything stays in your browser.
AI Token & Cost Calculator FAQ
What is a token?
A token is a chunk of text that a language model processes as a single unit. Tokens can be whole words, parts of words, punctuation, or whitespace. The word "infrastructure" is one token in most tokenizers, but "tokenization" gets split into "token" and "ization" by some models. Understanding tokens is essential for estimating API costs and staying within context window limits.
Why do GPT-6 and GPT-4 give different token counts?
GPT-6 uses the newer o200k_base tokenizer with a 200,000-token vocabulary, while GPT-4 uses cl100k_base with 100,000 tokens. The larger vocabulary means GPT-6 can represent more words and subwords as single tokens, typically producing 5-15% fewer tokens for the same text. This translates directly to lower API costs.
How accurate are the Claude and Gemini estimates?
Anthropic and Google do not publish their tokenizers. Our estimates are based on empirical testing and known characteristics of their tokenizer architectures. Claude typically produces roughly 15% more tokens than GPT-6's o200k_base for the same text, while Gemini is closer to 5% more. These are reasonable estimates for cost planning, but exact counts may vary.
Is my text sent to any server?
No. All tokenization happens in your browser. The tokenizer models are downloaded once and cached locally. Your text never leaves your device. This tool is safe to use with proprietary or sensitive content.