Token Inflation Lab
Analyze how different tokenizers process your text. A "cheaper" model can be more expensive if its tokenizer is inefficient.
Input Text
188 Characters27 Words
GPT-4o/5.5 (o200k)36 Tokens
Inflation vs Baseline+0.0%
ArtificialIntelligenceisrapidlyevolving.Tokenizationistheprocessofconvertingtextintoaformatthatamodelcanunderstand.Efficienttokenizersreducecostsandincreasespeed.
Claude 3/444 Tokens
Inflation vs Baseline+25.0%
ArtificialIntelligenceisrapidlyevolving.Tokenizationistheprocessofconvertingtextintoaformatthatamodelcanunderstand.Efficienttokenizersreducecostsandincreasespeed.
Llama 3/440 Tokens
Inflation vs Baseline+12.0%
ArtificialIntelligenceisrapidlyevolving.Tokenizationistheprocessofconvertingtextintoaformatthatamodelcanunderstand.Efficienttokenizersreducecostsandincreasespeed.
Gemini 2/342 Tokens
Inflation vs Baseline+18.0%
ArtificialIntelligenceisrapidlyevolving.Tokenizationistheprocessofconvertingtextintoaformatthatamodelcanunderstand.Efficienttokenizersreducecostsandincreasespeed.
The Hidden Cost: Token Density
Most engineers compare models based on price per million tokens. However, Token Density is just as important. A model with an older tokenizer (like Claude 2 or GPT-3.5) might turn the same paragraph into 30% more tokens than GPT-4o.
In 2026, OpenAI's o200k tokenizer is the gold standard for efficiency, especially for non-English languages and code, where inflation on other models can exceed 50%.