AI models struggle with counting due to tokenization, not comprehension

AI models like GPT-4o and Claude struggle with simple counting tasks because they process text as tokens, not characters or words. A single word like "strawberry" can be represented by one token or multiple, depending on capitalization and context, making it difficult for the AI to isolate and count specific letters. This tokenization process, often using byte pair encoding, is crucial for understanding how AI models interpret and process language, directly impacting costs and context window limitations, as pricing and limits are based on token counts rather than character or word counts. AI

IMPACT Understanding tokenization is key for optimizing AI costs and performance, as it directly affects pricing and context window usage.

RANK_REASON Article explains a technical aspect of LLM tokenization and its implications, rather than announcing a new model or product.

Read on dev.to — LLM tag →

• C#
• Claude
• GPT-3.5
• GPT-4
• GPT-4o
• GPT-5
• Microsoft.ML.Tokenizers
• o200k_base
• Sennrich, Haddow and Birch
• tiktoken

• model release
• infra

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